NHS Digital Data Release Register - reformatted

University Of Oxford projects

2885 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

🚩 University Of Oxford was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. University Of Oxford may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.

A Study of Cardiovascular Events iN Diabetes – PLUS (ASCEND PLUS) - Recruitment agreement — DARS-NIC-655024-S2H5Q

Opt outs honoured: Anonymised - ICO Code Compliant, Identifiable (Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: Yes (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2022-12-07 — 2024-12-06

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Customer - Data Quality Report - Aggregate (Comms)
  2. Customer - Data Quality Report - Aggregate (Recruitment)
  3. Demographics
  4. Mailing - Cohort - Non-aggregate (Comms & Recruitment)

Objectives:

BACKGROUND
Around one in 11 adults worldwide has diabetes: a long-term condition where a person’s blood sugar levels are too high.
There are two main types of diabetes:
• Type 1 diabetes – where the body’s immune system attacks and destroys the cells that produce the hormone insulin so that no insulin is produced. This is treated with insulin injections.
• Type 2 diabetes – where the body does not produce enough insulin, or the body’s cells do not react to insulin. This usually occurs later in life and is often treated with tablets. Some patients eventually need to be treated with insulin.
People with diabetes are more likely to suffer from other major health problems. These include heart and circulatory problems (including heart attacks and strokes), high blood pressure and dementia. Diabetes can cause kidney disease, problems with feeling in the feet, and eye problems that may affect vision.

ASCEND PLUS is a clinical trial led by the University of Oxford. It is a randomised, double-blind, parallel-group, placebo-controlled event driven trial designed to test the hypothesis that oral semaglutide reduces cardiovascular events and other complications of diabetes in people with type 2 diabetes mellitus (T2DM) without a prior heart attack or stroke. The study will use streamlined methodology to randomise approximately 20,000 people with T2DM in the UK and follow them during a scheduled treatment period with a median duration of approximately 5 years.

OUTCOMES DATA (Not part of this agreement however for future information)
With consent, the ASCEND PLUS team will collect linked healthcare data from NHS Digital and other organisations for the 5-year scheduled treatment period and the following 20 years in order to find out the medium and long-term effects of oral semaglutide (this linkage for consented trial participants will be covered by a separate Data Sharing Agreement).

THE AIM OF THE TRIAL
The ASCEND PLUS trial aims to provide evidence about both the efficacy and safety of prolonged treatment with oral semaglutide. The hypothesis of the ASCEND PLUS trial is that treatment with oral semaglutide reduces cardiovascular events and other complications of diabetes in individuals aged at least 55 years, with T2DM, without a history of a heart attack or stroke, and without any upper or lower Haemoglobin A1c (HbA1c) threshold.

STUDY TEAM
Any reference to "the study team" in this agreement refers to members staff substantively employed by University of Oxford directly working on the ASCEND PLUS programme.

PATIENT AND PUBLIC INVOLVEMENT AND ENGAGEMENT (PPIE)
Two lay members were recruitment to the trial Steering Committee (TSC) and attended the first TSC meeting in June 2021 and subsequent meetings, ensuring patient and public involvement in the high-level strategic decisions for the trial.

Between June and October 2021, the ASCEND PLUS team convened six patient and public focus groups, largely involving people with type 2 diabetes. The groups included people from diverse backgrounds across the UK, with the exception of Northern Ireland, as the trial will not take place in this location. The trial’s proposed consent model formed a key focus of each group. There was strong support for potential trial participants to have a choice of consent method (self-directed online consent or consent via nurse telephone or video call interview). The trial procedures were amended in light of the advice from these groups to offer this choice within the reply form. The use of patient data without consent (to invite patients to join the study) formed a focus of the group discussions at 5 of the meetings. There was broad support for this process to enable the study to recruit the large number of participants required.

The trial has convened the ASCEND PLUS Public Advisory Group (ASCEND PAG), a diverse group of patients and public, to input into the whole life-cycle of the trial. Members were recruited from the existing Nuffield Department of Population Health public advisory panel and the focus groups described above. The ASCEND PAG give feedback, advice and opinions across different aspects of the trial including recruitment materials, participant questionnaires, website development, strategies to maintain participant adherence and engagement, and dissemination of the trial results. The first meeting was held on 6 September 2021. Group members have subsequently provided input into all the patient-facing trial documents. As a result of feedback from the ASCEND PAG and the focus groups, the material was divided into three leaflets; an initial information leaflet to be sent out with the invitation letter and then a full participant information leaflet and a separate data protection leaflet, which are to be sent to individuals who express an interest in the trial. Text and content change recommendations from ASCEND PAG members were implemented throughout, unless these were inconsistent with the study processes.

This has resulted in documents which are easier to understand and more inclusive. For example, for the invitation letter, ASCEND PAG members edited the transparency notice wording to plain English and added the text box, ‘If you would like this information in a non-English language or other format, such as large print, please contact us’ to the start of the invitation letter. In other places, the ASCEND PAG requested a change to the order of the information presented. For example in the participant information leaflet the section, ‘Will taking part affect my medical insurance or travelling?’ was moved earlier in the document.

The ASCEND PAG also co-developed the consent animation, which is part of the self-directed online consent process. First they contributed to the development of the script and then provided feedback on the images used in the storyboard, most of which were implemented. The ASCEND PAG was also instrumental in the selection of which quality of life questionnaires to include in the protocol and have reviewed the draft screening form questions and provided detailed feedback.

To optimise the wording for the letter invites, NHS DigiTrials team in NHS Digital have tested different letter variants with members of the public. Initially, NHS DigiTrials conducted desk research and developed hypotheses on behavioural techniques that would help optimise invitation letters.

NHS DigiTrials analysed the results and focussed on people with Type II Diabetes in order to help inform invites for the ASCEND PLUS trial. For people with this health condition, all experimental letter variants outperformed the control on all reported measures of taking action. As a result of this, the content of the invitation letters for the ASCEND PLUS trial may be amended slightly to align with the optimum performing letters from the testing

LEGAL BASIS FOR COMMON LAW DUTY OF CONFIDENTIALITY
University of Oxford as data controller, are requesting to use NHS Digital data to support a clinical trial called ASCEND PLUS. This agreement is specifically to support the recruitment of a cohort by writing out to individuals who meet the required eligibility criteria.
For the first element of the recruitment process, the legal basis for the identifiable data from NHS Digital to flow to Paragon Customer Communications Ltd for the purpose of University of Oxford inviting them to join the ASCEND PLUS trial is Section 251 approval (NHS Act 2006), approved by the Confidentiality Advisory Group (CAG).

For the second element of the recruitment process, the University of Oxford are relying on consent. Interested potential participants will return a reply form to University of Oxford which will include consent to obtain participant information. Further details on this are detailed within Processing Activities section.


GDPR LEGAL BASIS FOR PROCESSING OF PERSONAL DATA
University of Oxford, as sole Data Controller, are using Article 6(1)(e) "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller." As part of the application process, the requirement for the data requested has been assessed and NHS Digital is content that it is appropriate, necessary and proportionate for the performance of the task described in the purpose statement and that there is no other reasonable and less intrusive means for the data processor to achieve their purpose.

It is acknowledged that University of Oxford has no direct relationship with the potential research participants up until the time (if they choose to) they respond to the postal invitation by returning the reply form to University of Oxford. At that point, University of Oxford relies on the potential research participant’s consent to
A) obtain participant identifiable information from NHS Digital, and
B) issue the participant with more information regarding the trial and make contact to undergo trial screening and solicit consent to participate in the trial.
Potential participants may experience minor inconvenience of being contacted and invited to take part in the research programme. However, the inconvenience is restricted to the receipt of a letter through the post. Therefore, the likelihood of any adverse impact on the data subjects is low and the severity of any such impact is judged to be minimal.

Additionally (as health data is a special category of Personal Data), University of Oxford is using Article 9(2)(j): Special category data used for “Archiving in the public interest, scientific or historical research or statistical purposes”. If the Study Team demonstrate that oral semaglutide is beneficial in a wide range of patients with type 2 diabetes then the results could change national and international guidelines and more patients could be offered the treatment. This should reduce the risk of complications in patients with diabetes which is in the interest of the public.

INCLUSION AND EXCLUSION CRITERIA FOR INVITATION
The trial will be conducted throughout the UK* and the aim is to recruit a total of 20,000 participants. It is anticipated that about 18,000 of these participants will be recruitment through the collaboration with NHS Digital in England but this may change as the trial recruitment progresses.
Lists of potentially eligible individuals will be generated from electronic searches of centrally held NHS datasets at NHS Digital
*NOTE: NHS Digital will be providing data for England only. The study will be obtaining the related data from the other devolved nations from the relevant organisations.

Invitation inclusion criteria:
• Reside within England
• Age at least 55 years (this is to ensure that the individuals taking part in the trial are at sufficiently high risk of the relevant cardiovascular disease outcomes (e.g. heart attack and stroke).
• Have type 2 diabetes (based on either a relevant diabetes ICD-10 code in their Hospital Episode Statistics data and/or receipt of diabetes treatment in the NHSBSA Medicines data)

Invitation exclusion criteria:
The overall cohort of participant aims to be representative across UK geographical locations, age, and gender. It is possible as part of the creation of each NHS Digital mailout, that specific criteria will be applied to the searches to create the invitee list for that mailout. This may involve increasing the proportion of invitees from particular criteria groups to facilitate balancing the overall cohort make up.
In addition, participants should not have a recorded history of heart attack, stroke, recent cancer or dementia.

Recruitment is expected to begin around December 2022 and take place over 2 years. For the first year of recruitment, approximately half a million patients will be identified and contacted. With an assumed response through to randomisation rate of 2%- , approximately 9,000 patients should be recruited, however this recruitment target is an estimate and may be subject to change. The study team anticipate that a further half million invites will be required in the second year, however, should the study team require a substantial change to the recruitment target or number of invitees, an amendment to this agreement will be required with justification for the change, with appropriate Ethics and CAG approval.

REQUESTING NOT TO TAKE PART IN THE PROGRAMME:
In addition to the National Data Opt-out, members of the public will be able to specifically request not to be contacted for the ASCEND PLUS trial. University of Oxford will promote this information via the dedicated programme website (http://www.ascend-plus-trial.org) and via local media for two weeks prior to letters being issued to enable those who do not wish to receive a letter to declare this. NHS Digital will also promote this information via a dedicated page on its website (http://digital.nhs.uk/ascend-plus). There will also be an option for people to register their request not to take part in ASCEND plus by telephone. In order to provide a single point of contact for all trial related queries, individuals who do not want to be contacted for ASCEND PLUS but are unable to access the study-specific opt-out form on the NHS Digital website will be asked to call the trial Freephone number at the University of Oxford. The trained switchboard team will then record the study specific opt-out on the NHS Digital website on the individuals behalf with no personal data entered into the University of Oxford systems.

NHS Digital will ensure that anyone who has registered for the study specific opt-out will be excluded from the cohort selection and as a result their details will not be on the list passed to Paragon CC for invitations to be sent.

MAILOUTS
Potential participant will be sent one invitation for the ASCEND PLUS trial unless any of the following criteria apply:
1. A national data opt-out has been applied
2. A trial specific opt-out has been applied

Each letter will be mailed with a reply form and the Initial Information Leaflet.

ORGANISATION’S ROLES AND RESPONSIBILITIES:
University of Oxford is the applicant and data controller, who also process data. They are responsible for the programme and overseeing the work carried out to aid recruitment into the programme. They are also responsible for providing the core eligibility criteria for participants.

University of Oxford are responsible for:
1. Generating the invitation request(s) and sending to NHS Digital on a regular basis
2. Monitoring uptake by invitees (i.e. numbers of invitees consenting to participate in the programme)
3. Monitoring scope of the population sending back reply forms
4. Adjusting the selection criteria for invitations to adjust for underrepresentation of target populations taking up the programme, as necessary.
5. Compiling weekly cohorts of interested participants (from the returned reply forms) and securely transferring to NHS Digital for linkage with further participant identifiers and vital status.

NHS Digital are acting as a data processor on behalf of University of Oxford and are responsible for:
1. Applying the inclusion and exclusion criteria to NHS Digital Datasets to generate a list of invitees
2. Feeding back to University of Oxford the number of invitees actually fulfilled out of the total target population, via an aggregate report with small numbers suppressed.
3. Removing objections or opt outs (where a national data opt-out has been registered, as well as special categories of people for whom the data should not be disseminated. The purpose of the restriction is to ensure that patient information that might imply a location is protected.)
4. Sending the list of invitees on to University of Oxford's third-party provider (Paragon Customer Communications Ltd) for generating the invitation letters and mailing these out
5. Agreeing with University of Oxford key processing timelines, including
a. Time from submission to Paragon Customer Communications to mailout
b. Date of the mail outs
c. Daily cut-off times (i.e. after which processing will take place the next day)
d. Time to feedback to University of Oxford the numbers selected
6. Where the number of invitees is less than the population available, invoking a system to choose invitees at random
7. Maintaining a record of people who have returned reply forms to University of Oxford (details of which University of Oxford will supply to NHS Digital securely via Secure Electronic File Transfer (SEFT) and ensuring that these participants are excluded from a second or any further rounds of invitations.
8. Maintaining a record of people invited to the study ensuring they are excluded from any further rounds of invitations.
9. Processing the cohort of interested participants (approximately on a weekly basis) and providing vital status update and updated cohort information securely back to University of Oxford.
10. Processing the cohort of interested participants (on an ad-hoc basis) against the cohort of invitees to produce an aggregate recruitment conversion report, with small numbers suppressed. This report is to be securely shared with University of Oxford.

Paragon Customer Communications are acting as a processor of University of Oxford. Their responsibility is to receive the lists of invitees from NHS Digital and mail out to them accordingly.

A 'return to sender' address will need to be included on the letters. When letters are unable to be delivered to the participant address provided by NHS Digital - they will be returned to Paragon Customer Communications where they will be shredded. Paragon Customer Communications will provide University of Oxford with an aggregate report of number of returned letters.

FUNDING and COMMERCIAL PURPOSE.
The ASCEND PLUS trial was initiated and designed by investigators at the University of Oxford aiming to improve the health of people with type 2 diabetes. The trial is funded through a grant to the University of Oxford from Novo Nordisk, a Danish multinational pharmaceutical company. Additionally, Novo Nordisk - who manufacture the treatment being studied, oral semaglutide - will also be providing the study treatment for the trial. Oral semaglutide is the only oral medication in this class. There are other drugs in this class produced by other companies, however these are given by injection so would not be suitable for the ASCEND PLUS trial design and may be more difficult for patients to take. This is why the study team approached Novo Nordisk rather than any other manufacturer for this study. In the interests of transparency, it is stated here that if the results show that oral semaglutide is beneficial for a wide range of people with diabetes, then this could also increase revenue for the manufacturer Novo Nordisk. Nordisk will have no influence over any of the findings and will have no ability to suppress any outputs produced by the study.

NHS Digital record level data covered by this Data Sharing Agreement will not be shared with any funders and final decision making on processing of the data rests with University of Oxford senior trial management team who are all substantive employees, therefore the funders are not considered Data Controllers or Data Processors on this agreement. The ASCEND PLUS study is sponsored by The University of Oxford. The protocol and procedures have been developed by the investigators at the Clinical Trial Service Unit, University of Oxford with contributions from Novo Nordisk. The Steering Committee determine the scientific objectives of the trial, ensure adequate progress towards those objectives and review any papers prior to publication. As is usual with this type of trial, the Steering Committee has representatives from the funder and also has other experts from other institutions to advise the trial management team. The Steering Group will not have access to or process any NHS Digital record-level Data.

Expected Benefits:

One in 11 people worldwide has diabetes including around 5M people in the UK. Individuals with diabetes have increased risks of adverse cardiovascular health outcomes (such as heart attacks and strokes) which can be fatal or disabling, and of other health problems such as dementia. People with diabetes can also develop complications such as kidney disease, reduced vision, amputation and pain or numbness in their feet (neuropathy). There are several new treatments for diabetes, including oral semaglutide and similar drugs given by injection, but clinical trials have only tested them in selected individuals with very high cardiovascular risks.

ASCEND PLUS is testing oral semaglutide, the first oral Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA), in a wide range of people with type 2 diabetes. The main aim is to assess the effects of the treatment on adverse cardiovascular health outcomes but the trial will also assess effects on other complications of diabetes. Oral semaglutide and other GLP-1 RAs control blood sugar, reduce weight and, in high-risk patients, reduce the risk of adverse cardiovascular health outcomes. However, these medications are not widely used, partly because they have not been shown to be beneficial in most patients with type 2 diabetes who don’t already have cardiovascular disease. The trial will also be able to find out the long-term effects of oral semaglutide. There is some evidence that oral semaglutide may protect against dementia and kidney disease but this is not proven.

If ASCEND PLUS shows that oral semaglutide is beneficial in a wide range of patients with type 2 diabetes then the results could change national and international guidelines and more patients could be offered the treatment. This should reduce the risk of complications in patients with diabetes.

The main results are expected in 2028 with long-term follow-up continuing for 20 years after that. The study team aims to present results at scientific conferences and publish in high-impact peer reviewed journals. The study team plan to distribute plain English results co-developed by the ASCEND PAG to surviving study participants by mail and shared with the public via the trial website.

The trial should also generate important methodological insights, tools and resources to benefit future research. These will be share where possible during the trial through presentations, publications and via collaboration with Health Data Research UK and the MRC-NIHR Trials Methodology Research Partnership.

Outputs:

As a result of this recruitment agreement with NHS DigiTrials, the ASCEND plus study team are hoping to recruit to target having posted out adequate numbers of invitations to potentially eligible participants.

Identifiable health data requested from NHS Digital will only be used to identify and invite potential participants. NHS Digital record Level Identifiable data will only be available to University of Oxford after participants have expressed interest and returned the consenting reply form.

Progress against recruitment targets may be reported to the University of Oxford, the funder Novo Nordisk and the Trials Steering Committee in an aggregated and suppressed format.

Key recruitment targets have been set for first year of recruitment and will be closely monitored and reported. NHS DigiTrials forms the main route to recruitment for the trial and the sole method of recruitment in England.

Successful recruitment will enable to trial to test the effects of oral semaglutide in a wide range of people with type diabetes. The main results of the trial are expected in 2028 and should inform National and International guidelines. Examples of these guidelines could include the NICE guideline on “Type 2 diabetes in adults: management”, the American Diabetes Association Standards of Medical Care in Diabetes guidelines and the diabetes guidelines from the European Society of Cardiology and the European Association for the Study of Diabetes. The study team aim to publish the main trail report in a high-impact medical journal with a public summary on the trial website.

Results will also be communicated directly to trial participants.

Processing:

As data controller, the University of Oxford will provide to NHS Digital the core eligibility criteria for those potential participants who will receive the initial invitation letters and information sheets. University of Oxford refine the population that receive these invitations based on first half of a postcode, making adjustments as required to ensure adequate representation of target populations.

NHS Digital would be using a mailing provider (Paragon Customer Communications) to fulfil the communications. Paragon Customer Communications will use Research Ethics Committee (REC)-approved template invitation letters and would add address details and unique barcode references onto the letters prior to mailing it out. All identifiable data provided to Paragon Customer Communications by NHS Digital will be done so under the legal basis of Section 251 support as provided by the Confidentiality Advisory Group (CAG) for this element of the ASCEND PLUS trial recruitment.

Data processing is carried out by employees of NHS Digital who have been appropriately trained in data protection and confidentiality. NHS Digital will access records allowing them to gather the following information needed to determine suitability for invitation to the ASCEND PLUS trial.

COHORT SPECIFICATION:
• University of Oxford are accountable for providing the specification to NHS Digital for each mail out. These specifications will be based on a combination of multiple postcodes, age limits, sex at birth and ethnicities for potential participants. The information they will provide to NHS Digital on each occasion is:

• Lower and upper age limits (55 and over as per the inclusion criteria).
• Male / female percentage split, if required.
• A selection of postcodes (first half of postcode only), if required.
• The number of invitations required for each request.
• The geographical specifications are based on ensuring a broad range of individuals from across England.
• At a pre-determined point, University of Oxford will transfer details of the specification to NHS Digital via SEFT. This will be on a flexible ad-hoc basis, determined by the number of responses received for each request, but could be up to once weekly.

COHORT IDENTIFICATION:
• Using the inclusion and exclusion criteria as specified by University of Oxford, NHS Digital will interrogate the Patient Demographics Service (PDS), NHS Business Services Authority (NHSBSA) Medicines Dispensed in Primary Care and the Hospital Episode Statistics (HES) datasets and extract all those potential participants who meet the criteria within the latest specification as provided by University of Oxford.*
• NHS Digital will then remove any records where a national data opt-out has been registered, as well as special categories of people for whom the data should not be disseminated. The purpose of the restriction is to ensure that patient information that might imply a location is protected.
• The remaining records will have their relevant contact details (Forename, Surname, Address, Postcode) extracted ready for despatch to Paragon , as well as a unique barcode reference for each potential participant.

*NOTE: Although the HES APC and NHSBSA Medicines datasets are being cross-referenced to produce the cohort of potential participants for this agreement, these datasets are not considered as data products on this Data Sharing Agreement as no record level HES APC or NHSBSA Medicines data will be shared with either University of Oxford or Paragon Customer Communications. The processing of HES APC and NHSBSA Medicines data is covered under a separate Data Processing Agreement between NHS Digital and University of Oxford

COHORT DISSEMINATION AND MAILOUT
• Each time NHS Digital create and disseminate an extract, the records will be added to a mailing list cohort dataset, including individual participant IDs.
• Every time a fresh extract is produced, it will be checked to ensure that any records appearing in this mailing list dataset are removed in order to prevent potential participants receiving multiple invitations.
• NHS Digital will provide Paragon Customer Communications with Forename, Surname, Address, Postcode and Unique barcode reference via SEFT.
• Paragon Customer Communications will then mail out to individuals as required.
• All potential participants will receive an invitation letter containing their Name, Address and Postcode and Unique barcode reference,
• Paragon Customer Communications will destroy all data received from NHS Digital 30 days after mailing as instructed by the University of Oxford.

PARTICIPANT EXPRESSION OF INTEREST VIA CONSENT REPLY FORM
The invitation letter is mailed with an enclosed Initial Information Leaflet, a Reply Form and a Freepost envelope. The information includes the Freephone telephone number of the ASCEND PLUS team, the study e-mail address and the study website URL, so that potential participants can find out more about the study at this stage if they chose. If the recipient is potentially interested in taking part, they are asked to complete the Reply Form. This is pre-populated with the person's name and unique barcode reference. The potential participant is asked to add their telephone number and e-mail address and indicate whether they would prefer to engage with the study online or via telephone / video call with a research nurse. They can also add any special requirements such as large print or non-English language. By returning the Reply Form the potential participant is giving permission for the University of Oxford to receive their personal data including receiving their Name, address, postcode, NHS number, date of birth, gender and GP practice details from NHS Digital, so that the study team can complete the screening assessment, and, if they are eligible and consent to the study, inform their GP. Each reply form is checked and logged by the study team. The name and unique identifier will be extracted from the Reply Form using a barcode scanner to reduce errors. Permission to use personal details in order for University of Oxford to make further contact is clearly described on the reply form.

INTERESTED PARTICIPANT COHORT LINKAGE TO ADDITIONAL DATA
On a weekly basis, the University of Oxford will securely provide NHS Digital with the unique identifiers (barcode reference) for all those individuals who have returned their reply form. NHS Digital will use the unique barcode reference to link back to the potential participants full name, address, postcode, NHS Number, date of birth, gender and GP practice details, from the Demographics dataset. These data items will be returned securely to University of Oxford via SEFT.

Personal data for patients who have return the reply form (and therefore given permission for University of Oxford to receive their details in order to do the screening assessment) but then either don’t complete the screening assessment or are not eligible at screening and so don’t provide full informed consent for the trial will be held until the end of recruitment and then destroyed. University of Oxford retain this data until the end of recruitment in case a potential participant changes their mind and decides to attend screening, becomes eligible or has any other queries about the trial. University of Oxford feel this is in line with their permission given by returning the reply form. After recruitment is completed and there is no possibility of them completing the screening assessment and therefore no rationale for University of Oxford to hold their personal data longer.

AGGREGATE RECRUITMENT CONVERSION REPORT
On an ad-hoc basis (up to once a week), NHS Digital will compare the cohort of invitees to the cohort of interested participants (who have returned their reply form to University of Oxford) to produce a report detailing the percentage uptake of participants in relation to the inclusion and exclusion criteria provided by University of Oxford. This report will only contain aggregate data with small numbers suppressed and is represented within this Data Sharing Agreement under section 3B (Additional Data Access Requested) as “Customer – Data Quality Report – Aggregate (Communications)”

The permitted territory of use for data provided by NHS Digital for this agreement is England and Wales.

Potential participants will receive one postal invitation for the ASCEND PLUS study; at present there will be no further attempts to remind or re-approach patients who do not respond. The study team may consider sending out an additional reminder in future, and if required, will submit an amendment to this agreement and seek relevant ethical approval. It is possible that patients who move from England to Wales or Scotland during the recruitment period, may receive a further invitation from a search conducted by other data custodians in those regions but this is likely to only affect a small number of individuals and an explanation would be provided.

An aggregate report with small numbers supressed (as per the HES Analysis Guide) containing information about gender, age and geographic demographics of the mailing cohort will be provided to University of Oxford from NHS Digital for the purposes of confirming that inclusion and exclusion criteria have been met for each mailing. This report will also contain the total number of people who received the mail out and the number of people removed from the cohort prior to mailing based on trial-specific opt out, national data opt out and special categories of people for whom the data should not be disseminated, such as those on a witness protection programme.

The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement. The data from NHS Digital will not be linked to any other data other than those outlined in this Agreement.

HES and ECDS DISCLOSURE CONTROL / SMALL NUMBER SUPPRESSION
In order to protect patient confidentiality, when presenting results calculated from HES record level data, outputs will contain only aggregate level data with small numbers suppressed in line with HES Analysis Guide. When publishing HES data, data processors must make sure that:
• National-level figures only may be presented unrounded, without small number suppression
• cell values from 1 to 7 (inclusive) are suppressed at a sub-national level to prevent possible identification of individuals from small counts within the table.
• Zeros (0) do not need to be suppressed.
• All other counts will be rounded to the nearest 5.
Data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

SYMPLIFY Trial Communications via NHS DigiTrials request — DARS-NIC-661736-Y2Q9R

Opt outs honoured: Identifiable (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Sensitive

When:DSA runs 2022-11-04 — 2023-11-03

Access method: One-Off

Data-controller type: GRAIL BIO UK LTD, UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Mailing - Cohort - Non-aggregate (Comms & Recruitment)

Expected Benefits:

Keeping participants informed of results of trials they are involved with is a high priority according to the Health Research Authority and is best practice. The communications will also provide an opportunity to inform participants regarding the planned sharing of data with USA so ensure transparency.


GRAIL Bio UK Ltd and University of Oxford hope that by communicating with participants via this mailout, that they will help to keep them engaged with the work of SYMPLIFY and make it clear that participants are partners in the research.

The letters will also provide an opportunity to remind participants that information about how their data are handled is available on the trial website.

Outputs:

The key immediate output will be a mailing delivered to the participants of the SYMPLIFY trial.

The ultimate output of this data processing will be participants being mailed a letter to inform them of an update to the SYMPLIFY trial and to remind them of the flow of data being sent to the USA. No other outputs are expected (such as results, presentations or reports) as a result of this Data Sharing Agreement. The SYMPLIFY trial has a separate data sharing agreement (DARS-NIC-604851-W0M3S) for outcomes data relating to the study, which should generate outputs such as results, presentations or reports.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e: employees, agents and contractors of the Data Recipient who may have access to that data).

Proposed Methodology::

>Cohort:
1. GRAIL Bio UK Ltd will create a list of participant Study IDs (excluding anyone GRAIL Bio UK Ltd knows to have withdrawn consent for all forms of follow-up), NHS Number and Date of Birth and provide this securely to NHS Digital using Secure Electronic File Transfer (SEFT)
2. The cohort is validated using an automated pipeline to check for errors.
3a. If the file has more than 2% errors, the file is sent back from NHS Digital to GRAIL Bio UK via SEFT to be reviewed and corrected.
3b. If the file has less than 2% errors, this will be accepted by NHS Digital and processed using an automated pipeline to check for vital status and addresses.

>Vital Status Check
NHS Digital will perform a vital status check and remove any additional participants known to have died (whom GRAIL Bio UK Ltd may not have been aware of due to the intermittent nature of the vital status update that SYMPLIFY receives).

>Data Out
After performing the vital status check, NHS Digital will then retrieve the latest address and postcode for the remaining participants:
• A file containing participant's Title, Forename, Surname, Address and Postcode will be sent to NHS Digital's third party mail house, Datagraphic Ltd from NHS Digital via SEFT.
• Datagraphic Ltd will then mail out to individuals using an ethically approved letter provided by GRAIL Bio UK Ltd.
• All participants will receive a letter containing their Title, Name, Address and Postcode.
• Datagraphic Ltd will destroy all data received from NHS Digital within 20 working days after mailing as instructed by NHS Digital.

NOTE: For this agreement, no data will be returned to GRAIL Bio UK Ltd. GRAIL Bio UK Ltd do not store participant address details for the SYMPLIFY study therefore do not require for these to be returned. GRAIL Bio UK Ltd obtain outcomes data (including Civil Registration - Deaths data) via a separate agreement (DARS-NIC-604851-W0M3S) and therefore do not require vital status information to be returned under this agreement.

> Return to sender and follow up queries
The letter will provide details for how to contact the SYMPLIFY team at University of Oxford for follow up enquiries. A 'return to sender' address will need to be included on the letters, which will be Datagraphic Ltd. When the returned item is received, Datagraphic Ltd will carry out an automatic reconciliation using a 2D Mailcare barcode contained within the address window, which identifies the mail date and originating client. The unopened letter will be scanned, and the return recorded. The database is updated with a ‘reason code’ for the undelivered mail items, alongside the original address fields, and the piece of mail is then securely destroyed.

>Opt Outs
The mailing(s) will include information for participants on how to opt out of SYMPLIFY should they wish. This information will also be on the trial website and included in the privacy notice. Participants will be able to write or e-mail their intention to the SYMPLIFY team at University of Oxford who will update their records accordingly.

Any such participants would be removed from the list of participant IDs that GRAIL Bio UK Ltd send to NHS Digital for any subsequent mailing(s) (along with those who have died, withdrawn consent for follow-up or elected to receive communications electronically). v

MR1086 - The Oxford Vascular Study (consent cohort) — DARS-NIC-653950-W8D4Z

Opt outs honoured: Identifiable (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2022-07-01 — 2025-06-30

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Demographics
  3. Civil Registrations of Death

Objectives:

The following provides background information on the purpose of the original study:

The Oxford Vascular Study (OxVasc) began in April 2002 to determine mortality, disability, psychological morbidity, cognitive decline and cost of care following stroke, transient ischaemic attack (TIA), Acute Coronary Syndrome (ACS) and acute peripheral vascular events in patients registered in one of eight GP practices in Oxfordshire.

ACS is the leading cause of death in the developed world, causing more than twice as many deaths as stroke. However, mortality data underestimates the burden of stroke. Stroke is the main cause of neurological disability in the developed world, and a common cause of dementia, depression, epilepsy, falls and fractures. The incidence, case fatality, longer term sequelae of stroke and ACS have never been measured in the same population at the same time. Comparison of OCSP (Oxford Community Stroke Project) and OXMIS (Oxford Myocardial Infarction Study) which took place in the early 1980s suggests that mortality due to stroke is lower than that due to ACS, but overall incidence is similar, and the total clinical instance of stroke may be greater. There are no data from the UK on recent time trends in age and sex specific incidence or disability rates for stroke and ACS. However, there have been major changes over the last 20 years in the life-style, primary and secondary prevention treatments and particularly in population demographics. A formal comparison would provide a firm basis on which local and national policy decisions about allocation of limited NHS funding for clinical services and limited governmental funding for medical research could be made.

OxVasc is one of a number of cohort studies funded by the National Institute for Health and Care Research (NIHR) to identify simple low cost interventions and to inform the development of clinical trials to improve the treatment outcomes of vascular disease in the short and long term. By recruiting all eligible participants from a defined population and following them up over a long period of time, OxVasc reduces recruitment bias so the results are more generalizable to the population as a whole and can identify whether the benefits of any intervention are maintained (e.g. sustained blood pressure monitoring and treatment, carotid surgery).

The GDPR legal bases for processing the data held under this Agreement are Article 6(1)(e) (processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller) and Article 9(2)(j) (processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes). This research into the health outcomes for people who have suffered from stroke or other vascular events is a task in the public interest under General Data Protection Regulation (GDPR) as it aims to improve care for all patients considering undergoing this type of process – informing clinicians and commissioners of variation and outcomes and complications to support work to improve and standardise treatment selection choices. The protocol has had appropriate ethics committee approval and CAG support (all documentary evidence is included in this application.

Since 2008, mortality and demographic data were supplied to the University of Oxford by ONS and subsequently the Health and Social Care Information Centre (which has since become NHS Digital) for the purpose of this research study. The overall aims of the study have not changed since 2002 and the benefits of a cohort study of this length and detail will continue to improve the public’s health through disease prevention, earlier disease diagnosis and better disease management. The study to date has had significant impacts (as described in the benefits section) and it is expected that further measurable benefits will be outputs that underpin NICE and other Department of Health strategies for detection, management and treatment of vascular disease.

The minimal information is requested under this agreement to meet the aims of the study and ensure integrity of study outputs, specifically:

Date of death-survival after vascular event to evaluate treatment interventions, natural history of disease and to stop further contact for follow up visits.
Place of normal residence and place of death-health economic analyses of recovery and use of assisted living/institutional care.
Cause of death (including ICD10 codes and free text) to establish underlying and contributing factors to death.

The University of Oxford previously flowed identifying information to NHS Digital for the purpose of list cleaning. The University of Oxford used the list cleaning service to provide a newsletter to participants. Upon receipt of the Demographics data, University of Oxford will destroy this data and provide a data destruction certificate to NHS Digital as it will no longer be required.

No attempt is made to contact families after the death of a participant notified to the study team through this process.

The analyses are determined by the Principal Investigator (PI) and the study statistician and performed by the University of Oxford research team to support the aims and objectives of the study. Ethical approval and grant funding from the Wellcome Trust and the NIHR Oxford Biomedical Research Centre have been granted for the study, for the same purposes as described in this Agreement.

The progress is evaluated annually and new analyses are added or completed based on findings to date and the length of time required to collect outcomes (cause of death) to determine the prognosis of different presentations of vascular disease and/or achieve statistical power to answer the research question. For example, mortality data is required to determine the outcome (disability or death) and time course of bleeding requiring medical attention in patients taking long-term antiplatelet treatment after acute vascular events. This is then used to estimate the age-specific numbers needed to treat to prevent upper gastrointestinal bleeding with routine proton-pump inhibitor co-prescription.

Not all of the work of the Oxford Vascular Study involve use of data on mortality obtained from NHS Digital but use of the data will be important in some analyses in order to determine firstly the impact of any treatment on survival and secondly the health economic value of any intervention e.g. prevention of recurrent stroke and subsequent health resource use.

The data subjects are patients registered in one of eight GP practices in Oxfordshire who have had a stroke, transient ischaemic attack (TIA), Acute Coronary Syndrome (ACS) or acute peripheral vascular events. These are the same practices (and associated demographic characteristics of the previous studies-OCSP/OXMIS) allowing valid comparisons over time. Since 2002, participants have been recruited from hospital, outpatients’ clinics or home following referral from a collaborating GP and interviewed by a researcher following written consent. Clinical information is gathered from participants about their health, medical history, family history and current treatment. The study team extract information about their vascular event and related conditions from health records for the duration of the study in order to follow up on participants health status. Regular face-to-face or telephone contact is maintained with participants and their GPs over the first two years and they are contacted again at five and ten years after entering the study. However, over this period participants may move out of Oxfordshire or register with a different GP. In order to achieve the study aims, accurate information on the date and cause of death of participants in the cohort to evaluate long term morbidity and mortality for the study population is required. For this reason, national data is required. Data will need to be shared at a record level as agreed between the University of Oxford and NHS Digital to flag and receive date and cause of death for each individual cohort member and in order to produce accurate matching. There is no alternative way to obtain all cause mortality data sufficiently robust to achieve the aims of the study. All results will be presented as aggregated data.

The earliest participants were recruited while the study was in a pilot phase and they were all asked to re-consent to participate in the full study when version 2 of the consent form (dated 28/12/2006) came into use. Under this Agreement, the University of Oxford is permitted to share with NHS Digital details of participants who have given consent using version 2 of the consent form or any subsequent version. The current Agreement does not permit the processing of data relating to any participant who was recruited using an earlier version - which did not include a declaration of consent for "information held by the NHS and records maintained by the General Register Office" to be used to maintain contact and follow up their health status - and did not re-consent using version 2 or a subsequent version. The data for these participants is to be obtained using section 251 support from the Confidential Advisory Group (CAG) under a separate Agreement, DARS-NIC-148369-8PPWK.

The University of Oxford is the sole data controller who also process the data supplied by NHS Digital under this Agreement. No other organisation determines the purpose for data processing or has any access to the data. Funding for OxVasc is provided by the Wellcome Trust and the NIHR Oxford Biomedical Research Centre. Past funding has been provided by the Stroke Association which is still acknowledged on some of the study outputs. The funders expect the Nuffield Department of Clinical Neurosciences to undertake research such as this but the funding body can not access data nor has a role in analysis or interpretation.

Yielded Benefits:

Expected Benefits:

The overall aims of the Oxford Vascular Study are to improve the public’s health through disease prevention, earlier disease diagnosis and better management of known risk factors. Results from the study to date have been used to underpin NICE guidelines and other Department of Health strategies (some of these benefits are described in yielded benefits to date) by providing evidence for ways to improve diagnosis of disease and how to effectively treat common risk factors such as high blood pressure.

The Oxford Vascular Study has ongoing funding from the Wellcome Trust and the NIHR to continue recruitment and complete follow up of the 25 year cohort. This will allow the study to continue providing new evidence to inform stroke prevention and vascular disease generally for the benefit of the population at large, with the continuing recruitment and long term follow up of OxVasc participants. These benefits fulfil the requirements necessary for data processing outlined in Article 6(1)(e).

The study also benefits the individual participants by providing:
1. Rapid assessment and treatment following TIA and minor stroke in order to identify the cause and provide treatment.
2. Ongoing assessment of vascular risk factors (BP, cholesterol), health care advice (smoking cessation, lifestyle advice) at follow up, enabling participants and the collaborating GP to improved secondary prevention of vascular disease.

Mortality data from NHS Digital also potentially reduces distress to participant’s relatives by attempting to contact them for follow up/information about circumstances of death.

The OxVasc study has been running for over 20 years, during which time many improvements in assessment/diagnosis and treatment of TIA, stroke and ischaemic heart diseases have been made. Analysis of morbidity and mortality over this time period is expected to provide additional insights into how further gains can be made in stroke prevention and care. An example of how long term cohort studies like OxVasc can direct further research is in stoke incidence in younger age groups, which appears to be increasing. A new study to better understand the role of treatable risk factors in young stroke patients which could potentially be informative for future clinical guidelines are now underway. (https://www.medicalresearchfoundation.org.uk/projects/contribution-of-the-presence-susceptibility-to-and-control-of-modifiable-vascular-risk-factor-in-young-stroke-and-tia-a-prospective-cohort-and-nested-case-control-study). This study is separate from the OxVasc study and does not use that data held under this Agreement, however, the new study builds on OxVasc research to improve treatment for young stroke victims.

Outputs:

The study overall has produced over 250 peer reviewed publications on incidence of disease, risk factor management, prognosis and outcomes. Peer-reviewed manuscripts on original research arising from the study are subject to the Wellcome Trust open access policy and are available to all free of charge on publication. A statement on data used and data sharing is provided in line with the individual publisher guidelines and the NIHR. The data received under this Agreement will continue to be used in the same way as previously outlined and is important for use in analyses of the effects of new/extended uses of primary and secondary prevention of vascular disease over a long period of time.

Research aims and findings from the Oxford Vascular Study are summarised on the study website (www.ndcn.ox.ac.uk/research/oxvasc) and presented at open days organised by the NIHR Oxford Biomedical Research Centre (BRC), Nuffield Department of Clinical Neurosciences and public involvement and engagement groups. Talks on OxVasc and related topics (e.g., high blood pressure, vascular dementia) are also available on YouTube (https://oxfordbrc.nihr.ac.uk/research-themes-overview/stroke-and-vascular-dementia/videos-stroke-and-vascular-dementia). Selected results of the study have been reported in the local, national and international press.

Participants are informed of progress with posters displayed with results of the study to date in the participating GP practices and the general information booklet which participants are given on entry to the study and updated yearly.

Yearly reports on the progress of this research have been given to the funders with all outputs and impacts for the previous year. Many of the outputs arising from the Oxford Vascular Study include data on mortality obtained from NHS Digital, often to corroborate information reported by hospitals, GPs and/or relatives of participants on cause of death. Data from NHS Digital provides the collated ICD10 codes, and these data continue to be important in some analyses together with the detailed clinical information collected from participants as part of the study. A statement regarding sharing of data collected from and about participants is available on the study website.

All outputs (presentations, posters, peer reviewed publications and oral presentations at conferences) report numbers at an aggregated level, with small numbers suppressed.

Processing:

The following provides background on the processing activities undertaken for the original study:

The study data, including data provided by NHS Digital under previous versions of this Agreement, are held by the University of Oxford. The data are stored electronically on University of Oxford central servers which are connected to the main University of Oxford’s network. At no time will sole employees of John Radcliffe Hospital have access to the data held on the server for University of Oxford.

Identifying details of participants have previously been supplied to ONS and subsequently NHS Digital so that their patient records could be flagged and mortality data could be reported to the study. The University of Oxford will flow identifiers (Surname, First name, NHS number, date of birth, study ID) to NHS Digital to update the cohort to flag participants recruited after 2018.

The data disseminated under this Agreement will only cover participants who have consented to this. Where section 251 support has been granted to meet the Common Law Duty of Confidentiality, the data for these participants is disseminated under a separate Agreement for this study: NIC-148369.

The following datasets are processed by the study:

i. Civil Registration Mortality data – details of participants’ deaths including date and cause;
ii. Demographics – details of embarkations or lost to follow up

Data received from NHS Digital will be linked to unique study ID using date of birth and NHS number to ensure a perfect match to each participant’s date of vascular event to calculate outcomes (Kaplan-Meier survival analyses). All data supplied by NHS Digital will be used only for the approved Medical Research Project MR1086-The Oxford Vascular Study. No data will be shared with any individuals or agencies outside of the study team and all study staff are substantive employees of the University of Oxford.

The data is held in an access-controlled server room within the University of Oxford Medical Sciences Division, situated on the Old Road Campus and connected to the main University network, located behind a firewall. Physical access is limited to Computer Services Department staff. Data will be encrypted using industry standard techniques meeting the Information Governance Toolkit standard (RBQ).

PANORAMIC: Platform Adaptive trial of NOvel antiviRals for eArly treatMent of covid-19 In the Community — DARS-NIC-605115-L0W3V

Opt outs honoured: Anonymised - ICO Code Compliant, Identifiable (Statutory exemption to flow confidential data without consent, Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006)

Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 - s261(5)(d); Health and Social Care Act 2012 – s261(2)(c), CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 - s261(5)(d); Health and Social Care Act 2012 – s261(2)(c), CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-12-10 — 2022-03-31

Access method: Ongoing, System Access, One-Off
(System access exclusively means data was not disseminated, but was accessed under supervision on NHS Digital's systems)

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. COVID-19 Access to Summary Care Records
  3. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Critical Care
  6. Medicines dispensed in Primary Care (NHSBSA data)
  7. Emergency Care Data Set (ECDS)
  8. Hospital Episode Statistics Outpatients
  9. Uncurated Low Latency Hospital Data Sets - Admitted Patient Care
  10. Uncurated Low Latency Hospital Data Sets - Critical Care
  11. Civil Registrations of Death
  12. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  13. Hospital Episode Statistics Admitted Patient Care (HES APC)
  14. Hospital Episode Statistics Critical Care (HES Critical Care)
  15. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

Despite high uptake of vaccination against COVID-19, the disease remains prevalent in the UK and in many countries around the world, with many patients continuing to require hospital admission. Early treatment with antiviral agents may prevent progression to the later phase of COVID-19. Therefore, there is an urgent need to identify treatments for COVID-19 for use in the community early on in the illness that prevent the need for hospital admission and improves time to recovery. It is therefore vital that the University of Oxford use this opportunity to accelerate enrolment into COVID-19 therapeutics trials.

The PANORAMIC (Platform Adaptive trial of NOvel antiviRals for eArly treatMent of covid-19 In the Community) trial is the only national priority clinical trial evaluating potential novel antivirals for COVID-19 in the primary care setting, endorsed by the Chief Medical Officers (CMOs) of all four devolved nations. The primary aim is to determine the effectiveness of selected antiviral agents in preventing hospitalisation and/or death in higher-risk patients with a confirmed positive SARS-CoV-2 PCR test result.

PANORAMIC is:
• Recruiting across the whole UK* - anyone age 18** or over who match the participant criteria can participate, regardless of location.
• For both recruiters and patients it is a simple process to complete enrolment on to the trial.
• Obtaining consent, checking eligibility, issuing study medication and materials, and follow-up is managed remotely through a central facility at the University of Oxford.

*PLEASE NOTE - The University of Oxford is obtaining data from NHS Digital related to residents in England only.
** PLEASE NOTE - The first treatment included in the PANORAMIC trial has conditional licensing for adults and therefore the study team are unable to include under children at this stage. Depending on the advice from the Antiviral Taskforce regarding future treatment arms, and their licensing conditions, the inclusion criteria may be amended in future to include under 18’s.

Primary objective -
To determine whether antiviral treatment in the community safely reduces non-elective hospitalisations/deaths in higher risk, symptomatic patients with confirmed COVID-19

Secondary objectives - To explore whether trial treatment reduces:
1) Time to recovery (defined as the first instance that a participant report of feeling recovered from the illness)
2) Participant reported illness severity, reported by daily rating of how well participant feels, enabling identification of sustained recovery.
3) Duration of severe symptoms and symptom recurrence
4) Contacts with the health services
5) New infections in household
6) To investigate the safety of antiviral agents
7) Longer term effects
8) Cost effectiveness

The study team are requesting the Covid-19 UK Non-hospital Antigen Testing Results (pillar 2) dataset in order to identify and successfully recruit potential patients into the trial, early on in their stage of COVID-19 disease. A positive PCR test is essential for inclusion into the trial. The positive PCR test result will be participant reported prior to randomisation, and confirmation of the positive result will be sought at a later date via the Pillar 2 dataset.

The University of Oxford would like to receive names and contact details (including preferably a telephone number and email address) of people who have received a positive COVID-19 swab result from the Pillar 2 testing system. The trial team, based at the University of Oxford, will then contact these potential participants, inform them about the trial and if they are happy, go on to screen and consent them into the PANORAMIC trial. Daily, the trial team would like to receive identifiable record-level data on a random cohort (up to 500) of people aged 18+.

The trial already has a centre set up and operating remotely at the University of Oxford to manage this recruitment in a timely manner. Management of the recruitment to the trial will be undertaken by substantive employees of the University of Oxford who have been appropriately trained in data protection and confidentiality.

The question of whether ‘cold calling’ is appropriate has been considered for this application. As time is of the essence for recruitment into PANORAMIC, the telephone is the most efficient and quickest means to ensure direct contact with the individual, who can answer questions instantly over a call. The study team would also like to use the Short Messaging Service (SMS) and email option for invitation, for those patients who they cannot reach via telephone or those who require a follow-up information. During the calls, clear explanation will be given to individual about how the trial has been able to contact them and what to do if they do not wish to be contacted again (i.e. registering a National Data Opt-out). The trial team will apply the Telephone Preference Service. The trial team will also ensure the required comorbidities are discussed early on in the calls so as to not to get the individual’s hopes up if they are not in fact eligible for the trial.

Other considerations that have been taken into account in relation to contacting individuals:
• The data relating to positive COVID-19 tests is sent to NHS Digital at the same time that it is sent to the Business Services Authority, the latter process triggering the SMS to the individual informing them of their result. It then takes around four hours for the Covid-19 UK Non-hospital Antigen Testing Results (pillar 2) dataset within NHS Digital to be updated with this information. Given that this information then needs to be extracted from the dataset at some point in the next 24 hours, then used by the trial team to make contact with the individual, the risk of the individual being informed of their test result by the trial team before they have read their SMS is small. However, the trial team should have a suitable script prepared to deal with this slim possibility.
• The chances of people having multiple positive COVID-19 test results are rare, and rarer still is the likelihood that they will be one of the 500 people extracted from the thousands of daily test results to be sent to the trial team on more than one occasion. Therefore, the risk of an individual being contacted twice for recruitment into PANORAMIC is extremely low.
• NHS Digital recognises that there are likely to be more requests of this nature in future and therefore, if multiple trial require extracts of people to contact, suitable controls need to be in place within the extract process to ensure that individuals are not getting contacted for recruitment into trials more than is reasonably expected.

Patient and Public Involvement & Engagement (PPIE)
The Panoramic trial has had extensive PPIE engagement to support development of trial materials and processes and to discuss the plans for efficient and safe use of patient data and has plans for significant engagement throughout the period of the study.

The main study PPIE group includes members of five different cultural and faith communities and representatives of those with learning disabilities. They have had three meetings prior to this application, focussing on acceptability of trial processes and optimising patient-facing materials including redesigning the Pictorial Patient Information Sheet (PIS) and co-developing a summary front page for this document. They have reviewed the online trial interface and instructions for participants. This group will continue to meet at least every month when the trial commences recruitment.

The study team have begun to convene additional PPIE groups in Scotland, Northern Ireland and Wales to focus on nation-specific recruitment and dissemination issues and will work with individual cultural and community leaders to ensure inclusion of the most diverse range of participants possible.

Request to use SCR for consented participants:

The PANORAMIC trial team are requesting to access the Summary Care Record (SCR) for patients recruited into the trial for the purposes of ensuring timely prescribing and safe patient care. SCR access will be essential to successfully recruit participants in the very restrictive timeframe of five days since symptom onset. For the first treatment arm, the trial is run remotely, therefore removing the need for participants to be near to a GP practice. Screening and contact with the trial team is all completed online. The trial team are requesting access to Summary Care Records as part of this application as it will not be possible within the five day window of recruitment from symptom onset, for the trial team to request such information regarding safe prescribing from the participant’s GP.

The PANORAMIC trial team therefore to seek permission for the clinical trial team, a group of dedicated doctors and nurses who are fully qualified, study-trained, accredited and registered, to review consenting patients’ Summary Care Records, in order to be able to confirm key information obtained from the patient relevant to safe patient care in the trial. Only nurses and doctors who are registered and accredited with the General Medical Council of the UK or the Nursing and Midwifery Council of the UK who are clinical members of the PANORAMIC study team would review the Summary Care Record for information relevant to safe prescribing and participant care.

The Summary Care Record will be used as a timely, second check regarding medication, allergy, and co-morbidities information to support reconciliation, to ensure safe prescribing. These are all elements of a patient’s SCR with additional information which is a subset of their wider GP record. This access provides an immediate available information source to meet the need to support safe prescribing in a proportionate and timely way. In addition, the Summary Care Record will provide a further safety check, in that access to it will facilitate the opportunity for double-checking participants’ NHS Number and GP practice.

There is a five-day window from symptom onset to enrolment into the trial and so SCR access is critical to confirm eligibility within this very limited time-period.

Access is sought only for those people who have screened eligible to be part of the trial, and who have already signed informed consent for participation in the study and for the trial to access their clinical records. All potential participants are asked specifically for permission for the trial team to access their medical records and have the opportunity to decline this access. In addition, they will be asked specifically and separately for permission about access to their Summary Care Record. For a patient who has provided consent to join the trial and for their SCR to be accessed, the data will be accessed by a clinician to support safe prescribing decisions immediately prior to prescribing medications. A Summary Care Record will only ever be accessed on one occasion for each individual participant/

Access to SCR is required for the period that the trial is recruiting - currently this is December 2021 until Sept 2023.

This request for SCR access for PANORAMIC, along with the similar request for the sisters trial, PRINCIPLE, together form a first-of-type access request and as an interim position it is included in the Data Sharing Agreement. This is a holding position whilst considerations are undertaken to agree whether to offer this as a service more widely to clinical trials for patient safety purposes. This allows time for an appropriate access and approval process to be developed in future, but in the meantime ensures that SCR access is recognised within the research data access process alongside the normal direct care process for access to SCR.

FOLLOW UP DATA:
In this agreement, the University of Oxford are requesting access to record-level data from the following data sets in order to receive follow up data on trial participants:

> Hospital Episode Statistics (HES) Admitted Patient Care (APC)
>HES Critical Care (CC)
> Civil Registration (Deaths) data set
> Medicines dispensed in Primary Care (NHSBSA data)

The trial follows up all consented participants for 28 days and then at three and six months following randomisation.

Due to the high rate of recruitment, the Data Safety Monitoring Committee review the trial data weekly and can ask for recruitment into a trial arm to be stopped immediately. Therefore trial data needs to be made available as soon as possible so that the team can quickly issue a press statement and publication of results if required to do so. There is a need to capture the required data in a more timely and efficient manner via monthly extracts from NHS Digital.

HES APC, HES Critical Care, and Civil Registration of Deaths datasets will be essential to collect the primary outcome, which relates to all-cause hospitalisation/death.

LEGAL BASIS FOR COMMON LAW DUTY OF CONFIDENTIALITY
The lawful basis for the release and use of the confidential data (Covid-19 UK Non-hospital Antigen Testing Results (pillar 2) and Summary Care Record (SCR)) being shared under this version of the agreement is Regulation 3(4) of the National Health Service (Control of Patient Information Regulations) 2002 (COPI) to require NHS Digital to share confidential patient information with organisations entitled to process this under COPI for COVID-19 purposes. The only permitted activities under this Data Sharing Agreement (DSA) are for COVID-19 purposes and within bounds of Reg 3(2) COPI. Reg 3 (2) COPI states that: "2) For the purposes of this regulation, “processing” includes any operations, or set of operations set out in regulation 2(2) which are undertaken for the purposes set out in paragraph (1)." The research relates to the monitoring and managing of COVID-19 and would therefore be covered by Reg 3(1)(d) of COPI.

Consent is in place to allow the pseudonymised follow-up data to be provided to the study team from NHS Digital and details of the trial and expected data flows are explained on the latest version of Patient Information Sheet which will be hosted on the trial website: https://www.panoramictrial.org/ (currently in development).

Only the study team at the University of Oxford will access the requested data. No other organisations are involved in the processing or storage of NHS Digital record level data. NHS Digital Record level data will not leave the UK.

LEGAL BASIS FOR PROCESSING DATA
The University of Oxford, as the Data Controller who is also processing the data will process Personal data under GDPR Article 6 (1) (e) - Processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. As a higher education establishment, the University conduct research to improve health care and service and the data requested is necessary for the performance of a task carried out in the public interest.

Additionally, under GDPR Article 9(2)(j) processing of Special Category Personal Data (of which Health data is one) is necessary for archiving for research purposes. Data minimisation process is being followed and only data that is required specifically for the purposes of this study has been requested, to protect the rights of the data subjects.
Article 9(2)(h) is additionally being used to cover the processing of SCR specifically: ‘processing is necessary for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, the provision of health or social care or treatment or the management of health or social care systems and services’ as it is being used as a timely, second check regarding medication, allergy, and co-morbidities information to support reconciliation, to ensure safe prescribing.

The PANORAMIC trial is funded by the National Institute of Health Research (NIHR), but do not make any decisions determining the purposes and means of the processing of data or the study purpose and methodology, and are therefore not considered a Data Controller. Additionally, the NIHR will not have access to any NHS Digital record level data and are therefore not considered a Data Processor.

A collaborator agreement is in place between University of Oxford and the institutions listed on the protocol for this clinical trial. The study team have confirmed that the institutions listed within the protocol will not have access to NHS Digital record level data and therefore, in line with NHS Digital’s DARS standards, does not consider these other institutions as a joint Data Controller or Data Processor for this agreement.

Expected Benefits:

The NIHR has commissioned a research consortium to deliver a complex community-based clinical trial platform for NOVEL antiviral candidates, to enable an evidence-based approach to identify whether such treatments are effective in treating COVID-19. PANORAMIC hopes to uniquely expand the evidence about the effectiveness of novel antivirals to benefit COVID patients in the NHS and worldwide.

Antivirals, if deployed rapidly, have the potential to break chains of transmission, reduce symptoms and hospitalisations, all of which will protect the vital gains of the vaccination programme, particularly if new variants of concern emerge which reduce vaccine efficacy. The emergence of the SARS-CoV2 virus has had a profound impact on the UK population, especially in relation to those more at risk groups. It is vital to ensure that a significant rise in infections and spread of the virus in the population is controlled as far as possible. The development of antiviral treatments is integral to a longer-term response to COVID-19 and will enhance pandemic preparedness in the years ahead. In addition to clinical data showing reductions in viral load and time to alleviation of symptoms or illness duration, other data are of great importance to Antiviral Taskforce (ATF) and DHSC in terms of national policy. These include data on reducing hospitalisations and mortality as well as data on reducing secondary transmission in households.

The primary focus of the trial will be early treatment of confirmed (PCR positive) SARS-CoV2 infections in high-risk individuals to prevent hospitalisation, reduce symptoms, and speed up recovery, thus reducing clinical impact of the virus on individuals and the strain on NHS hospitals. Individuals where the vaccine is less effective, such as the immunosuppressed or the elderly are key targets for this type of treatment. Additionally, antivirals may be particularly useful in managing outbreaks, working alongside other public health interventions, to prevent infection in known contacts of positive cases and to offer protection to those who are not vaccinated or do not respond to vaccination.

Outputs:

All outputs will be aggregated with small number suppression applied as per the HES analysis guide.

The PANORAMIC trial will recruit to target much quicker than using current methods if the team can receive the requested NHS Digital identifiable data. The statistical analysis team aim to receive follow up data in a more timely manner, therefore answering the COVID-19 treatment in the community question more quickly with the aim of preventing COVID-19 patients being hospitalised so in turn reducing NHS burden.

The trial team intends to disseminate results via media channels: pre-prints/publications in peer-reviewed journals such as the New England Journal of Medicine (NEJM); press releases in local and national newspapers; BBC news coverage; Department of Health and Social Care (DHSC) press briefings; DHSC Social media updates; via the Antiviral Taskforce webpage; Twitter and Facebook University of Oxford accounts. Results will be disseminated to trial participants via the trial website, supported by the University of Oxford. The study team will provide regular updates to the NIHR Evaluation, Trials and Studies Coordinating Centre.

From the start of the trial (beginning of December 2021), the data will be reviewed on a weekly basis to determine whether the antiviral treatment arms meet the superiority/futility criteria. The trial aims to recruit participants within 4 months (by the middle of April 2022) to identify whether the initial antiviral agent is effective in treating COVID-19. The first interim analysis is scheduled for the start of January 2022, after 1,000 study participants have been randomised in the trial.

The study team will update participants by signposting them from their ‘end of trial letter’ to the trial website for the latest results and information. Due to the limited resources of the study trial team and the potential number of study participants involved, the study trial team have made the decision that distributing newsletters or email updates would be difficult to manage at such a scale. The study trial team believe that signposting to the website also ensures that the most up to date information is always available to participants, as newsletters can soon become outdated due to the fast-paced and evolving nature of this platform trial. The study team are also considering the use of video/audio updates and other formats to the website depending on capacity.

The trial is of national and international relevance during this pandemic.

Processing:

Covid-19 UK Non-hospital Antigen Testing Results (pillar 2) - The processing of the data will be as follows:

• On a daily basis (seven days a week) NHS Digital will interrogate the Pillar 2 data and extract up to 500 individuals at random who are aged 18 or over who have received a positive COVID-19 test result in the previous 24 hours.
• The individuals will be residents of England only.
• Filters will be applied to remove patients who have registered a National Data Opt Out, as well as special categories of people for whom the data should not be disseminated. The purpose of the restriction is to ensure that patient information that might imply a location is protected.
• Individuals who have signed up for the Telephone Preference Service will not be contacted via Telephone.
• The flow from NHS Digital to University of Oxford will be automated via a Secure Electronic File Transfer service called SEFT.
• University of Oxford will use the data provided to make outbound contact to ask if eligible individuals would be interested in being recruited into the trial.
• The study aims to recruit 200 people into the trial per day, 1000 participants before January 2022.
• The number of individual contact details supplied by NHS Digital to University of Oxford will be reviewed once the take-up rate is better understood.
• On an interim basis, the PANORAMIC trial will use the Pillar 2 Data flow already provided under DARS-NIC-411161-G4K7X-v5 (PRINCIPLE trial) - noting that this data is provided to the same study team at the University of Oxford. Once a separate data flow is in place specifically for PANORAMIC, this permission will be revoked.

The trial team at University of Oxford will hold the data securely adhering to all Information Governance (IG) Policies in the Department. The trial team will call these contacts to inform them of the trial, screen, consent and randomise them.

It will take 5,319 patients per arm to answer the question for the drug of that arm. The trial has pre-defined futility and superiority (failure and success) criteria, which will determine when it closes and whether it reaches the sample size. The trial will involve Usual Care and 2-3 antiviral agent arms.

Participants will be randomised using a secure, fully validated and compliant web-based randomisation system. Once deemed eligible, the medically qualified clinician or research nurse from the central clinical team or Hub will randomise the participant. Participants will be randomised to one study arm using equal allocation ratios corresponding to the number of eligible arms in the trial. For instance, if there are two active interventions (A & B), the allocation ratio will be 1:1:1 for Usual Care, active A, active B (respectively), such that 33% of participants are randomised to Usual Care. If there are 3 active interventions, the allocation ratio will be 1:1:1:1, such that 25% of participants are randomised to Usual Care. Patients must be eligible for at least two arms (Usual Care and at least one novel antiviral intervention). Stratification will be by age and vaccination status. The randomisation database will automatically alert the relevant Investigational Medicinal Product (IMP) distributor and the participant, trial team and legal representative if applicable will be notified electronically of the treatment allocation. If the participant does not have an email address, they will be notified by telephone.

Statistical data analysis will be carried out via University of Oxford owned devices connected to the University of Oxford network either directly in person or remotely, using an appropriate statistical package. To remotely access the devices requires a secure 2-factor authenticator (VPN) and users are then able to securely access the secure server on the University’s IT framework. All data analysis will be conducted within the confines of the University’s secure server, and will not be downloaded to remote devices for storage or processing.

Berry Consultancy in the USA will also be undertaking data analysis, however this will be performed using non-NHS Digital data. No NHS Digital data will be processed by any organisation not already stated in this agreement, nor will any NHS Digital data be sent, stored or processed outside of the UK.

The identifiable data received from NHS Digital will be deleted on a weekly basis as the trial team will no longer require it.

• Summary Care Record (SCR) for consented participants:

Once participants are recruited into PANORAMIC, the trial team will access their Summary Care Record. This will be through ‘SCR Core’ or ‘SCR Additional Information’, depending on what is available for each participant.

SCR Core includes:
• current medication
• allergies and details of any previous bad reactions to medicines
• the name, address, date of birth and NHS number of the patient

SCR Addition Information includes:
• significant medical history (past and present)
• reason for medication
• anticipatory care information (such as information about the management of long term conditions)
• end of life care information (from the SCCI1580 national dataset)
• immunisations.

Data is minimised as access is only for the consented cohort for the trial.
The Clinical trial team will use SCR and build on best practice by identifying the patient’s NHS Number after a demographic search and using this to confirm the patient’s identity as well as ensuring the NHS Number is noted on any paperwork being returned to the patient’s practice regarding their participation in the trial.

The SCR Team at NHS Digital will use a position for the trial team which allows access to SCR via SCRa/Spine Portal and does not include emergency access. SCR access is time limited for the duration of the trial; this will be achieved with the use of roles to be applied to smartcards to be time limited for the duration of the trial.

The trial team will not retain information obtained from SCR once the eligibility checks have been done: they will record that the SCR has been accessed, and the audit trail within the SCR will also log who has accessed the record.

• Follow up data (HES APC, HES Critical Care, Medicines dispensed in Primary Care data and Civil Registration of Deaths):

On a monthly basis the trial team will send to NHS Digital the relevant data items for those cohort members who have hit the 28 day follow up window.

The University of Oxford will provide NHS Digital with the following information via Secure Electronic File Transfer (SEFT) for consented participants: Study ID, NHS Number, Date of Birth, trial recruitment date, withdrawal date (if applicable).

NHS Digital will link the cohort members to the aforementioned datasets and return the record-level pseudonymised outputs to the Study team at the University of Oxford via SEFT.

NHS Digital Record Level Data will not be linked to any other datasets.

HES and ECDS DISCLOSURE CONTROL / SMALL NUMBER SUPPRESSION
In order to protect patient confidentiality, when presenting results calculated from HES record level data, outputs will contain only aggregate level data with small numbers suppressed in line with HES Analysis Guide. When publishing HES data, data processors must make sure that:
• National-level figures only may be presented unrounded, without small number suppression
• cell values from 1 to 7 (inclusive) are suppressed at a sub-national level to prevent possible identification of individuals from small counts within the table.
• Zeros (0) do not need to be suppressed.
• All other counts will be rounded to the nearest 5.
Data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

WAX: Weight Bearing in Ankle Fractures. A randomised clinical trial of weight-bearing following operatively treated ankle fracture. — DARS-NIC-504846-J6X8M

Opt outs honoured: Anonymised - ICO Code Compliant (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2022-10-20 — 2025-10-19

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Emergency Care Data Set (ECDS)
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Outpatients
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)
  6. Hospital Episode Statistics Critical Care (HES Critical Care)
  7. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The University of Oxford requires access to NHS Digital data for the purpose of the following research project: WAX: Weight Bearing in Ankle Fractures. A randomised clinical trial of weight-bearing following operatively treated ankle fracture.

The study aims to determine whether functional outcomes after early weight-bearing in patients with operatively treated unstable ankle fractures are not worse than adopting a delayed weight-bearing regime which is usual care.

University of Oxford will analyse the dataset to:
1. Investigate the difference in risk of adverse events between the trial treatment groups in the first 12 months post-surgery.
2. Investigate the resource use, costs and comparative cost utility between the trial treatment groups in the first 12 months post- surgery.

This study is a randomised clinical trial, which is the best method to compare treatments to guide the care of patients. Randomisation will be used to produce two groups of patients: those given advice to walk on their operated ankle 2 weeks after surgery, and those who wait until 6 weeks. Patient follow-up will extend to 12 months.

It is funded by the National Institute for Health Research (NIHR) Research for Patient Benefit (RfPB) programme, reference number PB-PG-1217-20029. There will be a report submitted to the funder currently planned for the end of February 2023. The study cohort will go through a one-year follow up in January 2023. The data has been requested until 2025 to allow peer review of submitted manuscripts and/or responses to readers, as there may be a need for additional analyses.

The University of Oxford relies on GDPR Article 6(1)(e) as the lawful basis for processing the data within this application. There is public interest for patients, healthcare staff and the NHS, as this research will decrease uncertainty and allow standardisation of care and promotion of development of pathways for more efficient and cost-effective care.

The study requires processing of special category data and relies on Article 9(2)(j) as a lawful basis for processing data.

Data for this project has been minimised to ensure researchers only have access to the data they require to carry out the statistical and scientific processing of the data and to meet the purpose of the project for which there is a public interest.

NHS Research Ethics Approval has been granted for this study and all participants have prospectively consented to share their data in line with the processing described in this agreement.

The project is not part of a wider project, collaboration, or associated work.

The data subjects will be the trial participant cohort who fulfil all eligibility criteria as defined in the project Protocol. Every participant has provided prospective consent to access their personal data.

The cohort will include adults (18 years+) undergoing surgery for an ankle fracture. All participants will be treated non-weight-bearing until their two-week postoperative follow-up visit. They will then be instructed to either begin weight-bearing on the injured leg or remain non-weight-bearing for an additional 4 weeks. The decision on which instruction they are given will be made by chance using a process called randomisation so that neither patients nor surgeons can influence the choice. All other care will be as per usual treatment. Participants will report how well their ankle is healing and working, and their quality of life using questionnaires at intervals over the first year following surgery. Differences in healthcare costs will also be compared as another element of this research.

The University of Oxford recruited 562 participants over a 21-month period (between 13th January 2020 and 29th October 2021), from more than 20 hospitals. At least 436 participants were required in this study. This number was calculated based on previous scientific research to ensure that the study was large enough to reach a firm conclusion about its aims. The published results will inform NICE recommendations and will influence clinical practice.

The participant population for this study consisted of adult patients with an operatively treated ankle fracture:

Inclusion Criteria
• Age 18 years and above.
• The patient has undergone operative fixation for an unstable ankle fracture.
• Surgery was performed within 14 days of the injury.
• In the opinion of the treating surgeon, the participant might benefit from early weight-bearing.
• Able and willing to give informed consent.

Exclusion Criteria
The participant may not enter the study if ANY of the following apply:
• A lack of protective sensation (e.g. peripheral neuropathy).
• Inability to adhere to trial procedures.
• Bilateral operatively treated ankle fractures.
• Already in a trial for ankle fracture.
• Patient has received a hindfoot nail to treat index fracture.

There are around 170 ankle fractures each day in the UK. Many of these injuries heal with support in a plaster cast or splint, but some require surgery to restore the natural alignment of the bones and fix them in place with screws and plates. This improves how the ankle works once the fracture has healed.

Following surgery for an ankle fracture, patients are commonly told not to walk on the affected leg for six weeks in order to allow the bones to heal. Restricting the weight put through the affected leg may reduce the chance of surgical complications such as infection, breakage of the plates and screws, and loss of alignment requiring revision surgery. However, this restriction has been associated with problems such as blood clots, muscle weakness, stiffness, and poor recovery. It is unclear that the traditional six weeks period of limited walking is of any benefit. A recent national review found that surgeons gave patients very varied instructions following ankle fracture surgery, indicating that overall, UK surgeons have differing opinions about the best extended treatment pathway.

There has been little high-quality research in this area. The National Institute for Health and Care Excellence (NICE) and the James Lind Alliance (JLA) Priority Settings Partnerships have identified this question as one of their top priorities for research in trauma.

The objectives that are addressed through this data processing are:
1. Investigate the difference in risk of adverse events between the trial treatment groups in the first 12 months post-surgery.
2. Investigate the resource use, costs, and comparative cost utility between the trial treatment groups in the first 12 months post- surgery.

Emergency Care Data set (ECDS), Hospital Episode Statistics (HES) Admitted Patient Care and Outpatient datasets will be used.

Each of the requested datasets provide distinct data that are not available elsewhere and are required to adjust for between-participant variation and to determine outcomes necessary to answer the research questions described in the objectives.

Data will be required at the level of the participant in order to construct an adequately explanatory statistical model to address the research questions; all data will be de-identified prior to transfer to University of Oxford.

Only 4 years of data for each participant is required in order to fully describe important characteristics of the participants to determine between-participant variation. This includes the one-year follow-up data to determine any adverse events that required treatment and the associated costs.

Only consented trial participants in England will be included in the requested cohort. The team carried out a multicentre trial in order that the results are generalisable to NHS practice (for a better comparison) and therefore require data from participants across England.

In planning the study with patient representatives and gaining NHS Research Ethics approval, the team explored alternatives means to address these objectives and this approach was considered both proportionate and appropriate.

The research team is minimising data requested to only the trial participant cohort; data for each participant in the cohort will be requested for the period of time that they are involved in the trial follow-up.

University of Oxford is the sole data controller and processor for these data. No other third-party organisations are involved in this study. The study team plan, and have ethical approval to, archive the de-identified data required for the study for 3 years beyond the end of the study. It is usual to do this in this type of clinical trial so that if there are queries around the study, the interpretation or the statistical methods used can be answered fully and if necessary with reference to the original data.

Outputs:

At the conclusion of this study, it is hoped that the University of Oxford will have provided the most robust evidence available to infer whether patients who have had surgery for an ankle fracture should wait 6 weeks before walking on the operated leg or walk on their operated leg sooner following surgery. Patients and members of the public will help design a publicity strategy so that the results of the study are distributed outside of the routine scientific literature.

A report will be produced, which will inform the full update to the National Institute for Health and Care Excellence (NICE) Guidance NG38 (Fractures (non-complex): assessment and management) in 2024. Plain English outputs will include papers and web and blog media. A major international free-to-access publication is planned, alongside two national and two international presentations.

The University of Oxford have worked with members of the public who have personal experience of lower limb fractures and have knowledge of how weight-bearing advice can affect patients’ lives. A Patient and Public Involvement (PPI) member has access to the wider pool of experienced representatives who make up the Oxford Trauma and Emergency Care Patient and Public Involvement Group (the study's own PPI group). This group provides wider review of materials and provides input in reviewing plain language literature prior to dissemination, giving a broader forum for review, and providing efficient utilisation of the Patient and Public Involvement member. Using the INVOLVE guidance, the University of Oxford have given the members information on how they can be involved in the research and the type of support and training available to them.

Patients sit on the trial management and steering committees and have a key role in drafting trial documents for participants, attending conferences and assisting with the publication of the results. A Patient and Public Involvement member and a clinical expert led on the final drafting of the patient written intervention instructions.

For wider dissemination, the patient representatives will lead dissemination to the patients and carers directly through their extensive network of patient advocacy organisations which include the ARUK Centre for Epidemiology, Wales Centre for Primary and Emergency Care (Including Unscheduled) Care research (PRIME) and the Oxford Link and other local interface organisations.

They have already helped to generate a plain language summary for patients and the public. This document is available in paper copy, podcast and as a blog. An abstract will be submitted to the biannual NIHR INVOLVE Conference (https://www.invo.org.uk/current-work/) and a PPI member will give a presentation. Posters will also be prepared with the PPI team for inclusion at any workshop or conference where relevant PPI is being discussed.

In addition, to disseminate directly to study participants, findings will be more widely available locally through posters in appropriate outpatient rooms and liaising with identified service user groups.

All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide. Data will be aggregated and presented at the level of the randomised treatment arm. If applicable data within cells will be suppressed if they are small values to reduce the risk of re-identification.

No participant-level data falling under this agreement will be shared with any third-party.

The dissemination strategy will consist of three strands. The first will ensure that patients and the public are informed of the trial results; the second will engage practitioners and health-care providers, and the third will inform national guideline and policymakers.

Patients, patient advocacy groups, and members of the public:
Our patient representatives will lead dissemination to the patients and carers directly through their extensive network of patient advocacy organisations which include the ARUK Centre for Epidemiology, Wales Centre for Primary and Emergency Care (Including Unscheduled) Care Research (PRIME) and the Oxford Link and other local interface organisations.

They will help generate a plain language summary for patients and the public. This document will be available in paper copy, podcast and as a blog. An abstract will be submitted to the biannual INVOLVE Conference and a Patient and Public Involvement member will give a presentation. Posters will also be prepared with the PPI team for inclusion at any workshop or conference where relevant PPI is being discussed. In addition, to disseminate directly to study participants, findings will be more widely available locally through posters in appropriate outpatient rooms and liaising with identified service user groups.

Health care providers:
The trial team will work with the Oxford NIHR Biomedical Research Centre (BRC) and Collaborations for Leadership in Applied Health Research and Care (CLARHC) media teams to maximise the reach of the press and publicity outputs from this study. The team has costed the application to include one free-to-access publication in the mainstream literature. The final results will be submitted for presentations at annual meetings of the British Orthopaedic Association (BOA) and the Orthopaedic Trauma Society (OTS). The findings will be presented to the entire NHS via the NHS national electronic Library for Health (NHS Evidence). International ‘reach’ of the published research findings will be supplemented by presentations at high visibility meetings such as the Orthopaedic Trauma Association (OTA) Annual Meeting (US) and European Federation of National Associations of Orthopaedics and Traumatology (EFORT) Annual Congress (Europe).

In addition, the team is developing complementary systems incorporating non-traditional media. The Chief Investigator has been developing an enhanced web presence through blogging on the leading UK trauma and orthopaedic websites. These blogs engage both trauma and research communities. They have been very successful and have provided a means for rapid dissemination. The team plans to expand this activity into additional subject-specific and general blogs such as the British Medical Journal (BMJ).

National guidelines:
The research team will use their established network involvement to disseminate these research findings. These include the NIHR Clinical Research Network, and specialist interest groups (British Orthopaedic Association, Orthopaedic Trauma Society, Orthopaedic Trauma Association and The European Federation of National Associations of Orthopaedics and Traumatology).

The team will alert the relevant NICE standing committee to the results of the trial by notifying their surveillance team.

The study team is due to report in March 2023 and inform the full update to NICE Guidance NG38 in 2024. Progress in the work has been successful despite COVID and they are confident of hitting this timeline. De-identified data will be stored securely for a period of 3 years by University of Oxford following the final report to respond to any queries regarding the study.

Processing:

A file of unique identifiers and patient-level identifiers (NHS number, date of birth, sex, and postcode) will be sent from the University of Oxford to NHS Digital. The cohort includes 562 participants.

NHS Digital will link HES data for each patient identified in the cohort using the matching data file (containing NHS number, date of birth, gender, and postcode) to the unique identifier. The HES data will be at patient level and de-identified. NHS Digital will destroy the linkage file once linkage is achieved. The de-identified HES data, which will include special category health data, with the linked ID will be sent to the University of Oxford.

There will be no subsequent flows of data.

The processing organisation is University of Oxford. Two processing/storage sites will be used: Botnar Research Centre and Nuffield Department of Primary Care Health Sciences.

The trial team will initially prepare the linkage file as described above. On receipt of the linked, de-identified data from NHS Digital the trial team will carry out a prospective economic evaluation, conducted from an NHS and personal social services perspective, using the data provided by NHS Digital, augmented with participants’ self-reports. The economic evaluation will estimate the difference in the cost of resource inputs used by participants in the two arms of the trial, allowing comparisons to be made between the two weight-bearing strategies following ankle fracture fixation and enabling costs and consequences to be compared. Resource utilisation will be captured through the data provided by NHS Digital. The costs of the treatment options, including supplementary interventions (e.g., revision surgery) and rehabilitation inputs will be estimated using NHS reference costs and standardised to current prices. Health-related quality of life will be collected from participants’ self-report at randomisation, and at 6 weeks, 4- and 12-months post- randomisation using the EuroQol EQ-5D-5L measure; responses will be used to generate quality adjusted life-years (QALYs). The economic evaluation will be framed as a cost-utility analysis with results expressed in terms of incremental cost per QALY gained. The team will use non-parametric bootstrap estimation to derive 95% Confidence Intervals (CIs) for mean cost differences between the trial groups and to calculate 95% CIs for incremental cost- effectiveness ratios. The magnitude and significance of the coefficients on the interactions between the covariates and the intervention variable will provide estimates of the cost-effectiveness of the treatment options by participant subgroup.

The data will not be linked to any other data and only the linkages described are permitted under this Agreement.

Routine statistical procedures to suppress small cell numbers (less than 5) will be used to reduce the risk of re-identification. No attempt will be made in the processing to re-identify individuals.

Data processing will only be carried out by substantive employees of the University of Oxford who have been appropriately trained in data protection and confidentiality.

Data will only be accessible to these employees in a designated, locked, secure data processing office with standalone computers in accordance with the data security policies of the University of Oxford, Big Health Data Group (NDORMS), and Nuffield Department of Primary Care Health Sciences.

EXTEND Study: Needs-Assessed Care for Early Psychosis — DARS-NIC-474674-R3F7S

Opt outs honoured: Anonymised - ICO Code Compliant (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 - s261(5)(d)

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2022-11-25 — 2025-11-24

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  2. Civil Registration - Deaths
  3. Emergency Care Data Set (ECDS)
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Mental Health Services Data Set
  7. Civil Registrations of Death
  8. Hospital Episode Statistics Accident and Emergency (HES A and E)
  9. Hospital Episode Statistics Admitted Patient Care (HES APC)
  10. Mental Health Services Data Set (MHSDS)

Objectives:

The University of Oxford requires access to NHS Digital data for the purpose of the following research project: EXTEND Study: Needs-Assessed Care for Early Psychosis

The EXTEND study team are researchers from universities across the UK, led by the Chief Investigator from the University of Oxford.

The following is a summary of the aims of the research project provided by the University of Oxford:
'The purpose of this research is to investigate the impact of alternative durations of Early Intervention in Psychosis (EIP) care on service users’ health outcomes. This will include understanding and contextualising the existing variations in the duration of EIP care provided and service user outcomes, and estimating the cost-effectiveness of an alternative, flexible, needs-assessed EIP service. To do this, the study team proposes to create and analyse a pseudonymised dataset linking routine health service data from NHS Digital, to a clinical audit dataset held by the Royal College of Psychiatrists (National Clinical Audit of Psychosis- NCAP).'

Early Intervention in Psychosis (EIP) services are phase-specific, multidisciplinary, community-based mental health teams that treat people who are experiencing, or who have recently experienced, their first episode of a psychotic illness. EIP services are the principal pathway for the treatment of an emergent psychosis in the NHS in England.

It is not known what the optimal length of treatment should be for individuals within EIP services. It is not known whether the length of treatment, in itself, improves or reduces the chance of a ‘good’ outcome. The majority (58%) of individuals with First Episode Psychosis (FEP) will have an initial remission of their symptoms after the first episode of psychosis and 38% will reach eventual symptomatic and functional recovery. There is also a significant proportion who do not reach remission nor recovery criteria after their FEP, representing 3000 people a year in England. Furthermore, there is a range of illness trajectories following a first episode, meaning the eventual outcome is not easy to predict at the beginning of the illness.

Given this range of possible outcomes and trajectories, it is likely that a flexible, needs-focused treatment is required for those who experience FEP, rather than the current standard package of 3 years of assertive community treatment for everyone. It is possible, therefore that some people would benefit from longer treatment within EIP to help sustain the improvements made in the first few years. Conversely, it is possible that the length of treatment in itself is not relevant, but it is the delivery of NICE-recommended treatments that is the most effective component of treatment, which could be delivered over a shorter timeframe.

The following NHS Digital data will be accessed:
• Hospital Episode Statistics (HES) Admitted Patient Care (APC)
• HES Accident & Emergency (A&E)
• Emergency Care Data Set (ECDS)
• Mental Health Services Data Set (MHSDS)
• Civil Registration Deaths (mortality)

The requested data from MHSDS, ECDS, HES and mortality will be linked with the pseudonymised records from the National Clinical Audit of Psychosis (NCAP). The linked dataset will provide data on a large representative cohort of patients receiving care from EIP services, together with detailed data on health service use and outcomes. This will allow an analysis of the objectives of the EXTEND study - the impact of the duration of EIP care on outcomes.

The level of the data will be pseudonymised.

The data will be minimised as follows:
• Limited to data for a study cohort identified from NCAP data in 2019/20 and 2020/21 which the Royal College of Psychiatrists will provide to NHS Digital.
• Limited to data between 2016/17 and latest available at the time of dissemination (expected in 2023).

The University of Oxford is the data controller and is the organisation responsible for ensuring that the data will only be processed for the purpose described above.

The lawful basis for processing personal data under the UK GDPR is:
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;

The lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.

The funding is provided by the National Institute for Health Research (NIHR). The funding is specifically for the EXTEND study - Personalised Care for Early Psychosis, funding is in place until May 2025.

Cardiff University and Imperial College London (co-investigators on the EXTEND project) will be data processors acting under the instructions of the University of Oxford.

The Office for National Statistics (ONS) is a data processor. The role of ONS role is limited to hosting and providing access to the NHS Digital data in their Secure Research Service (SRS). ONS will receive the NHS Digital data and will upload it to the Secure Research Service (SRS). NHS Digital data will not be stored on any premises other than those of the Office for National Statistics.

Other co-investigators on the project (including from Pennine Care NHS Foundation Trust, Leeds and York Partnership NHS Foundation Trust, Keele University, Centre for Mental Health and Manchester Metropolitan University) do not require access to the NHS Digital data and do not determine the means and purposes for processing for this specific study. These institutions are conducting qualitative, policy or project management work as part of the broader NIHR grant. This includes a Manchester Metropolitan University (MMU) professor who is the co-chief investigator (and is therefore listed as leading the project on publicly available material, including the privacy notice) but does not determine the means and purposes for the processing in this agreement.

This EXTEND study is part of an overall programme of research funded through the NIHR through a Programme Grant for Applied Research (PGfAR) and for which the Professor from MMU is Programme Chief Investigator. There are several work packages that different universities are leading and this study is being led by the Professor from University of Oxford as Chief Investigator for this specific study. Therefore, whilst the Professor at MMU has responsibility for the overall programme of research, for the purpose of this specific processing Oxford University is the sole data controller.

Data will be accessed by a PhD student affiliated with Imperial College London. The individual has completed mandatory data protection and confidentiality training and is subject to the University of Oxfords' policies on data protection and confidentiality. The individual accessing the data will do so under the supervision of a substantive employee of Imperial College London. Imperial College London would be responsible and liable for any work carried out by the individual. The PhD student would only work on the data for the purposes described in this Agreement.

A Public and Patient Information and Engagement (PPIE) group was consulted regarding the collection of the data for the purposes described above.

The study has convened an EXTEND patient and carer Involvement Group (EXTEND-InG), chaired by the two PPI co-investigators, which has already met to discuss the proposed data linkage and the methods for informing individuals affected.

Expected Benefits:

The findings of this research study are expected to contribute to evidence-based decision-making for policymakers, and local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment, and experience of healthcare users relevant to the subject matter of the study.

The use of the data could
• help the health and social care system to better understand the health and care needs of populations.
• lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
• advance understanding of regional and national trends in health and social care needs.
• inform planning health services and programmes, for example, to improve equity of access, experience, and outcomes.
• inform decisions on how to effectively allocate and evaluate funding according to health needs.
• provide a mechanism for checking the quality of care. This could include identifying areas of good practice to learn from or areas of poorer practice that need to be addressed.
• support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).

This study intends to estimate the impact of alternative models of provision of Early Intervention in Psychosis care. If it finds that extended, or more flexible, duration of EIP care benefits patients experiencing First Episode Psychosis (FEP) and is cost-effective, it could guide the direct improvement of the National Health Service (NHS).

Evidence generated from the study may have relevance to tens of thousands of people with a diagnosis of psychosis. Furthermore, the data generated has the potential to impact on their carers, clinicians, service managers and Integrated Care Boards (ICBs).

Through the publication of findings in appropriate media, the findings of this research may add to the body of evidence that is considered by the bodies, organisations, and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.

The linked NCAP data and NHS Digital data may be suitable to be used as a resource for future research into care for early psychosis, subject to the required approvals.

Outputs:

The outputs will not contain NHS Digital data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the datasets from which the information was derived.

The outputs of the processing will be national-level aggregates and modelling. Some statistics will be reported at the EIP team level (<100 individuals). Statistical disclosure risk for small numbers will be managed in line with the ONS protocols on disclosure risk for health data.

The expected outputs of the processing and how they will be communicated to relevant recipients through the following dissemination channels:
- Interim reports to the funder on the progress of data linking and causal inference.
- End of programme reports to the funder on each of the key work packages: understanding existing duration in EIP care; causal impact of different durations of EIP care on patient outcomes; cost-effectiveness of alternative duration EIP provision.
- Submissions to open-access peer-reviewed journals (e.g. Lancet Psychiatry or British Journal of Psychiatry).
- Presentations at national and international conferences (e.g. International Early Intervention Association / International Congress of The Royal College of Psychiatrists).
- Specific reports on findings for healthcare policymakers.
- Guidance for EIP service providers to aid in the development of a national implementation strategy if the intervention is found to be effective.

Production of policy-focused and Patient and Public Involvement (PPI)-focused outputs (such as blogs, videos, briefings and short reports) will be ongoing throughout the course of the funded project period (June 2022 to May 2025). The funder requires interim reports at 9 months and 18 months. Final reports, including journal submissions, are intended to be completed by the end of the funded project period (May 2025).

Processing:

Royal College of Psychiatrists (RCPsych) will transfer data from NCAP to NHS Digital. The data will consist of identifying details (specifically NHS Number, age, gender, and partial postcode) along with a unique person ID ('pseudonymous key') for the cohort to be linked with NHS Digital data.

NHS Digital will provide the relevant records from the HES APC, HES A&E, ECDS, MHSDS, and Civil Registration Deaths datasets to ONS. The data will contain no direct identifying data items but will contain a unique person ID which can be used to link the data with other record level data (NCAP data) already held by ONS.

RCPsych is also sending directly to ONS the same unique person ID with their pseudonymised NCAP data which the EXTEND study team will then use to link to the NHS Digital data via the unique person ID.

The ONS will store the NHS Digital data as part of its Secure Research Service (SRS), which only grants access to accredited and approved researchers. The ONS Information Asset Owner (IAO) will take overall responsibility for the Controller’s data after delivery to ONS. The ONS IAO will ensure all data are managed in accordance with Standard Disclosure Control (SDC), and all applicable Data Protection Legislation.

Researchers from the University of Oxford, Cardiff University and Imperial College London will have access to the pseudonymised linked dataset through the ONS SRS.

Once researchers and their research projects are accredited or approved, projects using the SRS have a project space created. Data sets requested for projects will be mapped to the project space. Researchers named on projects will then be provided with their account details and instructions on how to access the SRS. Access to the SRS is through a safe setting. Safe settings may be in safe rooms on ONS sites, in safe rooms on other certified sites, or through an organisation that has an Assured Organisational Connectivity Agreement with ONS and which maintains current certification.

The data will not leave England/Wales, at any time.

Access is restricted to employees or agents of the University of Oxford, Imperial College London and Cardiff University who have authorisation from the University of Oxford Chief Investigator.

All personnel accessing the data have been appropriately trained in data protection and confidentiality.

The NHS Digital data will be linked at person record level to the NCAP data obtained from RCPsych.

ONS will not be linking any of their datasets to the NHS Digital data (i.e. the NHS Digital data will be held in isolation on SRS but will be linked by the pseudo key to the NCAP data (all pseudonymised).

There will be no requirement and no attempt to reidentify individuals when using the data.

Researchers from the University of Oxford, Cardiff University, and Imperial College London will conduct statistical analysis to understand the existing variation in duration of EIP care, estimate the effect of increasing EIP duration from 3 to 5 years and estimate the cost-effectiveness of alternative models of EIP provision. All statistical outputs from this work will be checked by the ONS for disclosure and will take the form of aggregate statistics and modelling.

Active Monitoring for AtriaL Fibrillation - AMALFI trial — DARS-NIC-470203-Y2L7J

Opt outs honoured: Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2022-08-01 — 2025-07-31 2022.09 — 2022.12.

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Emergency Care Data Set (ECDS)
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Critical Care
  5. Hospital Episode Statistics Outpatients
  6. Medicines dispensed in Primary Care (NHSBSA data)
  7. Civil Registrations of Death
  8. Hospital Episode Statistics Admitted Patient Care (HES APC)
  9. Hospital Episode Statistics Critical Care (HES Critical Care)
  10. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The University of Oxford requires linked healthcare data for consented participants in the Active Monitoring for AtriaL Fibrillation (AMALFI) study for baseline characterisation of participants and follow-up purposes. This includes centrally-collected data on primary care records, medications dispensed in the community, mortality, and Hospital Episode Statistics (HES) datasets.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide, and is estimated to affect over 1 million people in the UK. In AF, the atria (upper chambers of the heart) beat in an uncoordinated way, which disturbs the normal blood flow and can lead to the formation of blood clots inside the heart. These can travel through the bloodstream and create a blockage in the arteries supplying the brain, causing a stroke. Patients with AF are at a 5-fold increased risk of stroke, but this risk can be effectively reduced by up to two thirds with anticoagulation (blood-thinners). However, AF can occur only in short and infrequent episodes that make it hard to capture and start treatment, and it may also not cause any symptoms; as a result, some patients might have undetected AF until the time when they have a stroke.

A potential solution for this problem is to actively look for silent AF through screening in patients who are considered at risk. This is already routinely done in primary care when patients over the age of 65 years present for some other reason (for example for a routine appointment or a flu jab), in which their doctor might assess their pulse or do an electrocardiogram (ECG). However, this assessment is very short and is unlikely to detect short and infrequent AF episodes. New technology such as extended cardiac monitors and digital wearables offer the potential for increased and more frequent monitoring periods, and easier access to patients - but they are also more expensive and create additional workload for the healthcare system. At the moment, there is no conclusive evidence that screening for silent AF improves either clinical outcomes or patient quality-of-life, or if employing new AF screening strategies is a cost-effective strategy (and if so, how exactly it should be performed).

The AMALFI study is a randomised clinical trial of screening for subclinical (undiagnosed) AF in elderly patients with no previous AF, who are at increased risk of both AF and a subsequent stroke – this selection is based on a CHA2DS2VASc score of 3 or higher in men and 4 or higher in women (the CHA2DS2VASc score is a standard tool used by clinicians to assess stroke risk in patients with AF and help them decide who should receive anticoagulation). AMALFI is comparing a two-week remote continuous cardiac monitoring period with a ZioPatch to usual care alone: consenting participants will be assigned to one of the two groups by chance, in a similar way to tossing a coin, called “randomization”. The study has recruited 5,043 people through primary care practices in England. Recruitment started in 2019 and finished on February 28th 2022. The cohort is composed of 5,043 people, minus those that have withdrawn consent (6 thus far). This number of unconsented individuals may increase within the proposed duration of this Agreement, in which case, the University of Oxford will send NHS Digital an updated cohort. The main outcome of the study is the proportion of participants with newly-detected AF in both arms 2.5 years after randomisation (as shown in primary care records), with additional analyses planned in subgroups of age and sex, and at 5 years. The study may then continue collecting follow-up data for a period of up to 20 years after the initial 5 years. This long follow-up period was envisaged so that further data may be collected for longer-term outcomes (i.e. beyond 5 years), if a longer timeframe for assessment is considered important following the initial results.

If shown to be effective (and cost-effective), this approach to screening could form the basis for a potential future nationwide screening program, which could prevent stroke, disability, and premature death in this patient population. A second objective of the study is to develop streamlined procedures for running clinical trials in primary care, namely by comparing the outputs if using data collected directly from primary care practices or centrally-held data such as the datasets managed by NHS Digital.

AMALFI is sponsored by the University of Oxford and funded by the National Institute for Healthcare Research (NIHR) through the NIHR Oxford Biomedical Research Centre, with additional support from the NIHR Thames Valley and South Midlands Clinical Research Network (CRN) in the form of logistical support with recruitment only. The protocol and study procedures have been developed by a team of investigators spanning the Nuffield Department of Population Health (NDPH), the Radcliffe Department of Medicine, and the Nuffield Department of Primary Healthcare Sciences (all of which are departments of the University of Oxford) and is being run by the Clinical Trial Service Unit (CTSU) at NDPH.

Trial procedures are simple and remote, with no physical sites or dedicated staff apart from a small team at the CTSU, and no study visits. Eligible patients are being identified from primary care records in participating practices associated with the CRN. The practice runs a search of their records and generates a list of eligible patients, which is then uploaded to DocMail (a standard NHS mailing service). DocMail processes the mailing of the study documentation and, if interested, patients will return a completed questionnaire and consent form to the study team at CTSU – this includes their personal details such as name, address, date-of-birth, and NHS number. After this, participants are randomised and mailed a letter with or without a patch to self-administer (depending on the treatment allocation). Patients in the active/screening group are asked to wear their patch for 14 days and then return it using an enclosed box to iRhythm, the patch manufacturer, which analyses the data collected via the patch. iRhythm have not had and will not have any involvement in the design, conduct, analysis, or reporting of this study, nor will they have any access to NHS Digital data. A monitoring report is provided to the study team and the relevant findings shared with the patient’s GP, who manages subsequent treatment if needed. There are no study visits and follow-up data is only currently being collected from electronic record extractions at the individual practices taking part in the study. Information on quality-of-life will be collected through remote completion of mailed EQ5D (EuroQoL-5 Dimensions) questionnaires at two occasions (and for all participants at the same time): once the overall study recruitment has finished, and again approximately 2.5 years after that (i.e., coinciding with the timing of the primary outcome assessment). These questionnaires are a standard tool used in both research and clinical care settings within the NHS to assess several domains of quality-of-life (QoL; mobility, self-care, usual activities, pain/discomfort, anxiety/depression).

The University of Oxford will be the sole data controller who also process data for the study. The University of Oxford is the Data Controller as they determine the purposes for which and the manner in which the personal data disseminated under this Agreement are to be processed. The data will be stored and processed at the University of Oxford in secure servers and under strict access restrictions, and it will not be released to any other organisations or used for other purposes other than this study.

Altogether, the study requires the following datasets:
1. Medicines Dispensed in the Community (NHSBSA data)
2. Civil Registrations (mortality data)
3. HES Critical Care
4. HES Admitted Patient Care
5. Emergency Care Data Set
6. HES Outpatients

The study has also identified the need for General Practice (GP) Data, but at the current time, this data is not available. A national collection of primary care data would be a valuable resource for this study to draw upon for the research.

The data requested will be used to assess the study outcomes (namely the detection of AF) as well as other important events, such as the initiation of medication for AF, the need for additional appointments and tests, as well as hospital admissions and deaths. Altogether, these data will help the University of Oxford understand if AF screening can improve AF detection rates, what are the associated costs of doing so, and if there are particular safety signals of concern. It may also provide some initial indication of whether this procedure can improve clinical outcomes, although this is not the main purpose of the study. In particular, the NHSBSA data will be used to calculate the number of people starting anticoagulation in the active and control groups, which in turn will help the University of Oxford understand what are the likely benefits and risks of anticoagulation for subclinical AF in reducing strokes versus bleeding events.

AMALFI is collecting medications data via two routes: primary records extracted from each GP practice taking part in the study, and the centralised, nationwide NHSBSA dataset. Both sources will be used to assess rates of initiation of anticoagulation and other medications in the study cohort, and their impact on effectiveness (stroke, heart failure) and safety (bleeding) events resulting from new diagnoses of atrial fibrillation.

Given the novelty of the NHSBSA data, it is not yet known how well it overlaps with other pre-existent data sources (primary care records, in this case), and whether the NHSBSA dataset can be used on its own to retrieve medication exposure. If that was the case this would potentially improve clinical trial efficiency significantly via simplified data collection, both in AMALFI and beyond.

This work is to be undertaken in line with the NHSBSA Direction of assessing effectiveness and safety of medicines as the team is developing the methodological work required to guide future research using these data.

Finally, the conjunction of clinical events (detailed in the Civil Registrations, Medicines, HES Admitted Patient Care and HES Accident & Emergency datasets) with information on healthcare resource use (complemented by the HES Outpatients data) will provide a rich resource to assess the relative cost implications of screening from the perspective of the healthcare system, which will be crucial to inform future discussions on implementation. An additional aim of this work will be to undertake trial methodology research, specifically comparing the NHSBSA medicines data to medication data that is already being collected by the study from local GP practices. This exercise will focus on comparing the numbers of people identifying as taking particular drugs in each data source (rather than trying to audit the data collected in the NHSBSA dataset), and is hoped to help pave the way for broader use of the NSHBSA dataset for clinical trials.

Data minimisation will be pursued by only requesting datasets directly related to the outcomes of interest to the trial (clinical outcomes using primary care, admissions, and mortality data; medication initiation using primary care and dispensed medicines data; and health-care resource use using the remaining HES datasets), and requesting only the fields needed in each dataset; these have been reviewed by a clinician and a health-economist to ensure that only the necessary data is requested. Data is also only being requested for as far back as 5 years before the start of recruitment, i.e., from 2014/15 to latest available.

The objective of undertaking the AMALFI research study aiming to improve future patients’ health is the justification for processing under GDPR Article 6(1)(e) and Article 9(2)(j). In particular, AMALFI aims to provide high-grade evidence on the potential efficacy and cost-effectiveness of atrial fibrillation screening in the NHS, which it is hoped will inform future considerations regarding whether and how to roll-out a nationwide screening program, much like the existing programs for breast, cervical, and colorectal cancer. All participants in AMALFI have provided written informed consent to take part in the study, including specific consent for access to data held by NHS Digital to be provided to the University of Oxford for the purpose of this study. The study has received favourable Research Ethics Committee approval from the London - Bromley Research Ethics Committee (REC reference 19/LO/0220) and is registered in the International Standard Randomised Controlled Trials registry (reference ISRCTN15544176) and the NIHR portfolio; the Integrated Research Application System (IRAS) reference number is 234837.

Recruitment under each version of the consent:
Consent Form (v1.1) 06-FEB-19; (284 consented; in use May 2019 - August 2019) - this version only had HES/ONS wording.
Consent Form (v2.0) 15-AUG-19; (1000 consented; in use February 2020 - December 2020) - this version only had HES/ONS wording - edit not related to linkage.
Consent Form (v2.1) 27-APR-20; (2045 consented; in use February 2021 – July 2021) - this version only had HES/ONS - edit not related to linkage.
Consent Form (v3.0) 01-MAY-21; (812 consented; in use September 2021 - October 2021) - this version had GP/Medicines wording.
Consent Form (v3.1) 25-OCT-21; (902 consented; in use November 2021 - January 2022) - this version had GP/Medicines wording - edit not related to linkage.

The University of Oxford established the Clinical Trial Service Unit (CTSU), now within the Nuffield Department of Population Health, in the 1980s to conduct large trials such as the International Study of Infarct Survival (ISIS) trials. Since then CTSU has successfully completed a number of landmark studies including the 20,000 participant Heart Protection Study, the 9500 participant SHARP study, the 26,000 participant HPS-2/THRIVE study and the 30,000 participant HPS-3/REVEAL study. More recently, CTSU has coordinated the 40,000+ RECOVERY trial of treatments for COVID-19, which showed that dexamethasone, tocilizumab, and the REGN-COVID2 monoclonal antibodies were effective in reducing all-cause mortality in this population.

AMALFI is sponsored by the University of Oxford and is adopted onto the NIHR Portfolio as an academic study. iRhythm are the manufacturers of ZioPatch, the device which is being used in the study, and are providing it free of charge. iRhythm have not had and will not have any involvement in the design, conduct, analysis, or reporting of this study.

Although iRhythm could have an indirect commercial benefit if the study was to be positive (i.e., expanded use of the Zio Patch device in UK practice), this would only occur as a consequence of the study providing evidence of a benefit for public health (and pending a detailed assessment by policymakers such as the National Institute for Health and Care Excellence and others, whom would only recommend screening with the Zio Patch if any commercial benefit gained by iRhythm was outweighed by the benefits to health and social care). Moreover, the study will include cost-effectiveness analyses that will factor in not only the benefits but also any costs from the proposed intervention, producing important data for health economics analyses to be conducted in the future by policymakers, such as the National Institute for Health and Care Excellence.

Expected Benefits:

1. Provision of the data requested may help AMALFI provide important results that may affect standard clinical practice in the UK and beyond, by generating reliable evidence on the efficacy and cost-effectiveness of remote screening for subclinical AF using a self-applied patch. In particular, the datasets requested in this data dissemination are expected to form the basis for the assessment of the impacts of AF screening on anticoagulation rates, hospital admissions, outpatient appointments and A&E attendances, and deaths. While several monitoring devices are available, there is scarce randomised evidence of the added value of using such devices on top of usual care, particularly in a longer time frame (which may lead to AF cases being detected regardless of the use of the device, diluting its benefit and therefore cost-effectiveness). It is therefore expected that results from AMALFI might be incorporated in future National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network (SIGN), and UK National Screening Committee guidelines; in particular, and depending on what the results of efficacy and cost-effectiveness show, AMALFI might provide the basis for a recommendation of targeted screening in elderly patients with additional comorbidities using a wearable patch in the UK, most likely in the primary care setting (and potentially managed via a central coordinating system replicating the methodology used in AMALFI). These results also have the potential to affect the care of millions of people worldwide, with potentially significant benefits in terms of reduction of fatal or disabling stroke and reduced healthcare costs (by means of reduced number of hospitalisations or A&E attendances) – the extent of which will be assessed by the detailed efficacy and cost-effectiveness analyses planned.

2. AMALFI is a streamlined and remote study with minimal data collection undertaken by participants, and none by their GPs. Participants are only asked to complete a short one-page questionnaire at enrolment (with remote EQ5D questionnaires planned at 2.5 and 5 years after inclusion), while GPs will perform a standard data extraction from their records at approximately 1, 2.5, and 5 years after randomisation. While these simple procedures make it easier for both participants and GPs to take part, they are limited in their capacity to provide detailed data that is of interest to the study (such as hospitalizations, secondary care appointments, mortality, and others). Therefore, access to data held by NHS Digital for participants in the AMALFI study will be crucial for detailed assessments of the impact of AF screening on a range of outcomes that would be hard to capture through more bespoke methods. Moreover, the provision of data on primary care records and medications as held by NHS Digital will allow the study team to potentially drop the need for GPs to run any data extractions for follow-up, further reducing the burden of taking part in research and making clinical trials more efficient, cost-effective, and attractive to busy clinicians. The comparative assessments of data collected directly from GPs or similar data held by NHS Digital may fuel the use of centrally held data by other researchers. The methods developed as part of this study may then be shared with the scientific community to improve the development of streamlined trials in cardiovascular disease and other fields.

3. The results should inform future discussions on the potential implementation of a nationwide screening program for subclinical atrial fibrillation and will be developed and disseminated to the wider public with the assistance of the Public Advisory Panel at the Nuffield Department of Population Health. If such a program were to be implemented based on these results, it could potentially involve several thousands of eligible patients in the UK each year, while paving the way for similar programs in other countries around the world.

4. Finally, the University of Oxford will aim to reach policymakers such as the National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network (SIGN), and UK National Screening Committee (UKNSC). Although the University of Oxford does not expect an immediate policy change following the results of this study, the data produced may be used by both bodies to guide future recommendations for subclinical AF screening in UK practice. The University of Oxford will do this by registering as a stakeholder with these institutions and commenting on their AF management and screening guidance documents based on the findings.

Outputs:

The main results of AMALFI are expected in mid-late 2024, with long-term results in 2026. Further long-term results may be published if a longer timeframe for assessment is considered important following these results.

Dissemination of the results will be aimed at three different audiences: 1) clinical and research community; 2) patients and charities; and 3) policymakers.

The results will be disseminated widely within the clinical and research community, including presentation at relevant conferences (such as the European Society of Cardiology congress or the European Heart Rhythm Association congress) and publication in a high-impact medical journal such as Circulation, Journal of the American College of Cardiology, or European Heart Journal. Further academic papers (including results of cost-effectiveness analysis and papers about the trial methods) will be published in high impact, peer-reviewed journals (possible outlets include Trials, Clinical Trials, EuroPace, European Heart Journal Quality of Care and Clinical Outcomes) and on the trial website.

For patients and charities, a non-technical summary of the main study findings will be sent to participants and relevant charities, such as the British Heart Foundation and Arrhythmia Alliance (as well as NIHR who has funded the trial up to this stage) and published on the study website. Patient and Public Involvement will be sought in the design and sharing of outputs from the Public Advisory Panel at the Nuffield Department of Population Health (https://www.ndph.ox.ac.uk/research/participant-panel).

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

The medicines data is not deemed disclosive and information on a GP level is available in the public domain. However, should the study team consider that published information poses a risk of re-identification, the following suppression methodology should be applied:
· Zeros should be shown.
· 1-7 to be rounded to 5.
· Any other numbers rounded to nearest 5.
· Rounding unnecessary for averages etc.
· Percentages calculated from rounded values.
· If zeros need to be suppressed, round to 5.

Processing:

The flows of data:

1. The University of Oxford will provide NHS Digital with personal identifiers (sex, date-of-birth, and NHS number) plus a study ID for a cohort of consenting participants in the AMALFI study, via Secure Electronic File Transfer (SEFT). The University of Oxford will resend the cohort to NHS Digital before each data dissemination to ensure that any potential new withdrawals of consent are removed.

2. NHS Digital will link the cohort to the datasets requested and send the data back to University of Oxford via SEFT. The data will contain
- identifiable data of date-of-birth and NHS number.
- for the Civil Registration Deaths dataset, Cause of Death will also be flowed which is classed as identifiable as it is a free text field and so could potentially contain identifying information.
- requested data from the datasets.

University of Oxford are requesting that date-of-birth and NHS number (which is provided in step 1 above) are flowed back to University of Oxford for the purpose of linkage validation by the research team. The research team would like to ensure that the NHS Digital data is correctly linked given a specific cohort is being studied and many of the study outcomes will rely on the provision of correctly linked data.

The study ID is used to link the information received from NHS Digital to the cohort. However, this does not allow the University of Oxford to confirm that the data actually belongs to the correct person – therefore it is standard practice to cross-check identifiers provided by NHS Digital with those that the University of Oxford already hold to confirm correct linkage (note that NHS number and date of birth are already known to the University of Oxford, therefore NHS Digital will not be providing data that is not already held).

If a participant withdraws their consent to participation in the study (including for data linkage with NHS Digital), their wish will be recorded and they will not be included in the cohort sent to NHS Digital for linkage. The linkage cohort will be updated before each data dissemination to accommodate any potential new withdrawals of consent. Data from withdrawn participants is stored up until the point of withdrawal, with no further data collection taking place after that point.

The record-level identifiable data received from NHS Digital will be stored in a secure location within England and Wales and will only be accessed by individuals within the Clinical Trial Service Unit who have authorisation to access the data for the purpose(s) described, all of whom are substantive employees of the University of Oxford and have been appropriately trained in data protection and confidentiality. The raw data will be securely held at the CTSU within The University of Oxford in a restricted database (with access limited to a very small number of individuals), and it will not be shared outside the University of Oxford.

Data will be stored in an encrypted study database in a pseudonymised form, with identifiable fields such as NHS number or names encrypted in situ using state-of-the-art encryption methods (in line with the NHS Data Security and Protection toolkit). Access to this database is restricted to the study team and on a need to know basis, protected with username and password, and can only be accessed from internal networks. Information stored in the database can be accessed either via an internal desktop program (used to log information and contacts with participants), or directly within the database server. For the desktop program, only the study team individuals who have direct contact with participants can see identifiable fields. Within the database server, access to the identifiable fields is locked with a decryption key held by the two database managers, who are qualified programmers and substantial employees of the University of Oxford. The data will not be used by individuals outside the AMALFI study team and therefore there are no perceived risks of re-identification at this stage (outside the group of people who already have access to identifiable information as a consequence of their roles in managing the trial and contacting with participants).

Besides the data linkage described in this Agreement, AMALFI is also collecting electronic primary care records directly from participating GP practices via local data linkage; besides this, no other linkage is currently being pursued. The linked data received from NHS Digital will be stored separately from the primary care data collected via the participating GP practices until the data analysis stage (where it may be combined to produce the study outcomes). The data to be provided will not be matched to publicly available data.

The data collected via this Agreement will be used to determine the presence of atrial fibrillation diagnoses and associated symptoms (primary care and HES APC), quantify and characterise reasons for hospital admission (HES APC) and death (Civil Registrations), assess medication use (primary care and Dispensing data). In addition to this, the University of Oxford will further estimate healthcare resource use (hospital admissions, A&E attendances, outpatient appointments, need for diagnostic tests) for health economics analyses (using HES APC, HES critical care, Emergency Care Data Set, and HES outpatients).

OPtimising Treatment for Mild Systolic hypertension in the Elderly: a randomised controlled trial (OPTiMISE) — DARS-NIC-459340-M8R2R

Opt outs honoured: Anonymised - ICO Code Compliant (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2022-07-01 — 2025-06-30

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Emergency Care Data Set (ECDS)
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Civil Registrations of Death
  6. Hospital Episode Statistics Accident and Emergency (HES A and E)
  7. Hospital Episode Statistics Admitted Patient Care (HES APC)

Yielded Benefits:

This is a new data request, so no benefits have yet been yielded.

Outputs:

All findings from the proposed research are intended to be published in peer-reviewed journals under an open-access license, with target journals including the British Medical Journal (BMJ), Journal of the American Medical Association (JAMA) Internal Medicine and Hypertension. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be presented in published outputs. It is anticipated that the study findings will support better patient-centred management plans for the prevention of cardiovascular disease in older individuals and therefore will be available for the next iterations of the NICE hypertension and multi-morbidity guidelines.

The University of Oxford intend to present results at national scientific meetings (e.g. of the British and Irish Hypertension Society, Society for Academic Primary care, British Geriatrics Society) and international conferences (e.g., for the European Society of Cardiology, North American Primary Care Research Group). The University of Oxford will publish a summary of our findings on the study website (https://www.phctrials.ox.ac.uk/studies/optimise) and submit a final study report to the MHRA and ethics committee who provided approval for the study. Where appropriate, we will press release the results of the research in conjunction with their publication in scientific journals, and fully engage with any arising media inquiries that result. Social media (e.g. Twitter) will be used to draw attention to the work and stimulate debate, particularly when it is presented at conferences or published in the lay and scientific media.

Multiple channels of communication will be used to disseminate study findings including written feedback to study participants, plain English summaries, newsletters and community engagement events. We will work closely with our PPI representatives to ensure results are presented and disseminated in a patient friendly manner. An article discussing the issues raised by the research will be written for ‘The Conversation’, an online newspaper written by academics which is free and easy to read for the general public.

The University of Oxford will aim to present the findings of their analyses using these data at conferences in late 2022 and throughout 2023 and publish the final results in publications and online in early 2023.

Processing:

1. Participants who have consented to the OPTiMISE trial will have their data initially linked with the primary care data which is already held as part of ORCHID.
2. The University of Oxford will provide NHS Digital with a list of NHS numbers and date of births along with a unique Study ID for the OPTiMISE cohort.
3. A specific member of the University of Oxford IT team within the IT department will share the identifiers with NHS Digital, not the research team.
4. NHS Digital will send back to University of Oxford the pseudonymised, record level cohort data with Hospital Episode Statistics (HES), Admitted Patient Care (APC) and Accident & Emergency (A&E) data, Emergency Care Dataset (ECDS) data and mortality data included. Files will be sent securely back to University of Oxford via the Secure Electronic File Transfer System (SEFT).
5. University of Oxford will download and store the data within their secure ORCHID trusted research environment.
6. University of Oxford will process the pseudonymised data to meet the aims and objectives of the study (as detailed in ‘Objectives for Processing’). This will include matching the pseudonymised data from NHS Digital with the pseudonymised study data using the unique study ID. There will be no subsequent flows of data from the University of Oxford which has not already been suppressed in accordance with the small number suppression rules in the HES Analysis Guide.

Once data have been provided by NHS Digital, the University of Oxford will have no need to re-identify participants for the purpose of data processing. All patient identifiers are stored (securely and) separately from the main study database. Patient identifiers will be permanently destroyed at the earliest opportunity, in line with ethical and GDPR requirements and University policy.

The data will be controlled and processed by a group of substantive staff who are all based at the University of Oxford and under an employment contract. All staff are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Query Language (SQL) developers, Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) practice liaison officers, a project manager and a head of department. All staff will access NHS Digital data from secure workstations or secure laptops with encrypted drives within the group’s secure network. No data will be accessed outside of the location where the data is stored.

Analysis will be done by the clinical trials unit within the Nuffield department of Primary Care Health Sciences at the University of Oxford.

MR415 COHORT STUDY OF CANCER INCIDENCE AND MORTALITY AMONGST WOMEN TREATED FOR SUPERTILITY — DARS-NIC-147910-HHGGZ

Opt outs honoured: Anonymised - ICO Code Compliant

Legal basis:

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-12-06 — 2022-12-05

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Personal Demographics Service

Objectives:

This Data Sharing Agreement permits the retention of the data provided under previous iterations of this Agreement but no further processing, for an interim period. This is a pragmatic approach to provide an active Agreement whilst enabling the University of Oxford to complete the necessary actions to enable a subsequent application to extend the Agreement meeting all applicable data sharing standards as published in NHS Digital’s website (see: https://digital.nhs.uk/services/data-access-request-service-dars/dars-guidance).

The data supplied by NHS Digital to Childhood Cancer Research Group will be used only for the approved Medical Research Project.

Yielded Benefits:

In any future application, the applicant will be required to provide details of the actual benefits achieved as a result of the study.

Expected Benefits:

This Agreement permits the secure retention of the data only and no other processing.

In any future application, the applicant will be required to provide details of the expected benefits resulting from the study.

Outputs:

This Agreement permits the secure retention of the data only and no other processing.

No new outputs will be produced under this Data Sharing Agreement.

In any future application, the applicant will be required to provide details of the outputs that were produced and disseminated by the study as well as details of any future outputs planned.

Processing:

Under this Agreement, the data may be securely stored but not otherwise processed. No new data will be provided by NHS Digital under this Agreement.

Astrazeneca/Oxford - Re-use of existing data for data validation exercise — DARS-NIC-480562-G9R5X

Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: Yes (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-07-09 — 2021-09-08

Access method: One-Off

Data-controller type: ASTRAZENECA UK LIMITED, UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. COVID-19 Hospitalization in England Surveillance System
  2. COVID-19 Second Generation Surveillance System
  3. Secondary Uses Service Payment By Results Accident & Emergency
  4. Secondary Uses Service Payment By Results Episodes
  5. Secondary Uses Service Payment By Results Outpatients
  6. Secondary Uses Service Payment By Results Spells
  7. COVID-19 Second Generation Surveillance System (SGSS)

Objectives:

The University of Oxford and AstraZeneca wish to use data currently held by University of Oxford (disseminated under DARS-NIC-381683) to do initial internal development work ahead of receiving a fresh data extract under DARS- NIC-459114. The work will support the preparatory work for the data management of the extract building models and internally validating them against other work that the University of Oxford are involved in / are in the public domain.

DATA CONTROLLERS AND PROCESSORS
The University of Oxford (Oxford's Royal College of GPs (RCGP) Research and Surveillance Centre (RSC)) are Joint Data Controllers with AstraZeneca Limited UK (also known as AstraZeneca Global). The data will only be processed by University of Oxford's Royal College of GPs (RCGP) Research and Surveillance Centre (RSC) and by Momentum Data. University of Oxford have subcontracted a part of the analysis to Momentum Data who will be acting as data processors on the instructions from University of Oxford and AstraZeneca UK Limited.

LEGAL BASIS
The lawful basis for processing data under GDPR has been reviewed and been assessed as acceptable. The University of Oxford process data under Article 6(1)(e): "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller" as they are a Public Authority.

AstraZeneca UK Limited process data under Article 6(1)(f): “Legitimate interests: the processing is necessary for your legitimate interests or the legitimate interests of a third party, unless there is a good reason to protect the individual’s personal data which overrides those legitimate interests. (This cannot apply if you are a public authority processing data to perform your official tasks.)”

Additionally, the University of Oxford and AstraZeneca UK Limited process the Special Category Health Data under Article 9(2)(j): "processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject" as the data are required for research purposes in the public interest.

Expected Benefits:

The benefits will be internally initially to ensure that the data managers are ready to work on the extracted data for the Vaccination effectiveness studies as soon as they are available thus enabling the results of the study to be published more rapidly.

Outputs:

No outputs are expected other than preparing databases for the receipt of the extracted data once available via the main research agreements.

Processing:

Date will only be accessed by staff substantively employed by University of Oxford who are a data controller and processor and Momentum Data, who are a data processor. No data will leave the University of Oxford.

The data will be controlled and processed by a group of staff who are all substantive employees of the University of Oxford. Additionally, for the purpose of this study, given the tight turnaround and the urgency in providing these analyses in the interest of public health, University of Oxford have subcontracted a part of the analysis to Momentum Data who will solely be acting as data processors on the instructions from University of Oxford and AstraZeneca (joint data controllers). Analysts from Momentum Data will first have to complete the IG training in order to get access to the secure environment at the University of Oxford. Although acting as data processors, all the analysis and processing will take place within the secure environment at University of Oxford. (ORCHID)

University of Oxford staff are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

Data will only be accessed by individuals within the RSC who have authorisation. The authorisation process includes:
(1) Contractual requirement to follow IG principles;
(2) Using the email registered with Human Resources to complete IG training and to return the certificate;
(3) Staff email is authorised by the IT department for one year to access the secure network and staff computers are configured to allow this;
(4) At any point the project managers or Head can have access to the secure network turned off.

Momentum will be data processors, however they will only access data within a secure environment via a secure client and can’t download data.

Improving outcomes for patients having shoulder replacements: guiding patient selection, evaluating cost-effectiveness and informing NHS provision — DARS-NIC-432598-Q6S0C

Opt outs honoured: Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-10-01 — 2024-09-30 2022.02 — 2022.12.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. HES:Civil Registration (Deaths) bridge
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Outpatients
  5. Civil Registrations of Death - Secondary Care Cut
  6. Hospital Episode Statistics Admitted Patient Care (HES APC)
  7. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

In this agreement, the University of Oxford requires Hospital Episode Statistics (HES) Admitted Patient Care (APC) and HES Outpatients (OP) data and linked Civil Registration (mortality) Secondary Care data for the purpose of a longitudinal, retrospective study to investigate temporal trends, geographic trends and variations in access for shoulder replacement surgery in the NHS. The study team wish to study the complications that follow surgery, and to predict the projected future burden of shoulder replacement surgery on the NHS which is crucial for service provision planning across the country for the next 25 years.

This study will be undertaken at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) at the University of Oxford. This study has been funded by the National Institute for Health Research (NIHR) through a Doctoral Fellowship Award and will attempt to answer key questions raised by patients, carers and researchers through the national 2015 James Lind Alliance Priority Setting Partnership. The study has also been endorsed by the National Director of Clinical Improvement for the NHS and the British Elbow and Shoulder Society (BESS). Further patient and public involvement through the Shoulder Research User Group (SHRUG) has reiterated the importance of this study to patients.

Shoulder pain is associated with increased health care utilisation and accounts for 20% of disability claims for musculoskeletal disorders. Degenerative shoulder osteoarthritis causes pain, functional limitation and disability and has an estimated prevalence between 4% and 26%. Patients with bilateral shoulder arthritis can rapidly lose function and be unable to self-care. Shoulder arthritis therefore leads to significant morbidity, particularly in an ageing population. Over 45,000 shoulder replacements were undertaken in the UK between 2012 and 2020.

Despite its high prevalence, there is ongoing treatment uncertainty with no high-quality evidence to guide the choice of the different shoulder replacements that are marketed. An evidence review conducted in 2009 (American Academy of Orthopaedic Surgeons) was published but unable to identify any strong evidence to support any of their 16 recommendations on shoulder replacement surgery. A 2010 Cochrane review reporting the effectiveness of different surgeries for shoulder arthritis determined that the overall lack of evidence precluded any conclusions being drawn about the benefit and safety of shoulder replacement surgery. In 2020, another Cochrane Review confirmed an ongoing lack of high-quality evidence on the topic of shoulder replacements. The 2020 NICE guideline on hip, knee and shoulder replacements further highlights the need for high-quality evidence to inform the surgical management of shoulder osteoarthritis and the need for clinical and cost-effectiveness comparisons between different types of shoulder replacements. With improving shoulder replacement outcomes being a priority for patients, there is an urgent need for better evidence and service planning in the NHS.

This project is aligned with the National Health Service (NHS) agenda. The NHS Improvement (NHSI) Getting It Right First Time (GIRFT) programme is dedicated to improving patient outcomes at a reduced cost to the NHS, by reducing regional variation in practice guided by evidence-based care. While other joint replacement procedures are undergoing service provision changes, much better evidence is now needed before any acceptable and effective national guidance for shoulder replacements can be made. Through this proposed project, researchers at the University of Oxford will gain an improved understanding of the burden of shoulder replacement surgery on the NHS now and in the future by analysing temporal trends, geographic trends, and any variations in access to shoulder replacement surgery based on population demographics.

This proposed project will form part of a larger research agenda on improving the outcomes of patients having shoulder replacements that will also include analysis of data from the National Joint Registry (NJR). This agreement containing the request for HES and Mortality data will specifically address one Work Package of the larger research project, namely the current and future burden shoulder replacement surgery on the NHS, including costs of shoulder replacement surgery and an analysis of any geographical variations or inequity of access.

While data from the National Joint Registry (NJR) collected as a part of the larger research project will provide useful surgery-specific information over a limited timescale, the HES and mortality data requested in this agreement will represent a more comprehensive dataset of NHS secondary care in England and enable a long-term follow up of patients having shoulder replacements allowing an assessment of complications, further surgery, as well as changes in practice, regional provision per population and any variations in access per population demographic. This will all inform future NHS service provision of shoulder replacements for maximum benefit to all patients and society.

The requested pseudonymised, record-level HES and mortality data will enable the research team to fulfil its aims and address patient and healthcare information needs. The NDORMS study team will explore how the change in service provision (number of surgeons and hospitals) corresponds to the demand for shoulder replacements. Trends in associated healthcare costs will be estimated through HES Admitted Patient Care as well as HES Outpatients data. Civil Registration (Mortality) data will enable analysis of the association between surgery and mortality and its temporal and geographic variations, including equality of access across England and across different patient groups.

NDORM’s lawful basis for processing data under GDPR has been reviewed and been assessed as acceptable. The University of Oxford process data under Article 6(1)(e): "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller" as they are a Public Authority.

Additionally, the University of Oxford process the Special Category Health Data under Article 9(2)(j): "processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject" as the data are required for historical research purposes in the public interest. This research is in the public interest as there is very little high-quality evidence available to inform shoulder replacement service provision and workforce planning.

The University of Oxford is the sole data controller who also processes data. When the results of the study are made available, the study group will work closely with British Elbow and Shoulder Society (BESS) and NHSI GIRFT to improve translation of the study results into national policy. No record level data processing will be undertaken by these groups. All outputs will be aggregated with small number suppression applied as per the HES analysis guide.

This study will be funded by the NIHR who will publish summary details of the study on their website but will not be involved in data processing or decisions about data analysis.

Expected Benefits:

The University of Oxford hopes that this study informs NHS commissioners and healthcare professionals about the likely future burden of shoulder replacements on the NHS both in terms of cost and workforce planning. Key information about the geographic variation in surgical outcomes should help inform patient-choice and decision making as well as highlight modifiable organisational factors that will be useful to NHS commissioners to improve the service provision of shoulder replacements. NDORMS hope to work closely with NHSI so that the evidence from the temporal and geographic analysis of trends using HES data translates to improved provision.

This study forms part of a larger research agenda to improve the outcomes of UK patients having shoulder replacements as part of a surgical trainee’s doctoral thesis at NDORMS. Together with the current study, this overarching project aims to provide patients and clinicians with long overdue evidence to inform shared decision-making around shoulder replacement surgery with the aim of improving patient outcomes. It hopes to provide answers to key research questions raised by patients and the public through the James Lind Alliance Priority Setting Partnership (JLA PSP) and recent Cochrane and NICE evidence reviews.

The target date for the above outputs and dissemination that is expected to translate to measurable benefit is 36 months from receipt of the data.

Outputs:

Throughout this project the University of Oxford aims to actively engage with key stakeholders including NHS managers, professional bodies and patients and the public for results interpretation and dissemination. The research team at NDORMS has strong national collaborations that expect to facilitate effective dissemination of the research outputs to researchers, scientists and healthcare policymakers:

1. NHSI/Getting It Right First Time (GIRFT): A Professor of Orthopaedics (co-applicant on the study) advises GIRFT and has previously authored the new recommendations for provision of elbow replacement surgery in the NHS. The NHS National Director of Clinical Improvement fully supports this study so it is hoped it will gain high-level attention, enabling the results to directly translate to service provision changes on a national scale.

2. British Elbow and Shoulder Society (BESS): A Professor of Orthopaedic (co-applicant on the study) is President Elect for BESS and hopes to be able to facilitate the updating and authorship of BESS national guidelines depending on the results of the study.

3. National Institute for Health and Care Excellence (NICE): NDORMS expects to update NICE on any important findings which have the potential to translate to national change in clinical practice.

4. National Joint Registry (NJR): Two co-applicants on this study (a Professor of Orthopaedics and a Senior Research Fellow in Medical Statistics and lead data analyst for the NJR) work closely with the NJR. Dissemination to surgeons and the public will be supported by the NJR who reference important research projects in their annual report, NJR symposium at the British Orthopaedic Association, and through NJR patient information.

5. NIHR Oxford Biomedical Research Centre: Dissemination through the press, media and charities.

Researchers also hope to present the study findings at the below national and international conferences in the form of podium and poster presentations:

1. British Orthopaedic Association (BOA): This is the largest Orthopaedic conference in the UK and it is expected provide NDORMS with a platform to present the study results to all British Orthopaedic surgeons. This meeting is particularly well attended by Orthopaedic trainees and offers an excellent opportunity to create maximum impact on future consultant surgeons.

2. British Elbow and Shoulder Society (BESS): NDORMS hopes to disseminate the results of the study through BESS to all British shoulder surgeons in the form of conference presentations at the annual scientific meeting.

3. European Shoulder and Elbow Society (SECEC): Dissemination to shoulder surgeons on an international level

All outputs will adhere to the HES analysis guide with data being shown in aggregate form only with small numbers suppressed as per the HES analysis guidance.

Patient and Public Involvement (PPI):
NDORMS recognises the importance of meaningful PPI involvement and patients have been involved at all stages of this project. The importance of this study was identified and prioritised by patients and the public through their participation at the 2015 James Lind Alliance Priority Setting Partnership on shoulder surgery. NDORMS has already worked with patients from both the North (Shoulder Research User Group) and South (Oxford) of England to formulate this study plan including the larger research agenda on shoulder replacements. The study team have worked with the PPI Manager at the NIHR Oxford Biomedical Research Centre to identify patient representatives. The study team are setting up a patient advisory group for this study and a patient expert from the NICE Guidelines Joint Replacement Committee has already joined. The study team have collaborated with the PPI Senior Research Officer at the NIHR Research Design Services to make plans for effective dissemination of the study results so that they are easily accessible and interpretable to the wider patient community and to the public.

The study team plan to disseminate the study findings through scientific publications at peer-reviewed journals. These publications are expected to be open-access to maximise dissemination to the target audience of researchers, scientists, patients and the public. Open-access fees have been secured in the NIHR funding for this project. NDORMS hope to work with the new BESS Expert Patient Group to co-produce patient-friendly summaries including patient perspectives of the project results and infographics. NDORMS expect to publish a full and complete account of that research in the NIHR Health Services and Delivery Research Journal, ensuring the research is reported fully, and publicly available via the NIHR Journals Library website and Europe PubMed Central. A webpage is also expected to be developed within the NDORMs website for this study in order to improve dissemination of the results to the public.

Processing:

The University of Oxford will not be providing any patient data to NHS Digital but will provide NHS Digital with a list of OPCS codes for data minimisation. NHS Digital will link HES APC and HES OP data to Civil Registration Mortality data, preparing a pseudonymised, case-level dataset. This dataset will be transferred to the University of Oxford via Secure Electronic File Transfer Service (SEFT) and securely stored and processed as described below. Based on historical data and the projected number of shoulder replacements over the coming year (2021) the study team are expecting the resultant data extract cohort size to contain approximately 75,000 procedures (1998-2021).

The data requested is as detailed below – one drop of record level pseudonymised data:
HES APC data spanning the years 1998/1999 to 2020/2021,
HES OP data spanning the years 2003/2004 to 2020/2021,
Civil Registration (Deaths) Secondary Care Cut linked to the HES data spanning 1998/1999 to 2020/2021

DATA MINIMISATION
NDORMS has considered data minimisation and has taken the necessary measures to ensure the requested data is fully justified and limited to the required cohort. Details of these are outlined below:

- Datasets and linkages: The study is focused on patients who have had a shoulder replacement. The data required are limited to only hospital inpatient and outpatient episodes and mortality records for adult patients aged 18 years and over who have had a shoulder replacement since 1998 identified using the OPCS procedure codes code assigned to the hospital episode. The study team will provide a list of operation codes (OPCS) to ensure a minimised, yet sufficient cohort of patients are identified. The datasets requested are to be pseudonymised as this will enable the research questions to be answered least intrusively.

Civil Registration Mortality data linkage is required for two reasons. First, it is crucial to know whether a patient is still alive when undertaking survival analysis in such a longitudinal study. Second, it is important to identify the mortality rate following shoulder replacement or following a direct complication of shoulder replacement surgery. Both the cause of death as well as the date of death is therefore required, and the 23-year follow-up will enable researchers to produce the most accurate estimate of shoulder replacement burden and projected future burden to the NHS.

- Years and filtering: Complications after shoulder surgery including revision surgery for failing implants can occur many years after the index procedure, and the 23-year follow-up data requested will allow these events to be accurately captured. In order to most accurately analyse the temporal trends in shoulder replacement surgery, The University of Oxford are requesting long-term data covering 23 years. This will enable researchers to identify trends that occurred over a number of years which may correspond to changes in practice or the introduction of national guidelines. Having long term data is also critical to the production of the most accurate estimate of shoulder replacement burden and projected future burden to the NHS over the next 25 years.

Temporal trends spanning a number of years will also allow researchers to best forecast future costs and burden on the NHS. National (England only) data is required to identify geographic variation in the demand and provision of shoulder replacement surgery and to investigate whether deprivation has an influence on patients’ outcomes following shoulder replacement. It also helps to target the expansion of healthcare resources to the areas of greatest need in future.

- Episodes and fields: The study team require details of all the data subjects’ episodes of inpatient and outpatient care before the episode where they had their shoulder replacement (excepting Maternal episodes/birth episodes are not required as they are not relevant to this study) in order to gain a better understanding about their past medical history which may contain important information about risk factors for complications and revision surgery. Limiting previous episodes for the data subjects based on ICD codes may introduce bias and prevent the identification of important associations between risk factors and outcomes, so it is necessary to include all prior episodes. There is limited high-quality evidence to be able to confidently identify which risk factors are important predictors of outcome following shoulder replacement. It follows therefore that all previous patient episodes and code sets are required in order to ensure all true risk factors and associations are highlighted, and to prevent inadvertently discarding risk factors due to assumption. Furthermore, certain comorbidities may have been identified in patient episodes that may not be directly related to their shoulder replacement episode (e.g. medical comorbidities identified during a patient’s attendance for appendectomy). All subsequent hospital episodes for the data subjects are also required to ensure all inpatient and outpatient events that may be associated with the shoulder replacement procedure including revision surgery, hospital appointments and other treatments (however long after the index operation) are accurately captured and their costs accounted for to reflect the true burden of shoulder replacements.

DATA ACCESS
Once the pseudonymised data has been received by the University of Oxford from NHS Digital:
- The HES and Civil Registration Mortality datasets will be held on a password protected University of Oxford Computer on an encrypted drive in the Secure Data Room at the NDORMS Botnar Research Centre. NDORMS holds an up to date NHS Data Security and Protection Toolkit which provides data security assurance for processing and storing NHS data. Data will be encrypted to AES-256 standard as per IG07 NDORMS Confidential Data Storage and Destruction Policy.

All data will be processed only by substantive employees of University of Oxford. Access to the data will be restricted to named individuals working on the project and based at the NDORMS Botnar Research Centre who have received suitable training and will only access the data for the purposes described in this agreement. Those accessing NHS Digital data at NDORMS are required to take the University wide annual information security awareness module which is mandatory for all staff and students, and undergo an induction to the secure data room with the Information Governance Manager.

DATA ANALYSIS:
NDORMS will carry out statistical analysis to model the change in volume and secondary care costs of shoulder replacements over the 23-year period provided by the NHS Digital HES data. Correlations between variables will also be investigated such as disease prevalence and patient demographics. Geographic variations in outcome will be explored across hospital trusts and Clinical Commissioning Groups and the influence of these factors on a variety of outcomes (such as length of hospital stay, waiting times, revision and complications) will be highlighted. Variations in access to shoulder replacement surgery across different geographic area, different patient cohorts including levels of deprivation will be reported. Surgical volume will be projected for the following 25 years by applying age- and sex-standardised shoulder replacement rates to national (England only) population forecasts, a method previously applied to the projection of cataract surgery growth in Canada. This will be contrasted with different scenarios of projected growth in the service provision in England based on the analysis of observed trends from the NHS Digital HES data.

At the end of the study, the data will be safely held in a password protected University Computer at the Botnar Research Centre for 60 months and, in that time, it will be assessed only to answer questions arising from the publication and other publicity. The interim expected timeframe for completion of the data processing, production and dissemination of the outputs is 36 months, with a further 36 months retention of data after this to respond to changes based on peer review comments from journals and from funding bodies. An active data sharing agreement will be held with NHS Digital during this full data retention period.

All data will be processed only by substantive employees of University of Oxford who have been appropriately trained in data protection and confidentiality.

Data will not be linked to any other record level data. No attempts will be made to re-identify any individual from the data being supplied.

In order to protect patient confidentiality, when presenting results calculated from HES record level data, outputs will contain only aggregate level data with small numbers suppressed in line with HES Analysis Guide.

University of Oxford- National Core Studies Can phenotypes developed from enhanced remote primary care assessment of COVID-19 be used to identify a cohort of community cases, and enable comparison of recovered and long COVID? — DARS-NIC-431881-N8B0N

Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-03-23 — 2021-08-31

Access method: Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON, UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  5. Diagnostic Imaging Dataset
  6. Emergency Care Data Set (ECDS)
  7. Mental Health Services Data Set
  8. Secondary Uses Service Payment By Results Accident & Emergency
  9. Secondary Uses Service Payment By Results Episodes
  10. Secondary Uses Service Payment By Results Outpatients
  11. Secondary Uses Service Payment By Results Spells
  12. Civil Registrations of Death - Secondary Care Cut
  13. COVID-19 Second Generation Surveillance System (SGSS)
  14. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  15. Diagnostic Imaging Data Set (DID)
  16. Mental Health Services Data Set (MHSDS)

Objectives:

Since the outbreak of COVID-19 in Wuhan, China, and the subsequent pandemic, Public Health England (PHE) has commissioned the Oxford- RCGP RSC, based at the University of Oxford, to incorporate the monitoring of COVID-19 into its virology surveillance scheme. A vital part of this work has been to monitor the number of suspected COVID-19 cases in the community and ultimately establish the effect of COVID-19 infection on hospitalisation as the outcome measure.

OVERALL AIM
Primary aim of the RECAP (Remote COVID-19 Assessment in Primary Care) project is to assist primary care providers to improve patient care and health outcomes. Most COVID-19 patients are diagnosed and managed remotely by GP’s. This helps reduce the burden on hospitals. However, due to the wide number of possible symptoms, it is hard to predict and diagnose COVID-19.

Through this project, we aim to develop and validate an early warning score for GPs based on data collected to a remote GP consultation and then link this to the outcomes such as hospital admissions as a measure of clinical deterioration.

The purpose of this application is to link data held by NHS digital to support the RCGP RSC to conduct observational epidemiological studies that inform the national public health response to COVID-19. The RCGP RSC dataset includes individual patient level up-to-date primary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care.

For the purpose of this study, datasets coming into ORCHID as part of the MAINROUTE study (DARS-NIC-381683-R6R6K) will also be utilised.

The datasets included are:
- Detailed demographic and risk factor data.
- COVID-19 appointments: information on whether or not a virology swab was taken and the outcome of the swab
- Non-COVID-19 appointments
- Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE
- Co-morbid conditions
- Medication which may be associated with better or adverse outcomes.
- Test results
- Referrals made
- A&E visits
- Inpatient appointments, including critical care
- COVID-19 SARI Watch (Formerly CHESS)
- Mortality data (if applicable)

Additional datasets under DARS-NIC-431355-B1L8W will also be utilised:
- COVID-19 Second Generation Surveillance System (SGSS) – (Pillar 1)
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar2)
- Civil Registrations (Deaths)– Secondary Care Cut

The GDPR Lawful basis for processing the requested data under this agreement are;

Imperial College London:
Since Imperial College are the sponsors of this study, they are joint data controllers.

All the data will flow into the secure environment at University of Oxford, thus are data controllers. Furthermore, only University of Oxford will be processing the data.

The data controllers for the processing being undertaken within this agreement are University for Oxford and Imperial Collage London who have together the joint responsibilities. Some of the data which will be accessed under this agreement will be data which is already in the hands of University of Oxford under a different agreement DARS-NIC-381683 for which University of Oxford operate as a Data processor for on behalf of Pubic Health England (PHE) and the Royal Collage of GPs (RCGP) and for DARS-NIC-431355-B1L8W where University of Oxford are a data controller for different purpose.

The lawful basis for processing the data are as follows:

Article 6(1)(e) (Public Task processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller).

Article 9(2)(j) (processing is necessary for reasons of public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interest of the data subject.

Expected Benefits:

The RECAP (Remote COVID-19 Assessment in Primary Care) project aims to assist primary care providers to improve patient care and health outcomes. Most COVID-19 patients are diagnosed and managed remotely by GP’s. This helps reduce the burden on hospitals. However, due to the wide number of possible symptoms, it is hard to predict and diagnose COVID-19.

Through this project, an early warning score for GPs based on data collected to a remote GP consultation will be developed and validated which will then be linked to outcomes such as hospital admissions as a measure of clinical deterioration.

Outputs:

Specific Outputs for this study are:
• To track the impact of COVID-19, visual descriptions (dashboards) of the number and rates of patients presenting specific symptoms (primary care data), being tested for specific tests, hospitalisation outcomes confirming deterioration
• Subgroups of data will be identified to enable display by GP practice, region, age group, gender, and ethnicity.
• Establishing a risk score for the predictability of COVID-19 diagnosis.

Furthermore, articles will be published in international scientific journals.

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

Flows of data:
- Data are extracted from practices that are members of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network by Wellbeing. The University of Oxford subcontracts with Apollo to do this as part its contractual responsibilities.
- The University of Oxford will provide NHS digital with a list of pseudonymised NHS numbers and pseudonymised date of birth for the cohort each quarter.
- NHS Digital will link the cohort to the requested datasets and send pseudonymised linked datasets securely back to University of Oxford.
- University of Oxford will store the data on the secure network.
- University of Oxford will process and aggregate pseudonymised data to produce approved reports for surveillance (as part of the National surveillance process); and for the purpose of COVID-19 vaccine pharmacovigilance and quality improvement.

No identifiable data items will be passed into or out of NHS Digital

SALTING METHODLOGY:
The University of Oxford will follow a salting method in a manner that all the data will be non-identifiable. The process is as follows:
1. An encryption salt is held by a designated staff member of the University of Oxford Medical Science Division who is not a member of the ORCHID staff.
2. When a data linkage is required, the encryption salt holder sends the encryption salt to the data provider (NHS Digital)
3. The data provider will hash personal identifiers (in the data requested by ORCHID) using a hashing algorithm
4. The hashing algorithm is SHA2-512.
5. To make this key unique, an encryption salt is added at the end of the NHS number (e.g. NHS number= 12345678 ; SALT (held by someone other than ORCHID staff) = bob. So, hashing would take place using the SHA2-512 alogrithm by 12345678bob = return pseudonymised data)

The RCGP RSC data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

Data will only be accessed by individuals within the RSC who have authorisation that are substantive employees of University of Oxford. The authorisation process includes: (1) Contractual requirement to follow IG principles; (2) Using the email registered with Human Resources to complete IG training and to return the certificate; (3) Staff email is authorised by the IT department for one year to access the secure network and staff computers are configured to allow this; (4) At any point the project managers or Head can have access to the secure network turned off. There is special authorisation to have access to the main database.

Only three SQL developers and one senior project manager can access the main database. Surveillance databases are
created for approved analyses once they have been agreed by the RCGP RSC approval committee. This agreed protocol
includes the list of variables required for the database. The SQL developers create separate databases for individual projects only including the required variables, for the required time interval.

The additional linkages will be added to the data that the University of Oxford already receives from the RCGP RSC network practices and PHE reference laboratories.

This process for previous projects linking different sets of data, and the linkage has been successful, provided both parties use the same pseudonymisation algorithm (SHA-512).

There will be no requirement nor attempt to re-identify individuals from the data. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

The use of national data is needed as the University of Oxford are a national surveillance centre and the cohort are from across England and Wales.

The use of pseudonymised NHS numbers are essential as the request to link to the data that the University of Oxford already received from the RCGP RSC network general practices and PHE reference laboratories.

NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

Outcomes of Patients who survived Treatment on an Intensive Care unit for COVID-19 in England and Wales (OPTIC-19): a comparative retrospective cohort study — DARS-NIC-419335-H5P8T

Opt outs honoured: Anonymised - ICO Code Compliant, Yes (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-09-15 — 2023-09-14 2021.11 — 2022.12.

Access method: One-Off

Data-controller type: INTENSIVE CARE NATIONAL AUDIT & RESEARCH CENTRE (ICNARC), UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Emergency Care Data Set (ECDS)
  3. GPES Data for Pandemic Planning and Research (COVID-19)
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Hospital Episode Statistics Outpatients
  7. MSDS (Maternity Services Data Set)
  8. MSDS (Maternity Services Data Set) v1.5
  9. Civil Registrations of Death
  10. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
  11. Hospital Episode Statistics Accident and Emergency (HES A and E)
  12. Hospital Episode Statistics Admitted Patient Care (HES APC)
  13. Hospital Episode Statistics Outpatients (HES OP)
  14. Maternity Services Data Set (MSDS) v1.5

Expected Benefits:

Through addressing questions about the impacts of severe COVID-19 on subsequent adverse events among survivors, the outputs of this work hope to inform:
- Clinicians regarding the ongoing/future risks to their patients
- Patients now and in the future of their short, medium and long-term risks.
- The wider health care community and health services that are tailored to the treatment of patients during and after ICU. These organisations are hope to include the ICUsteps charity and Oxford Critical Care Forum.

This work hopes to identify potentially modifiable risk factors that may lead to identification and treatment of patients who have survived treatment for COVID-19 on the ICU. Ultimately the benefit will depend on which risk factors are identified and the degree to which they can be modified in clinical practice. For example, if survivors of severe COVID-19 have an excess risk of cardiovascular disease, these patients might benefit from statin therapy even if identified as low risk by current primary care guidelines (e.g. as predicted by QRISK-2).

ICNARC is one of few organisations internationally who collate detailed data on intensive care patients at scale. For this reason, the study team expect their study to be one of the largest to inform the follow-up care of patients who have survived severe COVID-19.

Through publication of several scientific papers and presentations at scientific meetings on the topic the study team hope to raise the understanding of the scale of the risks faced by patients following ICU. This work should lead to further funded work that result in prevention and treatment options.

Outputs:

This study aims to publish at least two journal articles describing the risk faced by survivors of COVID-19 treated on an ICU. The first article aims to report six month outcomes (target date: 1st October 2021) and the second aims to include one year outcomes (target date: 1st December 2021).

The study team will be responsible for publishing outputs in high-impact open access peer-reviewed journals, such as the British Medical Journal, Intensive Care Medicine and the Lancet. All outputs will be aggregated with small number suppression applied as per the HES analysis guide. All published output aim to be accompanied by a corresponding press releases including lay summaries of the findings and its applicability to patients. When major findings are published, the Departmental press office aims to assist with press releases, social media messages and interviews.

The study hopes to also be promoted through the CCRG Twitter handle (@KadoorieCentre), which has over 1000 followers comprising of patients and health care professionals. Findings hope to be be presented at national and international conferences to experts in the field – for example, the UK Intensive Care Society State-of-the-Art annual conference and one of the European Society of Intensive Care Medicine’s bi-annual conferences.

The study team aim to present their results to relevant patient groups and their families via the ICU charity “ICUsteps” (https://www.icusteps.org/). They hope to also discuss their progress with the Oxford ICU patient forum, whose members include former ICU patients and their relatives. The study team hope to specifically seek the advice of both groups on how best to present the study results to the wider public. The study team developed a patient notification strategy with input from the Oxford Research Ethics Committee (REC) and members of the Oxford ICU Forum (a group of previous ICU patients, families, and lay members). The study will fully implement the National Data Opt-out and will therefore exclude any records related to patients who have notified the NHS of their wish for their data not to be used in research.

For a previous study, the study team were advised by the ethics committee that placing posters in the relative rooms or waiting areas of the participating ICUs to notify patients of the study, “was not practical and should not therefore be used”. All members of the Patient and Public Involvement (PPI) panel agreed with the ethics committee that displaying posters would not be appropriate. However, the PPI group advised that information should be available on a study website. The group advised that the website summary should be brief and accessible, with links to the detail (such as formal privacy policy) available. Members of the previous study PPI group were also consulted for this study (OPTIC-19) and were very clear in their support.

The aim is to achieve these outputs within the 15 month study period.

The study website (https://www.ndcn.ox.ac.uk/research/critical-care-research-group-kadoorie-centre/research-studies/six-month-outcomes-after-surviving-treatment-for-covid-19-disease-on-an-intensive-care-unit-in-england-optic-19) will be updated with all the details of the above.

The study team aim to engage with senior critical care clinicians in Oxford and Thames Valley should the results suggest how clinical follow-up of patients treated for COVID-19 on the Intensive Care Unit (ICU) might be improved.

Processing:

The Data Flow for this agreement will consist of the below:
a. ICNARC will identify the study cohort and allocate participants a unique study ID.
b. Identifiers (NHS Number, Date of Birth, Gender and Post Code) and the unique study ID will be submitted by ICNARC to NHS Digital using their secure electronic file transfer (SEFT) system.
c. NHS Digital will link to record-level HES, MSDS, GDPPR and Civil Registrations using the patient identifiers provided.
d. NHS Digital will then remove identifiers to pseudonymise the extracts.
e. NHS Digital will send the pseudonymised record-level extracts of the above-mentioned data products for the duration of the study via SEFT to the Critical Care Research Group at the University of Oxford.

The pseudonymised data from NHS Digital (HES/MSDS/GDPPR/Civil Registration data) will be linked by The Critical Care Research Group at the University of Oxford to health data and National audits from NHS Wales Informatics Service (NWIS), Barts' health NHS Trust (on behalf of the National Institute for Cardiovascular Outcomes Research (NICOR), Kings College London (on behalf of the Sentinel Stroke National Audit Programme (SSNAP), and the UK Renal Association (UKRR) and then subject to a process of data cleaning and data quality assessment. The study will also link data from the UK Obstetric Surveillance System (UKOSS) to identify patients who were pregnant at the time of ICU admission.

The resulting pseudonymised dataset will then be used for statistical analysis in keeping with a pre-defined statistical analysis plan.

No data will be matched to publicly available data and there will be no requirement/attempt to re-identify individuals.

As ICNARC is responsible for and runs the CMP program, ICNARC will always hold a copy of both the source data (the CMP program) and the study specific pseudo-anonymous data (for analysis purposes). Due to pre-existing privacy commitments if a patient contacted ICNARC and requested their data was removed from the data set then they would be obliged to do so. During the period the study is running, patients who raise requests to have their data removed from the study with either ICNARC or the CCRG will be identified by their ICNARC ID (by ICNARC) and their data removed from the study database by the CCRG/ICNARC. After the study period ends, the study team will destroy the ledger linking the ICNARC CMP ID to the OPTIC-19 Study ID making it impossible to directly link the study dataset to the individual. In terms of the study itself, all analysis will be performed on the anonymous dataset and there is no intention or requirement to re-identify the data at a later stage.

Both ICNARC and Oxford are collaborators on this project and run separate specialist analysis platforms and expertise within the groups (hence the co-data controller relationship). As result we will require the work to be performed by both teams in both environments. All data processing at the Critical Care Research Group at the University of Oxford and ICNARC will take place using secure systems. The data will not leave these systems at any time. All data held within these environments are owned and run solely by the respective data controllers/processors/data guardians.

The final pseudonymised study data will be stored securely within the CCRG Data Safe Haven and ICNARC servers for a minimum of five years after the end of the study, in keeping with the MRC Retention Framework for Research Data and Records.

Data processing is only carried out by substantive employees of the University Of Oxford and ICNARC who have been appropriately trained in data protection and confidentiality.

All data processing within the Critical Care Research Group at the University of Oxford takes place within a secure system which is designed in keeping with the principles of NHS Digital Security Assurance requirements. The data does not leave the system at any time and users connect via a remote desktop connection to a protected analysis environment that runs within the system - all connections are via encrypted tunnels into the system. The remote desktop connection prevents uses from transferring files or copying data in either direction. No data is copied or transferred to the remote device/client machine. Access to the environment is protected by two firewalls, departmental Virtual Private Network (VPN) and requires strong key based login.

The system is located in a locked, access-controlled server room within the CCRG research offices. All remote machines are university owned, standard build computers that conform to Cyber Essentials Plus accreditation and are compliant with the NHS Digital Data Security and Protection Toolkit (DSPT).

The study data set will never be released from this environment and it will be deleted at the end of its retention period.

Members of the study team, both in Oxford and at ICNARC, will undertaking statistical analysis of the study data set. For this reason, a copy of the pseudonymous data set will also be held at ICNARC (conforming with NHS DSP). Data will be stored on secure servers at ICNARC (office in London) or servers which are owned by an authorised contractor called Exponential-e (https://www.exponential-e.com/). Exponential-E are a contractor authorised by ICNARC and provide protection with a leading anti-virus protection. The anti-virus protection that is provided to ICNARC's servers provides protection against all currently known malware whereby is being updated on a daily basis with over 400,000 variations of updates per day. In addition to this signature based protection the level ICNARC also has advanced signatureless protection. This protects against the latest attacks more commonly known as “zero day” . These attacks are seen more with ransomware attacks and will detect an attack in progress, stop it in its tracks and then clean any effected server. The console then provides a function of reporting for analysis to review. Exponential-e are compliant with ISO 9001; ISO 27001; ISO 14001; ISO 22301; ISO 50001; ISO 20000 and also hold a Health & Social Care Network Complaint certificate of compliance. Employees of Exponential-E will not access the data, but will provide storage/back-up, and as such, are listed as processors.

Identifiable data held in the Case Mix Programme will be kept separate to the pseudonymised dataset for analysis. No attempt to reidentify participants will be made.

The data will be accessed from a networked PC via a Local Area Network. Firewalls are in place to prevent unauthorised remote access. Access to the networked PC is via username and password. All data analysis will be conducted within the confines of the ICNARC secure server, and will not be downloaded to remote devices for storage or processing. Only authorised members of staff working on the study will access the data. The CCRG will confirm that this data set is deleted at the end of the same study retention period.

Investigating a vaccine against COVID-19 — DARS-NIC-396423-H4Z6Z

Opt outs honoured: Identifiable (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2020-08-20 — 2020-12-31

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Permission to Contact

Objectives:

This Data Sharing Agreement authorises the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials. The data will be processed on behalf of the data controller, the University of Oxford, by the data processor, NHS Digital, for the purpose of supporting recruitment to participate in a COVID-19 vaccine trial being run by the University of Oxford.

The following provides background to the Permission to Contact (PtC) Service:

NHS Digital has agreed to work in partnership with the National Institute of Health Research (NIHR) to build and host a first of type online Permission to Contact (PtC) Service on nhs.uk where members of the public can register their details and give their permission to be contacted by researchers working on NIHR approved UK coronavirus vaccine trials about participating in those trials. As at 14th August 2020 there are two such trials. One is run by Oxford University and the other by Imperial College London. This PtC Service, which is called “Sign Up to be Contacted about Coronavirus Vaccine Studies” on the nhs.uk website was launched as a national service on 20th July 2020.

This Service enables participants to:
• Provide permission for NHS Digital to share an individual’s details provided through the Service with the researchers undertaking COVID-19 UK vaccine trials for the purposes of researchers contacting that individual about taking part in those trials.
• Provide their permission to be contacted by NHS Digital about progress and outcomes from CV19 vaccine studies and in relation to the development of the PtC Service, including to inform them of opportunities to participate in other types of health research.

The data collected from individuals who sign up includes sufficient information to achieve the following purposes:
• Matching potentially eligible participants to eligibility criteria provided by the vaccine trials for their specific studies. This data will comprise of age, sex, geographic locations, type of employment, and a number health question e.g. about whether they have long-term health conditions.
• Providing relevant details of potentially eligible participants which have been obtained through the Service to researchers. This will allow the researchers to contact the participants with a view to discussing their taking part in a trial and if so, to obtain their further permission to take part in the trial.
• NHS Digital will provide access to the information obtained from individuals through the Service via the existing Data Access Request Service (DARS) process available to researchers working on UK COVID-19 vaccine trials sponsored by the National Institute of Health Research. The Service will only provide researchers with the data collected directly from individuals themselves through the Service.

The contact details will be used to invite potentially eligible individuals to undertake an eligibility assessment and, if eligible, to give informed consent to participate in this trial. NHS Digital, as data processor acting on behalf of the Oxford Vaccine Study, will be sending the email to eligible participants.

The following provides information about the University of Oxford’s clinical trial:

A new virus causing respiratory disease emerged in Wuhan, China in December 2019 and has since rapidly spread to many other countries around the world, despite unprecedented containment efforts. The virus is part of the Coronavirus family which can cause respiratory infections ranging from the common cold to more severe diseases. This virus causes the disease known as COVID-19. Common symptoms of COVID-19 include fever, tiredness, and dry cough. Whilst about 80% of infected people have no or mild symptoms and will recover from the disease without needing special treatment, 1 in every 6 people who gets COVID-19 becomes seriously ill. Older people and those with underlying medical problems are more likely to develop serious illness. Thousands of deaths have been reported so far. The World health Organisation declared the COVID-19 epidemic a Public Health Emergency of International Concern on 30th January 2020.

There are no currently licensed vaccines and only a limited number of specific treatments for COVID-19.

The University of Oxford is undertaking a study to assess how well people of all ages can be protected from COVID-19 with a new vaccine called ChAdOx1 nCoV-19. This study will also provide valuable information on safety aspects of the vaccine and its ability to generate good immune responses against the virus.

The University of Oxford aims to enrol small numbers of older adults (aged 56-70 years, then 70+ years) before expanding to large numbers of adults across all ages (18+ years). After this, the University of Oxford will also assess the vaccine in a small cohort of children (5-12 years). In total the University of Oxford aims to enrol up to 12,330 volunteers.

The Permission to Contact (PtC) Service will be utilise to assist in recruitment of individuals aged 56+ who will be contacted and invited to participate in the vaccine trial.

Personal data can be lawfully processed under GDPR Articles 6(1)(e) and 9(2)(j) as this is a task in the public interest and classed as research.

Consent allows the data subjects’ confidential data to be used for this purpose satisfying the duty of confidence.

The organisations involved in the processing under this Agreement are the University of Oxford, the data controller, and NHS Digital which, as data processor, will extract a subset of individuals’ information from the PtC dataset and send these individuals emails inviting them to contact the University of Oxford to sign up to participate in the trial.

The University of Oxford will not directly access any of the data under this Agreement.

Organisations involved in the wider vaccine trial are:
• AstraZeneca UK Limited – who have licensed the vaccine and undertaken some of the vaccine manufacture and trial sample analysis
• Public Health England - research collaborator
• Numerous clinical trial sites (primarily NHS trusts)
• Various academic collaborators

None of these organisations have any involvement in the decisions taken by the University of Oxford in respect of the data under this Agreement.

The other organisation involved as the trial funder is the National Institute for Health Research (NIHR)/Department of Health and Social Care. The NIHR has supported the University of Oxford’s request to use the PtC service.

Expected Benefits:

The primary benefit of using the data will be to recruit participants for the clinical study/trial in a manner which:
• Enables individuals to volunteer in advance to participate in COVID-19 vaccine trials as an alternative to other potentially more intrusive mechanisms, e.g. sharing data with researchers about individuals under section 251 consents or COPI notices, which although lawful is initially less transparent.
• Allows researchers to identify a suitable cohort and recruit them quickly into the vaccine trials – thus reducing the overall time to recruit into the trials and to accelerate the delivery of an effective vaccine to treat individuals to manage the COVID-19 outbreak and to save lives.
• Reduces burden on research staff in identifying and contacting potential clinical trial participants.
• Supports the Vaccines Taskforce objectives to drive forward, expedite and coordinate efforts to research and then produce a coronavirus vaccine and make sure one is made available to the public as quickly as possible.

Outputs:

The information from NHS Digital will be used to facilitate contact with individuals who are potentially eligible and who have indicated willingness to potentially participate in studies/trials of COVID-19 vaccines.

This is expected to result in individuals entering the trials screening process with a view to them participating, with fully informed consent, in the Investigating a Vaccine Against COVID-19 study.

The main trial results of Investigating a Vaccine Against COVID-19 study are expected to inform development of a safe and effective vaccine against COVID 19.

Processing:

NHS Digital will extract a list of patients meeting the following criteria:

Volunteers must be:
• 56 years old or older and living in Sheffield, Northwick Park, Edinburgh or Liverpool, OR
• 70 years old or older and living in Birmingham or Hull (Castle Hill)

Volunteers must NOT:
• have had a positive COVID-19 swab/PCR/'antigen' test (antibody tests of any result are acceptable so if your database does not make a distinction between these, then ignore this item)
• be receiving current treatment for cancer
• have a bleeding disorder
• be pregnant, breastfeeding, or planning a pregnancy

NHS Digital will identify all individuals within the PtC dataset meeting the above criteria and will extract their names and email addresses.

It is not known in advance how many individuals meeting the above criteria will have records in the PtC dataset. The number may be amended and the process may be repeated depending on the level of response.

NHS Digital will write to the individuals in the subset inviting them to participate within the trial using ethically approved text provided by University of Oxford. The email will remind the individuals of the background of the permission to contact programme and give them the opportunity to state that they do not wish to be contacted again.

Individuals will not be contacted multiple times under this Agreement and NHS Digital will record the fact that the individuals have been contacted to ensure compliance with the maximum number of contacts outlined as part of consent.

No other processing of the data will take place and the data will not be linked with information from any other sources.

The University of Oxford will not hold or access the data directly and no data will be shared more widely. The data will only be accessed by NHS Digital employees.

Request to share information from the Shielded Patient List (SPL) for Covid-19 Purposes — DARS-NIC-381632-M4D9L

Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Other-Health and Social Care Act 2012 Section 261(1) and Section 262(2)(e)

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2020-05-27 — 2020-09-30

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Shielded Patient List

Objectives:

Background and Aims
The University of Oxford are requesting access to the Shielded Patient List (SPL) to support the development and validation of a new risk prediction tool to identify people in the England at high risk of severe outcomes from COVID-19 infection.

The first cases of infection caused by coronavirus SARS-CoV-2 (COVID-19) in the UK were confirmed on 24th January 2020 and the first UK death on 28th Feb 2020. Since then the disease has spread rapidly through the population. There are no vaccines, preventative or curative treatments for COVID-19 disease and only one possible disease modifying treatment so the government has used social distancing as a population-level intervention to limit the rate of increase in cases.

Case series of confirmed COVID-19 have identified age, sex, certain co-morbidities, and ethnicity as potentially important risk factors for susceptibility to infection, hospitalisation, or death due to infection. In addition chronic use of some medications at the time of exposure has been suggested as a potential risk factor for infection or severe adverse outcomes due to infection, although the evidence is currently too limited to confirm or refute these concerns. Understanding these risk factors is important especially where exposure, risk factors or medication could be modified in individuals or at a population scale to alter the likelihood of infection or adverse outcomes. Furthermore, associations between medications and improved outcomes, if confirmed from large cohorts, might provide important insights into disease mechanisms and pathogenesis.

As illustrated by a recent systematic review, prediction models for COVID-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. Three models have been identified that predict hospital admission from pneumonia and other events (as proxy outcomes for COVID-19 pneumonia) in the general population. Eighteen diagnostic models were identified for detecting COVID-19 infection (13 were machine learning based on CT scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. The systematic review indicated that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic8.

Thus, the Data Controller proposes to develop and validate a new risk prediction tool to identify people in England at high risk of severe outcomes from COVID-19 infection. This research will form the basis for a rapid research study to inform national COVID-19 and shielding policy. The study will describe the development and validation of novel COVID-19 risk prediction equations for initial use in the NHS in the UK but potentially available internationally (subject to local validation). It is anticipated that the equations will be widely available for use and that the equations will be updated regularly as understanding of COVID-19 increases, better data become available and as the underlying population changes or the virus itself mutates

The purposes for sharing the requested data are set out below (Agreed Purposes):
• NHS Digital has agreed to share a pseudonymised identifier (Pseudonymised NHS Number) and COVID 19 risk code (High or moderate if present) for each patient on the SPL (the Disclosed Data) with the Data Controller for the research purposes below.
• The Data Controller proposes to develop and validate a new risk prediction tool to identify people in England at high risk of severe outcomes from COVID-19 infection. This research will form the basis for a rapid research study to inform national COVID-19 and shielding policy. The study will describe the development and validation of novel COVID-19 risk prediction equations for initial use in the NHS in the UK but potentially available internationally (subject to local validation). It is anticipated that the equations will be widely available for use and that the equations will be updated regularly as understanding of COVID-19 increases, better data become available and as the underlying population changes or the virus itself mutates.

Cohort
University of Oxford will undertake a cohort study in a large population of primary care patients using the QResearch® database (version 44). They will include all practices in England who had been using their EMIS computer system for at least a year. The University of Oxford will randomly allocate three quarters of QResearch practices to the derivation dataset and the remaining quarter to a validation dataset.

The University of oxford will identify a second validation dataset from GP practices using a different system (e.g. using the TPP clinical system). This could either be achieved through (a) the new OpenSafely platform or (b) an extract provided by TPP to Oxford to link to QResearch (in line with REC approvals ref 18/EM/0400 obtained 01.04.2020) The advantage of (a) is that it available now and accessible by one of the investigators. The advantage of (b) is that it will allow TPP data to be linked to mortality, HES, ICNARC and COVID-19 national datasets since these datasets are already held by the QResearch team at Oxford and updated regularly (weekly or monthly).

Open cohorts of patients aged 0-100 years registered with practices on or after 1st January 2020 will be identified. Patients who do not have a valid NHS number will be excluded. Patients will enter the cohort on 1st Jan 2020. Patients will be censored at the earliest date of the diagnosis of the relevant outcome of interest, death (non-COVI-19) or the date of most recent data for each outcome.

It is relevant to note here that Data which is being anonymised by NHS Digital to share with the University of Oxford for QResearch includes data which has been collected from GPs about patients who may have registered a Type 1 as the data was shared for a direct care purpose – namely to identify those who are extremely clinically vulnerable and who need to shield. However, there is no mechanism to identify those patients to exclude them from this dissemination. Given their data will be anonymised when shared with University of Oxford, the public interest in those individuals who are on the SPL being given appropriate advice on how to protect themselves from COVID-19 and that this research is directly about identifying those who are at most risk, it is considered the objective of the research and the benefits it will bring to those what are shielding and others who may need to shield, overrides any objection registered by a patient.

Legal Basis
The SPL data was obtained by NHS Digital under the COVID-19 Public Health Directions 2020 which permit NHS Digital to collect and analyse data for COVID-19 Purposes and to share the Disclosed Data with the Data Controller for the Agreed Purposes under section 261(1) and s261(2)(e) of the Health and Social Care Act 2012 (2012 Act).
The Agreed Purposes are COVID-19 purposes for the promotion of health as required by s261(1A)(b) of the 2012 Act as the study will inform policy in relation to shielding and will develop a tool that will provide information about the COVID-19 risks to individuals. Under the General Data Protection Regulation 2016 (GDPR), NHS Digital is relying on Article 6(1)(c) Legal obligation: the processing is necessary for complying with the law (not including contractual obligationswith the Data Controller for the Agreed Purposes above. As this is health information and therefore special category personal data
NHS Digital is also relying on Article 9(2)(g) – substantial public interest and para 6 of Schedule 1 DPA – statutory purpose, to share the Disclosed Data for the Agreed Purposes.

Oxford University does not have a s251 approval for the data it holds for QResearch as it is considered to be anonymised data (in context) and HRA have confirmed that CAG approval is not required.

The data in the QResearch database is not considered to be confidential patient information and the Confidentiality Advisory Group (CAG) has confirmed to the Data Controller, including in March 2020 in relation to its COVID-19 research, that section 251 support from CAG is not required.
As the NHS number for patients from the SPL will be replaced with a pseudonymised identifier by NHS Digital, the Disclosed Data when shared with the Data Controller and used with other data in the QResearch Database will also be anonymised data, as the Data Controller and those who will process the Disclosed Data for the Agreed Purposes will not be able to identify the individuals to whom the Disclosed Data relates. As the Disclosed Data will be anonymised data, there will be no breach of the common law duty of confidence through the processing by the t Data Controller of the Disclosed Data for the Agreed Purposes. As the Disclosed Data will by anonymised data when processed by the Data Controller, it will not be regarded as personal data and therefore is not subject to GDPR and the Data Protection Act 2018. NHS Digital will publish details about the sharing of the Disclosed Data with the Data Controller in its Data Release Register and on its website page about the SPL List here https://digital.nhs.uk/coronavirus/shielded-patient-list/distribution.

Data Requested
NHS Digital has produced a Shielded Patient List (SPL) which contains details of those individuals who have been identified by clinicians as being extremely vulnerable in relation to the COVID-19 virus as a result of their pre-existing medical conditions. The University of Oxford has requested NHS Digital to share with it certain anonymised information identified below as Disclosed Data from the SPL, for the development and validation of a risk prediction algorithm to estimate short term adverse outcomes from COVID-19 disease which can be used as a risk stratification tool and to inform national shielding policy as more fully detailed below.
Data to be disclosed will come from the Shielded Patient List (SLP):
Shielded Patient List (SPL)
Versions 1.0, 2.0, 3.0, 5.0 and 7

The data refers to list records of who have been identified as Clinically Extremely Vulnerable (CEV) and added to the Shielded Patient List.

The data to be disseminated is:
• Pseudonymised Identifier (Pseudonymised NHS Number)
• COVID 19 risk code (High or moderate if present)

The sole data controller and processor is the University of Oxford.

Expected Benefits:

It is important for patients, staff and the NHS that there is one widely used, validated tool which is consistently implemented across the service and which is supported by the academic, NHS and patient communities. This will then help ensure consistent policy and clear communication between policy makers, professionals and the public.

The risk algorithms can be used in various ways (examples below are based on the various ways which www.qrisk.org has been implemented and used across the NHS over the last 12 years).
1. Within a consultation between the patient and a clinician with the intention of sharing the information with the patient to assess management options.

For example, a 54-year old Asian man wishes to know his risk of serious COVID-19 disease in order to modify risk factors (lifestyle, medication, occupational exposure etc). This could be achieved through development of a risk calculator for use within a consultation.

2. To risk electronically stratify populations to target clinical interventions towards different groups of patients based on levels of risk.

For example, a GP practice needs to identify patients shielding or prioritisation for vaccination (once one is available). This could be achieved through the implementation of the equations as risk stratification software embedded in GP clinical computer systems. This will ensure the tool can be applied to up-to-date electronic health records for direct clinical care purposes.

3. To model impact of interventions or changing policy (e.g. shielding, prioritisation for vaccination, occupational health, health economic analyses) through the analysis of the equations are applied to consolidated research databases.

For example, DH/PHE/NHS Digital need to assess the impact of changing guidelines on the risk categories or thresholds at a national or regional level e.g. how many patients would be reclassified as high/medium/low risk and what would the resource implications be?

4. Adapted for use by the general public to improve communication and understanding of risk (David S to add more) through implementation into web-based tools.

For example, a school or community needs to highlight risk factors and link to recommendations in behaviours to help reduce transmission of COVID-19.

5. Use by researchers to help generate new knowledge or insights.

For example, a risk stratification tool could be used to identify high risk patients to be invited to join a clinical trial or to adjust an analysis for baseline risk factors.

Outputs:

COVID-19 is an emerging pathogen which presents a significant threat to the population in terms of increased morbidity and mortality, particularly among vulnerable groups such as those with pre-existing disease.

The primary objective and thus output of the study will be the development and validation of novel COVID-19 risk prediction equations for initial use in the NHS in the UK but potentially available internationally (subject to local validation). It is anticipated that the equations will be widely available for use and that the equations will be updated regularly as understanding of COVID-19 increases, better data become available and as the underlying population changes or the virus itself mutates. It is also important to recognise at the outset that there will be limitations to any model that is produced and that the use of the model reviewed and updated regularly to ensure it remains fit for purpose.

All outputs produced will be in the form of aggregated reports with small number suppression applied.

Processing:

The Disclosed Data will be linked to other data held by the Data Controller in the QResearch Database, a GP practice research database. The database is linked at individual patient level to hospital admissions data, cancer registrations and mortality records obtained from the Office for National Statistics. In 2020, two additional national databases were linked to QResearch for COVID-19 research: the national registry of COVID-19 RT-PCR positive test results held by Public Health England (PHE) and the Intensive Care National Audit and Research Centre (ICNARC) Case Mix Programme (CMP) database. It is proposed that a register of healthcare workers is also added to the QResearch database and that together with the Disclosed Data this will also enable the research objectives for the study to be expanded to specifically examine the risks for both groups of people (shielded and health care workers) which could directly inform national policy regarding the maintenance and operation of the SPL and also quantify the risks experienced by health care worker to inform occupational health considerations.

• All records in the QResearch database are de-identified and are linked using a project specific pseudonymised NHS number. The pseudonymisation keys are not held by the Data Controller. The Disclosed Data will be de-identified by NHS Digital before it is shared with the Recipient and NHS Digital only will hold the pseudonymisation key.
• The Recipient will ensure that the Disclosed Data when linked with other data in the QResearch database will remain de-identified and will be considered to be anonymised in context in accordance with the ICO Code of Practice on Anonymisation.1

The Disclosed Data will be securely shared on or around 24 May 2020 with the University of Oxford via SEFT.

The Data Controller will ensure that when processing the Disclosed Data that it will not be processed in a way which would enable the identify of any individual to be ascertained either directly or indirectly and as such that the Disclosed Data will be maintained as anonymised data by the Data Controller who will ensure there are sufficient controls in place to achieve this as required by the ICO Code of Practice on Anonymisation.

If any of the Disclosed Data becomes identifiable and personal data through the processing activity carried out by the Data Controller, the Data Controller shall: a.) immediately cease processing the Disclosed Data and shall notify NHS Digital and agree changes to these terms of release which will require the Data Controller to have a legal basis to continue to process the Disclosed Data; and b.) comply with the GDPR, the Data Protection Act 2018, all applicable law concerning privacy or the processing of personal data and the Common Law Duty of Confidence when processing the Disclosed Data.

The Data Controller may process the Disclosed Data for the Agreed Purposes only.

NHS Digital will share the Disclosed Data in a one-off transfer securely with the Data Controller on or around 24 May 2020. If any further versions of the Disclosed Data are required, this will be agreed with the NHS Digital Caldicott Guardian.

The Data Controller will ensure that the Disclosed Data is subject to the same level of security and shall be stored and processed in the same locations as the other data disclosed to it by NHS Digital through the NHS Digital Data Access Request Service (DARS) under Data Sharing Agreements reference numbers NIC-240279 and NIC-375354.

The Data Controller shall if required apply for updated Research Ethics Committee approval in relation to the inclusion of the Disclosed Data in the COVID-19 research it is carrying out, if this is required (noting that the Disclosed Data is anonymised data which would be made available to the Data Controller.

The Data Controller and the Processor will on completion of the processing activity for the Agreed Purposes securely destroy the Disclosed Data (including any copies it was necessary for it take for the Agreed Purposes) and on the request of NHS Digital shall provide a data destruction certificate signed by the lead applicant.

NHSD will pseudonymise the NHS number. There will be no identifiable information in the data shared with QResearch. The data will be linkable via the pseudonym to other datasets University of Oxford holds all of which are anonymised in context due to the control in place under contracts under which they are shared and through the controls put in place by Oxford.

QResearch
QResearch is a high-quality research database established in 2002 which has been used extensively used for the development of risk prediction tools which are widely used across the NHS as well as a wide range of high impact epidemiological research. QResearch is a large, representative validated GP practice research database nationally. Until April 2020, there were 1205 practices contributing covering a population of 10.5 million patients. Following a recruitment invitation, the database has now doubled to 2519 practices in England, 193 in Northern Ireland and 3 in Scotland which will cover approximately 21 million current patients. There are currently no practices in Wales.

All data shared under this agreement will be processed and stored in secure locations within England and Wales and will not be shared outside University of Oxford, other than in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

4CHILD - Four Counties Database of Cerebral Palsy, Vision Loss and Hearing Loss in Children (Berkshire, Buckinghamshire, Northamptonshire, Oxfordshire) — DARS-NIC-148239-M8RTP

Opt outs honoured: Identifiable (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-10-11 — 2021-10-10

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report

Objectives:

The University of Oxford received data from ONS and subsequently NHS Digital from 2003 onwards for the purpose of 4Child. 4Child is a database which was established in 1984 to collect information about children with cerebral palsy and/or severe vision loss and/or hearing loss born to residents of Berkshire, Buckinghamshire, Northampton and Oxfordshire. It was used as a resource to carry out surveillance, research and service evaluation on the causes and consequences of the three potentially disabling conditions of cerebral play, vision loss and hearing loss; to access the effectiveness of interventions during pregnancy and soon after births; as well as to assess the need for services to support affected children.

Formerly called the Oxford Register of Early Childhood Impairments (ORECI) 4Child has been collecting information about children with cerebral palsy (CP), sensorineural deafness or severe vision loss born to residents of Berkshire, Buckinghamshire, Northamptonshire and Oxfordshire since 1984 and has information about 2,700 children with one or more of these impairments.

The register was set up against a background of uncertainty of the contribution of increased numbers of low birth weight survivors on the numbers of disabled children in the population. At the time, there were no routinely collected and easily accessible data on early childhood morbidity and so the register was set up as a framework to examine clinical associations of disabling conditions, to assess services, and to assess the effectiveness of perinatal intervention.

The aims of the database when it was established were to:

i. Monitor the prevalence of cerebral palsy, vision loss and hearing loss in children from 1984 onwards in the four counties of Berkshire, Buckinghamshire, Northamptonshire and Oxfordshire.
ii. To provide a research platform and support research and service audit initiatives using data from 4Child, in order to contribute to knowledge and understand of the causes and consequences of the impairments and how they might be prevented and better managed.
iii. To develop links with other researchers and collaborate in research within the UK, Europe and other centre around the world.

The 4Child database was run by the 4Child research team within the National Perinatal Epidemiology Unit (NPEU) at the University of Oxford.

The operation of the 4Child database involved receiving information about children who were born or lived during in early childhood (up to age 5 years) in one of the four counties, who were suspected and then diagnosed as having one of the three impairments: cerebral palsy, vision loss or hearing loss; of note some children have more than one of these impairments.

The information about the affected children was provided by any health professional who came into contact with the child during early childhood – so called multi-source notification. Multi-source notification is the mechanism used to ensure that no children were missed. The 4Child team at the University of Oxford also received information from the Office for National Statistics about any children who died. This enabled the team to carry out research into death rates for children with these impairments and to understand the reasons why some children and adults with these impairments die earlier than would normally be expected.

The information received about the children was identifiable personal data and included the names, addresses and dates of birth of the children. Receiving identifiable information was necessary to enable the team to identify when a child was notified to the team more than once so that that any duplicate notifications could be removed. The identifiable information was also used to enable collection of follow-up information about the children to obtain details about the extent and impact of their impairment. Using the information, with relevant regulatory permissions, the team were able to contact some families to invite them to participate in research.

Funding was provided to support the work of 4Child from a number of sources over the years. Latterly it was funded by the Department of Health but this final grant ended in 2010. The 4Child research team was disbanded once the funding ran out. Only one member of the research team remains in the NPEU.

As it was not possible to obtain further funds the database closed to registrations of newly diagnosed children in 2010. Nevertheless, the existing database is an invaluable and unique source of whole population data about children with these three important impairments as currently there is no other similar information collected in England in this systematic way.

In view of the fact that was not possible to obtain further funding to continue the active work of the database a decision was recently made to remove all the identifiable personal information on the database and this process was completed in August 2019. This means that all names, addresses, postcodes, dates of birth, dates of notification and diagnosis, where applicable dates of death, and all other date-related information held in the database have been deleted. All relevant date information was replaced with age at the event. The information held includes the cause of death.

The database will be securely archived to comply with good practice and also to preserve the resource for potential future research subject to the necessary approvals including an application to NHS Digital.

The need to keep the data in the long term will be subject to review. Initially, the mortality data will be retained for two years. Before the end of this period the University of Oxford will decide whether there is a continuing purpose for the mortality data which would justify keeping the information for longer. In the event a decision is made not to continue holding the data from death certificates, this information will be deleted from the database. At that stage the University of Oxford will also review the value of continuing to hold the rest of the data in the 4Child database.

This Data Sharing Agreement will permit the retention of the data previously supplied by NHS Digital and predecessor organisations. No other processing activities will be permitted. No new data will be supplied and/or linked to the dataset and no uses of the data (e.g. for research) are permitted.

Yielded Benefits:

The 4Child register contributed vital data to enable important research into the trends, aetiology and consequences of cerebral palsy to be carried out in: the 4Child region, across the UK as part of the UKCP collaboration and across Europe as part of the SCPE collaboration. The other key contribution that the 4Child data made was tracking the trends in the prevalence of cerebral palsy in the four counties. These were published in a series of annual reports and made available to clinical staff, policy makers and service planners. One of the important uses of the prevalence data was in the planning of service provision for children with cerebral palsy based on the number of affected children and the extent and severity of their impairments. Over 90 peer-reviewed research papers using data from the 4Child register were published prior to the closure of the register. Examples of the research conducted using 4Child data include: 1. A study to describe trends in the prevalence of cerebral palsy in preterm and low birthweight infants which demonstrated that over the period 1980 to 1996 the prevalence fell indicating the beneficial impacts of improved maternity and neonatal care over this period (Platt et al 2007). 2. A aetiological study to investigate the effects of gestational age at birth on the risk of cerebral palsy which identified and quantified the role of inflammatory factors which influenced the gestational-age specific risks, including, intrapartum hypoxia, neonatal sepsis, pre-eclampsia (Greenwood et al 2005). 3. A study to describe the long term consequences of cerebral palsy and the impact of cerebral palsy on subsequent risk of premature mortality and the predictors of mortality. This study demonstrated that the number and severity of impairments were the strongest predictors of risk of death (Hemming et al 2005).

Expected Benefits:

This data will be retained to comply with University of Oxford guidance and policy on good clinical practice. It also preserves a unique database of children with cerebral palsy, vision loss and hearing loss in England, which could still yield future benefits.

Processing:

This 4Child programme is now completed and closed.

The 4Child register was originally a disease register to enable surveillance, research and service evaluation to be carried out. The register is no long active.

This Data Sharing Agreement permits the retention of the data previously supplied by NHS Digital and predecessor organisations, which has now been pseudonymised.

This agreement permits processing of the data for the purpose of secure storage and back up.

This agreement does not permit any further processing that involves analysis, linkage, onward sharing. If further data processing is required the applicant must submit an amendment request to NHS Digital before data is accessed.

The data originally requested from NHS Digital was for use in the 4Child Four Counties Database of Cerebral Palsy, Vision Loss and Hearing Loss (PIAG 4-09 b)/2003).

The intention is to archive the pseudonymised dataset for the immediate future to preserve it as a resource to be used in future research subject to funding and the necessary approvals.

Any further analysis will only take place following an amendment to this Agreement that would allow further processing of the data.

The data will be stored in the National Perinatal Epidemiology Unit (where it has been stored since inception). The NPEU is part of the Nuffield Department of Population Health at the University of Oxford. The data will be stored on NDPH servers since this is the department in which the NPEU sits.

MR461 - A long term follow-up study of Aperts Syndrome — DARS-NIC-148106-PP9LS

Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-08-01 — 2024-07-31

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report

Objectives:

Civil Registration mortality data and Cancer Registration data were supplied to the University of Oxford for the purpose of a long-term follow-up study of individuals with Apert Syndrome.

This study is now complete and closed. This study ran from 1994 to 2013. Data processing finished in 2015, when information on ages at event (death, cause of death, still living on 08/05/2013), with all identifiers removed, were provided to a statistical epidemiologist substantively employed by the University of Oxford. The findings (now in aggregated form) have been ready for publication since 2015 but this has been delayed pending clarification of the legal basis for retaining the data.

This Agreement permits the release of the publication, and the processing of the data for the purpose of secure storage and back up to ensure it is possible to verify the conclusions published from this study..

This Agreement does not permit any further processing that involves analysis or linkage other than for the purpose of verifying findings in line with the original objectives of the study by repeating previous analyses described in this Agreement. Following publication of the study findings, it is possible that the findings will be questioned or challenged by third parties through direct contact with the University of Oxford, contact via the publishing journal or an open letter. In such circumstances, the University of Oxford may re-run the previously analyses undertaken to verify that the published results were accurate and may write a response to be issued directly to the challenger or published. The University of Oxford may not use the data to undertake different analyses to those undertaken during the original analysis.

This Agreement does not permit any onward sharing of the data. The data controller may process the data for the purpose of audit.

If any further data processing is required in addition to the above purposes or if the data needs to be moved to a different location/organisation the applicant must submit an amendment request to NHS Digital and receive formal approval in an amended Data Sharing Agreement before data is accessed.

Since 1960 there has been very little further research into Apert syndrome in this country until in 1994 a new study was created at the University of Oxford. The purpose of this new study at the University of Oxford was to try to find out what causes Apert syndrome. Apert syndrome is a rare malformation syndrome comprising two distinctive features, namely a characteristic appearance of the face and skull due to early closure of the skull bones (craniosynostosis) and bony fusions of the fingers and toes (syndactyly). This due to an alteration in one of the many thousands of genetic instructions which human beings carry from the time of conception. This study has already managed to identify which particular genetic instruction is altered, but much work needs to be done to try to understand why the alteration occurs in the first place, and how it causes the features of Apert syndrome.

In 1994 the University of Oxford sought permission from the head researcher of the 1959 study (who was based at the University of Sheffield) in order to obtain the names and dates of birth of the 24 participants in the 1959 study. These are the only data subjects.

The University of Oxford used these details to obtain data from the Medical Research service under the Office of Population Censuses and Surveys (OPCS). in January 1995. The service subsequently transferred to the Office for National Statistics (ONS) and then the Health and Social Care Information Centre (now known as NHS Digital). This detail was used to give the University of Oxford two options;
1 - Should the participant have passed away at time of flagging, the University of Oxford obtained the cause of death, and cancer registration data to understand whether there was an increased incidence of certain cancers.
2 - Should the participant be alive at time of flagging, the University of Oxford would invite them through their General Practitioner to take part in the study.

This study is considered to be in the public interest because it provides new (not previously available) information on the long-term survival of the medical disorder Apert syndrome. The retention of the data, which is now pseudonymised, is not expected to raise ethical issues because no individually identifying information on ages and causes of death is held.

It should be noted that this (currently unpublished) study provides very valuable information for people with Apert syndrome and their parents and carers, because it establishes for the first time that many individuals with the condition live into their 50s-70s. Moreover, although mortality is higher than average before this age, the causes are varied with no one frequent cause (for example a particular type of cancer), being revealed by the data. There is a strong public interest in making these findings widely known by publication in the medical literature.

Data collected were mortality and cancer data from 1994 until 2013. Initially data were supplied by OPCS/NHSCR on whether each individual was already deceased, and if so, the cause of death. In the case of individuals still alive, tracing of dates and causes of death, or cancers, continued (following necessary approvals after each administrative reorganisation) by ONS/NHSIC/HSCIC until 8 May 2013

Since then, actions have been taken to pseudonymise the Apert Syndrome data. These included converting date of birth to week and month of birth, deleting names, and deleting NHS Numbers. No identifying details are held by the University of Oxford in respect of this study.

The data controller is the University of Oxford which is also the only organisation which has processed this data. The analysis of the data completed in 2015, when information on ages at event (death, cause of death, still living on 8/5/2013), with all identifiers removed, were provided to a University of Oxford statistician who has completed a paper on this Apert syndrome. No external parties have been involved in this work. The findings have been ready for publication since 2015, but this has been delayed pending clarification of the legal basis for retaining the data. Following advice from NHS Digital, the University of Oxford has pseudonymised the data.

Yielded Benefits:

None, as the work has remained unpublished up to this point.

Expected Benefits:

This data will be retained to comply with guidance and policy on good clinical practice and regulations. It also preserves a unique database which could still yield future benefits for young families by looking at the effects on children in the longer term.

It is expected that this will improve knowledge of long-term prognosis of Apert syndrome, and causes of mortality, in adulthood. This will assist health professionals in providing better quality, evidence-based information to patients with Apert syndrome and their parents. Improved medical knowledge about this rare condition represents a public good.

The peer-reviewed publication will provide the objective data supporting the broad conclusions. The Headlines article will make these data known to the patient/parent constituent groups.


The benefits anticipated are two-fold:
(i) Psychological: Apert syndrome is a serious disorder providing many challenges for affected children and their parents. It will be very reassuring for families to know that being affected by Apert syndrome does not in addition indicate a high likelihood of early mortality as an adult because of a particular later-onset disease.
(ii) Scientific. It is known that one of the most common genetic associations of endometrial cancer is the identical FGFR2 mutation to that causing Apert syndrome, but occurring as a somatic mutation (ie, in a particular cell in the body at a later stage of life, rather than something you are born with). It is of great scientific interest that being born with the identical mutation is not necessarily associated with markedly increased risk of a similar type of cancer.


Major action/change is likely to be empowerment provided by new knowledge and associated psychological benefit, given that the new knowledge is largely reassuring regarding prognosis. The impact is small at population level, because this is relevant only to Apert syndrome (prevalence ~1 in 65,000); but nevertheless important for this group of individuals.

This agreement seeks to assure data integrity for a reasonable period of time (up to 5 years) following dissemination. This will enable evidence supporting findings to be examined, if necessary, should be findings be questioned or challenged for reputable scientific reasons.

Outputs:

This study is now completed and closed.

On approval of this Data Sharing Agreement, a completed publication from 2015 will become publicly available. No new analyses will be undertaken using the data under this Agreement.

An online article would be prepared for Headlines, the UK Craniofacial Support Group, so that parents of children with Apert syndrome, and affected young adults, would be made aware of the findings. No new use of the data would be required for this. It would simply be targeting the already-existing information to a specific audience and in simple language.

A summary of the findings would be published in a peer-reviewed medical genetics journal, for example, American Journal of Medical Genetics.

Causes of death, or whether still alive at the end of the study, would be summarised in 5-year bins. As there are only 24 data subjects in the study, each 5-year bins contain fewer than 5 individuals and consequently if someone knew of a person who had Apert syndrome and was in the 1959 study and died within a specific 5-year age-range, they may be able to identify that person. However, the only additional information they may be able to determine about this individual from the summary data might be their primary cause of death which is a matter of public record. Death and cancer data would be summarised in the form of Kaplan-Meier survival curves or similar demonstrating the proportion of individuals still alive at a given age.

An Open Access charge would be paid to the publisher of the peer-reviewed article, to ensure that the article could be read by anybody wishing to do so.

The Headlines web-based article would ensure that the knowledge reached the relevant patient/parent group.

The Publication is expected to be made available for submission of manuscript for peer-review towards the end of 2019, with publication and associated Headlines article in 2020.

This is the sole planned data dissemination and therefore integral to the overall purpose of the work.

Processing:

This study is now completed and closed.

This Agreement permits processing of the data for the purpose of secure storage and back up.

This Agreement does not permit any further processing that involves analysis or linkage other than for the purpose of verifying findings in line with the original objectives of the study by repeating previous analyses described in this Agreement.

This Agreement does not permit any onward sharing of the data. The data may be viewed for the purpose of an audit.

If any further data processing is required in addition to the above purposes or if the data needs to be moved to a different location/organisation the applicant must submit an amendment request to NHS Digital and enter into an Amended Data Sharing Agreement before the data is accessed.

In the event that the data needed to be accessed for the purposes of audit or to enable verification of previous findings, the dataset with authorisation from the Principal Investigator will be accessed by the study statistician only for the purposes of verifying results of previous analyses by rerunning analyses that were previously undertaken. Data would be accessible only for as long as is required to enable verification of the analyses and to write a response as appropriate.

The data may not be transferred to any other location and may only be accessed by substantive employees of the University of Oxford for the purposes described above.

The data originally requested from NHS Digital was for use in the long-term follow-up study of Apert Syndrome. Civil registration mortality and Cancer Registrations data were used to follow-up individuals involved in the study.

Study data needs to be retained and accessible for 5 years after publication.

RCGP Research Surveillance Network Observational Research Umbrella (RCGP RSC ORUm) — DARS-NIC-381683-R6R6K

Opt outs honoured: No - Statutory exemption to flow confidential data without consent, Anonymised - ICO Code Compliant, No (Statutory exemption to flow confidential data without consent)

Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2021-02-14 — 2024-02-13 2021.04 — 2022.12.

Access method: Ongoing

Data-controller type: PUBLIC HEALTH ENGLAND (PHE), ROYAL COLLEGE OF GENERAL PRACTITIONERS

Sublicensing allowed: No

Datasets:

  1. Mental Health Services Data Set
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Diagnostic Imaging Dataset
  5. Emergency Care Data Set (ECDS)
  6. Secondary Uses Service Payment By Results Accident & Emergency
  7. Secondary Uses Service Payment By Results Episodes
  8. Secondary Uses Service Payment By Results Outpatients
  9. Secondary Uses Service Payment By Results Spells
  10. COVID-19 Second Generation Surveillance System (SGSS)
  11. Diagnostic Imaging Data Set (DID)
  12. Mental Health Services Data Set (MHSDS)

Objectives:

Since the outbreak of COVID-19 in Wuhan, China, and the subsequent pandemic, PHE has commissioned the RCGP RSC to incorporate the monitoring of COVID-19 into its virology surveillance scheme. A vital part of this work has been to monitor the number of suspected COVID-19 cases in the community in a timely way.

PHE and the RCGP are Joint Data Controllers for this request. The RCGP Research Surveillance Centre (RCGP RSC) is based at the University of Oxford. The RCGP RSC is a growing network of over 1200 GP surgeries based in England. University of Oxford is the data processor.

PHE

Public Health England (PHE) holds a contract with the Royal Collage of Practitioners (RCGP) who in turn hold a contract with the University of Oxford to deliver information to support surveillance and monitoring of vaccine efficacy on Influenza.

RCGP

The Royal College or GPs (RCGP) Research and Surveillance Centre (RSC) has over 50 years’ experience of undertaking surveillance and research activities, predominantly in influenza surveillance. Pseudonymised patient data is extracted from over 1600 practices on a weekly basis, feeding into the disease surveillance and research funded through Public Health England (PHE). The COVID-19 activities set out in this agreement fall within the wider Disease Surveillance activities.

University of Oxford – are a data processor. The secure network which holds the physical data is at the University of Oxford. The University of Oxford acts as Data Processor on behalf of the Data Controller (RCGP and PHE).

The lead Professor who is the RCGP RSC Director, has moved his main appointment from the University of Surrey to the University of Oxford. Oxford currently provide the academic and clinical informatics input to inform data usages and ensure this adheres to contract held with PHE. Additionally, the study produces research outputs from the University of Oxford (these outputs have small numbers suppressed and Oxford are therefore not listed as a data processor).

The surveillance function of the RCGP RSC provides a unique platform upon which to build population based observational epidemiological studies designed to inform the national public health response to COVID-19. Direct COVID-19 analyses will study for example which patient-level characteristics are associated with COVID-19 infection, predictors of adverse outcomes, and potential treatments. Indirect COVID-19 analyses will for example provide near real-time monitoring to inform strategies to mitigate the indirect effects of the national response to COVID-19 on other "COVID-19 sensitive" non-communicable diseases.

Built on high quality primary care electronic health records data, the Joint Data Controllers for this request (PHE and RCGP) hope to add to the existing RCGP RSC HES (Critical Care, Outpatients, A&E, Admitted patient care) and Civil Registration (mortality) Data (CRD) linkages to support the priority observational COVID-19 studies outlined below.

OVERALL AIM

The study aims to establish an umbrella agreement for data linkages to support the RCGP RSC to conduct observational epidemiological studies inform the national public health response to COVID-19.

PRIORITY OBSERVATIONAL WORKSTREAMS

The following three priority workstreams outline analyses underway or in set-up using the RCGP RSC dataset.

1. RGGP RSC COVID-19 SURVEILLANCE

Aim - to identify whether there is undetected community transmission of COVID-19, estimate population susceptibility, and monitor the temporal and geographical distribution of COVID-19 infection in the community.

Specific objectives
1 a. To monitor the burden of suspected COVID-19 activity in the community through primary care surveillance and clinical coding of possible COVID-19 cases referred into the containment pathway

1 b. To provide virological evidence on the presence and extent of undetected community transmission of COVID-19 and monitor positivity rates among individuals presenting ILI or acute respiratory tract infections to primary care. PHE see all specimens (identified by NHS Number within their laboratory department) then pseudonymise this identifier to allow linkage. The PHE data and NHS Digital data will all be pseudonymised using the same algorithm so that a fully linked record for each person in the database will be available for the research team. The analysis will therefore be done by the team at an individual level but without the need to know who that individual is.

1 c. To estimate baseline susceptibility to COVID-19 in the community and estimate both symptomatic and asymptomatic exposure rates in the population through seroprevalence monitoring

1 d. To pilot implementation of a scheme for collection of convalescent sera with antibody profiles among recovered cases of COVID-19 discharged to the community. PHE see all specimens (identified by NHS Number within their laboratory department) then pseudonymise this identifier to allow linkage. The PHE data and NHS Digital data will all be pseudonymised using the same algorithm so that a fully linked record for each person in the database will be available for the research team. The analysis will therefore be done by the team at an individual level but without the need to know who that individual is.


2. DECISION-COVID: DEfining the CharacterIStIcs Of Individuals with suspected Novel COronaVIrus Disease and risk factors for development of the disease.

Aim - To better understand the characteristics of patients being tested for COVID-19 and to determine the associations between demographics, comorbidity and medications on the likelihood of developing COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, death).

Specific objectives
2 a. Identify patient demographics and co-morbidities that predict the diagnosis of COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, pulmonary events, death).

2 b. Identify medications that are associated with and increased or decreased risk COVID-19 infection and complications (e.g. hospitalisation, admission to an intensive care unit, pulmonary events, death).


3. MAINROUTE-C19: Monitoring Attendance, INvestigation, Referral, and OUTcomEs in Primary Care: impact of and recovery from COVID-19 lockdown

Aim - To describe and analyse the impact of the COVID-19 lockdown on presentation patterns, diagnoses, monitoring and outcomes of common non-communicable diseases, such as cancer, cardiovascular disease, diabetes and mental health.

Specific objectives
3 a. To produce practice-level data analytics on presentation, management and diagnoses of common non-communicable diseases and preventive health activities before, during and after COVID-19 lockdown, by region, practice, gender, and age

3 b. To examine the effect of the COVID-19 lockdown (and release) on presentation, management and diagnoses of common non-communicable diseases and preventive health activities by region, practice, gender, age and ethnicity

3 c. To determine the effects of the changes in presentation, management and diagnosis on long-term outcomes such as hospitalisation, morbidity and mortality, and some condition-specific outcomes.


EXISTING DATASET

The main aim of this application is to build on the exiting RCGP RSC database. The RCGP RSC dataset includes individual patient level up-to-date primary and secondary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care. For example, the database contains the following variables for each patient (where present):
• Detailed demographic and risk factor data.
• COVID-19 appointments: including information on whether or not a virology swab was taken and the outcome of the swab
• Non-COVID-19 appointments.
• Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE
• Vaccination status: date of vaccination, type of vaccination
• Co-morbid conditions
• Medication which may be associated with better or adverse outcomes.
• Test results
• Referrals made
• A & E visits
• Inpatient appointments, including critical care
• Outpatient appointments
• Mortality data (if applicable).

Existing linkages include CRD and HES data provide key information about the outcomes of care:
• HES: Critical Care
• HES: Outpatients
• HES: A&E
• HES: Admitted patient care
• CRD (mortality) data

ADDITIONAL LINKAGES REQUESTED

Additional individual level linkages to the entire RCGP-RSC cohort will support the priority analyses outlined above. Individual patient level data is required because individual patient level linkage allows much more precise statistical analyses to be made, compared with comparing aggregate data. Additional historical and updating linkages are requested to the following additional datasets:

• Cancer Registration Data
• Secondary Uses Service Payment By Results Episodes
• Secondary Uses Service Payment By Results Outpatients
• Secondary Uses Service Payment By Results Accident & Emergency
• Secondary Uses Service Payment By Results Spells
• Mental Health Services Data Set
• Diagnostic Imaging Dataset
• Emergency Care Data Set (ECDS)
• COVID-19 Hospitalisation in England Surveillance System (CHESS) Dataset
• Second Generation Surveillance System (SGSS) Dataset


Historic data are needed because longitudinal data better enable the RCGP RSC to predict what might happen in the future; even a small increase in the ability to understand flu and COVID-19 and its associated morbidity and mortality would offer benefits for patients and the NHS. Both historical and future data are needed in order to build a robust database and reporting system using up-to-date primary and secondary care data at the individual patient level, which can be easily queried. This will enable the study group to answer a wide range of questions which will have an impact on the provision of health care in England. For example, the data will be used to answer questions posed by PHE, who make many decisions about healthcare, such as the vaccination programme, or preventative measures. In MAINROUTE, for example, longitudinal data will allow time series analyses to be conducted as part of objcetive 3 b. which will compare primary care activity before, during and after "lockdown" to establish whether changed in primary care activity are associated with changes in disease presentation and outcome.

The same pseudonymisation algorithm will be applied to all data involved in this study (and any other studies) so the researchers can draw scientific conclusions for a study population. The PHE data and NHS Digital data will all be pseudonymised at University of Oxford prior to researcher access using the same algorithm so that a fully linked record for each person in the database will be available for the research team.

REGULATORY FRAMEWORK

The GDPR Lawful basis for processing the requested data under this agreement are;

Public Health England;
Article 6(1)(e) (Public Task processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller).

Article 9(2)(h) (processing is necessary for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, the provision of health or social care or treatment or the management of health or social care systems and services)

and

Article 9(2)(i) (processing is necessary for reasons of public interest in the area of public health, such as protecting against serious cross-border threats to health or ensuring high standards of quality and safety of health care and of medicinal products or medical devices).
PHE exist to protect and improve the nation's health and wellbeing, and reduce health inequalities.

RCGP;
Article 6(1)(f) processing is necessary for the purposes of the legitimate interests pursued by a controller, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. This shall not apply to processing carried out by public authorities in the performance of their tasks.

Article 9(2)(i) (processing is necessary for reasons of public interest in the area of public health, such as protecting against serious cross-border threats to health or ensuring high standards of quality and safety of health care and of medicinal products or medical devices).

Additionally the request for data is supported by PHE as they have an emanation of the Secretary of State for health and social care, to both self-approve the use of Control of Patient Information Regulation 3 and to grant this approval to third parties processing confidential patient information without consent for purposes that fall under the scope of Regulation 3.

This authority to has been in existence since PHE was established in 2013 although the large majority of the Regulation 3 approvals granted since that date have been internal to PHE; only a very small number have been granted by PHE to third parties. Specifically the work being undertaken under Reg 3 in this application is limited to Communicable Disease surveillance and other risks to public health’.

The data will not be shared with third parties and only used within the data processors listed in this agreement. Data disseminated under this application can only be used for different purposes after those different purposes have been approved by NHS Digital under separate applications and a live DSA is in place.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

Expected Benefits:

The surveillance work conducted by the RCGP RSC on behalf of the Data Controllers is used by Department of Health, NHS England and PHE to monitor trends in a number of infectious conditions. Specifically for COVID-19, the RSC aims to identify whether there is undetected community transmission of COVID-19, estimate population susceptibility, and monitor the temporal and geographical distribution of COVID-19 infection in the community. In addition, the RCGP RSC will describe and analyse the impact of the COVID-19 lockdown on presentation patterns, diagnoses, monitoring and outcomes of common non-communicable diseases, such as cancer, cardiovascular disease, diabetes and mental health. Furthermore the analyses conducted under RCGP RSC ORUm will lead to a better understanding of the characteristics of patients being tested for COVID-19 and the associations between demographics, comorbidity and medications on the likelihood of developing COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, death).

Specific benefits

The linkages described in this protocol can help assess the severity and mortality of a given condition, thereby alerting PHE on whether larger measures should be implemented. This could lead to improved healthcare and reduced mortality of certain conditions. Additionally, the linkages allow the RCGP RSC to identify how COVID-19 lockdown has put additional pressure on the health system in terms of delayed testing and referral, meaning that plans can be put in place in order to prevent or deal with these pressures during subsequent waves of the pandemic.

By supporting RGGP RSC COVID-19 SURVEILLANCE the researchers will be able to augment direct RCGP RSC COVID-19 surveillance using dedicated national COVID feeds (CHESS/SGSS) and enable full care pathway analysis from presentation to community providers (GP/111) through to secondary/tertiary care (ECDS/HES/SUS).

By supporting DECISION-COVID linked data will allow analyses to determine associations between demographics, comorbidity and medications of patients presenting to GP and the likelihood of developing COVID-19 and subsequent complications such as hospitalisation, admission to an intensive care unit, death (ECDS/SUS/HES/ONS), and to characterise socioeconomic and ethnic disparities in patients being tested (CHESS/SGSS) for COVID-19.

By supporting MAINROUTE-C19 linked data will enable an end-to-end description of the impact of the COVID-19 lockdown on the clinical pathway in terms of presentation patterns (GP RSC), testing (RCGP RSC/SUS/HES/DID) diagnoses (GP RSC/SUS/HES/Cancer/MHDS), monitoring (RCGP RSC) and outcomes (GP RSC/SUS/HES/Cancer/ONS) of common non-communicable diseases, such as cancer, cardiovascular disease, diabetes and mental health.

Outputs:

Specific Outputs for this study are:

• To track the impact of COVID-19 lockdown, visual descriptions (dashboards) of the number and rates of patients presenting with specific symptoms (primary care data), being tested for specific tests (including DID data), or referred for particular conditions will be presented over time (at weekly frequency) from 2018 will be hosted online. The raw data will be overlaid by 7-day moving averages, adjusted for seasonality. Subgroups of data will be identified to enable display by GP practice, region, age group, gender, and ethnicity.
• Using HES, SUS, ECDS, Mental Health Services Data, and Cancer data, outcomes will be examined through 7-day moving averages and presented graphically over time for the years 2018, 2019 and 2020 onwards to descriptively compare levels of activity.

Findings from this study will also contribute to existing outputs as follows:

• The RCGP RSC weekly report is circulated to a selected list of recipients on Wednesdays and it is publicly available on Thursdays at 2 pm at the RCGP RSC website (http://www.rcgp.org.uk/clinical-and-research/our-programmes/research-and-surveillance-centre.aspx). This report currently covers incidence rates of 37 infectious and respiratory conditions in England. It is expected that, in future, hospitalisation trends will be included. This is incorporated into the syndromic surveillance carried out by PHE on a daily basis, which allow them to determine any urgent priorities for local health protection teams.
• Similar to this, an annual report is published covering the annual trends of the 37 conditions. Each year, this report has a new theme which is explored in a paper submitted to a peer-reviewed journal (usually British Journal of General Practice). Themes explored include demographic disparities in disease presentation, higher rates of consultations for lower respiratory infections for boys, and urban/rural disparities of presentation.
• In January of every year, the University of Oxford provide a mid-season flu cohort to PHE with data up to the end of December. This is a fully pseudonymised patient-level extract collected by a PHE statistician using a secure drive. This data extract contains details of influenza swabbing, chronic conditions, and vaccination status for each patient. It is hoped to be able to include details of emergency attendances or admission around influenza, pneumonia, or lower respiratory tract infection events. At the end of the flu season (varies from March to May), a second extract is provided updating the first, with data recorded after December.
• The data from both of these extracts is used to estimate seasonal influenza vaccine effectiveness, stratified by comorbidities and demographics. HES data allow the University of Oxford/PHE and RCGP to include the impact of any changes in effectiveness, assessed through changes in hospital admissions/emergencies due to respiratory conditions. The results are published at the mid-season and at the end of season stage, on the peer-reviewed journal Eurosurveillance.
• Important results from either of these will be further analysed and presented at the RCGP annual conference, the PHE annual conference, and the PHE annual epidemiology conference.

Processing:

Flows of data:
• Data are initially extracted from practices that are members of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network by Wellbeing. The University of Oxford subcontracts with Wellbeing to do this as part its contractual responsibilities. The data are pseudonymised at source within the Wellbeing extraction process.

• The University of Oxford, on behalf of RCGP RSC, will provide NHS digital with a list of hashed NHS numbers and hashed date of birth for the cohort. NHS Digital will be operating under instruction as a data processor from the Data Controllers in this agreement to process the cohort data as per the details set out in this agreement and return the linked data asset. That data will flow back from NHS Digital using the same hashed algoritham therefore the research team will only be accessing pseudonymised data.

The Hashing process is as follows:
1. An encryption salt is held by a designated staff member of the University of Oxford Medical Science Division who is not a member of the research team.
2. When a data linkage is required, the encryption salt holder sends the encryption salt to the data provider (NHS D)
3. The data provider NHS D in this case will hash personal identifiers (in the data requested under this agreement) using a hashing algorithm
4. The hashing algorithm is SHA2-512.

The data are then linked across the datasets requested in NHS Digital using hashed NHS numbers.

On receipt of the data from NHS Digital University of Oxford will then link the NHS Digital data with the cohort data already held in the University using the same hashed algorithm. The data will be pseudonymised in a consistent manner so that the research team are then only working with a fully pseudonymised dataset. Each individual in the cohort will have a fully linked record.

NHS digital will provide back linked data including hashed NHS Number and hashed DOB:
• Cancer Registration Data
• Secondary Uses Service Payment By Results Episodes
• Secondary Uses Service Payment By Results Outpatients
• Secondary Uses Service Payment By Results Accident & Emergency
• Secondary Uses Service Payment By Results Spells
• Mental Health Services Data Set
• Diagnostic Imaging Dataset
• Emergency Care Data Set (ECDS)
• COVID-19 Hospitalisation in England Surveillance System (CHESS) Dataset

• University of Oxford will store the data on the secure network.

• University of Oxford will process and aggregate pseudonymised data to produce approved reports for surveillance (as part of the National surveillance process); and quality improvement.

The data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

All record level data will be held and stored within England and Wales.

Detailed explanation of flows of data:

a) Data flow from RCGP RSC network member practices to University of Oxford: Wellbeing extract the data from the practices. Patients who have opted out of data sharing do not have their data extracted, unless they have consented to a specific surveillance programme or study. This extract provides the study with information about patient’s visits to general practices including the date of the appointment, the reason for the visit and any relevant vaccination information. The University of Oxford also receive patient’s NHS numbers and date of births which are pseudonymised using SHA-512 algorithm. Detailed information about this algorithm is held in a separate location by IT services at the University of Oxford. It is this department who will share the identifiers with NHS Digital for the linkage leaving the research team only access to the pseudo data.

b) University of Oxford Storage and processing of data: The data about patients registered with RCGP RSC general practices is stored on the secure server at the University of Oxford which can only be accessed from the University of Oxford. The data will be processed within secure network and dedicated analysis server of the Surveillance Group. The secure network is located behind a firewall within the University’s network, all in-bounded connections are blocked, but out-bounded connections are allowed. Patient level data are held in the database server within the RSC Group’s secure network.

c) Pseudonymised data will be stored on the database server within the RSC’s secure network once fully linked with the NHS digital returned data. The pseudonymisation algorithm is held in a separate location by IT services at the University of Oxford.

d) University of Oxford process and aggregate pseudonymised data to produce reports. For example, University of Oxford on behalf of RCGP RSC provide a mid-season flu cohort to PHE with data up to the end of December. This is a fully pseudonymised patient-level extract collected by a PHE statistician using a secure drive. The University of Oxford also produce an end of season report, an annual report and weekly reports that are available to the public and use aggregated data on rates of infectious and allergic conditions.

The RCGP RSC data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

Data will only be accessed by individuals within the RSC who have authorisation that are substantive employees of University of Oxford. The authorisation process includes: (1) Contractual requirement to follow IG principles; (2) Using the email registered with Human Resources to complete IG training and to return the certificate; (3) Staff’s email is authorised by the IT department for one year to access the secure network and staff’s computers are configured to allow this; (4) At any point the project managers or Head can have access to the secure network turned off. There is special authorisation to have access to the main database.

Only three SQL developers and one senior project manager can access the main database. Surveillance databases are created for approved analyses once they have been agreed by the RCGP RSC approval committee. This agreed protocol includes the list of variables required for the database. The SQL developers create separate databases for individual projects only including the required variables, for the required time interval.

There will be no requirement nor attempt to re-identify individuals from the data by the research team. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.




The Oxford Risk Factors And Non Invasive Imaging Study (ORFAN) Arm 4 — DARS-NIC-409610-J6L1F

Opt outs honoured: Yes - patient objections upheld, Anonymised - ICO Code Compliant, Yes (Section 251 NHS Act 2006)

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2021-03-25 — 2024-03-24 2021.03 — 2022.12.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Medicines dispensed in Primary Care (NHSBSA data)
  2. Civil Registration (Deaths) - Secondary Care Cut
  3. COVID-19 Hospitalization in England Surveillance System
  4. Emergency Care Data Set (ECDS)
  5. HES:Civil Registration (Deaths) bridge
  6. Hospital Episode Statistics Accident and Emergency
  7. Hospital Episode Statistics Admitted Patient Care
  8. Hospital Episode Statistics Critical Care
  9. Hospital Episode Statistics Outpatients
  10. Civil Registrations of Death - Secondary Care Cut
  11. Hospital Episode Statistics Accident and Emergency (HES A and E)
  12. Hospital Episode Statistics Admitted Patient Care (HES APC)
  13. Hospital Episode Statistics Critical Care (HES Critical Care)
  14. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The Oxford Risk Factors And Non Invasive Imaging Study (ORFAN) at large has been operational since 2015. Originally the study was limited to arms 1, 2 and 3. Arm 4 has been developed conceptually from 2018, and came into practical existence from 2019 to both address outstanding scientific questions that cannot be answered with the limited pool of participants in arms 1-3, and to further develop and validate novel biomarkers that have emerged from ORFAN Arm 1-3 and other Ox-HVF studies (see below). ORFAN Arm 4 is funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre and The British Heart Foundation (BHF).

ORFAN Arm 4 study is a multi-centre observational cohort study involving patients who have had a computed tomography (CT) angiography or CT chest scan.

The study is applying to NHS Digital in order to link study participant's patient identifiers with data held by NHS Digital in order to develop and validate cardiovascular disease risk assessment tools that will lead to earlier detection of disease risk and prevent heart attacks and strokes, amongst other cardiovascular diseases. These tools are for usage in the clinical environment and are intended to change patient care for the better.

The purpose of the project is to develop new and better biomarkers of cardiovascular disease risk using novel approaches to the analysis of CT scans. The overriding purpose of this work is to reduce the large burden of morbidity and mortality that cardiovascular disease such as heart attack and stroke have in our society.

The NHS Digital data requested in this agreement contribute in a fundamental way to the achievement of this purpose by allowing the research team to know what diseases and clinical events had occurred prior to the CT scan of relevance to the study, and what disease and clinical events have occurred for participants following the CT scan. This data is critical to the successful development of image analysis tools as it enables accurate differentiation of participant characteristics, namely imaging characteristics detected from within the CT scans, that can be utilised to predict patient risk.

A further major purpose of the ORFAN Arm 4 project is to understand the cardiovascular disease ramifications of COVID-19 infection. The study team know that deleterious impacts on blood vessels and the heart due to acute COVID-19 infection are commonly reported, but they do not know the long-term impacts or how to predict which patients are most at risk of morbidity due to such disease effects. Access to data related to COVID-19 infection and later cardiovascular disease diagnosis and clinical events, along with relevant CT scans of such patients’ hearts and vessels will enable a much more accurate assessment of this risk for individual patients.

THE ORFAN STUDY PROTOCOL AND OBJECTIVES
The study has two main objectives:
1) To investigate whether biomarkers, including imaging biomarkers, of metabolic risk can predict major adverse cardiovascular events
2) To identify novel biomarkers able to predict cardiovascular disease pathogenesis and extent of pre-existing vascular disease, including in those with COVID-19 infection.

The specific primary objective of the ORFAN study is to investigate whether biomarkers of disease risk can predict major adverse cardiovascular events.

In order to achieve this objective, the primary outcome measures for the study are:
1. Measurements of plasma/imaging markers of cardiometabolic risk - not directly relevant for this application, although the data requested will be linked to the pseudo-anonymised plasma and imaging data already held by the applicant
2. Atherosclerosis progression by Computerised Tomography - not directly relevant for this application, although the data requested will be linked to the relevant pseudo-anonymised imaging data already held by the applicant
3. Major adverse cardiovascular events over 10 years - relevant for this agreement.

The relevant study secondary objective is to identify novel biomarkers able to predict cardiovascular disease pathogenesis and extent of pre-existing vascular disease
The secondary outcome measures that are relevant to this application:
1. Measurement of modification to cardiovascular disease pathogenesis and modulation of pre-existing vascular disease that is attributable to infection with coronavirus disease 2019 (COVID-19), and other pre-existing vascular disease.

The ORFAN Study was assigned COVID-19 Cardiovascular Disease UK Flagship Project status by the NIHR-BHF in May 2020 and has received ethical approval to pursue scientific inquiry into specific risk that is conveyed by COVID-19 infection in regard to stroke and coronary artery disease. The data from this agreement will form a major part of this inquiry so the study team can understand the heart disease risk that COVID infection confers, and will enable the creation of biomarkers for assessing patients individual risk of heart disease complication following COVID-19 infection.

The ORFAN study at large consists of 4 Arms of study. Arms 1, 2 and 3 include a total of 15,500 prospectively recruited participants who are directly consented to be included within the study. The ORFAN Study research arms 1,2 and 3 include directly consented patients for whom the University of Oxford collects and processes data for research purposes. The outcome data of these participants are collected through NHS Digital with whom a data sharing agreement has been signed (DARS-NIC-392669-T1F8B). This agreement herein does not include the collection, processing or storage of any data associated with patients in ORFAN Arms 1, 2 or 3.

ORFAN is part of the Oxford cohort for Heart, Vessels and Fat (Ox-HVF), which means the results from this study may be interpreted together with the findings of other Ox-HVF projects. The Ox-HVF cohort is a cluster of clinical studies run from the University of Oxford that together provide results allowing the deployment of a multi-level strategy to understand the mechanisms of cardiovascular disease, specifically heart attack and stroke.

This agreement concerns ORFAN Arm 4 only, which is a retrospective study arm which includes up to 100,000 adult participants (75,000 - in the UK; 25,000 – internationally; this application only concerns participants located within England and Wales) who have undergone CT chest, abdomen and pelvis scans for clinical purposes at a participating NHS Trust radiology department. The ORFAN study team are focussed on CT coronary angiograms in this project - scans that only include the heart and surrounding blood vessels and tissues.

ORFAN Arm 4 focuses on collecting CT images, patient demographics and clinical information including clinical outcomes and medication usage to aid the development of new CT image analysis algorithms and software tools for the practical application of these algorithms via both traditional and artificial intelligence approaches. ORFAN Arm 4 will provide the required statistical power to allow automation of image analysis processes such as the automated calculation of image analysis techniques developed by the University of Oxford including the perivascular Fat Attenuation Index, the coronary artery Fat Radiomic* Profile and the Atriomic Stroke Algorithm (a novel risk algorithm interrogates the heart atria (the top chambers of the heart) to extract radiomic features, hence ‘atriomic’), as well as the development of new imaging biomarkers - this is the focus of this project. The development of these algorithms and tools has, and will, lead to much improved patient care for those at risk of heart attack and stroke.

* In the field of medicine, radiomics is a discipline and collection of methods concerned with the extraction of a large number of statistical features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye.

Following the COVID-19 pandemic, the ORFAN study programme pivoted to include relevant research objectives related to the non-invasive assessment of cardiovascular damage caused by infection with the virus. This work has been designated by the NIHR and the British Heart Foundation as a COVID-19 Cardiovascular Disease UK Flagship Project. The influence of COVID-19 infection on the outcome measures of the study will be explored, and novel tools to assess the impact of COVID-19 infection on blood vessels and the heart will be developed. This agreement is of fundamental importance for the success of ORFAN Arm 4. Access to NHS Digital held data will enable the accurate ascertainment of disease status for all relevant study conditions within all participants. The breadth of relevant diseases and clinical activity for this research is large due to the complexity of accurately adjusting imaging analysis for all conditions.

The analysis that will occur will involve the adjustment of patient risk profiles for all clinical events and medication usage prior to the CT scan of interest, and adjustment of all clinical events and medication usage following the CT scan of interest. It is not possible to accurately build risk assessment models without both the prior and post CT scan patient data.

As an example, a specific project that will make use of Arm 4 data that has also received significant funding is explained here. This project is the development and clinical translation of the Atriomic Stroke Algorithm, which has received a BHF Translational award (TG/19/2/34831 – ‘Using radiomics and artificial intelligence to predict cardio-embolic stroke’) to validate a novel imaging biomarker for the direct prediction of stroke risk from CT images. This project intends to make use of ORFAN Arm 4 data to provide the most accurate risk assessment of individual stroke risk available. This award has unlocked new technical ability in the ORFAN project as it has funded a very powerful computer capable of processing many thousands of CT scans for deep-learning purposes. Deep-learning is the field of artificial intelligence concerned with the automated interpretation of images, in the same way a computer can identify if a photo portrays a cat or a dog, a computer can also identify if a CT scan portrays a heart or a stomach, to use a rudimentary example. In this project, the computer will learn to identify patients with inflammation around their heart – inflammation that places them at increased risk of stroke. This computer has been purchased and installed at the University of Oxford for this project. The award also funds suitably qualified engineers to work on the ORFAN study utilising this computer for CT scan analysis purposes.

The University of Oxford research team who will receive the pseudonymised data from NHS Digital will never receive linkage files that enable the matching of the participants clinical information with their identifiable information.

The sole Data Controller is the University of Oxford who also process the data. The team is led by the Professor of Cardiovascular Medicine at the University of Oxford.

There is a third-party company, Caristo Diagnostics Ltd, which is a University of Oxford spin-out company from the Antoniades Laboratory. This company is involved in the wider ORFAN study, along with other research studies coordinated from the Professor Antoniades laboratory as part of the Ox-HVF stable of studies. This company will NOT be involved in any processing of NHS Digital data relating to this agreement and will have no data shared with it. Caristo Diagnostics Ltd also do not play any role in determining the means by which any data will be processed under this agreement.

There are no funders or commissioners directly involved in the collection or processing of data. The ORFAN study has received funding from a number of sources, however the British Heart Foundation has awarded monies specifically for ORFAN Arm 4. The specific project is the development and clinical translation of the Atriomic Stroke Algorithm via a BHF Translational award (TG/19/2/34831 – ‘Using radiomics and artificial intelligence to predict cardio-embolic stroke’). This project intends to make use of ORFAN Arm 4 data to provide the most accurate risk assessment of individual stroke risk available, as is in keeping with the ORFAN scientific aims. The British Heart Foundation has no control over the methodology of the study nor direct access to NHS Digital data and is therefore not considered a Data Controller.

The lawful basis for processing data under GDPR has been reviewed and been assessed as acceptable. The University of Oxford process data under Article 6(1)(e): "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller" as they are a Public Authority.

Additionally, the University of Oxford process the Special Category Health Data under Article 9(2)(j): "processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject" as the data are required for statistical purposes in the public interest.

The public interest relevant to this work is that the research stands to greatly improve the way in which patients – that is, the public of the UK – receive care in relation to heart attack and stroke. These are the top causes of morbidity and mortality in our community and there is huge public interest in improving patient risk assessment and the management of that risk to save lives.

Yielded Benefits:

The University of Oxford ORFAN team have previously produced multiple results that have been published academically and reported upon within the lay press. Some noteworthy examples include: Evidence that obesity may not be necessarily bad for all patients, and those patients with high body mass index may be protected against cardiovascular mortality because fat in the body may secrete protective substances. This is called the obesity paradox and has major implications for the treatment of patients with heart diseases (published in the journal Diabetes in 2015, link to press coverage about the obesity paradox: https://www.telegraph.co.uk/news/science/science-news/11657811/Why-obesity-protects-against-heart-diseaseand-heart-attack.html). The University of Oxford (ORFAN team) has recently identified a significant therapeutic target for the treatment of heart diseases, and that discovery led to intense research to develop new drugs to modify this target (presented in the European Society of Cardiology 2018 Congress, and received the Best Poster Award). The University of Oxford ORFAN group has developed a novel imaging biomarker (see Antonopoulos et al in Science Translational Medicine, 2017), namely the Fat Attenuation Index (FAI), which has been shown to be a marker of vascular inflammation at early disease stages. Validation of this biomarker in large cohorts of patients with residual cardiovascular risk showed that FAI is able to detect patients at high risk for cardiac mortality and is also predictive of non-fatal heart attacks. This permits reclassification of an individual's risk, above and beyond the current state-of-the-art diagnostic tools, with strong implications for guiding medical management in patients and guiding the use of primary and secondary prevention measures. The development of this technology is a significant example that highlights the strength and unique ability of the Ox-HVF cohort in combining data across different and diverse fields - from clinical and epidemiological data to basic science and imaging data to outcome data (requested in the current application) - to create new, boundary-pushing ideas that promote health and serve the public interest. Of note, FAI was featured by iNews as one of the ten health innovations that could soon be on the NHS (https://inews.co.uk/news/health/the-ten-health-innovations-that-could-soon-be-on-the-nhs/). bFAI has been included into the recent Up-To-Date clinical guidance for use in patients with chest pain (https://www.uptodate.com/contents/cardiac-imagingwith-computed-tomography-and-magnetic-resonance-in-the-adult) and can be used to detect patients who may need intense medical therapy to prevent future heart attacks.”

Expected Benefits:

The dissemination of the data stands to have a huge impact on the provision of cardiovascular care within the UK and further afield. The creation of novel means to assess a patient’s individual risk for heart attack and stroke could revolutionise the way that clinicians currently deal with these deadly diseases. Current means to inform a patient of their risk for both heart attack and stroke rely on population wide data based on demographics (such as age) and clinical measures such as blood pressure. Although these are important risk factors, these clinical tools fail to consider some of the most fundamental causes of heart disease that can now be detected, such as inflammation around the heart and the effect of adipose tissue adjacent to the heart muscle, and hence provide poor accuracy for patients.

The personalisation of risk assessment, as the ORFAN study sets to create via access to NHS Digital data and other sources, unlocks personalised clinical approaches that can provide the right therapy or monitoring for the right patient, at the right time, more often than is currently achieved – saving clinical time and health service resources while improving the health outcome for the patient. The COVID-19 arm of ORFAN Arm 4 is set up to explore the cardiovascular consequences of COVID-19 infection and stands to provide important benefits to patients in regards to the personalised assessment of disease risk for Covid patients following their acute illness with the virus. This benefit stands to be hugely important to the UK, with very high Covid rates and unknown consequences for heart disease and how to assess risk for individual patients.

The dissemination of ORFAN Study findings is in the public interest due to the profound benefits that this work could have on patient care for those at risk of very common conditions including heart attack and stroke. These conditions remain the top two leading causes of death in our society, and efforts to reduce early mortality have stalled in recent years. The ORFAN Study intends to create tools for the most accurate assessment of cardiovascular disease risk available to patients. The dissemination of these tools to clinicians, once appropriately validated, is entirely within the interests of the UK public so that they can receive better care. The dissemination of the requested NHS Digital data for this project is fundamental to the success of the scientific aims, and is firmly in the interest of the UK public.

The outputs hope to facilitate the improvement of patient care in the interests of the UK public. The benefits of processing this data are to develop tools that stand to directly improve patient care in regards to common cardiovascular disease including heart attack and stroke. The dissemination of the research findings should enable uptake of these technologies within the UK and abroad, and provide better management options to patients so that they can avoid the morbidity and mortality associated with cardiovascular disease.

The linkage of NHS Digital data to patients identified by local NHS Trust clinical care teams as being suitable for inclusion in the ORFAN Study arm 4 is fundamental to the achievement of the scientific aims of the ORFAN Study, particularly to create novel disease risk assessment tools for heart attack and stroke. The statistical power to test and validate the novel risk assessment tools is not possible without the requested data. The study team hope that the
analysis of these data as disseminated by NHS Digital will enable the creation of the necessary tools, as the study team will be able to adjust the analysis for the relevant clinical events/disease diagnoses/medications that the patient had prior to the CT scan and after the CT scam. Without this information, the analysis is not possible and there would be no advancement of this scientific field and no advancement of early risk assessment through imaging biomarkers for heart attack and stroke.

ORFAN Arm 4 compliments the other arms of the ORFAN study where direct patient consent is obtained. ORFAN Arms 1, 2 and 3 all involve the direct consent of patients, and the direct follow up of these patients over time. It is not feasible for enough participants to be enrolled into these consented study arms to enable the research to successfully fulfil the scientific objectives and create tools for clinical usage. ORFAN Arm 4, and the requested dissemination of NHS Digital data as a part of that arm, will facilitate the necessary statistical power to develop and test the tools that emerge from ORFAN Arms 1-3 and the other complementary studies within the Oxford Heart Vessel and Fat research group (Ox-HVF) coordinated by the University of Oxford.

Such synergy within the Ox-HVF projects facilitates rapid translation of basic science into clinically useful tools. An obvious continuation of this same project would be to test the perivascular Fat Attenuation Index in the ORFAN Arm 4 cohort, to investigate if the biomarker is able to accurately predict patients who will go on to suffer from heart attack following their CT scan. Such an experiment requires large numbers of participants, with complete datasets of background risk factors and events (diagnoses prior to the CT scan), the CT scan itself, and outcomes data (diagnoses/events following the CT scan).

The Study expect to be publishing key findings related to the development of novel disease risk assessment for cardiovascular disease within 2-3 years of the DSA commencing. The study team expect novel diagnostic and risk assessment tools to be incorporated into clinical practice within 5 years from the DSA commencing, and further publication of the use of these tools from within the ORFAN study, reliant on this agreement by 10 years.

The outputs are hoped to change the clinical approach to patients at risk of heart attack and stroke who have been referred to receive a CT scans that includes their heart. The tools that the ORFAN study intends to create are for the advanced interpretation of these scans, and are hoped to provide clinician with information related to individual disease risk for the specific patient. This information is not currently available from these scans. This information can then be used to change lifestyle and medical therapy as appropriate and/or to change monitoring of that specific patient as appropriate, with the aim being to reduce the patients individual risk for harm. A simplified example of how such tools can impact on an individual patient’s care is provided: A patient has chest pain and is referred for a CT scan of their heart; CT scans are the NICE and ESC recommended first line investigation for chest pain. The scan shows clear coronary arteries free from blockage however when the novel Fat Attenuation Index (FAI) is assessed on the scan the patient is found to have highly inflamed coronary arteries. This patient is likely at elevated risk of heart attack despite no visible blockages. This patient could then be placed on anti-inflammatory medication and monitored carefully for a period as opposed to discharge without any change in therapy and monitoring, as is currently the status quo for a patient with arteries free of blockage. The FAI is a previously developed novel CT measure of coronary artery inflammation developed by the Antoniades laboratory using ORFAN and other studies.

The development of such imaging biomarkers enables more personalised disease risk assessment for patients. These biomarkers are concerned with individualised assessment of risk for common conditions such as heart attacks, strokes and atrial fibrillation. Risk assessments for such conditions are currently based on classical approaches to risk factors that provide blunt population level risk data that are difficult for clinicians and patients to interpret. Risk assessments that make use of biological processes that directly relate to the disease, such as measuring the level of inflammation surrounding the coronary arteries, as is possible with the Fat Attenuation Index, and relating this to other information such as a patients age and cholesterol level, can provide highly accurate and personal risk assessments that clinicians and patients can utilise to reduce risk of events. These risk assessments can be utilised to enact closer monitoring of patients, to motivate lifestyle changes and to guide pharmacological therapies in a way that has not previously been possible.

45’000 CTA scans (CT scans of the heart) are performed in UK per year to manage chest pain, expected to increase to >350’000 when the 2016 NICE guidelines are fully implemented. The 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes recommends CTA as the first line test for the diagnosis of coronary artery disease in all symptomatic patients. This class 1 recommendation will further see the use of CTA increase across Europe. Currently, 80% of CTAs reveal no significant coronary disease, a number that is expected to increase with increasing scan rates. In addition, non-contrast CT scans of hearts are routinely used for screening the population for coronary calcification, as part of risk stratification. As these scans are all focused on the coronaries, any other information is missed. By way of example utilising stroke: An automated technology that can provide risk stratification for stroke and heart attack as a standard element of clinical CTA reporting, may predict (and potentially prevent) up to 900 strokes/year based upon current uptake of CTA in the UK. The study team expect that the method could identify patients with atrial myopathy at risk for cardio-embolic stroke, over and above clinical risk prediction tools such as the widely utilised CHA2DS2-VASc score, so they can be referred for ECG monitoring or possible anticoagulation therapy to prevent events. The proposed technologies to be developed in the ORFAN study can be applied retrospectively to existing cardiac CT scans, acquired with or without contrast, or prospectively as part of usual clinical care.

The financial impact of heart attack and stroke on UK society is very large. Again, utilising the stroke example: 1 in 6 people in the UK will have a stroke during their lifetime, with enormous socioeconomic impact. The average cost of stroke per person is £40,000 in the first 12 months after the event (cost of incident stroke), followed by a lifetime average cost of £25,000/person/year. Grossly, the overall annual cost of stroke to UK society is £26 billion. The rate of first-time strokes in people >45 years old is expected to increase by 59% in the next 15 years, with worldwide stroke-related illness, disability and early death set to double by 2035. The prevention of every stoke via enhanced tools for the detection of risk stands to save enormous resources within the UK’s NHS, and could be an immensely valuable tool for clinicians to wield to both prevent events, but also to better target therapies to those that actually need them – preventing waste.

The possible benefits are hoped to be achieved by the study sponsor, the University of Oxford. Intellectual property developed through this work will be owned by the University of Oxford. Benefit will be measured in the short-term by the impact of peer-reviewed academic publications and presentations of the work at international cardiology conferences. The final result of this work is anticipated to be a number of automated risk algorithms that empower both patients and clinicians with highly accurate prediction of disease events, such as a heart attack or stoke, within the following 5 years. This risk prediction is currently very poor and enhanced prediction ability is critical for managing patients in regard to lifestyle changes, monitoring and the initiation or escalation of medical therapy. The long-term benefit will be measured by the total number of patients for whom the disease assessment tools are applied to clinically, and how many clinical events are assessed to have been prevented through the technology. This longer-term impact will require specific study to understand, and the ORFAN study will enable this through the 10-year follow-up period as requested in this DSA. It should be possible to adjudicate which patients do go on to suffer clinical events via the requested annual NHS Digital data extractions, hence allowing an ongoing enquiry into the effectiveness of the diagnostic and risk assessment tools that the study team propose to develop.

Results related to the development of novel imaging tools for the prediction of heart attack and stroke will be available within 2 years of the commencement of the DSA. The results are hoped to be published and subsequently updated in the following years once further clinical outcomes data is collected from NHS Digital, as requested. The project is forecast to be finally concluded at the end of the 10 year follow up period as agreed to within the agreement, at which time all final analysis of the outcomes will occur and final validation papers will likely be published, although the study team anticipate by then the tools developed for the assessment of individual patient risk to be adopted clinically and clinical trials to be ongoing as to the effectiveness of the tools.

Outputs:

The major outputs from this data processing are twofold:
1) The creation of novel disease risk calculating algorithms algorithms (patient assessment algorithms and the necessary software tools to practically apply the algorithms) for conditions such as heart attack and stroke ready for incorporation into patient care within health services, and
2) the communication of these outputs via scientific and lay-person publications.

The research outputs to convey the results will be led by peer reviewed publications in leading international journals, presentations in international and national scientific meetings and subsequent media reporting and public engagement lead by the University of Oxford.

Journals being targeted to submit to/publish in:
i) The New England Journal of Medicine (NEJM) (impact factor 74.7)
ii) The Journal of the American Medical Association (JAMA) (impact factor 45.5)
iii) The Lancet (impact factor 60.4)
iv) Circulation (impact factor 23.6)
v) Journal of the American College of Cardiology (JACC) (impact factor 20.5)
vi) British Medical Journal (BMJ) (impact factor 30.2)
vii) European Heart Journal (EHJ) (impact factor 22.7)

The study team intend major findings of this work to be published in the top-tier general medicine journals (NEJM, JAMA, Lancet, BMJ) as opposed to more cardiac specific findings which will be aimed at the top cardiology journals (Circulation, JACC, EHJ). This enables far greater readership and targeting of impact. All publications in major journals such as those listed here will be open access, meaning any reader from anywhere on Earth can access the full text of the research article without any cost.

Congresses and conferences targeted to submit work to:
i) Scientific sessions of the American Heart Association
ii) Scientific sessions of the European Society of Cardiology
iii) Scientific sessions of the American College of Cardiology
iv) Scientific sessions of the British Cardiac Society

The outputs from this work will be both immediate - with publications in high impact journals within 2-3 years of the commencement of the agreement as well as long-term - when diagnostic biomarkers are implemented in clinical practice. As the ORFAN study is expected to continue collecting outcomes data for at least the next 10 years, the cohort will continue to generate outputs as more events accumulate over time. These long-term impacts have the potential to change clinical practice worldwide and save lives from improved assessment of cardiovascular disease risk.

Specific algorithms that the ORFAN Study team wish to create and/or validate through the ORFAN arm 4 study include:
1) An algorithm for the specific risk assessment of ischaemic stroke in those with and without atrial fibrillation (provisionally called the Atriomic Stroke Algorithm)
2) An algorithm for the specific risk assessment of cardiovascular complications in those with COVID-19 infection
3) An algorithm for the likelihood of success of invasive catheter ablation in those with atrial fibrillation
4) Validation of the perivascular Fat Attenuation Index (FAI) algorithm for heart attack risk. The FAI algorithm is an already established algorithm for the accurate assessment of heart attack risk, discovered in the ORFAN Study. FAI involves the assessment of CT scan features, in particular attenuation – the CT term for density, of fat surrounding the coronary vessels.
5) Validation of the Fat Radiomic Profile (FRP) algorithm for heart attack risk. The FRP algorithm is a another already established algorithm for long-term heart attack risk discovered in the ORFAN Study. This algorithm relies upon many radiomic features extracted from CT scans from around the coronary vessels. These features cannot be seen by the naked eye, and so require computer extraction and assessment which is what the FRP computes.
6) Likely other algorithms related to specific at risk population depending on data quality and statistical power

All such algorithms are stand-alone patient assessment tools, and any intellectual property created from within the ORFAN Study will be owned by the University of Oxford, and associated intellectual property will be controlled by the University for licensing to health services for inclusion in clinical practice.

The ORFAN research team at the University of Oxford has no personal intention to monetise or generate income from the research outputs generated from the ORFAN Arm 4 study. Were discoveries to be made in the ORFAN Arm 4 study, individual researchers within the ORFAN team would be listed inventors on any patents that the University of Oxford may file.

The study team expect to be publishing important findings related to the development of novel disease risk assessment for cardiovascular disease within 2-3 years of the DSA commencing. It is possible this may occur earlier if the extraction of data in the first year produces enough scientific power for robust results. They expect novel diagnostic and risk assessment tools to be incorporated into clinical practice within 5 years from the DSA commencing, and further major publications related to the use of the tools developed from within the ORFAN study by the end of the 10 year data retention date.

THE LEVEL OF DATA THAT WILL BE CONTAINED IN THE OUTPUTS:

The level of data contained in the outputs is aggregate data with small number suppression applied as per the disclosure rules for the various data sets that has been augmented via statistical processes. No individual participants data is ever published individually, with the nature of this scientific work demanding high numbers of participants data for the testing and validation of disease risk calculators.

The dissemination activities of the ORFAN Study team are focussed on high-impact peer reviewed journals and oral research presentations at international conferences. Other activities through which the ORFAN study findings are communicated include through NHS Trust clinical grand-round meetings at NHS Trusts who deliver services relevant to those explored in the study, NHS Trust newsletters delivered to staff emails and through high level meetings with NHS executives and clinical leads at NHS Trusts to discuss the suitability for the clinical tools to be tested within the clinical environment at their Trusts. This work also involves audit related work to model the impact of novel tools developed from within ORFAN on current service usage, as has occurred within Oxford University Hospitals NHS Trust already.

To target the lay audience, the ORFAN team uses the following approaches and is guided by the Research Services team at the University of Oxford and the Public Engagement team at the Cardiovascular Medicine Division of the University of Oxford:

a) Website (www.oxhvf.com); this is updated with all the most up to date information regarding the outputs of the research. This is a public-facing website, and the public can read about the latest outputs of the study.

b) Newsletters; when major findings or general outputs are available, the ORFAN team post newsletters both on the website and in hard-copy form to ORFA Arm 1-3 participants. This is not possible for Arm 4, as we do not hold patient identifiable information. Media attention including articles in BBC News, The Guardian, The Financial Times and CBN concerning work published in the prestigious journal ‘Science Translational Medicine’ are the sorts of topics highlighted in newsletters (see example from the Antoniades Lab: https://test188076.files.wordpress.com/2018/01/newsletter-adiporedox-15-09-2017.pdf).

c) Press releases; the ORFAN team have an active involvement in outreach activities of the University of Oxford, Oxford University Innovations and the British Heart Foundation communications team, and the major findings from their study lead to press releases, and from there they are distributed to the lay press.

d) Workshops and patient and public involvement; the ORFAN team participate in workshops for patients as part of the Biomedical Research Centre in Oxford, and through that they inform the patients about their research and ask for their involvement in the design of protocols, feedback on research procedures and more, through Patient and Public involvement (PPI) panels

e) Social media – the ORFAN Study group frequently share results and outputs to their social media presence on Twitter, Facebook and LinkedIn.

Exploitation of results/outputs:
All intellectual property and knowhow is owned by the University of Oxford, as the sponsor of the ORFAN Study. The University of Oxford, through Oxford University Innovation, maintains the ability to licence any technology that is created within the ORFAN Study for commercial use or for use within health services such as the NHS.

Processing:

The purpose of this agreement is to carry out health data related research. The agreement is to provide patient identifiable data for the purposes of linkage to health data held by NHS Digital. To achieve this clinical care teams from 13 NHS Trusts who are collaborating on the study will provide the minimum required patient identifiers to NHS Digital (NHS Number and Date of Birth, and Post Code). This is the only flow of identifiable data that will be received by NHS Digital.

The collaborating 13 NHS Trusts with local clinical care teams participating in ORFAN Arm 4 are:
Oxford University Hospitals
Royal United Hospitals Bath
Milton Keynes University Hospital
University Hospitals of Leicester
Barts Health
Royal Brompton and Harefield
Leeds Teaching Hospitals
Royal Papworth Hospital
Guy's and St Thomas’
The Royal Wolverhampton
Sandwell & West Birmingham Hospitals
University Hospitals Birmingham
Manchester University

There is a single cohort group that will be included in the ORFAN Arm 4 study, however the participants of this cohort group will come from a number of different NHS Trusts across England. The data subjects are adults (18-99 year old) who have undergone a relevant CT scan at a NHS Trust where the local clinical team is collaborating on the ORFAN Study Arm 4.
The relevant CT scans include only clinically indicated and successfully performed scans, specifically a CT angiogram (CTA) or CT chest, abdomen and pelvis scan.

The data required is concerned with identifying what diagnoses, clinical events and therapy occurred both prior to the relevant CT scan (from 2005 onwards) and after the CT scan. The data required includes hospital episodes (emergency department attendances, inpatient admissions, critical care admissions, and outpatient clinics), with the focus on diagnoses made during those episodes and procedures that occurred at those episodes. The data required also includes medicine prescribed in the community both prior to and after the CT scan as this greatly influences risk of future disease through the reduction of risk. To address the research objective of how COVID-19 infection influences cardiovascular disease risk the study team will request data related to COVID-19 infection and its severity. Death data is also needed to know what patients in the study have passed away, otherwise these patients will be incorrectly included in certain analysis. The data sets requested from NHS Digital are:

- Civil Registration (Deaths) - Secondary Care Cut
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- Emergency Care Data Set (ECDS)
- Hospital Episode Statistics Accident and Emergency
- Hospital Episode Statistics Admitted Patient Care
- Hospital Episode Statistics Admitted Critical Care
- Hospital Episode Statistics Outpatients
- Medicines dispensed in Primary Care (NHSBSA data).

The first drop of all historical data would be after this agreement is signed off, and a second drop using THE SAME full Arm 4 ORFAN cohort in October 2023 for the periods 2020/21, 2021/22, and 2022/23.

The ORFAN Arm 4 data is analysed only for the purposes of the ORFAN project – to develop and validate imaging biomarkers of cardiovascular disease risk. The ORFAN Arm 4 data as received from NHS Digital will be analysed alongside the actual CT images from the linked patients. This is performed in a completely pseudonymised manner with the only link between the data extracted from NHS Digital and the original CT scan being the study ID originally assigned by the local NHS Trust clinical care team where the patient received care.

The second follow up of patient outcomes via NHS Digital data will further enable the correct ascertainment of individual patient risk through further refinement of imaging biomarkers as increasing numbers of patients suffer from clinical events of relevance to the ORFAN Study.

METHODOLOGY
For the first drop of NHS Digital data at the start of the agreement, the clinical care teams at each collaborating NHS Trust will generate a list of their own patients who met the participation requirements for ORFAN Arm 4, this list will be established and stored within the local NHS Trust firewall at the site where the patient was treated in the first instance. The clinical care teams will assign a unique ORFAN Arm 4 study ID to each participant. When ready and instructed to do so, the clinical care teams will send their ORFAN Arm 4 patient list, including identifiers NHS Number, Date of Birth, and Postcode, plus OFAN Study ID. Cohorts will be sent in via Secure Electronic File Transfer (SEFT) service. 13 Trusts in total will provide their cohorts to NHS Digital.

NHS Digital combines the 13 Trusts' cohorts to create one full ORFAN Arm 4 cohort and links and extracts the required data fields from the required data products and removed identifiers (leaving the Study ID).

NHS Digital send the pseudonymised data extracts to the Study team at University of Oxford via SEFT. No data is sent back to the 13 NHS Trusts that provided the patients that are included in the cohort.

For the second drop of NHS Digital data in October 2023, NHS Digital retrieves the full ORFAN Arm 4 cohort from storage and links and extracts the required data fields from the required data products and removed identifiers (leaving the Study ID). NHS Digital send the pseudonymised data extracts to the Study team at University of Oxford via SEFT.

There is no subsequent flow of pseudonymised data from the data recipient, the University of Oxford, to any other organisation.

DATA MINIMISATION
The study team consider the data they have requested as adequate for the scientific aims of the ORFAN Study protocol, and will achieve the purposes of creating novel risk prediction tools for common cardiovascular diseases. All data requested is relevant to the risk of cardiovascular disease and its complications, and most importantly the request is limited to the achievement of the ORFAN Study aims only.

The NHS Digital datasets requested will be linked to the ORFAN Arm 4 cohort of approx. 75,000 participants.

The study only request linkage to datasets held by NHS Digital that are fundamental to the scientific aims of the ORFAN Arm 4 study. No requested linkages are superfluous to the study or that hold data that will not be utilised in statistical models as proposed in the ORFAN Study protocol. The datasets requests can not be reduced because all datasets as requested hold necessary data for the successful completion of the ORFAN Arm 4 project.
The scientific purpose of the ORFAN Arm 4 require pseudonymised data only, and no identifiable data is requested.

The number of years requested corresponds to the scientific aims of the ORFAN Study. The study team require to know the background medical conditions, hospitalisations and procedures that the participants had in order to adjust the statistical analysis to account for prior risk. This is necessary when creating risk algorithms that seek to predict an individual's risk of a specific medical condition. The study team request data linkage for the years supported by the CAG. This is from 2005 through until the current time, with repeat extraction supported for 10 years following the commencement of CAG support. The ORFAN Study team has compromised on how many years to request linkage for, as the more retrospective years of linkage included within the study, the more accurate the risk prediction algorithms would become. However, 16 years of previous risk of events (2005 through 2021) will provide enough relevant information regarding the participants in ORFAN Arm 4 to allow successful adjustment of the risk algorithms. For example, if a patient had a stroke 5 years prior to the CT scan this will mean that patient is analysed in a different way to patients who did not have such an event.

This study must consider geographic differences in exposure to cardiovascular disease risk factors, differences in socio-economic status and differences in access to health care. It would not be acceptable to include all 75,000 participants from the Oxford University Hospital (OUH) NHS Trust as this analysis would be prone to bias. On more practical terms, it is not possible to enrol patients from a single or smaller number of NHS Trusts due to the fact that most CT radiology services only have several thousand patients relevant to inclusion in the study. For example, the OUH NHS Trust has at most 6,000 relevant participants who could be enrolled in ORFAN Arm 4. For this reason, the study team must extend to multiple NHS Trusts so that the total number of participants is appropriate to achieve the scientific aims of the study.

The study team are only considering participants from across England and Wales. The cohort needs to include all episodes of care across this area as events that occurred at any relevant hospital must be considered in the algorithm. The cohort is limited to those aged 18 years and over. The cohort is primarily limited by the fact inclusion requires a participant to have received a study relevant clinical computed tomography (CT) scan. For the most part for ORFAN Arm 4 this means the participant must have received a clinical CT coronary angiogram (a specific CT scan of the heart).

The ORFAN Study requires all episodes of care, both elective and emergent, to achieve the purpose, bar maternity episodes. The study team do not require maternity episodes and do not require unborn child and neonatal records. Elective episodes are important as these often capture procedures of relevance to cardiovascular disease risk.

The study team have selected the minimum number of fields per dataset that are required for the successful fulfilment of the ORFAN Study scientific objectives. These fields are all necessary for the adjustment of the risk algorithms for cardiovascular disease and as such these fields will be used in the models, or to adjust specific risk factors by severity and/or the confounding of treatment (medical and surgical) prior to being fed into the models. Dates of death can be transformed into the format MM/YYYY.

Following receipt of the linked pseudonymised data from NHS Digital by the ORFAN Study team at the University of Oxford, the study team will perform statistical analysis to develop disease risk models in accordance with the scientific objectives of the ORFAN Study as outlined in the ORFAN Study protocol approved by the Health Research Association’s (HRA) Research Ethics Committee. This processing will include importing the data into statistical data analysis software using R (a programming language) and SPSS (SPSS Statistics is an IBM software package used for interactive, or batched, statistical analysis). The analysis will include the development of models for the prediction of disease such as stroke and heart attack and will involve the simultaneous analysis of CT imaging data (not sourced from NHS Digital).

There will be linkage of the NHS Digital data to the CT scan of the relevant patient that rendered them eligible for inclusion in ORFAN. This linkage occurs through the unique ORFAN Study ID originally assigned to the patient by the patient’s own clinical care team at the NHS Trust where they were treated. Along with sending the minimum required patient identifiers to NHS Digital for linkage, the local clinical care teams will also extract, de-identify and assign the corresponding unique patient ID to the relevant CT scan, and then send this scan to the ORFAN Study team. This will enable the ORFAN study team to analyse the scan in combination with the data received from NHS Digital, a key aspect in achieving the scientific aims of the study.

The ORFAN study team will also request data from national registries (or NHS Digital when available) such as National Institute for Cardiovascular Outcomes Research (NICOR) and the Sentinel Stroke National Audit Programme (SSNAP), both controlled by Healthcare Quality Improvement Partnership (HQIP) and will link the pseudonymised NHS Digital data to ORFAN study participants events recorded in these national registries. This linkage is through the unique ORFAN study ID only, not identifiable data.

There will be no matching of data to publicly available data sources. There will be no requirement or attempt to re-identify any study individuals in ORFAN Arm 4. No ORFAN Arm 4 patients will ever be contacted by the ORFAN Study team, or anyone else on behalf of the ORFAN study team.

Data processing will only be carried out by those who are substantive employees, with formal contracts, of the data processors or the data controller, the University of Oxford.

Once pseudonymised data is received from NHS Digital by the ORFAN Study team at the University of Oxford, the data is saved without any manipulation on secure University of Oxford servers located in University premises, namely the Acute Vascular Imaging Centre at the John Radcliffe Hospital, Oxford. An exact copy is saved on another secure University of Oxford server, namely the researcher server of the Oxford Centre for Clinical Magnetic Resonance Research, also at the John Radcliffe Hospital but in a separate building. This back up is only for disaster recover purposes and is not accessible to any ORFAN researchers/others bar senior University IT staff working in the Division of Cardiovascular Medicine. Both servers are in environment-controlled rooms, with uninterruptable power supplies and fire/flood monitoring. All server rooms require swipe card and physical keypad access, and users are credentialed by both the University of Oxford and the Oxford University Hospital NHS Trust.

The main copy and the back-up copy of the data is controlled by the study team lead, Professor of Cardiovascular Medicine, as the named ORFAN data controller. The study team lead will assign access to the dataset to credentialed researchers actively working on the ORFAN Arm 4 project.

The data will be held on servers owned by the University of Oxford, with the primary storage at the Acute Vascular Imaging Centre at the John Radcliffe Hospital and the secure back up at the Oxford Centre for Clinical Magnetic Resonance Research, also at the John Radcliffe Hospital but in a separate stand-alone building.

No third party organisations listed in the ORFAN Study protocol will receive pseudonymised data that has come to the University of Oxford from NHS Digital for ORFAN Arm 4. The University of Oxford and their ORFAN Study group is the only and final recipient of such data.

The investigators listed in the ORFAN Study protocol do not all retain rights to access ORFAN Arm 4 study data. Only the core ORFAN Study Team at the University of Oxford who have undertaken the appropriate training and have formal contracts in place with the University of Oxford may be eligible to access pseudonymised data disseminated to the University of Oxford from NHS Digital.

HES and ECDS DISCLOSURE CONTROL / SMALL NUMBER SUPPRESSION
In order to protect patient confidentiality, when presenting results calculated from HES record level data, outputs will contain only aggregate level data with small numbers suppressed in line with HES Analysis Guide. When publishing HES data, you must make sure that:
· cell values from 1 to 7 are suppressed at a local level to prevent possible identification of individuals from small counts within the table.
· Zeros (0) do not need to be suppressed.
· All other counts will be rounded to the nearest 5.
Data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

MEDICINES DISPENSED IN PRIMARY CARE DISCLOSURE CONTROL / SMALL NUMBER SUPPRESSION
The medicines data is not deemed disclosive and information on a GP level is available in the public domain. However, should the published information pose a risk of re-identification, the following suppression methodology should be applied:
• Zeros should be shown.
• 1-7 to be rounded to 5.
• Any other numbers rounded to nearest 5.
• Rounding unnecessary for averages etc.
• Percentages calculated from rounded values.
• If zeros need to be suppressed, round to 5.

COVID-19 Hospitalisations in England Surveillance System (CHESS) DISCLOSURE CONTROL POLICY
NHS Digital will only disseminate CHESS data collected from PHE where the information is linked to other information controlled by NHS Digital.

PRINCIPLE: Platform Randomised trial of INterventions against COVID-19 In older peoPLE — DARS-NIC-411161-G4K7X

Opt outs honoured: Yes - patient objections upheld, No - Statutory exemption to flow confidential data without consent, Identifiable, Anonymised - ICO Code Compliant, Yes, No (Statutory exemption to flow confidential data without consent)

Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 - s261 - 'Other dissemination of information', CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 – s261(2)(c), CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2020-11-05 — 2021-05-04 2020.11 — 2022.12.

Access method: One-Off, System Access, Ongoing
(System access exclusively means data was not disseminated, but was accessed under supervision on NHS Digital's systems)

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  2. COVID-19 Access to Summary Care Records
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Admitted Patient Care
  5. Civil Registration - Deaths
  6. GPES Data for Pandemic Planning and Research (COVID-19)
  7. Medicines dispensed in Primary Care (NHSBSA data)
  8. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  9. Civil Registrations of Death
  10. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

Over recent weeks, there has been an increase in the number of COVID-19 cases in the community and in hospitalisation. Currently, there are no treatments that have been proven in rigorous trials to help people with COVID-19 symptoms in the community recover quicker and reduce the need for hospital admission. It is therefore vital that the University of Oxford use this opportunity to accelerate enrolment into COVID-19 therapeutics trials.

The PRINCIPLE (Platform Randomised trial of Interventions against COVID-19 In older peoPLE) trial is the only national Urgent Public Health priority clinical trial evaluating potential therapeutics for COVID-19 in the primary care setting, endorsed by the CMOs of all four devolved nations . The trial aims to find out whether early treatment in the community speeds recovery and reduces the need for hospital admission for those with COVID-like-Illness.

PRINICIPLE is:
• Recruiting across the whole UK: anyone can participate, regardless of location.
• Light burden for both recruiters and patients; it only takes couple of minutes to complete enrolment on to the trial.
• Obtaining consent, checking eligibility, issuing study medication and materials, and follow-up is manged remotely through a central facility.

Primary objective - To assess effectiveness of trial treatments in reducing the need for hospital admission or death, for patients aged ≥50 years with comorbidity, and aged ≥65 with or without comorbidity and suspected COVID-19 infection during time of prevalent COVID-19 infections.
Secondary objectives - To explore whether trial treatment reduces
1) Duration of severe symptoms
2) Time taken to self-report recovery
3) Contacts with the health services
4) Consumption of antibiotics
5) Hospital assessment without admission
6) Oxygen administration
7) Intensive Care Unit admission
8) Mechanical ventilation
9) To determine if effects are specific to those with the infections syndrome but who test positive for COVID-19
10) Duration of hospital admission

The trial is currently recruiting via GP practices and the website and have c. 1000 participants but need to rapidly increase this to 3,000 (and beyond to support further trial arms). To achieve this, use of the Pillar 2 testing data is proposed.

The University of Oxford would like to receive names and contact details (preferably a telephone number) of people who have received a positive covid-19 swab result from the Pillar 2 testing system. The trial team based in Oxford will then contact these potential participants, inform them about the trial and if they are happy, go on to screen and consent them into Principle. Daily, the trial team would like to receive data on a random cohort (initially 200) of people aged 50+. They may need to increase this age to 65+ if those they contact screen as ineligible for the trial as they lack the required comorbidities.

Consideration has been given to whether the trial should be contacting individuals directly, and whether the recruitment could be managed through the Test and Trace service, i.e. the service are already set up to contact individuals and could inform them of the trial when they get in touch. However, given the use of contractors to operate this service, and thereby creating an extra layer to the process, this is unlikely to fit with the timescales the trial are working to (which ties in with why they have decided to switch from GPs as the primary source of recruitment). Additionally, the trial already has a centre set up and operating remotely to manage this recruitment in a timely manner.

The question of whether ‘cold calling’ is appropriate has been considered for this application, especially against alternatives such as SMS and emailing. As time is of the essence for recruitment into PRINCIPLE, telephone is the most efficient and quickest means to ensure direct contact with the individual, who can answer questions instantly over a call. This also ensures ‘human contact’, as opposed to SMS / emails, with trained and experienced CTU research nurses working directly to the trial team providing that contact. During the calls, clear explanation will be given to individual about how the trial has been able to contact them and what to do if they do not wish to be contacted again (i.e. registering a national opt-out). The trial team will decide whether to apply the Telephone Preference Service under their own discretion. The trial team will also ensure the required comorbidities are discussed early on in the calls so as to bot to get the individual’s hopes up if they are not in fact eligible for the trial. Lessons have been learned from a recent NHS Digital request for contact details provided to researchers contact people to donate blood and plasma, with careful attention paid to the various take up rates and any changes rates in these between the first and second waves of the pandemic. However, unlike that trial, PRINCIPLE could potentially be of direct benefit to the individual.

Other considerations that have been taken into account in relation to contacting individuals:
• The data relating to positive COVID19 tests is sent to NHS Digital at the same time that it is sent to the Business Services Authority, the latter process triggering the SMS to the individual informing them of their result. It then takes around four hours for the Pillar 2 dataset within NHS Digital to be updated with this information. Given that this information then needs to be extracted from the dataset at some point in the next 24 hours, then used by the trial team to make contact with the individual, the risk of the individual being informed of their test result by the trial team before they have read their SMS is small. However, the trial team should have a suitable script prepared to deal with this slim possibility.
• The chances of people having multiple positive COVID19 test results are rare, and rarer still is the likelihood that they will be one of the 200 people extracted from the thousands of daily test results to be sent to the trial team on more than one occasion. Therefore the risk of an individual being contacted twice for recruitment into PRINCIPLE is extremely low.
• NHS Digital recognises that there are likely to be more requests of this nature in future and therefore, if multiple trial require extracts of people to contact, suitable controls need to be in place within the extract process to ensure that individuals are not getting contacted for recruitment into trials more than is reasonably expected.

Expected Benefits:

The trial has co-primary endpoints: 1) Time taken to self-reported recovery; and 2) hospitalisation and/or death. The main objective of the trial is to assess the effectiveness of the interventions in reducing time to recovery and in reducing the incidence of hospitalisation and/or death for covid-19 sufferers.

Key secondary outcomes include: Hospital assessment without admission; Oxygen administration; Intensive Care Unit admission; Mechanical ventilation (components of the WHO Ordinal Scale); Duration of hospital admission; Duration of severe symptoms; Sustained recovery; Contacts with the health services; Consumption of antibiotics; Effects in those with a positive test for COVID-19 infection; WHO Well-being Index.

Target date - recruit 3000 by Dec 2020

Outputs:

The Principle trial will recruit to target much quicker than using current methods if the team can receive this data, therefore answering the covid-19 treatment in the community question more quickly with the aim of preventing covid-19 sufferers being hospitalised so reducing NHS burden.

The trial’s current sample size is 3000 participants which the aim is to recruit by Dec 2020.

The trial team will ensure that trial results are disseminated to all relevant parties (regular updates to the Therapeutic Taskforce, UPH committee, NIHR, DoHSC) and dissemination via media channels and to trial participants, supported by the University of Oxford. Publications will be produced as quickly as possible.

The trial is of national and international relevance during this pandemic.

Processing:

The trial is run remotely, therefore removing the need for participants to be near to a GP practice. Screening and contact with the trial team is all done online. The trial team will, however, contact the participant’s GP for information from their summary care record, to ensure safe prescribing – for example, that they will not be allergic to the proposed treatments.

The processing of the data will be as follows:

• On a daily basis (seven days a week) NHS Digital will interrogate the Pillar 2 dataset and extract 200 individuals at random who are 50 or over who have received a positive COVID 19 test result in the previous 24 hours.
• The individuals will be England-only.
• Filters will be applied to remove patients who have registered a national opt-out, as well as special categories of people for whom the data should not be disseminated, such as prisoners.
• Individuals who have signed up for the Telephone Preference Service will need to be taken into account.
• The flow from NHS Digital to University of Oxford will be automated via a SEFT account.
• University of Oxford will use the data provided to make outbound to ask if the individuals would be interested in being recruited into the trial.
• The aim is to recruit 100 people into the trial per day.
• The number of individual contact details supplied by NHS Digital to University of Oxford will be reviewed once the take-up rate is better understood.

The trial team in Oxford will hold the data securely adhering to all IG Policies in the Dept., the team will call these contacts to inform them of the trial, screen, consent and randomise them.

The identifiable data received from NHS Digital will be deleted on a weekly basis as the trial team will no longer require it.

QResearch - COVID-19 Risk Stratification project — DARS-NIC-382794-T3L3M

Opt outs honoured: No - data flow is not identifiable, No - consent provided by participants of research study, Identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 - s261(5)(d)

Purposes: Yes (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2020-06-01 — 2020-09-25 2020.06 — 2022.12.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No, Yes

Datasets:

  1. SUS plus - Admitted Patient Care (beta version)
  2. Civil Registration - Deaths
  3. COVID-19 Second Generation Surveillance System
  4. COVID-19 Hospitalization in England Surveillance System
  5. COVID-19 Vaccination Adverse Reactions
  6. COVID-19 Vaccination Status
  7. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  8. Emergency Care Data Set (ECDS)
  9. Hospital Episode Statistics Accident and Emergency
  10. Hospital Episode Statistics Admitted Patient Care
  11. Hospital Episode Statistics Critical Care
  12. Hospital Episode Statistics Outpatients
  13. COVID-19 Therapeutics Programme Data Set’
  14. HES-ID to MPS-ID HES Accident and Emergency
  15. HES-ID to MPS-ID HES Admitted Patient Care
  16. HES-ID to MPS-ID HES Outpatients
  17. MSDS (Maternity Services Data Set) v1.5
  18. Civil Registrations of Death
  19. COVID-19 Second Generation Surveillance System (SGSS)
  20. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  21. Hospital Episode Statistics Accident and Emergency (HES A and E)
  22. Hospital Episode Statistics Admitted Patient Care (HES APC)
  23. Hospital Episode Statistics Critical Care (HES Critical Care)
  24. Hospital Episode Statistics Outpatients (HES OP)
  25. Maternity Services Data Set (MSDS) v1.5

Objectives:

This agreement specifically relates to QResearch's urgent piece of COVID-19 work commissioned by the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) to prepare a COVID-19 risk stratification tool. To support this work, NHS Digital will provide a one-off release of the latest available SUS+ Admitted Patient Care (APC) and mortality data. This one-off release of data will be used to support the urgent risk stratification work, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active.

QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.

QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.

The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed on site at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication but not handling the data) may be employed by other UK universities. The NHS Digital data stay on site at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.

Only University of Oxford staff and the named data processors will have access to SUS and Civil Registration - Deaths record level data. External researchers will only have access to tabular outputs that are aggregate with small numbers suppressed in line with the HES Analysis Guide. Record level data are not shared with researchers outside of the University of Oxford.

Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.

Yielded Benefits:

The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs. Two other papers have been published - one in the BMJ describing the first version of the risk stratification tool and another in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome. Three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.

Expected Benefits:

The aim is to provide useful knowledge that patients, GPs and intensive care doctors can use to reduce the risk of severe COVID-19 infection within this pandemic.

Specifically it will help research to understand whether drugs commonly taken for chronic conditions such as hypertension or diabetes may exacerbate or reduce the severity of COVID-19 disease. It is hoped this study will be able to identify alternative drugs for patients with chronic conditions, as well as possible drugs to treat COVID-19; and recognise high-risk patients in primary care.

Around 14 percent of the adult population in England take anti-hypertensive medications, and around five percent receive medication to treat diabetes. The prevalence increases with age, making usage particularly common in those at risk of for severe COVID-19 infections. In many cases drugs from a different class could be used instead. If these drugs are increasing the risk of severe infection, they represent one of the few modifiable risk factors for severe COVID-19 infection. Medical and research communities need rapid large-scale accumulation of data on the outcomes of patients who develop COVID-19 infection whilst taking these drugs to allow appropriate risk assessment and clinical decision making for these patient groups. Other drugs in common use in primary care patients are believed to have anti-viral activity to COVID-19, such as hydroxychloroquine, used in rheumatoid arthritis, and lopinavir-ritonavir, used in the treatment of HIV.

There are also immune-suppressive therapies that may either increase the risk of severe illness by preventing the body’s response to infection, or attenuate the hyperinflammation syndrome associated with COVID-19 disease, so preventing severe disease.

The incidence of severe disease in patient groups taking these medications urgently needs to be established to guide both their management and investigation of COVID-19 treatment strategies.

ICNARC is already providing up-to-date information on the admission characteristics and outcomes of all patients with severe COVID-19 infection treated on an ICU in England, Wales and Northern Ireland.

Outputs:

The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.

Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.

Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.

The results tables within the papers will only contain statistical information with cell counts of > 5, being suppressed in line with the ICO code on anonymisation. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.

No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.

Processing:

EMIS and TPP process the GP data from the original data controllers (GP practices) and sends it to the University of Oxford. EMIS and TPP are not able to access or process any GP data once it is located at the University of Oxford.

EMIS and TPP are neither a data processor nor a data controller for the data provided by NHS Digital under this Agreement. EMIS and TPP are not able to access the HES data under any circumstances. EMIS and TPP have given permission for the GP data it supplies to be linked with the data from NHS Digital for purposes determined by the Principal Investigator at the University of Oxford.

Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the HES data. NHS Digital retains the salt key for this pseudonymisation, meaning that the University of Oxford are unable to re-identify the data but as described below they are able to link with GP data that was pseudonymised using the same Open Pseudonymiser tool. The University of Oxford will not be provided with a copy of the pseudonymisation salt key.

NHS Digital provide the pseudonymised data to the University of Oxford which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the SUS data. The data linkage is undertaken by an employee of the University of Oxford. No data items which would identify the data subjects are received by QResearch as the data is pseudonymised-at-source and at NHS Digital. Date of birth is rounded to year of birth before receipt by the University of Oxford. No other data linkage is permitted without further amendment to the data sharing agreement with NHS Digital. There is no requirement to re-identify individuals from the data and no attempts will ever be made to do this.

The resulting data are then used for undertaking primary research relating to COVID-19. The linked data are only accessed by approved research staff with substantive contracts employed by University of Oxford or its data processors. In order to support the urgent COVID-19 risk stratification work, a small number of employees from the University of Cambridge, University College London, London School of Hygiene and Tropical Medicine, and University of Liverpool may act as additional data processors. These organisations' staff will remotely access the data stored by the University of Oxford and will not store any additional copies of the data. These organisations will not have responsibility for determining the purpose for which, or the manner in which data will be processed, and they are only permitted to process data for the purpose of supporting this urgent COVID-19 risk stratification work. Data is only processed on site on secure servers at the University of Oxford. No individual level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement.

The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake data linkage or analysis of the data.

All outputs are restricted to aggregate data with small numbers suppressed in line with the HES Analysis Guide.

Regular reviews against the ICO code on anonymisation (2012) will be undertaken to ensure that the data remain anonymised and all appropriate controls are in place to minimise any risk of re-identification.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

R1 (D09) - Data support to COVID-19 RCT — DARS-NIC-365354-R3M0Q

Opt outs honoured: No - consent provided by participants of research study, Identifiable, Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No, Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2020-03-31 — 2023-03-30 2020.05 — 2022.12.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No, Yes

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Civil Registration - Deaths
  5. COVID-19 Second Generation Surveillance System (Beta version)
  6. SUS plus - Admitted Patient Care (beta version)
  7. GPES Data for Pandemic Planning and Research (COVID-19)
  8. Cancer Registration Data
  9. Medicines dispensed in Primary Care (NHSBSA data)
  10. Civil Registration (Deaths) - Secondary Care Cut
  11. HES-ID to MPS-ID HES Admitted Patient Care
  12. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  13. COVID-19 Vaccination Status
  14. Demographics
  15. Electronic Prescribing and Medicines Administration (EPMA) data in Secondary Care for COVID-19
  16. Emergency Care Data Set (ECDS)
  17. Hospital Episode Statistics Critical Care
  18. Civil Registrations of Death - Secondary Care Cut
  19. Hospital Episode Statistics Admitted Patient Care (HES APC)
  20. Civil Registrations of Death
  21. COVID-19 Second Generation Surveillance System (SGSS)
  22. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
  23. COVID-19 Electronic Prescribing and Medicines Administration (ePMA) in Secondary Care
  24. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  25. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

This is a new application from the University of Oxford. University of Oxford request to use the NHS Digital Clinical Trials Service for access to data for a study entitled Randomised Evaluation of COVid-19 thERapY (RECOVERY).

In 2019 a novel coronavirus-induced disease (COVID-19) emerged in Wuhan, China. A month later the Chinese Center for Disease Control and Prevention identified a new betacoronavirus (SARS coronavirus 2, or SARS-CoV-2) as the aetiological (causing or contributing to the development of a disease or condition) agent. The clinical manifestations of COVID-19 range from asymptomatic infection or mild, transient symptoms to severe viral pneumonia with respiratory failure. As many patients do not progress to severe disease the overall case fatality rate per infected individual is low, but hospitals in areas with significant community transmission have experienced a major increase in the number of hospitalized pneumonia patients, and the frequency of severe disease in hospitalised patients can be as high as 30%. The progression from prodrome (an early symptom indicating the onset of a disease or illness - in this case usually fever, fatigue and cough) to severe pneumonia requiring oxygen support or mechanical ventilation often takes one to two weeks after the onset of symptoms. The kinetics of viral replication in the respiratory tract are not well characterized, but this relatively slow progression provides a potential time window in which antiviral therapies could influence the course of disease.

In early 2020, as the protocol for this trial was being developed, there were no approved treatments for COVID-19, a disease induced by the novel coronavirus SARS-CoV-2 that emerged in China in late 2019. The UK New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) advised that several possible treatments should be evaluated, including (but not limited to) Lopinavir-Ritonavir, Interferon β, and low-dose corticosteroids. These groups also advised that other treatments will soon emerge that require evaluation. A World Health Organization (WHO) expert group issued broadly similar advice. This trial allows reliable assessment of the effects of multiple different treatments (including re-purposed and novel drugs) on major outcomes in COVID-19.

This study aims to compare several different treatments that may be useful for patients with COVID-19. These treatments have been recommended by the expert panel that advises the Chief Medical Officer in England. Some are tablets and some are injections. Although these treatments show promise, nobody knows if any of them will turn out to be more effective in helping patients recover than the usual standard of care at hospitals (which all patients will receive).

The treatments, given in addition to the usual care at hospital, are: Lopinavir-Ritonavir (commonly used to treat HIV); dexathasone (a type of steroid, which is used in a range of conditions typically to reduce inflammation) and Hydroxychloroquine. Hydroxychloroquine, a derivative of chloroquine, has been used for many decades to treat malaria and rheumatological diseases. It has antiviral activity against SARS-CoV-2 in cell culture. As of 07/04/2020 - Azithromycin has been added as an arm to the trial (replacing a previous drug type called interferon beta). Azithromycin is a macrolide antibiotic. The macrolides inhibit the growth of bacteria and are often prescribed to treat rather common bacterial infections. Azithromycin has immunomodulatory properties that has shown benefit in inflammatory lung disease.

As of 16/04/2020 - a second randomisation arm has been added to the protocol.
This is for participants who meet certain simple physiological and inflammatory criteria - for example those in the cohort receiving oxygen therapy or oxygen saturations <92%) who can be randomised for a second time to receive either tocilizumab (an immunosuppressive drug, mainly for the treatment of rheumatoid arthritis) or control (both in addition to the treatment assigned at the first randomisation)

Other arms can be added if evidence emerges that there are suitable candidate therapeutics. Conversely, in some patient
populations, not all trial arms are appropriate (e.g. due to contraindications based on co-morbid conditions or concomitant medication); in some hospitals, not all treatment arms will be available (e.g. due to manufacturing and supply shortages); and at some times, not all treatment arms will be active (e.g. due to lack of relevant approvals and contractual agreements). Therefore,other treatments may be added to the protocol as time goes on and more information is gathered. This will not impact or change the level of data that is required from NHS Digital - as the cohort in the trial remains the same, regardless of what drug is being trialed.

Data from the trial will be regularly reviewed so that any effective treatment can be identified quickly and made available to all patients. The RECOVERY Trial team will constantly review information on new drugs and include promising ones in the trial.

Patients are eligible for the study if all of the following are true:

(i) Aged at least 18 years
(ii) Hospitalised
(iii) Proven SARS-CoV-2 infection.
(iv) Suspected SARS-Cov-2 infection. In general, SARS-CoV-2 infection should be suspected when a patient presents with
(i) typical symptoms (e.g. influenza-like illness with fever and muscle pain, or respiratory illness with cough and shortness of breath); and (ii) compatible chest X-ray findings (consolidation or ground-glass shadowing); and (iii) alternative causes have been considered unlikely or excluded (e.g. heart failure, influenza). However, the diagnosis remains a clinical one based on the opinion of the managing doctor.
(v) No medical history that might, in the opinion of the attending clinician, put the patient at significant risk if he/she were to participate in the trial

The anticipated scale of the epidemic is such that hospitals, and particularly intensive care facilities, may be massively overstretched. Under some models of pandemic spread, up to 50% of the adult population may fall sick over a period of 8-12 weeks, of whom around 10% may require hospitalisation. In this situation, even treatments with only a moderate impact on survival or on hospital resources could be worthwhile. Therefore, the focus of the COVID-19 Core Protocol is the impact of candidate treatments on mortality and on the need for hospitalisation or ventilation. Critically, the trial (and subsequent data collection) is designed to minimise the burden on front-line hospital staff working within an overstretched care system during a major epidemic. Eligibility criteria are therefore simple and trial processes (including paperwork) are minimised.

The primary objective is to provide reliable estimates of the effect of study treatments on in-hospital death (with subsidiary analyses of cause of death and death at various timepoints following discharge).

The secondary objectives are to assess the effects of study treatments on duration of hospital stay; the need for (and duration of) ventilation; and the need for renal replacement therapy.

Data from routine healthcare records (including linkage to medical databases held by organisations such as NHS Digital) and from relevant research studies (such as UK Biobank and Genomics England) will allow subsidiary analyses of the effect of the study treatments on particular non-fatal events (e.g. ascertained through linkage to Hospital Episode Statistics), the influence of pre-existing major co-morbidity (e.g. diabetes, heart disease, lung disease, hepatic insufficiency, severe depression, severe kidney impairment, immunosuppression), and longer-term outcomes (e.g. 6 month survival) as well as in particular sub-categories of patient (e.g. by genotype).

Follow-up information is to be collected on all study participants, irrespective of whether or not they complete the scheduled course of allocated study treatment. Study staff will seek Follow-up information through various means including medical staff, reviewing information from medical notes, routine healthcare systems, and registries.

The study team require the following information from NHS Digital:

- Prompt feed from SUS on hospital discharge
- Quarterly feed on HES data
- Quarterly feeds on civil registration data (death certificate information)

This study is supported by a grant to the University of Oxford from UK Research and Innovation/National Institute for Health Research (NIHR) and by core funding provided by NIHR Oxford Biomedical Research Centre, the Wellcome Trust, the Bill and Melinda Gates Foundation, Health Data Research UK, and the Medical Research Council Population Health Research Unit, and NIHR Clinical Trials Unit Support Funding.

The new trial has been classed as an Urgent Public Health Research Study. It is one of a round of projects to receive £10.5 million as part of the £20 million rapid research response funded by UK Research and Innovation, and by the Department of Health and Social Care through the National Institute for Health Research.

Yielded Benefits:

By the end of April 2020, the Recovery trial had successfully established over 160 sites across the UK and recruited over 8000 participants treated in hospital for Covid-19. The data linkage already established to received SUS+ and other data is providing important information to the Data Monitoring Committee on a weekly basis about patients' recovery (i.e. discharge from hospital), in-hospital death and procedures required. Provision of complete and reliable data to the DMC through May and early June 2020 is critical to allow robust assessment of the effects of the the trial treatments with a major contribution to these data expected from analysis of the routine health care data requested under this agreement.

Expected Benefits:

- improving the health of the whole population by sharing information and expertise, and identifying and preparing for future public health challenges/COVID-19 challenges

- researching, collecting and analysing data to improve understanding of this public health challenge, and come up with answers to public health problems arising from COVID-19

- providing reliable information on potential treatments for COVID-19 and potentially changing the standard of care that the NHS offers which could improve outcomes for many thousands of patients

Outputs:

COVID-19 is an emerging pathogen which presents a significant threat to the population in terms of increased morbidity and mortality, particularly among vulnerable groups such as those with pre-existing disease.

The primary objective is to provide reliable estimates of the effect of study treatments on in-hospital death (with subsidiary analyses of cause of death and death at various timepoints following discharge).

The secondary objectives are to assess the effects of study treatments on duration of hospital stay; the need for (and duration of) ventilation; and the need for renal replacement therapy.

The interim trial results will be monitored by an independent Data Monitoring Committee (DMC). The most important task for the DMC will be to assess whether the randomised comparisons in the study have provided evidence on mortality that is strong enough (with a range of uncertainty around the results that is narrow enough) to affect national and global treatment strategies. In such a circumstance, the DMC will inform the Trial Steering Committee who will make the results available to the public and amend the trial arms accordingly.

The data requested will be used to evaluate the efficacy and safety of the study treatments and will help shape the public health response. The rapid feed will be used to ensure that the trial Data Monitoring Committee have complete, up-to-date information on the major outcomes in the trial on which to base their decisions. If they find compelling evidence of efficacy or safety then that arm may be stopped and – if effective – added to standard care across the NHS.

All outputs produced will be in the form of aggregated reports with small number suppression applied.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e: employees, agents and contractors of the Data Recipient who may have access to that data).

The University of Oxford will act as the trial Sponsor. The trial will be coordinated by a Central Coordinating Office within the Nuffield Department of Population Health staffed by members of the two registered clinical trials units – the Clinical Trial Service Unit and the National Perinatal Epidemiology Unit Clinical Trials Unit. Both of these units are at the University of Oxford. University of Oxford is the sole data controller for this piece of work. The data will be collected, analysed and published independently of the source of funding.

The initial proposal is to set up a feed of SUS APC data for the patients in a cohort list provided by University of Oxford. The process is as follows

• Details of the patient cohort (initial list and updates) to be emailed to a secure nhs.net account from another nhs.net account. It will be indicated that the email is in regards to the Oxford COVID-19 Study Trial. The initial list of patient identifiers will include study ID's and NHS numbers and date of birth.
• NHS Digital SUS team will report back any NHS Numbers which are not found in PDS (will do this for any new numbers that NHS Digital are sent as NHS Digital are sent them)
• Each week NHS Digital will provide a file of all records received by SUS for patients in the cohort (as updated with any new NHS Numbers)
• NHS Digital will send the extracts of data (baseline and deltas) to a MESH mailbox.
• The SUS APC extract will also include the date of death from PDS and the death status from PDS - this will all be sent as one extract in replacement of the Civil Registration Data Set for this iteration of the data sharing agreement.

Following on from the SUS APC dissemination - NHS Digital will do the following

• Link the cohort to Hospital Episode Statistics Data (HES) and provide back to University of Oxford

The linked data set will be sent back to Oxford via SEFT [Secure Electronic File Transfer System].


Further developments could include (and would be subject to approvals); the following:

• Automate the update of the patients in the cohort list
• Provide historic data for NHS numbers added to the cohort
• Use the Master Patient Service to try to identify the correct NHS Number based on patient name etc

All data shared under this agreement will be processed and stored in secure locations within England and Wales and will not be shared outside University of Oxford, other than in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

MR1483 - HPS-4/TIMI 65/ORION-4: A double-blind randomized placebo-controlled trial assessing the effects of inclisiran on clinical outcomes among people with atherosclerotic cardiovascular disease. Application for data for invitation. — DARS-NIC-172240-R4R0L

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251, Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(7), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2018-10-01 — 2020-06-30 2018.10 — 2022.12.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - List Cleaning Report
  2. Demographics
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Oxford requires demographic, mortality and HES data to recruit participants into the ORION-4 study.

Investigators at The University of Oxford have instigated this work to achieve benefits for patients by obtaining reliable evidence about the safety and effectiveness of a new cholesterol lowering medication called Inclisiran. Inclisiran is given as an injection 2-3 times a year and reduces bad (LDL ) cholesterol. The ORION-4 study will find out whether inclisiran safely reduces heart attacks, strokes and cardiovascular deaths in people who already have cardiovascular disease. If shown to be effective, this treatment could substantially reduce premature death and disability from these conditions. A secondary objective is developing streamlined trial methods that would benefit future research.

The ORION-4 study is co-sponsored by The University of Oxford and The Medicines Company. The protocol and procedures have been developed by the Chief Investigator at the Clinical Trial Service Unit, University of Oxford in an academic collaboration with the TIMI Group - an academic research group within Harvard University - and The Medicines Company which comprise the Steering Committee for the trial. The Steering Committee would determine the scientific objectives of the trial, ensure adequate progress towards those objectives and review any papers prior to publication. As is usual with this type of trial, the Steering Committee also has other international experts from other institutions to advise the trial management team.

In respect of the data under this Agreement, the University of Oxford is the sole data controller. While the Steering Committee signs off the high level plan, the University has full autonomy to determine what personal data is obtained and how it will be processed. The University of Oxford would determine how, when and by whom personal data is processed and is responsible for the security of those data.

The data will be stored and processed at the University of Oxford. Patient’s NHS and Hospital Numbers may be shared with the NHS Trusts that treated them so that the Trust can confirm patient’s eligibility by checking local laboratory results for cholesterol levels. Other than sharing these data items with NHS Trusts, data supplied by NHS Digital would not be released to any other organisation or used for any other purpose. No Harvard University employees will have access to record level data under this Agreement. The Medicines Company will not access data under this Agreement.

The University of Oxford established the Clinical Trial Service Unit (CTSU), now within the Nuffield Department of Population Health, in the 1980s to conduct large trials such as the International Study of Infarct Survival (ISIS) trials. Since then CTSU has successfully completed a number of landmark studies including the 20,000 participant Heart Protection Study, the 9500 participant SHARP study, the 26,000 participant HPS-2/THRIVE study and the 30,000 participant HPS-3/REVEAL study.

The aim of processing the data is to recruit consenting participants for a randomized trial. At the beginning of the trial, half of the participants will be put in the group to be given active inclisiran and the other half will be put in the group to be given placebo injections. This will be done by chance, in a similar way to tossing a coin, called “randomization”. The participants will be followed to find out whether the active inclisiran group experienced fewer heart attacks, strokes and cardiovascular disease outcomes than the placebo group thereby demonstrating the effects of the treatments. The trial will also seek to establish whether the treatment is safe by looking to see whether the active inclisiran group evidenced increased risk of any other health conditions/outcomes.

The demographic (including name, address and GP practice details), mortality and HES data are required to assist the University of Oxford in identifying and recruiting eligible participants. Potentially eligible participants will be invited on behalf of the Local Investigator using local NHS headed paper to attend a Screening Appointment at the ORION-4 clinic (usually in their local NHS Hospital). If they choose to attend, they will be invited to consent to participate in the clinical trial.

For those who provide informed consent, the University of Oxford will apply to NHS Digital for subsequent follow up data under a separate Data Sharing Agreement.

Yielded Benefits:

By 2020, the trial has established around 100 research sites across the UK and recruitment is progressing well. Because of the requirement of the trial to include people with higher cholesterol values a substantial proportion of people volunteering are not able to take part which has meant that recruitment has been slower than expected. However, because of the large-scale central invitation process using the data requested in this application, additional patients can be invited without substantial extra cost.

Expected Benefits:

The results of ORION-4 will be relevant to the 7 million people living with cardiovascular disease in the UK and many more around the world. Bad (LDL) cholesterol is a strong reversible risk factor for cardiovascular disease. Evidence from large, randomized trials have lead to the widespread use of statins in individuals with and at risk of vascular disease which has resulted in substantial benefits for patients. Recent research suggests that further lowering of bad (LDL) cholesterol results in additional reductions in the risk of heart attacks, strokes and death from cardiovascular disease. Inclisiran is a new medication which reduces LDL cholesterol by about half. If shown to be safe and effective and made widely available to high risk individuals, treatment with inclisiran might be expected to prevent a substantial proportion of the 0.5M heart attacks, 0.25M strokes and 0.15M deaths from cardiovascular disease which occur each year in the UK.

It is expected that the results of ORION-4 in 2025 will be incorporated into National and International Guidelines for the treatment and prevention of cardiovascular disease, including guidelines from the National Institute for Health and Care Excellence (NICE) and The Scottish Intercollegiate Guidelines Network (SIGN). Inclisiran is given by injection every 6 months and reduces bad (LDL) cholesterol by about half when given either alone or with statins. Other drugs which work in a similar way to inclisiran are available, but require 4 weekly injections and are expensive to produce. As a result they are not widely available for patients in the UK. Since inclisiran requires an injection only every 6 months and has lower production costs, it is likely that it will become available to patients if proved to be safe and effective in reducing the risk of heart attacks and strokes. Furthermore this treatment may overcome problems with medication adherence which are particularly problematic for long term preventative medicines. Therefore the results of ORION-4 are likely to change clinical practice in the prevention of cardiovascular disease both in the UK and around the world and would be expected to have a substantial impact on the numbers of heart attacks, strokes and deaths from cardiovascular disease globally.

The data from NHS Digital is extremely beneficial in recruiting the large numbers of participants needed for the success of this study. At CTSU large efficient trials have been conducted which produce reliable answers by recruiting large numbers of participants. Both the THRIVE and REVEAL trials each randomized over 8,000 patients in the UK using methods similar to those proposed for ORION-4 although in those studies patients' details were obtained direct from NHS Hospital Trusts rather than from NHS Digital. Many people invited to take part in research choose not to for a variety of reasons, therefore it is necessary to send very large numbers of invitations to successfully recruit to a large study like ORION-4. In REVEAL around 300,000 invitations were required to randomize about 8,000 people into the study. For ORION-4 it is expected that between 400,000 and 500,000 invitation letters would need to be sent to achieve the recruitment target. It would be considerably more difficult to achieve this without the data requested from NHS Digital.

Beyond the ORION-4 study, the recruitment methods using data from NHS Digital would inform trial design and produce benefits to future research. To reliably assess the effects of treatment it is necessary to randomize a large enough numbers of participants to avoid getting the wrong answer by chance and many randomized trials are too small to answer the research question reliably. Recruiting large numbers of participants is difficult and can be prohibitively expensive and many trials fail to reach their recruitment target. Cost-effective, streamlined methods of trial recruitment are needed to improve health by obtaining reliable knowledge about which treatments work and which are harmful. Using NHS Digital data to mail invitation letters to large numbers of potentially eligible people will provide just such a method to ensure the success of this trial and the methods developed would benefit future studies. CTSU is committed to developing streamlined methods of conducting randomized trials so that more trials can produce reliable answers. Members of the ORION-4 team are working with organisations such as the Clinical Trials Transformation Initiative (CTTi), a public-private partnership to develop practices aiming to increase quality and efficiency of clinical trials. Learning from the recruitment methods developed in ORION-4 will be shared by publications in open-access, peer reviewed journals, by courses run by CTSU and shared with other researchers using initiatives such as the CTTi.

Outputs:

The main trial results of ORION-4 are expected in 2025 and will inform the treatment of people living with cardiovascular disease around the world. The results will be disseminated widely, including presentation at relevant conferences and publication in an open-access, high-impact medical journal. Further academic papers (including results of cost-effectiveness analysis and papers about the trial methods) will be published in open-access, high impact, peer-reviewed journals and on the trial website. After the close of the study, additional results assessing the long-term effects of inclisiran obtained through data linkage or participant questionnaires will be published.

A non-technical summary of the main study findings will be sent to participants and relevant charities, such as the British Heart Foundation and Heart-UK, and published on the study website. For each paper a short presentation will be developed to summarise the key findings which will be presented at key conferences.

This study will use streamlined recruitment methods to identify and recruit 15,000 eligible participants cost-effectively. The United Kingdom will recruit around 12,000 participants and the United States of America will recruit the remaining 3000 participants. Recruiting such large numbers in the UK has a number of potential advantages including; enhanced efficiency and lower cost and enabling record linkage for important outcomes both during the scheduled treatment period and after the close of the trial, along with benefits to future UK research. The data requested in this application will be used to identify and recruit most of the 12,000 eligible UK participants by the end of 2019.

From experience with previous trials it is anticipated that between 20 and 30 people will need to be invited for each participant randomized into the study and therefore obtaining large numbers of potentially eligible participants is critical to successful recruitment. In previous studies lists of such individuals have been obtained from individual NHS Trusts, with Section 251 support. Obtaining the list of potentially eligible individuals from NHS Digital leads to the following benefits for the study; First, any delay in obtaining the data while waiting for local data analyst time and technical difficulties associated with dealing with different dataset formats from different NHS trusts would be avoided. Second, the risk of sending the invitation to the wrong address would be reduced. If data is sought directly from the NHS Trust and the patient has moved since they last visited that secondary care provider, then the address for invitation would be out of date. Thirdly, obtaining data directly from NHS Digital allows individual preferences about opt-outs to use of their data be upheld.

Obtaining GP practice information for each potentially eligible participant will help to ensure that correspondence is sent to the correct GP practice. Once a participant has been screened and provided consent, a standard ethics approved letter containing details of the trial and the individual’s current cholesterol lowering treatment, and highlighting the guideline recommended treatment for such patients, will be sent to their GP. The GP is asked to do two things before the randomization appointment 2 months later; (i) review the individual’s cholesterol lowering treatment and (ii) inform the trial coordinating centre if they have concerns about that participant entering the trial. It is therefore very important that letters go to the right practice. Previously, clinical trials have relied on patients to provide their GP details with subsequent checking by the coordinating centre. This is time-consuming and can lead to errors and it will be more efficient and accurate to use up-to-date GP information through NHS Digital’s List Cleaning service.

The List Cleaning outputs will reduce the risk of attempting to contact recently deceased individuals and potentially causing distress to living relatives. Furthermore the regular list cleaning will help to ensure that addresses are accurate. If the invitation were sent to the wrong address opened and the Participant Information Leaflet read carefully it would be possible to infer that the invited individual had vascular disease. Although the risk of this happening is low, it is important to minimise this risk by undertaking regular list cleaning.

Once recruitment is complete, reports will be generated using these data in order to describe the recruitment procedures for the trial (for example establishing the number of individuals invited to participate by age and sex). Such reports will not contain any identifying information (including small groups which could potentially be identified) and will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. This will be undertaken as soon as possible after the end of the recruitment period in 2020 and before the analysis of the main study results is complete in 2025.

Processing:

The University of Oxford will establish ORION-4 sites (generally in NHS Trusts which treated high numbers of individuals for heart attacks, strokes or cardiovascular conditions). Once a local NHS Trust has completed the study feasibility assessment, the University of Oxford will inform NHS Digital that that NHS Trust is now a confirmed site.

Using a combination of the Hospital Episodes Statistics (HES) and Personal Demographics Service (PDS) databases, NHS Digital will extract the details of patients aged 55 and over for whom a previous hospital episode with an ICD 10 code or other diagnostic code indicating eligibility for the trial has been recorded by one or more of those NHS Trusts. Patients who are deceased or who are not current registered with an NHS GP would be excluded.

NHS Digital will provide to the University of Oxford a file containing the following data items for potentially eligible participants:
• Name
• Latest address and postcode
• Date of Birth
• Sex
• ICD 10 or other diagnostic or procedure codes to indicate eligibility
• NHS Trust
• NHS Number
• Hospital Number
• GP practice code
• Admission date of most recent episode with an ICD 10 code or other diagnostic code meeting the inclusion criteria

The University of Oxford will undertake further work to ascertain eligibility. This may include sending a list of NHS and Hospital Numbers securely to the local NHS Trust so that they can undertake further eligibility checks based on the blood cholesterol levels as recorded in the Trust laboratory system.

During the study recruitment period, the University of Oxford will regularly submit batched lists of potentially eligible individuals back to NHS Digital who will provide a ‘List Cleaning’ service and report back the individuals’ current vital status, current address and current GP practice code. This information is required to minimise the risk of writing to recently deceased individuals or writing to incorrect addresses. The GP practice code is required so that a letter can be sent to the GP immediately after a participant’s screening appointment in order to inform the GP of the participants planned enrolment into the trial and allow the GP to opt that participant out of randomization if they feel appropriate.

The University of Oxford will write to the potential participants identified in the List Cleaning reports inviting them to an ORION-4 Screening Appointment at the ORION-4 clinic within the relevant NHS Trust. Should they choose to attend, at the appointment they will be invited to participate in the trial and give informed consent if they wish.

The data received from NHS Digital will only be accessed by individuals within the Clinical Trial Service Unit who have authorisation to access the data for the purpose(s) described, all of whom are substantive employees of the University of Oxford. The potential exception to this is that NHS and Hospital Numbers may be shared with the NHS Trusts that have previously treated the potential participants. This would not involve giving those NHS Trusts new information about the participants, since they recorded the data in the first place. It would simply be a practical step to enable the Trusts to identify the individuals in their own patient records in order to undertake a further eligibility check based on local laboratory blood cholesterol results.

After invitation, individuals who have not responded will be considered to have declined to participate. These individuals will not be invited again.

Data about those participants who declined to participate, or have been considered to have declined to participate through non-response, will be held initially in order to avoid inviting those individuals again. Once recruitment is complete, reports will be generated using these data in order to describe the recruitment procedures for the trial (for example establishing the number of individuals invited to participate by age and sex). Such reports will not contain any identifying information (including small groups which could potentially be identified). Once these reports have been completed, the data provided by NHS Digital for those individuals will be irrevocably deleted. This will be undertaken as soon as possible after the end of the recruitment period in 2020 and before the analysis of the main study results is complete in 2025.

The data provided under this Agreement will only be used for the purposes of recruitment and recruitment analysis and will not be used for any subsequent purposes within the clinical trial.

The short and long-term cardiovascular consequences of critical illness: The C3 Study — DARS-NIC-352725-V1X2R

Opt outs honoured: Anonymised - ICO Code Compliant, Yes (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-05-17 — 2024-05-16 2022.02 — 2022.11.

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Admitted Patient Care
  4. Civil Registrations of Death
  5. Hospital Episode Statistics Accident and Emergency (HES A and E)
  6. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The C3 Study: The short and long-term cardiovascular consequences of critical illness is designed to find out which patients are at risk of heart attacks/strokes up to several years after discharge from an Intensive Care Unit (ICU).

This study will also investigate whether treatments and events occurring in an Intensive Care Unit ICU contribute to this risk.

The mortality of patients who survive a period of treatment on an ICU is considerably higher than an age and sex matched general population. There is evidence of a high rate of major adverse cardiovascular events (MACE) amongst ICU survivors. MACE events include nonfatal stroke, nonfatal myocardial infarction, heart failure and cardiovascular death.

There are no data resources in existence that combine data collected during the episode of critical illness (occurring in ICU and during admission to hospital), with longer-term health data such as repeat hospital admissions, cardiovascular events, treatment and other related medical conditions.

The aim of this study will be to find out which patients are at risk of MACE after discharge from an ICU. This study will also investigate whether treatments and events occurring in ICU contribute to this risk.

In order to perform this study, a data resource needs to be generated that is sufficiently large and detailed. In an ICU, patients’ vital signs, treatments and blood tests are often electronically recorded as part of normal care. Following successful treatment on ICU patients remain admitted to the hospital before being discharged. During this time, they continue to accumulate electronic test results such as laboratory reports and diagnostic data. During this period on the wards they may also experience a heart attack or stroke which it is vital that is captured for purposes of the study.

By linking these highly detailed electronic health care records with NHS long-term follow-up data, the study team can unpick what factors increase patients’ long-term risks and identify patients at highest risk of having heart attacks/strokes years after ICU care.

This study will provide new knowledge about the associations between baseline cardiovascular risk, the disease resulting in ICU admission and therapies / events on ICU with subsequent MACE events, to allow the ongoing risk of these events to be determined. This may identify modifiable risk factors and allow for preventative treatments, improving the health outcomes of this vulnerable group of patients.

University of Oxford’s justification for processing is GDPR Article 6 (1) (e): The processing necessary to perform this task is in the public interest and the task has a clear basis in law. This is an issue of patients suffering critical illness and the clinicians treating it, what happens to patients after they leave ICU and whether the study team can identify those at risk and potentially prevent it.

The dissemination of the aggregated results of this study pose no risk to the public.
GDPR Article 9 (2)(j): processing is necessary for scientific research purposes and shall be proportionate to the aim pursued, respect the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

The data requested will allow the study team to build a novel database linking intensive care therapies and individual patient responses with longer term time to event data (both fatal and non-fatal) medical events requiring hospitalisation. Using this data, the study team can study in unprecedented detail both the event rate and methods of predicating who is at greatest risk or suffering an adverse event.

This study is staffed by two NIHR funded doctoral research fellows who are substantive employees of University of Oxford and work for the Critical Care Research Group in the University of Oxford. The themes of the two research programmes are focused on cardiovascular health after intensive care from both the myocardial event rate and the cardiac rhythm perspectives, respectively. This request relates to The C3 (short- and long-term consequences of critical illness) study (c3study.org). This is an NIHR funded observational study of patients admitted to certain intensive care units with the aim to find out which patients are at risk of heart attacks/strokes up to several years after discharge from an ICU. The team will also study how much the treatment the patients received on ICU contributes to this risk.

• Conduct a detailed review of what has been written in this area
• Find out how many patients suffer strokes/heart attacks after ICU care in England
• Work out which diseases and ICU treatments make heart attacks/strokes more likely
• Determine which patients are at greatest risk of heart attacks/strokes up to several years after leaving ICU

By linking these highly detailed ICU records with NHS long-term follow-up data, the study team can unpick what factors increase patients’ long-term risks and identify patients at highest risk of having heart attacks/strokes years after ICU care.
This study is a standalone project, and no wider collaboration is planned.

Retrospective observational cohort study of patients >16 years of age admitted to an Intensive Care Unit (ICU) in one of the study sites. This is a non-interventional cohort study comparing those patients who experience cardiovascular events and patients in the post ICU population who do not.

This study will provide new knowledge about the associations between baseline cardiovascular risk, the disease resulting in ICU admission and therapies / events on ICU with subsequent MACE events, to allow the ongoing risk of these events to be determined. By understanding who is at risk, it may be possible to modify known risk factors. Where the risk factors cannot be modified, the study team may be able to add preventative treatments.

The study team will create a new database to containing patients’ vital signs, treatments and blood tests, data routinely collected in ICU. By linking these highly detailed ICU records with NHS long-term follow-up data (HES & Civil Registration Data data), the study team can unpick what factors increase patients’ long-term risks and identify patients at highest risk of having heart attacks/strokes years after ICU care.

To understand how events in ICU effect cardiovascular risk after ICU discharge it is important to obtain data about subsequent events. Linking ICU data to key data items in the HES and Civil Registration data datasets are key in this regard.
Only pseudonymised data will be retained and used for analysis by the study team. HES data is an essential feature of this study providing the follow-up and coding data required to define the outcome of interest. HES data will be received on a per patient basis as it must be linked to the study dataset. However, only pseudonymised data will be received by the coordinating site (Critical Care Research Group Oxford University) from NHS Digital (all postcodes will be translated into LSOA codes by NHS Digital). No data from NHS Digital will be transferred to the participating sites. At the co-ordinating site the dataset to which it is being linked will not contain any direct identifiers

To adequately answer the study’s questions, many years’ worth of data is needed. This will provide the scale required to provide robust results and predictive model development. Relatively few people are admitted to ICU every year at each hospital (around 1000 admissions for a teaching hospital ICU). The study will include admissions dating back to 2006 (around the time the digital systems needed to conduct the study started to be used on ICU) to gain about 14k admissions to ICU per hospital. Some of these NHS Trusts have several ICUs but not all the ICUs have been using the digital systems for the entire 14 years. The study team therefore estimates that the 4 NHS Trusts currently in the study will provide around 84k admissions. The study needs a minimum of 5 years of HES data prior to the incident admission to ICU to assess co-morbidities and risk factors. The request for this number of years of data is therefore based on a recruitment period starting in 2006 to the current day and 5 years prior to the first admission.

Obtaining data from different types of hospital (district general and teaching hospitals) from different geographical locations, will make the study results more generalisable to the UK as a whole. The published data will only ever be published in aggregated form (this includes all mediums both published and websites etc.) and will apply small number suppression to prevent the results of small sub-groups of patients being published. The study results may will then be heeded by more hospitals, potentially benefiting more patients.

In studies of this size, individual patient consent is not feasible. No alternative study method was available that could provide the necessary data. The study team have therefore taken steps to ensure data processing is minimised and as unobtrusive as can be. The study team obtained full support from the Confidentiality Advisory Group.

The study will extract only data that will aid with the answering of the questions of the study. The study team require up to 120K ICU encounters (defined as a single visit to ICU) in order to overcome many of the challenges studying this outcome represents. The size of the cohort required has been determined by an a priori sample size calculation to provide adequate power to detect risk factors associated with MACE events after ICU admission. The number of years of historical data required were determined by this sample size. All patient episodes within the specified timeframe are required so ensure readmissions and repeated events are properly recorded. Cohort minimisation will occur at each participating site. Only those patients with valid admissions to participating ICUs will be included

HES – Used for timings and coding of co-morbidities and events that occur in the 5 years prior to ICU admission and the 5 years post.
Lower Layer Super Output Areas (LSOA) codes - Map different areas of the country to published deprivation indices which can be used to adjust for variations in deprivation across the England.
Civil Registration of Deaths – Limited to timing and cause of death with LSOA and occupation added for the purposes of correction for deprivation.

The study team have gone to some lengths to ensure that personal data collected will be relevant and limited to what is necessary in relation to the purposes of the study.

The organisations involved are:
a. Co-ordinating site (data controller, processor and guardian) – Critical Care Research Group, University of Oxford.
b. The participating sites (which will submit identifiers directly to NHS Digital and pseudonymised data to the participating site):
i. Oxford University Hospitals NHS Foundation Trust
ii. Royal Berkshire NHS Foundation Trust
iii. Imperial College NHS Foundation Trust
iv. King College NHS Foundation Trust

By having each site submit their identifiers directly to NHS Digital using their encrypted file transfer service, this improves data security as the identifiers do not need to be aggregated or stored for any period of time by the co-ordinating centre.

c. Internal linkage is performed by NHS Digital to Civil Registration of Deaths, HES APC and HES A&E.

The protocol and CAG application forms discuss third party linkage to NICOR via NHS Digital. This is currently on hold from a study management perspective and will not proceed without future amendment of both the CAG and this NHS Digital Data Sharing Agreement.

Expected Benefits:

Through addressing questions about the impacts of critical illness / ICU on subsequent cardiovascular disease amongst survivors, the outputs of this work will inform:

- Clinicians regarding the ongoing/future cardiovascular risks to their patients (journals and conference presentations)
- Patients now and in the future of their short, medium and long-term cardiovascular risks (twitter and specialist interest groups)
- The wider health care community and health services that are tailored to the treatment of patients during and after ICU (combination of journals, conference presentations, twitter and specialist interest groups)

Likely action/change/decision from this work:
Derive a method of identifying which patients are at risk of heart attacks, strokes and arrythmias and understand common risk factors. This work will identify potentially modifiable risk factors that may lead to identification and treatment of vulnerable patients.

Magnitude of impact:
The research will be able to estimate both the size of the affected population and therefore the number of potentially preventable cardiac events following ICU in England.

Actions leading to benefit:
Through publication of several scientific papers and presentations at scientific meetings on the topic the study team will raise the understanding of the scale of this problem faced by patients following ICU. This work should lead to further funded work that result in prevention and treatment options.

Measuring benefit:
Ultimately the benefit will depend on which risk factors are identified and the degree to which they can be modified in clinical practice.

Achievement:
The aim is to achieve these goals within the 3-year study period.

Outputs:

This study aims to publish at least two journal articles within the first 3 years of the study tackling the areas of cardiovascular risk and atrial fibrillation following critical illness respectively. These will be published in high-impact open access peer-reviewed journals (e.g European Society of Intensive Care Medicine Journal and Critical Care).

All published output will be accompanied by a corresponding press releases including lay summaries of the findings and its applicability to patients. Findings will be presented at national and international conferences to experts in the field.
Outreach will occur to specialist interest groups such as ex-patients and their families via:
- ICU charity “ICUsteps” (https://www.icusteps.org/)
- Intensive Care Society’s patient group
- Oxford patient forum

Outputs could include commentary, tweets and statements of support

When major findings are published, the departmental (Critical Care Research Group at University of Oxford) press office will assist with press releases, social media messages and interviews. The study has a significant social media presence using a Twitter account comprising of patients and health care professionals. All findings and comments will be manged through this Twitter account.

The study website will be updated with all the details of the above.

The study team will engage with policy makers, such as Intensive Care Society should the results suggest that areas of clinical practice might be improved.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)”

The participating sites will submit identifiers to NHS Digital. These individuals identifiers will be linked to the corresponding Hospital Episode Statistics (HES), HES Admitted Patient Care, HES A&E (Accident and Emergency) Data, and, Civil Registration Data by NHS Digital.

The individual sites will each contribute to the study cohort by uploading the local identifiers via the NHS Digital secure upload system. Members of the study team will attend the individual sites and assist with this process, but these identifiers will never be transferred to the co-ordinating site. All the participating / individual sites will be combined to form one study cohort by NHS Digital to form one single cohort.

Pseudonymised patient level data from HES and Civil Registration data will be requested from NHS Digital by the co-ordinating site, University of Oxford.

Data flow will consist of:
- Each participating site will allocate participants a unique study ID.
- Identifiers (specified below) will be submitted from each site to NHS Digital using their secure file transfer system
- NHS Digital will merge these identifiers into a single cohort
- NHS Digital will link to the requested datasets.
- NHS Digital will send the co-ordinating site (Critical Care Research Group – University of Oxford) historical and yearly updates from the above-mentioned data products for the duration of the agreement as specified.

The co-ordinating site will hold and process the pseudonymised dataset.
Data from NHS Digital (HES/ Civil Registration data), and the participating sites will be linked and then subject to a process of data cleaning and data quality assessment. The resulting dataset will then be used for statistical analysis in keeping with the statistical analysis plan detailed in the study protocol.
All linkage will be performed via/by NHS Digital. The study database (held by the co-ordinating centre will not hold any direct identifiers. The identifiers used for linkage to NHS Digital (listed also in the s251 approval form are)
- NHS number
- Data of birth
- Sex
- Postcode

There is no matching or linkage with any other public data.

There will be no requirement/attempt to re-identify individuals.

Data processing is only carried out by substantive employees of the data processor(s) and or data controller(s) who have been appropriately trained in data protection and confidentiality. All data processing takes please within a secure system which is designed in keeping with the principles of a data safe haven. The data does not leave the system at any time. The system is compliant with the NHS Digital DSPT Toolkit.

All data is held within the environment which is owned and run solely by the data controller/data processor and data guardian / co-ordinating site – Critical Care Research Group, University of Oxford.

CPinBOSS Study - Cerebral Palsy in the British Orthopaedic Surgery Surveillance Study — DARS-NIC-324368-Q0H5T

Opt outs honoured: Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-05-13 — 2024-05-12 2022.11 — 2022.11.

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The Cerebral Palsy in the British Orthopaedic Surgery Surveillance Study (CPinBOSS), funded by Action Medical Research (AMR), has been running since July 2019. This agreement requests HES data from NHS Digital for case ascertainment purposes. HES data is required to ensure that no potential cases that should be included in the study are missed. AMR will not have access to NHS Digital's Hospital Episode Statistics (HES) data/collected data from the study or specify any aims or objectives of the study. All people living with cerebral palsy, aged between 5-16 years, that have been referred for Single Event Multi Level Surgery (SEMLS) by either the treating clinicians or gait lab teams will be entered onto a database, forming the service evaluation aspect of the trial. The aim of the study is to undertake a national surveillance of this surgical activity encompassing all NHS Trusts in England. All of these patients and data are to come from participating NHS Trusts, which have appropriate Research & Development (R&D) and Sponsor approvals to participate within CPinBOSS. University of Oxford holds the data collected and only members of the CPinBOSS study team (all substantive employees of Oxford University) will have access to this database. The database will not be shared with any other institutions.

SEMLS is a surgical intervention that involves a minimum of two surgical procedures (bony or soft tissue) undertaken at a minimum of two different levels (e.g. hip and knee or thigh and calf) with the objectives to improve walking function within cerebral palsy patients. SEMLS has evolved over the past 30 years to replace repeated episodes of limited surgery. There are major differences between the Trusts that perform SEMLS in terms of patient selection and the choice of the specific surgical interventions. The primary objective of CPinBOSS is to identify the total number of patients that are eligible for SEMLS across all Hospitals in England, i.e. the incidence of children with cerebral palsy who fulfil the criteria for this type of surgery and to look at the variation in the surgeons’ criteria in selecting children for surgery by analysing the children’s clinical characteristics.

SEMLS operation would typically take a whole operating day of a surgical team. Therefore, any given surgical team is unlikely to be undertaking more than 4-5 such operations per month at the very maximum. The data collected from the recruiting NHS Trusts thus far support this estimate. With this in mind, it is highly unlikely that overwhelmingly large numbers will be involved. With the small numbers involved per centre it is expected that case ascertainment will be straightforward through comparison of HES and local PI submitted data.

This research was recently prioritised as one of the top-10 research priorities in children’s orthopaedics in a Delphi consensus amongst the British Society for Children’s Orthopaedic Surgery (BSCOS) members. The Delphi technique is a well-established approach to answering a research question through the identification of a consensus view across subject experts. This research was also prioritised in the top three topics at a recent James Lind Alliance Priorities Setting Partnership on paediatric lower limb surgery. The CP Cohort study is similar to the successful British Orthopaedic Surgery Surveillance Study (BOSS study), previously delivered in association with the BSCOS, confirming the feasibility of utilising this network for patient recruitment. The study design of CPinBOSS, including case ascertainment through HES data, followed the example of the BOSS study, which has now been completed successfully.

To undertake this national surveillance study, University of Oxford reached out to all NHS Hospitals nationally and invited participation by all clinical teams undertaking this type of surgery for children with cerebral palsy. A total of 24 NHS Trusts confirmed they undertake such surgery and were invited to recruit for the study. CPinBOSS has 19 NHS Trusts open for recruitment at the time of application (with a further 5 Trusts in current setup) across England. The recruitment period is from July 2019 – March 2021 with 2 years follow-up period. HES data is requested to provide case ascertainment, i.e. ensure that no cases are missed. It is particularly important for the case ascertainment to be national in order to capture cases undertaken in Trusts that have not formally declared this activity and are not recruiting for the study. This will enable the CPinBOSS research team to confidently report that CPinBOSS has captured national activity data with reasonable accuracy. If there are centres undertaking small volume activity and are not engaged with the clinical community, this would constitute important information for a national surveillance / service evaluation.

As the main objective of the study is to describe the incidence of the treatment across England , it is essential that University of Oxford do not miss any patients during the recruitment period. To ensure that University of Oxford have a full representative sample external sources will be used to check case ascertainment. Diagnostic CP codes will be searched within the Hospital Episode Statistics for England, the Patient Episode Database for Wales and the Scottish Morbidity Record and Cerebral Palsy Integrated Pathway Scotland (CPIPS) data (http://apcp.csp.org.uk/publications/cerebral-palsy-integratedpathway-scotland-cpips-dvd). The surgeon leads in hospitals with potential missed or duplicate cases will be contacted for confirmation. Only anonymised data will be reviewed.

HES data is requested for the recruitment period of the CPinBOSS study (July 2019 - Mar 2021) to ensure that University of Oxford have recruited all patients eligible for SEMLS. There are clear diagnostic (ICD) codes for diplegic cerebral palsy, these codes will be utilised to monitor hospital episodes throughout the study, which will be identified using the procedure codes provided.

The purpose of applying for HES data is to cross reference hospital admission for these patients with the information collected through the study. This will ensure that all cases of SEMLS are captured across England. In turn, this will contribute to the development of a comprehensive dataset which will reflect what is happening across the country with this patient population. Ensuring that University of Oxford capture all national activity in this clinical field and establishing the incidence of the condition is the central purpose of this study and cannot be achieved without the HES data. It is only with the cross referencing between data collected in the study with HES data that University of Oxford can ascertain complete case collection at national level.

University of Oxford are not seeking personal identifiable data, the request is for pseudonymised data. The data that is being sought will be utilised to identify NHS Trusts that have performed SEMLS over the specific recruitment period. The study team can then cross-reference with the recruitment statistics for CPinBOSS and identify if patients have not been included in the study. For example, the Oxford-based CPinBOSS Research group receive the HES data report indicating that in the first quarter of 2021 Hospital Trust X have undertaken 6 SEMLS operations. Trust X have provided the CPinBOSS research team data on 5 cases of SEMLS treated during the same period. The Study Coordinator of CPinBOSS contact the local PI at Trust X and inform them of the discrepancy in numbers, encouraging the PI to double check that no cases have been missed. No patient identifiable information is exchanged and only the total number of cases is discussed. There are then two potential outcomes: 1. The local PI at Trust X confirms that an extra case has been done but not included in the data provided to CPinBOSS. It is left with the local PI to encourage the local research team to upload the data of the additional case. The CPinBOSS team is no further involved with any chasing. No patient identifiable data are exchanged. 2. The local PI at Trust X confirms that NO extra case has been treated. The CPinBOSS team will then have no further involvement with this discrepancy. No patient identifiable data is exchanged.
Personal data will not be shared with the trusts in order to carry out this processing. Only counts will be shared.

All HES data will be held for 2 years post the end of the study (March 2025) so that research publications can be completed. It should be clarified here that a SEMLS operation would typically take a whole operating day of a surgical team. Therefore, any given surgical team is unlikely to be undertaking more than 4-5 such operations per month at the very maximum. The data collected from the recruiting NHS Trusts thus far support this estimate. With this in mind, it is highly unlikely that overwhelmingly large numbers will be involved. With the small numbers involved per centre it is expected that case ascertainment will be straightforward through comparison of HES and local PI submitted data.

University of Oxford and the CPinBOSS research team will conduct research following the General Data Protection Regulations (GDPR). Specifically, Article 6 1 (e) - Public Task and Article 9 2 (j) Archiving, Research and Statistics. Processing of the HES data is necessary for the research project to be conducted and will benefit the cerebral palsy population following dissemination of the peer reviewed published results, in line with Article 6 (e). In accordance with Article 9 (j) the research and statistical analysis is proportionate to the aim of the study and will respect the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

All data received from NHS Digital will be stored and managed by the University of Oxford (data processor and controller) and analysed by the trial statistician, a member of University of Oxford based at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), on secure, password protected servers in a locked university building within NDORMS.

CPinBOSS is funded by Action Medical Research (https://action.org.uk/).

Expected Benefits:

The total number of SEML surgeries that are performed on a yearly basis in the UK with children with cerebral palsy is unknown and there has been no large scale data collection on this. CPinBOSS is aiming to answer this primary question through a service evaluation whilst recruiting all NHS Trusts that perform SEMLS across the UK. The primary purpose of the study is to describe the incidence of the condition through identifying all the children treated nationally over a period of 18 months. This aim cannot be achieved with data collection alone as this would rely only on individual sites/hospitals entering data voluntarily. This is likely to lead to selective reporting that would affect the reliability and credibility of the study. It is hoped that cross referencing the data collected directly from the hospitals by the research team to those recorded in HES would ensure that University of Oxford do not miss cases and that the collected data truly represent the English activity over 18 months. This data sharing agreement will allow the research team to identify whether patients are being missed and not enrolled in the CPinBOSS study. This would facilitate chasing sites to report any unreported cases.

CPinBOSS is primarily a service evaluation with the primary outcome of identifying the total number of cerebral palsy patients that undergo Single-Event Multi-Level Surgery across the UK. The CPinBOSS research team have opened 19 recruiting NHS Trusts across England to report the total number of operations performed on cerebral palsy patients that meet the study inclusion criteria. Within the service evaluation there is a nested consented cohort where patients are approached to consent to be identified by the CPinBOSS research team. The patients that are recruited for the cohort are approached and consented directly by the research nurses and local PIs at the hospitals. The Oxford research team are not involved in the consent process. The patients are seen at either the gait lab or in clinic and then are approached by the local PI/research nurses and invited to consent to CPinBOSS cohort. This is different from the dataset that will be provided by the DARS team. There will be no transfer of personal identifiable data to/from the local hospitals or NHS Digital. It will just be the total number of operations performed over a specific period at that NHS Trust/hospital which will be discussed between the local site and the CPinBOSS research team.

CPinBOSS is not altering the care of the patients at the local NHS Trust as the only difference between the consented cohort and the service evaluation is that parents/guardians and children complete a consent/assent form, baseline patient reported outcome measures (PROMS), and follow-up PROMS at 1 and 2 years. It is hoped that this will allow the research team to identify whether the patients have improved their walking and mobility functions post-surgery and whether they are happy with the overall treatment and care they received. The patients that are identified via HES data will be included within the service evaluation and not be approached for the consented cohort. This is because the research team would not have received baseline PROMS prior to surgery.

The secondary objectives are to review the regional variation in total number of operations and to describe the clinical indication and surgeon decision making in the surgical management of these patients. It is hoped this will enable the research team to answer these objectively and provide recommendations for best practice, agreed standards and prioritizing future research.

It is hoped that publishing papers in high impact peer-reviewed journals will increase the evidence base around SEMLS within the CP population. There is little known about the management of these patients across England and it is hoped that this research project and the anticipated published results will help to increase knowledge and promote future research within this field.

CPinBOSS also aims to instruct policy and to produce agreed frameworks on the management of all CP patients across the UK that have been referred for SEMLS. Currently, there are no agreed standards in the care and management of these patients. This is why this HES application is so essential for the research team to achieve its goals in providing agreed policies that would significantly benefit the patients and to standardise practice within the UK. It is hoped this will result in a more agreed and suitable patient management pathway so that patients will receive the best possible treatment with the most up-to-date research supporting this.

Outputs:

All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide.

The primary output from this HES application is to double check and provide case ascertainment that University of Oxford have recruited all cerebral palsy patients (aged 5-16 years with GMFCS levels I - III) that have had SEML surgery over the recruitment period (Jul 2019 - Mar 2021). This will be analysed by the statistician and reported back to the trial management team. The level of data received from NHS Digital will be pseudonymised data which will reduce the risk of identifying patients.

The data will be written up into a high impact peer-reviewed research papers, such as the British Medical Journal or the Bone Joint Journal, whilst being presented at National and International conference following the completion of the study.

The aim will be to present the project at the British Society for Children's Orthopaedic Surgery and The European Paediatric Orthopaedic Society conferences. This will enable the research team to provide an update on the total number of SEML surgery performed over the recruitment period.

The data gained from CPinBOSS and HES will be utilised to inform and instruct policy, guidelines and frameworks on how patients with Cerebral Palsy are have their conditions managed across all Trusts within England. There is a current lack of coherence between Trusts and one of the major goals of this research is to inform policy, standardise treatment and care across the UK. This policy decision making will take place through the professional body BSCOS and its educational committee. BSCOS will then produce guidance/policy on best practice in this area by setting agreed standards for SEMLS treatment within the CP population that would emulate the practices of the centres that produce the best results. This study will also help to inform feasibility and design of future trials. However, should clinical trials not be feasible, this study will provide definitive prospective comparison of conventional vs minimally invasive SEMLS vs natural history that should have major impact on clinical practice.

Prior to the final scientific write ups of the trial, results will be communicated through the CPinBOSS website, aimed at paediatric orthopaedic surgeons treating children with cerebral palsy as well as paediatricians and physiotherapists. Newsletters will be published during the recruitment period and can be found here: https://www.ndorms.ox.ac.uk/clinical-trials/current-trials-and-studies/cpinboss-study. The results will also be presented at British Society for Children’s Orthopaedic Surgery conference in March 2021, along with further national and international meetings. The CPinBOSS study team will liaise with the parent/patient organisations which have been engaged throughout the conception and design of this study (STEPS, Action Cerebral Palsy) to produce lay summaries and infographics for the wide dissemination of the results to this group of children and their families.

Processing:

This data application is trying to answer a specific question, how many cerebral palsy patients have SEML surgery across England, and the wider project including the whole of the UK. The data sought relates to specific lower limb surgeries that have been performed on patients with cerebral palsy aged between 5-16 years old with Gross Motor Function Classification System (GMFCS) levels 1-3. The GMFCS system is a 5-level classification that differentiates children with cerebral palsy based on the child's current gross motor abilities, limitations in gross motor function, and need for assistive technology and wheeled mobility.

The data applied for are specific products relating to type of surgery (multiple ICD codes), NHS Trust of where the operations are performed and the date of the operations. Due to SEMLS comprising of multiple lower limb surgeries and that there are no specific ICD codes for SEMLS, the application includes a large range of operations. The statistician and the Chief Investigator, both based at University of Oxford will be able to identify if the patients have received the specific type of surgery (SEMLS) based on this minimal dataset.

The data applied for is for record level data of Hospital Episode Statistics Admitted Patient Care, which includes the following:
• Date of admission – To identify when the potential patients who underwent multi-level surgery can be easily identified by the local hospital/Trust.
• Primary & secondary diagnosis codes – To allow the research team to confirm the patients meet the inclusion criteria for this study and they have diplegic Cerebral Palsy. The research team will then be able to contact the local site team to enquire whether a patient was missed during the recruitment phase of CPinBOSS.
• Date of operation and status - To identify when the missed SEMLS patients were admitted so local site clinicians can identify these patients.
• Total number of procedures per episode – To allow the research team to confirm the patients actually underwent Multi-Level Surgery. For the purpose of the CPinBOSS study the research team have defined SEMLS as the intervention that involves a minimum of two surgical procedures (bony or soft tissue) undertaken at a minimum of two different levels (e.g. hip and knee or thigh and calf). This field will facilitate whether patients meet this definition so should be included within the study.
• Duration of the episode and the type – To allow the research team to confirm the patients are indeed undergoing SEMLS
• What hospital/Trust performed the operation – To identify which Trust/hospital the operations were performed for the research team to contact and chase for details. This will also allow for a total number of operations performed per site.
• Patient data including Age – To ensure patients meet the inclusion criteria for CPinBOSS (5 – 16 years old). Patients that fall outside of this age range will not be required within the CPinBOSS study.
• Ethnicity & Sex– To ensure that the patients that undergo SEMLS are representative of the general population and there is not selection bias based on specific ethnicity or sex.
• Socio-economic including Index of Multiple Deprivation – To ensure the patients that undergo SEMLS are representative of the general population and that there is not selection bias based on socio-economic status. This can be used to identify whether only a certain population is operated on.

Demographic data are requested to ascertain that the sample is representative of the general population and that patient selection is not biased by demographics, in other words that surgery is not predetermined/biased based on a specific ethnicities or sex of patients. The data request has been minimised and is targeted to a specific population that has lower limb surgery. This population includes children, aged 5-16 years old, with cerebral palsy (GMFCS levels 1-3) that had lower limb surgery, within England, over the course of the recruitment period of CPinBOSS (July 2019 to March 2021).

The data application applies for a one-way data flow (i.e. from NHS Digital to University of Oxford). All data received from NHS Digital will be stored and managed by the University of Oxford (data processor and controller – data stored on University of Oxford servers only and these are backed up daily) ) and analysed by the trial statistician, a member of University of Oxford based at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), on secure, password protected servers in a locked university building within NDORMS. All members of the CPinBOSS team have appropriate Good Clinical Practice (GCP) certificates and are appropriately trained in data protection and confidentiality. The Chief Investigator (CI) will oversee the whole project and the statistical analysis ready for dissemination. The data will be held for 2 years post end of the study for publication purposes.

Once the data is processed and provided to the research team, the statistician will identify the sites that have performed SEMLS and the total number of patients operated on during the previous quarter. This will provide the research team with in-depth knowledge of total number of SEML surgeries conducted at different hospitals across the UK. The research team cross-check number of patients recruited by each site to the HES data to identify whether the CPinBOSS trial has captured all patients that have had SEMLS over the recruitment period. Without the HES data University of Oxford cannot ensure that a full national representation of the incidence of this condition is collected.

There will be no attempt made to reidentify the patients from the data supplied by NHS digital under this agreement.

If the figures are different between the numbers that the Trust has specified and the numbers coming from HES, it would highlight potential missingness to the designated individual Trust identifying patients for the study. Based on the minimal dataset provided by HES it is likely that the treating clinicians, would be able to identify if the case is truly missing, or if it is a coding error. If truly missing, then the CPinBOSS team will prompt the local Trust team to enter the case details. If a coding error, the Trust team would be asked to highlight this to the study team. At no time would the study team (other than the individual's treating clinicians) be able to identify the individual. This would allow for a more detailed dataset and able to identify all patients fulfilling the inclusion criteria at participating sites across the England. This will ensure that the study result offers a reliable estimate of eligible participants, and ensures that the dataset represents the true sample of participants - to ensure that the results are widely generalisable, without bias emerging through missingness.

Security Assurance from University of Oxford Information Governance Team:
• As described in the Data Security and Protection toolkit, a list of all systems for storing or processing NHS Digital Data are on the asset register which is monitored regularly by the Information Governance Manager and The Research Centre IT Manager. The University of Oxford statutes include regulations relating to the use of Information Technology systems.
• Appropriate information security controls are implemented to protect all IT facilities, technologies and services used to access, process and store University information. The IT security baseline consists of approximately 80 specific requirements covering the following domains: Access control, System Acquisition and Development, Change Management, Incident Management, Monitoring and Logging, Network Security, Operational Security and Vulnerability management.
• The data from NHS Digital will be processed and stored safely and securely as per the Data Security and Protection Toolkit. Oxford University has a comprehensive Information Security Policy and all members of NDORMs are expected to abide by the departmental Information Security Policies

The data from NHS Digital will not be stored on cloud solutions at any time.

The department has a comprehensive policy and procedure for reporting breaches

Data will be processed and stored within University of Oxford servers, which are backed up daily. No data is transferred off site at any time.

MR576 - EPIC-Oxford. A prospective cohort study of 65,000 mainly vegetarian men and women, to examine how diet influences the risk of cancer, particularly for the most common types of cancer in Britain, as well as other chronic diseases. — DARS-NIC-148322-TMFVQ

Opt outs honoured: No - consent provided by participants of research study, N, Yes - patient objections upheld, Identifiable, Anonymised - ICO Code Compliant, Yes, No (Mixed, Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006, Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Other-Data was previously disseminated on the basis of: National Health Service Act 2006 - s251 - 'Control of patient information' , and Health and Social Care Act 2012 – s261(7). Data will be retained and new data will be disseminated on the basis of, Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2019-12-18 — 2022-12-17 2018.10 — 2022.11.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. MRIS - Flagging Current Status Report
  5. MRIS - Scottish NHS / Registration
  6. Civil Registration - Deaths
  7. Demographics
  8. Cancer Registration Data
  9. Hospital Episode Statistics Admitted Patient Care
  10. Hospital Episode Statistics Admitted Patient Care (HES APC)
  11. Civil Registrations of Death

Objectives:

EPIC-Oxford is a nationwide cohort study of approximately 65,000 men and women aged 20 and above who were recruited between 1993 and 1999 from throughout the UK. The study was designed to examine the effects of diet on long-term health, with a specific focus on vegetarians; 50% of the participants do not eat meat, with large numbers following lacto-vegetarian and vegan diets, and EPIC-Oxford is the only large prospective study in the world with dietary data and stored blood samples for a large number of vegetarians together with linkage for the whole cohort to medical records covering cancer diagnoses, hospitalisations and causes of death. To produce scientifically valid results it is essential that the whole EPIC-Oxford cohort can be linked with information from medical records, because if linkage was not complete there would be a high risk of the results being biased by showing spuriously low rates of disease in some dietary groups. All EPIC-Oxford participants provided written informed consent at recruitment to the study in the 1990s.

When the study first commenced the records available were for cancer registrations and causes of death. Linkage to data from HES became possible after the completion of the recruitment to EPIC-Oxford, and linkage to HES was first established in 2008.

The study website has been continuously updated since 2010 and has informed participants of the important publications which have been possible through linkage to the HES data, such as the 2013 paper showing for the first time that the risk of hospitalization or death from ischaemic heart disease was 32% lower in vegetarians than in non-vegetarians in the UK, which was widely reported in national media such as the BBC and national newspapers (http://www.bbc.co.uk/news/health-21258509 and http://www.telegraph.co.uk/news/health/news/9837285/Vegetarians-a-third-less-likely-to-develop-heart-disease.html).

EPIC’s research on the long-term health of vegetarians is unique in the world and is supported by a grant from the MRC (“Health of Vegetarians”). All this MRC-funded research, which is focused mainly on cardiovascular diseases, bone and joint health, and gastro-intestinal diseases, is completely dependent on continued ability to link the whole EPIC-Oxford cohort with the records from HES. The study is needed to improve understanding of the effects of diet on health and thus inform advice to governments, health professionals and the public about dietary choices to maximise the potential for long-term good health. Further aims include examining the roles of other lifestyle factors (including shift-work) and of endogenous hormones in relation to health. Many papers on diet and cancer risk have been published and now the availability of the HES data has enabled the extension of the research such as in the study's recent papers on ischaemic heart disease, diverticular disease and cataracts. The study’s overall aim is to provide reliable evidence on choices people can make in adult life to help increase their chances of staying healthy into old age. Further information can be found on the study website www.epic-oxford.org.

Study participants’ records are linked electronically to Hospital Episode Statistics for information on cause-specific hospital admissions, for example cancer diagnoses, cardiovascular disease, joint replacements and fractures. This is to examine the relationships between dietary, lifestyle and other potential risk factors with subsequent health. The aim is to contribute to knowledge of the epidemiology and aetiology of common diseases and other causes of hospital admissions. One of the primary outcomes is cause of mortality so continued receipt of this data is required.

Yielded Benefits:

The EPIC-Oxford study research using linked health data has direct public health relevance, in particular for the more than 1.2 million vegetarians in the UK (NHS Choices:https://www.nhs.uk/live-well/eat-well/healthy-eating-vegetarians-vegans/). For example, the research has shown that, compared to regular meat-eaters, vegetarians have a lower incidence of obesity, lower blood pressure, and a lower risk of ischaemic heart disease, diverticular disease, cataracts and of all cancers combined. This research provides important evidence which enables the NHS to recommend healthy vegetarian diets, and also more broadly expands scientific understanding of the effects of diet on health, which is essential for further development of dietary recommendations for optimum health by the NHS and Public Health England. Other work has shown that vasectomy does not increase the risk for prostate cancer, that shift work does not increase the risk for breast cancer, and that vegetarians are less likely to attend for breast cancer screening than non-vegetarians, all important findings for public health.

Expected Benefits:

Diet has been identified as the number one cause for the burden of disease worldwide, and by providing new evidence on the impact of diet on health EPIC-Oxford will contribute to reducing the work and cost to the NHS of diet-related ill-health.

The aim of EPIC-Oxford is to improve information on diet in relation to the risk of cancer and other chronic diseases, which offers huge potential for improvements in public health in the UK. The results are published in peer-reviewed publications and presented at conferences, and are also reported through national media. Over 500 peer-reviewed publications, mostly on diet and cancer, have included data from EPIC-Oxford: see http://www.epic-oxford.org/publications/.

EPIC-Oxford’s research relates directly to the health of 1.2 million people in the UK who follow vegetarian diets (NHS 2014). The long-term effects on health of a vegetarian diet are not well understood, and little is known about the health effects of a vegan diet. Previous research has demonstrated lower risks of ischaemic heart disease, stomach cancer and perhaps haematological cancers in vegetarians compared with non-vegetarians (Crowe et al 2013, Key et al 2009, 2014), but understanding of these relationships is incomplete. Further research is needed to assess both the potential beneficial effects of a vegetarian diet and also possible hazards associated with low intakes of some nutrients, such as protein, long-chain n-3 fatty acids, vitamin B12, vitamin D and calcium (particularly in vegans). As well as peer-reviewed scientific publications, the EPIC-Oxford website (www.epic-oxford.org) will be used to describe all findings, with lay summaries of findings when appropriate, copies of abstracts, and links to pdfs of full papers. The website provides information both for study participants and for a wider audience in the UK and worldwide and where appropriate we will also communicate with the NHS because the research will provide information to underpin their advice, e.g. as on their website: http://www.nhs.uk/Livewell/Vegetarianhealth/Pages/Goingvegetarian.aspx

EPIC-Oxford will directly benefit health care through the NHS by providing clinicians and other NHS health care professionals with up-to-date evidence-based guidance on the effects of diet on long term health and the risk of death. This will improve clinical health care and inform planners and policy makers to address demands on health and social care in the present and the future.

Target dates are ongoing.

Outputs:

Publications are produced on an ongoing basis. These do not identify individuals and contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide. Results are disseminated in peer-reviewed open-access papers in research journals, and related presentations to national and international colleagues, including clinicians. EPIC currently has funding to follow up patients until 2020.

The data is processed only by those named researchers and students within the Cancer Epidemiology Unit. For all of these outputs data is released only de-identified data in aggregate form (tabulations and figures showing analysis results at the minimum level of detail required, using small number suppression).

Publications and a summary of the research outputs are available to the public, to participants and to health researchers and clinicians through the study website (www.epic-oxford.org).
EPIC–Oxford results are reported in the media such as the BBC and national newspapers e.g. (http://www.bbc.co.uk/news/health-21258509 and http://www.telegraph.co.uk/news/health/news/9837285/Vegetarians-a-third-less-likely-to-develop-heart-disease.html) and its outputs reach a worldwide audience.

Future anticipated work using HES, ONS mortality and Cancer Registry Data:

The EPIC-Oxford’s Medical Research Council Grant MR/M012190/1 "Health of vegetarians" specifies a programme of research on the associations of vegetarian diets and related nutritional factors with the incidence of common diseases. In 2017 and 2018 the study will analyse:
• the relationships of vegetarian diets with the risk for ischaemic heart disease, extending previous published research on this topic with larger numbers of incident cases identified through HES and examining the extent to which the effects of a vegetarian diet may be explained by the consumption of saturated and polyunsaturated fatty acids, fruit and vegetables and dietary fibre. The intention is to complete this manuscript early in 2018 and to submit it to the British Medical Journal.
• In parallel analyses commencing in 2017, the study will examine the associations of vegetarian diets with risk for stroke, using the HES linkage to identify incident cases and to categorise then as ischaemic, haemorrhagic, or other types of stroke. Preliminary analyses based on a previous linkage to the HES data suggested that vegetarians had a somewhat higher risk of stroke than meat-eaters, but the number of cases was too small for robust analyses. With the new linkage to HES about 2000 cases of stroke are expected which will provide sufficient power to conduct reliable analyses, and to explore the possible roles of protein and vitamin B12 in determining stroke risk in vegetarians. The intention is to submit this manuscript in 2018 to the journal Circulation.
• Following these analyses of cardiovascular disease, at the beginning of 2017, the study will commence analyses of the relationships of vegetarian diets with the risk for musculoskeletal disorders: fractures of the forearm, wrist and hip, hip and knee replacement, and carpal tunnel syndrome. The study will examine whether associations of vegetarian diets with the risk for these disorders may be due to differences in intake of calcium and protein, and will aim to submit the papers to the American Journal of Clinical Nutrition in late 2018/early 2019.
• In 2019-2020 the intention is to examine vegetarian diet and gastrointestinal diseases including Crohn’s disease, ulcerative colitis and gallstones.
• The study’s Cancer Research UK grants on the Epidemiology and Aetiology of High Risk Prostate Cancer and the core grant of the Cancer Epidemiology Unit(CEU) specify a program of research on the risk for prostate cancer and the cancers in relation to and related factors and other diseases.

Conferences
The intention is to present the research findings at the following conferences:
• 2017 – Nutritional Society UK Symposium
• 2017 European Association of Urologists
• 2018 7th International Congress on vegetarian Nutrition., Loma Linda California.
• 2018 The National Cancer Research Institute (NCRI)
• 2019 The National Cancer Research Institute (NCRI)

Processing:

The majority of the EPIC-Oxford cohort are flagged on NHS Digital’s MIDAS system. NHS Digital provide monthly updates on participant events including removals and re-entries to NHS registration, cancer registrations and deaths including cause of death details.

The EPIC-Oxford team will supply a file of identifying details for additional participants of the EPIC-Oxford study that are not currently flagged within this cohort on the NHS Digital system. NHS Digital will then flag these members.

NHS Digital will link the full cohort to Hospital Episode Statistics records and supply pseudonymised linked data to EPIC-Oxford.

Using the study ID, the EPIC-Oxford team link the data with study participants’ records collected over time directly from the participants and from NHS Digital and ONS plus linked data from Scotland (via the Public Benefit and Privacy Panel for Health and Social Care) and from Northern Ireland (via the Central Services Agency). The linked data is stored separately from the patient identifiers. Participant identifiers linked to the study ID numbers are stored separately to the dataset for use in analysis and are held only for administrative purposes and for use in facilitating ongoing data linkage. The analysis dataset (containing the study participant’s linked records from the sources specified above) will not be re-linked with the identifiers. The analysis dataset contains full date of death for individuals whose deaths were reported prior to December 2016. This is the only identifiable field held within the analysis dataset. All subsequent data supplied by NHS Digital will be pseudonymised. It will contain month and year of death rather than full date of death. The analysis dataset also contains month and year of birth.

Only month and year of birth and/or death will be used in future analyses. All subsequent analyses use only subsets of the pseudonymised data. All such subsets are customised according to the characteristics relevant to the specific analysis containing only the minimum data required for the specific purpose.

Various types of analyses are undertaken on an ongoing basis for the overarching purpose of assessing cancer incidence, health risks and overall mortality. The data will be held only at the Cancer Epidemiology Unit at the University of Oxford. The datasets will be pseudonymised as described above before statistical analyses are undertaken.

The data will only be accessed by authorised members of the EPIC-Oxford study team, all of whom are substantive employees of the University of Oxford, or non-contractual DPhil,MSc students who complete University Research Services form agreeing to terms and conditions of the project, grant and latest Data Sharing Agreement. These are filed in Research Co-ordinators office with signed copies sent to Director of Research Services at University of Oxford. Access to ONS mortality data is restricted to individuals with Approved Researcher accreditation named within the Data Sharing Agreement. The data will only be used for the objectives of the study as described within the Data Sharing Agreement. The EPIC-Oxford study will not share any data supplied by NHS Digital with any other institution or individual outside of the study team at Oxford University.

MR1086 - The Oxford Vascular Study: incidence and outcome of stroke , transient ischaemic attack — DARS-NIC-148369-8PPWK

Opt outs honoured: N, Yes - patient objections upheld, Identifiable, Yes (Consent (Reasonable Expectation), Section 251 NHS Act 2006)

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c), Consent (Reasonable Expectation); Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2019-03-01 — 2020-10-02 2017.09 — 2022.11.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. Demographics
  4. MRIS - Scottish NHS / Registration
  5. MRIS - Flagging Current Status Report
  6. MRIS - List Cleaning Report
  7. MRIS - Members and Postings Report
  8. Civil Registration - Deaths
  9. Civil Registrations of Death

Objectives:

The Data supplied by the NHSIC to University of Oxford will be used only for the approved Medical Research Project MR1086.

Yielded Benefits:

Some of benefits of data collated in the Oxford Vascular Study (OxVasc) to date include: Emergency prevention of “threatened” stroke Major strokes are sometimes preceded by minor events – so called transient ischaemic attacks (TIA) or minor strokes. It was thought for many years that these events were relatively benign and that investigations were done on a non-urgent basis over weeks or months. Analyses of OxVasc outcomes showed that the risk of major stroke in the first few hours and days after these warning events was, in fact, very high (BMJ 2004), such that they were re-branded as a medical emergency in all international guidelines. A validated and refined simple risk scores (ABCD system) to triage high-risk individuals (Lancet 2005; Lancet 2007) and showed that delays to treatment substantially undermined benefits and showed that urgent use of existing treatments (aspirin, other antiplatelet drugs, blood pressure lowering drugs and statins) reduced the 90-day risk of major stroke by 80% (Lancet 2007; Lancet Neurol 2009. This simple, cheap but highly effective strategy was rolled out across the UK in the Department of Health’s National Stroke Strategy and NICE guidelines, is estimated to prevent 10,000 strokes per year in the UK alone, saving £200 million in NHS costs, and is now the standard of care worldwide. Further work has shown that most of the 80% reduction in the early risk of major stroke seen in OxVasc was due purely to aspirin, which also substantially reduces the severity of recurrent strokes. This new observation has major implications for public education - immediate self-administration of aspirin after possible TIA or minor stroke symptoms has the potential to prevent many millions of strokes worldwide at virtually no cost (Lancet, 2016). Screening for aortic aneursysms The schedule for abdominal aortic aneurysm (AAA) screening in men age 65 might have limited impact on overall AAA death rates if incidence of acute events is moving to older ages. Data form OxVasc showed two thirds of acute AAA occurred at ≥75 years of age, indicating screening older age groups should be considered. In addition, 25% of acute events were in women and the screening of nonsmokers at age 65 is likely to have very little impact on AAA event rates (Br J Surg. 2015, J Am Heart Assoc. 2015). Recovery after stroke Outcome in stroke trials is often based on an assessment of 3-month disability. How disability at this time point relates to longer-term outcomes will depend on late recovery, delayed stroke-related deaths, recurrent strokes, and nonstroke deaths. Data from OxVasc reaffirmed the use of outcome at 3 months in stroke trials. It also showed that although later recovery does occur, extending follow-up to 1 year would capture most long-term stroke-related disability. However, administrative mortality follow-up beyond 1 year has the potential to demonstrate translation of early disability gains into additional reductions in long-term mortality without much erosion by non-stroke-related deaths. Further work is underway to document the time course of long‐term quality‐adjusted life expectancy and healthcare costs in relation to early disability scores. (J Am Heart Assoc. 2017). The following is an extract from the publication ‘The National Institute for Health Research at 10 Years | An impact synthesis: 100 Impact Case Studies’ (see: https://www.rand.org/pubs/research_reports/RR1574.html): Costs arising from the treatment of stroke and costs incurred due to productivity loss of the UK population have been calculated to amount to approximately £8.9 billion a year [1]. Stroke treatment costs represent about 5 per cent of total UK NHS costs [1]. Research resulting from the Oxford Vascular Study (OXVASC), which is partly funded by the NIHR, has had significant impact on stroke prevention and the way minor strokes and transient ischaemic attacks (TIAs, or ‘mini strokes’) are managed, by informing clinical guidelines. The OXVASC study started in 2002 and provides data on the incidence and outcome of all acute vascular events occurring in the population in Oxfordshire [2]. The NIHR has contributed to the research in different ways, such as: funding specific research on cost savings arising from early detection of TIA and stroke, in phase 2 of the OXVASC study, and providing an NIHR Senior Investigator Award to one of the principal investigators [3]. The first phase of the OXVASC study showed that the risk of stroke after a TIA is greater than originally considered, that there is a narrow time-window for prevention, and that individuals who are at highest risk of having a stroke can be identified with a simple clinical score – the ABCD [2]. It also showed that the requirement for appointments could lead to a delay in referrals for patients with a suspected TIA or minor stroke. Based on learning from phase 1, the second phase of the research led to impacts on emergency treatment of TIA and minor stroke in primary care [2]. In this second phase, primary-care physicians were asked to send the patients immediately to the clinic, without any appointment, where treatment was initiated immediately if the diagnosis was confirmed. This led to an 80 per cent decrease in the 90-day risk of recurrent stroke in patients referred to the phase 2 clinic compared with those referred to the phase 1 clinic. In addition, clinic hospital admissions for recurrent stroke were lower when the requirement for appointments was removed, which translated to a savings of £624 per patient [4]. The Early use of eXisting PREventive Strategies for Stroke (EXPRESS) study, nested within OXVASC, determined the effect of more rapid treatment after a TIA and minor stroke in patients who are not admitted directly to hospital [4][5]. These findings have had an impact on service provision and professional education about TIA and minor stroke. This is demonstrated by the changes the research has produced in clinical guidelines. Findings from the EXPRESS study have informed the 2007 National Stroke Strategy; the 2008 National Institute for Health and Care Excellence guidelines Stroke: National Clinical Guideline for Diagnosis and Initial Management of Acute Stroke and Transient Ischemic Attack (TIA); and the 2012 Royal College of Physicians Intercollegiate Stroke Working Party’s National Clinical Guideline for Stroke [3]. The recommendations in these documents reflect the findings from the EXPRESS study that there is a need for identification of patients at high risk of subsequent stroke and early specialist intervention, including commencement of appropriate secondary prevention treatments. Based on the estimations from the EXPRESS study, it was calculated that emergency treatment of TIA and minor stroke in primary care would prevent about 10,000 strokes per year, adding up to savings of up to £200 million annually in acute care costs alone in the NHS [3]. Overall, the health and care system has benefited from improved stroke prevention as a result of determining the resource costs, health outcomes and cost-effectiveness in stroke care using evidence from the Oxford Vascular Study. Evidence 1] Saka Ö, McGuire A, Wolfe C. 2009. Cost of stroke in the United Kingdom. Age and Ageing 38 (1): 27-32. doi:10.1093/ageing/afn281 Study reporting the annual cost of stroke to the UK economy using a combination of direct and indirect cost measures. [2] National Institute for Health Research. 2016. Improving stroke prevention in routine clinical practice: Phase 2 of the Oxford Vascular Study (OXVASC) programme. As of 2 May 2016: http://www.nihr.ac.uk/funding/funded-research/funded-research.htm?postid=2164 Link to a project page on the National Institute for Health Research website, describing the OXVASC Study programme. [3] Research Excellence Framework. 2014. Reduction of stroke risk by risk stratification and urgent intervention after a transient ischaemic attack (TIA) or minor stroke. [Case study 14720.] As of 2 May 2016: http://impact.ref.ac.uk/CaseStudies/CaseStudy.aspx?Id=14720 The case study summarises the achievements of the team from the Stroke Prevention Research Unit in Oxford from early 2000 to 2013. [4] Luengo-Fernandez R, Gray AM, Rothwell PM. 2009. Effect of urgent treatment for transient ischaemic attack and minor stroke on disability and hospital costs (EXPRESS study): A prospective population-based sequential comparison. The Lancet. Neurology. 8: 235-43. doi: 10.1016/S1474- 4422(09)70019-5 This paper summarises the findings on the cost-effectiveness of the phase 2 intervention. It concludes that urgent assessment and treatment of patients with a TIA or minor stroke who were referred to a specialist outpatient clinic reduced subsequent hospital bed-days, acute costs and six-month disability. [5] Health Economics Research Centre. 2016. Resource costs, health outcomes and cost-effectiveness in stroke care: Evidence from the Oxford Vascular Study. Nuffield Department of Population Health. As of 2 May 2016: http://herc.medsci.ox.ac.uk/research/disease-cost-studies/studies-4/resource-costs-healthoutcomes-and-cost-effectiveness-in-stroke-care-evidence-from-the-oxford-vascular-study The page offers a comprehensive account of this part of the research of the Oxford Vascular Study, including the publications resulting from it. This project aimed to: 1) estimate the size and predictors of immediate and long-term (i.e. five years after the event) National Health Service resource use and healthcare costs of stroke and transient ischaemic attacks; 2) estimate the size and predictors of immediate and long-term health outcomes, including five-year life expectancy, patient disability, quality of life, and quality-adjusted life expectancy; and 3) assess if urgent clinical assessment and treatment of nonhospitalised patients with a minor stroke or TIA was cost effective.

Expected Benefits:

The overall aims of the Oxford Vascular Study are to improve the public’s health through disease prevention, earlier disease diagnosis and better management of known risk factors. Results from the study to date have been used to underpin NICE guidelines and other Department of Health strategies by firstly providing evidence for ways to improving diagnosis of disease and secondly how to effectively treat common risk factors such as high blood pressure.

Most outputs arising from the Oxford Vascular Study include data on mortality obtained from NHS Digital and these data continue to be important in some analyses together with the detailed clinical information collected from participants as part of the study.

Outputs:

No new outputs will be produced under this Data Sharing Agreement.

OxVasc is one of a number of cohort studies funded by the NIHR to identify simple low cost interventions and to inform the development of clinical trials to improve the treatment outcomes of vascular disease in the short and long term. By recruiting all eligible participants from a defined population and following them up over a long period of time, OxVasc reduces recruitment bias so the results are more generalizable to the population as a whole and can identify whether the benefits of any intervention are maintained (e.g. sustained blood pressure monitoring and treatment, carotid surgery).

The study overall has produced over 100 publications of incidence of disease, risk factor management, prognosis and outcomes, including:
- Change in incidence, mortality and risk factors for stroke from 1981 to 2004. (Lancet, 2004) showing the fall in incidence over the past 20 years is association with increased use of preventive treatments.
-Reported incidence, case fatality, burden and cost of all acute vascular events in a defined population. (Lancet, 2005)-
- Reported incidence and outcome of acute aortic dissection and Ischemic Peripheral Arterial Events from 2002-2012. (Circulation, 2013, 2015) showing uncontrolled high blood pressure remains the most significant treatable risk factor for acute aortic dissection and focussed use of existing treatments would be beneficial.

Yearly reports on the progress of the researchhave been given to the funders with all outputs and impacts for the previous year.

The study also benefits the individual participants by providing:
1. Rapid assessment and treatment following TIA and minor stroke in order to identify the cause and provide treatment.
2. Ongoing assessment of vascular risk factors (BP, cholesterol), health care advice (smoking cessation, lifestyle advice) at follow up, enabling participants and the collaborating GP to improved secondary prevention of vascular disease.

Research findings from the Oxford Vascular Study are summarised on the study website (www.ndcn.ox.ac.uk/research/oxvasc) and presented at open days organised by the NIHR Oxford BRC. Talks on OxVasc and related topics (e.g. high blood pressure, vascular dementia) are also available on YouTube. Results of the study have been reported in the local, national and international press.

Participants are informed of progress with posters displayed with results of the study to date in the participating GP practices and the general information booklet which participants are given on entry to the study and updated yearly.

Peer-reviewed manuscripts on original research arising from the study are subject to the Wellcome Trust open access policy and are available to all free of charge on publication. A statement on data used and data sharing is provided in line with the individual publisher guidelines and the NIHR. No data, even anonymised, from NHS Digital will be shared.

Processing:

Under this Agreement, the data already provided under previous iterations of this Agreement may be securely stored but not otherwise processed.

The University of Oxford is permitted to supply a file containing identifying details of study participants to NHS Digital.

NHS Digital will supply details of participants' current vital status, current status of registration with an NHS GP and latest known addresses for living participants registered with an NHS GP.

The University of Oxford will use this information to determine which study participants should appropriately be sent a patient notification by post.


The following provides background on the processing activities undertaken for the original study:

The study data, including data provided by NHS Digital under previous versions of this Agreement, are held by the University of Oxford in the Nuffield Department of Clinical Neurosciences Medical Sciences Division, based within the West Wing of John Radcliffe Hospital Oxford. The data are stored electronically on University of Oxford central servers which are connected to the main University of Oxford’s network. At no time will employees of John Radcliffe Hospital have access to the data held on the server for University of Oxford.

Identifying details of participants have previously been supplied to ONS and subsequently NHS Digital so that their patient records could be flagged and mortality data could be reported to the study. The University of Oxford will flow identifying information to NHS Digital for the purpose of list cleaning. The University of Oxford will use the list cleaning service to provide a newsletter to participants. The newsletter will be pre-reviewed by NHS Digital prior to publication.

The study database contains information collected directly from participants (e.g. family history, lifestyle) and from other sources including hospital and GP surgery notes (e.g. scan results, blood test results, blood pressure measurements). These are entered and coded within the database so they can be downloaded for analysis at a later date. The study database contains identifying data on each participant in order to keep up to date contact details for them so they can be contacted for follow up, but the database is encrypted so any data extracted for analysis does not have identifying details (names, addresses, etc.).

Other than the linkages described above, the data will not be linked with any other data.

No attempt is made to contact families after the death of a participant is confirmed through this process.

Data supplied by NHS Digital will be used only for the approved Medical Research Project MR1086-The Oxford Vascular Study. No data will be shared with any individuals or agencies outside of the study team. All study staff are employees of the University of Oxford.

The analyses are determined by the Principal Investigator (PI) and the study statistician and performed by the research team to support the aims and objectives of the study as outlined in the application for ethical approval and grant funding from the Wellcome Trust and the NIHR Oxford BRC.

The progress is evaluated annually and new analyses are added or completed based on findings to date and the length of time required to collect outcomes (cause of death) to determine the prognosis of different presentations of vascular disease and/or achieve statistical power to answer the research question. For example, mortality data is required to determine the outcome (disability or death) and time course of bleeding requiring medical attention in patients taking long-term antiplatelet treatment after acute vascular events. This is then used to estimate the age-specific numbers needed to treat to prevent upper gastrointestinal bleeding with routine proton-pump inhibitor co-prescription.

Not all of the work of the Oxford Vascular Study involve use of data on mortality obtained from NHS Digital but use of the data will be important in some analyses in order to determine firstly the impact of any treatment on survival and secondly the health economic value of any intervention e.g. prevention of recurrent stroke and subsequent health resource use.

The data is held in an access-controlled server room and connected to the main University network, located behind a firewall. Physical access is limited to Computer Services Department staff. Data will be encrypted using industry standard techniques meeting the Information Governance Toolkit standard (RBQ).

The HOME Study — DARS-NIC-113964-G3J0C

Opt outs honoured: Anonymised - ICO Code Compliant, No, Yes (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c); Informed Patient consent to permit the receipt, processing and release of data by NHS Digital, Health and Social Care Act 2012 – s261(2)(c); Other-S261(5)(d), Health and Social Care Act 2012 - s261(5)(d); Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c); Informed Patient consent to permit the receipt, processing and release of data by NHS Digital; Other-s261(5)d, Health and Social Care Act 2012 - s261(5)(d); Health and Social Care Act 2012 – s261(2)(c); Informed Patient consent to permit the receipt, processing and release of data by NHS Digital; Other-S261(2)(d), Health and Social Care Act 2012 - s261(5)(d); Health and Social Care Act 2012 – s261(2)(c); Informed Patient consent to permit the receipt, processing and release of data by NHS Digital

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2021-07-29 — 2024-07-28 2022.03 — 2022.10.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  2. Civil Registration - Deaths
  3. Emergency Care Data Set (ECDS)
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Hospital Episode Statistics Critical Care
  7. Hospital Episode Statistics Outpatients
  8. Mental Health Services Data Set
  9. Civil Registrations of Death
  10. Hospital Episode Statistics Accident and Emergency (HES A and E)
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Critical Care (HES Critical Care)
  13. Hospital Episode Statistics Outpatients (HES OP)
  14. Mental Health Services Data Set (MHSDS)

Objectives:

BACKGROUND – THE HOME STUDY:
NHS general hospitals have more than two million unplanned admissions of people aged 65 and older every year. These patients typically spend more time in hospital than those aged under 65. Prolonged hospital stays are known to be detrimental to older people and known to be caused in part by lack of attention to psychiatric problems such as delirium, dementia, and depression as well as psychological issues such as minor cognitive impairment or anxiety that may slow patients’ discharge from hospital. The University of Oxford Psychological Medicine Research group has developed a new approach to the identification and management of psychological problems (called Proactive Liaison Psychiatry or Proactive Psychological Medicine, PLP/PPM) which aims to reduce the time that older people spend in acute general hospital wards.

The HOME Study is a two-arm parallel-group randomised controlled trial. It aims to determine whether adding PLP/PPM to usual care reduces the time spent by older patients in acute hospital wards in the month (30 days) after randomisation (primary outcome), when compared with usual care alone. A number of secondary outcomes, including patients’ views of their length of time in hospital, their quality of life, their secondary healthcare use in the year post-randomisation and deaths will also be evaluated. The HOME Study will also determine the cost-effectiveness of adding PLP/PPM to usual care.

Participants in The HOME Study are adults aged 65 or over, who were admitted non-electively to a general hospital in Oxford, Exeter or Cambridge between May 2018 and March 2020. Recruitment to The HOME Study has now closed. Informed consent (or consultee agreement, in accordance with the Mental Capacity Act 2005, for patients who lacked capacity to consent) was obtained for trial participation.

AIM AND PURPOSE OF THIS AGREEMENT:
The aim of this agreement is to access the 'routine data' that participants have consented to the researchers using in order to complete The HOME Study.

The ‘routine data’ required are participants’ healthcare use in the year prior to recruitment, data on time spent in hospital in the month post-recruitment (trial primary outcome), healthcare use in the year post-recruitment (NHS secondary care resource use is a trial secondary outcome), and information on deaths (trial secondary outcome). These data are from the Hospital Episode Statistics (HES) Accident and Emergency, Admitted Patient Care, Critical Care and Outpatients datasets; the Emergency Care Dataset; the Mental Health Services dataset and the Civil Registration (Deaths) dataset.

These pseudonymised, record level data are the minimum required to analyse the secondary healthcare use and deaths of participants in The HOME Study, comparing the outcomes of participants allocated to PLP/PPM with those allocated to usual care. There are no alternative, less intrusive ways of achieving this purpose. It is important to receive the NHS Digital data described above as participants are likely to have received healthcare (and died) in different settings from those where they had their initial acute admission.

The data requested are limited to The HOME Study participants only and those variables required to address the study aims. As the aim is to collect data on specific individuals who are taking part in the study, the use of anonymised data is not feasible. The number of years requested and geographical spread of the data requested are defined by the HOME Study participants’ dates of randomisation and locations respectively.

ORGANISATIONS:
The University of Oxford (the study Sponsor) is the Data Controller responsible for determining the purpose and manner in which any personal data collected for clinical research are, or are to be, processed for this study.

The University of Oxford, the University of York and London School of Hygiene and Tropical Medicine are data processors for this study; researchers in these organisations will conduct statistical and health economic analyses.

The University of Oxford is a ‘public authority’, as defined in the Data Protection Act 2018, with a principal object of the organisation being research and its dissemination. The processing of identifiable personal data, including special category data, is necessary to carry out medical research that serves the public interest. The legal basis for processing personal data is: Article 6(1)e of the GDPR, ‘processing is necessary for the performance of a task carried out in the public interest’; and Article 9(2) j of the GDPR ‘processing is necessary for archiving purposes in the public interest, scientific or historical research purposes’.
The processing of sensitive personal data by The HOME Study researchers is in the public interest as it will provide information to guide doctors’ decisions about the care of patients and to guide policy makers’ decisions about NHS service provision. The processing is of medical data about particular individuals and will be related to their involvement in the randomised trial.

A number of other organisations are involved in the study but not in the processing any data disseminated under this agreement nor do they carry out any data controllership activities. These are: Oxford University Hospitals NHS Foundation Trust, Devon Partnership NHS Trust, Royal Devon and Exeter NHS Foundation Trust, Cambridge University Hospitals NHS Foundation Trust, Cambridgeshire and Peterborough NHS Foundation Trust (patient recruitment); University of Exeter, University College London, University of Nottingham, University of Manchester, University of Birmingham, Worcestershire Acute Hospitals NHS Trust (collaborators).

FUNDING:
The study is funded by the National Institute for Health Research Health Services and Delivery Research Programme. The funder has no role in the design or conduct of the study, and will not be processing or accessing any data.

Expected Benefits:

The dissemination of the data will allow the completion of The HOME Study, an NIHR-funded trial which is expected to have a large impact on the provision of psychiatric care for older people admitted to general hospitals in the UK and internationally.

Acute NHS hospitals have more than two million unplanned admissions of people aged 65 and older every year. The greater length of stay of older patients means that these admissions account for most of the available emergency bed days. The UK Department of Health set out a policy to shift care from hospitals to community settings. But despite this, the last decade has seen a large increase in emergency admissions, the majority of those being of people aged over 65, a trend likely to continue as the population ages.

Prolonged hospital stays are known to be detrimental to older people and known to be caused in part by lack of attention to psychiatric problems. The HOME Study aims to determine the effectiveness and cost-effectiveness of Proactive Liaison Psychiatry / Proactive Psychological Medicine (PLP/PPM) which is a new way of delivering psychiatric care for older general hospital inpatients that aims to address this problem of prolonged hospital stays.

The dissemination of the findings of The HOME Study is in the public interest due to the large benefits that this research could have for patient care. The trial results are expected to shape the decisions that are made about NHS investment in liaison psychiatry services. If the PLP/PPM intervention is effective and cost-effective, this model of care rolled out across the UK could improve the lives of the large number of older people admitted to NHS general hospitals every year, as well as saving NHS costs.

The use of NHS Digital data is fundamental to the achievement of the scientific aims of The HOME Study. The study protocol and analysis plan included the use of these data and all participants have agreed to their NHS healthcare data being used in this way.

The expected benefits include both a major contribution to our knowledge of how best to care for older patients in general hospitals, published in peer-reviewed journals, as well as information that could directly enable commissioners and policy makers to make decisions about NHS care. The knowledge that will be gained is expected to inform NICE guidance as well as the Royal College of Psychiatrists Psychiatric Liaison Accreditation Network system, which promotes the development of high quality, evidence-based, liaison psychiatry services and is linked to the Care Quality Commission’s new inspection regimen.

Outputs:

Over the year following receipt of the data (by the end of 2022), the findings of The HOME Study are expected to be published and disseminated as papers, reports and conference presentations.

The research is anticipated to be published in high impact peer-reviewed journals and presented at national and international conferences including those held by the Royal College of Psychiatrists in the UK, by the Academy of Consultation-Liaison Psychiatry in the USA, and the European Association of Psychosomatic Medicine in Europe.

It is hoped that the trial results will shape the NHS investment in liaison psychiatry services and will also inform the Royal College of Psychiatrists Psychiatric Liaison Accreditation Network system, which promotes the development of high quality, evidence-based, liaison psychiatry services and is linked to the Care Quality Commission’s new inspection regimen.

The trial is expected to also have major international impact. It is anticipated that the findings will be used by healthcare policy makers worldwide, in particular in the USA, due to the current substantial interest in developing liaison psychiatry services, including proactive service models, to meet the needs of older medical inpatients with multimorbidity.

The PLP/PPM intervention manual is intended to be made freely available to the NHS. If the trial results favour PLP/PPM we anticipate that the manualised intervention will form a framework for liaison psychiatry provision nationally.

A process evaluation-based commissioning and implementation guide is expected to provide key recommendations to service providers and purchasers, and form the basis for commissioning and quality assurance of services.

It is planned that the results will be made available to participants and the public on the study website. The HOME Study has a dedicated Patient and Public Involvement (PPI) panel, made up of people who have personal experience of being an older general hospital inpatient or being a caregiver for an older general hospital inpatient. The PPI panel members were actively involved in the development of the PLP/PPM intervention and the trial procedures as well as in training HOME Study research staff. The panel will meet with the research team to discuss the study findings and assist in their interpretation. They will also advise the team on how to best disseminate the findings and ensure that the results are clearly described.

The researchers intend to actively work with the University of Oxford communications team and the Science Media Centre (London) for assistance with effective dissemination of results via the press and social media.

The study team will also, when reviewing the results of the analysis, consider whether any change in individual facility visiting policies could impact the study, during and post the Covid-19 pandemic.

All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

The University of Oxford will provide identifiers (NHS number, date of birth, sex, name), a unique study ID and the participant’s date of recruitment to NHS Digital for consented participants in The HOME Study via a Secure Electronic File Transfer (SEFT).

The pseudonymised data requested from NHS Digital are from the Hospital Episode Statistics (HES) Accident and Emergency, Admitted Patient Care, Critical Care and Outpatients datasets; the Emergency Care Dataset; the Mental Health Services dataset (MHSDS) and the Civil Registration (Deaths) dataset, covering the period from May 2017 to March 2021. HES and MHSDS data will be minimised to the year prior to and the year post recruitment for each study participant.

The study manager at the University of Oxford holds the identifiable information for all members of the cohort, and will receive the NHS Digital data. The study manager has disseminated existing trial data (including participant study ID, date of birth and sex) for cohort members, obtained with consent/consultee declaration from their medical records and participant/proxy reports, to the relevant statisticians and health economists at the University of Oxford, University of York, and the London School of Hygiene and Tropical Medicine.

The study manager will disseminate subsets of NHS Digital data to the relevant statisticians and health economists at the University of Oxford, University of York and the London School of Hygiene and Tropical Medicine to complete the requisite analysis. There will be no requirement or attempt by data analysts at these organisations to re-identify individuals. Analysts will not be provided with participants’ names, NHS numbers or contact details. Existing trial data may be linked with NHS Digital data by analysts using the unique study ID where relevant to ensure participant information is complete and accurate for data analysis purposes.

The data requested will be used in the following ways:

Healthcare use (admissions, outpatient visits, Accident and Emergency attendances) in the year prior to randomisation and ethnicity – to describe trial participants at the time of their recruitment to the study.

Number of days spent as an inpatient in a general hospital in the month (30 days) post-randomisation – to compare the outcomes of participants allocated to PLP/PPM with those allocated to usual care. This is the trial’s primary outcome.

Healthcare use (admissions, outpatient visits, Accident and Emergency attendances) in the year after randomisation- to compare the outcomes of participants allocated to PLP/PPM with those allocated to usual care and to determine the cost-effectiveness of PLP/PPM compared with usual care.

Deaths - to compare the outcomes of participants allocated to PLP/PPM with those allocated to usual care.

Statisticians at the London School of Hygiene and Tropical Medicine will conduct the analyses that describe trial participants at the time of their recruitment to the study and compare the outcomes of participants allocated to PLP/PPM with those allocated to usual care. Health economists at the University of York will conduct the cost-effectiveness analysis.

The data will be stored as follows:

The University of Oxford will store the data on Oxford University’s Medical Science Division IT’s High Compliance system (HCS). This is a service for Clinical Trials Units (CTUs) and Medical Sciences Division Departments or Units which need to access applications securely, and manipulate and store very sensitive data. The HCS is a controlled environment within which sensitive data can be manipulated and de-classified for further processing.

The London School of Hygiene and Tropical Medicine will store the data on the Secure Data Server which can only be accessed at London School of Hygiene and Tropical Medicine. The data will be accessible only to named HOME Study researchers who have authorisation from the applicant.

The University of York will store and access the data on the Safe Haven. The data will be accessible only to named HOME Study researchers who have authorisation from the applicant.

Data will only be accessed and processed by substantive employees of the University of Oxford, The University of York and the London School of Hygiene and Tropical Medicine, and will not be accessed or processed by any other third parties not mentioned in this agreement. Data will only be accessed for the purposes of HOME Study analyses.

The data will be safely held in an encrypted form on the University of Oxford Medical Science Division’s High Availability Novell network for 5 years.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

MR565: The Million Women Study - addition of only 1 year complete HES data for 2018/2019. — DARS-NIC-389134-S8L1C

Opt outs honoured: No - consent provided by participants of research study, No - data flow is not identifiable, Identifiable, Anonymised - ICO Code Compliant, No (Reasonable Expectation, Consent (Reasonable Expectation))

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2020-05-21 — 2021-07-26 2017.06 — 2022.10.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. Hospital Episode Statistics Admitted Patient Care
  3. MRIS - Members and Postings Report
  4. MRIS - Cohort Event Notification Report
  5. MRIS - Scottish NHS / Registration
  6. MRIS - Bespoke
  7. Civil Registration - Deaths
  8. Demographics
  9. Cancer Registration Data
  10. MRIS - Flagging Current Status Report
  11. HES-ID to MPS-ID HES Admitted Patient Care
  12. Civil Registrations of Death
  13. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The Million Women Study (MWS) is a national study of women’s health funded by Cancer Research UK and the Medical Research Council. The study involves 1.3 million UK women, recruited in 1996-2001, who have given written consent for follow up of their health through their medical records, to examine how reproductive and lifestyle factors affect their future health.

Yielded Benefits:

The Million Women Study research using linked health data has already directly influenced health care. Results showed, for example, that women using hormone replacement therapy are at increased risk of breast cancer; this work, published in 2003, was shared with the Medicines and Healthcare Regulatory Agency and helped inform changes in prescribing guidance both for the UK and elsewhere. It is estimated that tens of thousands of cases of cancer worldwide have been avoided as a result of the subsequent fall in use of hormone therapy. Another example where NHS Digital data contributed to an influential paper was in showing that risk of blood clots after surgery was far higher, and lasted for much longer, than had been previously thought. This work is helping to inform European surgical care guidelines. MWS results on how characteristics of individuals affect participation and outcomes of bowel cancer screening are being incorporated into screening programme development. Recent work on the costs to the NHS of obesity-related conditions, and on breast cancer risk in relation to night shift work, has direct public health relevance. Participants in the Million Women Study are the first generation of women in the UK to have smoked to the same extent as a man and our findings show that female smokers died about 10 years earlier than non-smokers. These effects are much greater than had been reported previously and influence policy.

Expected Benefits:

The Million Women Study research using linked health data has directly influenced health care. Results showed, for example, that women using hormone replacement therapy are at increased risk of breast cancer; this work helped inform changes in prescribing, and it is estimated that tens of thousands of cases of cancer worldwide have been avoided as a result of the subsequent fall in use of hormone therapy.
Another example where HSCIC data contributed to an influential paper was in showing that risk of blood clots after surgery was far higher, and lasted for much longer, than had been previously thought. This work is helping to inform European surgical care guidelines.
Other influential MWS work with direct health care benefits is looking at how to best implement a screening programme for bowel cancer following a study into how characteristics of individuals affect participation and outcomes of screening.

Outputs:

The data set (HES data) will be used to examine relationships between lifestyle and reproductive factors (collected via questionnaire data) and a wide range of outcomes. These outcomes would mainly consist of a variety of cancer diagnoses, hip fractures and joint replacements and cardiovascular disease. CEU plan to publish their anonymised findings in peer reviewed scientific journals so that they can contribute to knowledge of common diseases and causes of hospital admissions.
CEU have successfully published papers using data from their previous HES extract (ET2535) and wish to continue using similar data together with extra information, to increase the understanding of patterns of disease groups including cancer diagnoses, joint replacements, fractures and cardiovascular disease. Further information can be found on the website at www.millionwomenstudy.org

Processing:

The cohort participants are already flagged with the HSCIC, and this will be used to link to the HES data.
Data will be used by teams of researchers/statisticians in the Cancer Epidemiology Unit (CEU) at the University of Oxford. The data will be used solely within the CEU and will not be shared with any other organisation.

ATEMPT: Antihypertensive Treatment in Elderly Multimorbid Patients (Pilot Study) — DARS-NIC-414909-M5W6W

Opt outs honoured: Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

Legal basis: , Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2022-05-05 — 2025-05-04 2022.09 — 2022.09.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. HES:Civil Registration (Deaths) bridge
  3. Hospital Episode Statistics Admitted Patient Care
  4. Civil Registrations of Death - Secondary Care Cut
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)

Expected Benefits:

An expected benefit of the ATEMPT pilot study aims to be an improved understanding of how to best manage blood pressure (BP) in older patients, in the presence of many underlying health problems, in particular when BP is not very high, and the effectiveness and safety of changing the number of prescribed antihypertensive drugs.

An additional benefit hope to be to understand the acceptability and tolerability of the intervention (using patient-reported outcomes) and to rule out any major excess harms (risk of serious adverse events) of managing BP in this way.

The findings from this pilot study are intended to provide the feasibility required to inform the planning of a larger, multinational, home-based study to assess the effect of treatment changes on patient-important outcomes. This trial could impact the treatment regime of millions of patients in the UK. The study is designed to minimise the burden of participation to patients. There is no need for clinic attendance. Participation and follow-up will take place at home using a bespoke IT system with much of the data collected remotely.

The experience of using bespoke IT-enabled systems to remotely recruit and monitor participants will, if shown to be effective, be considered for use in the recruitment and study management of other research trials. This could provide the means for other researchers to not only streamline research processes but also include participants in trials who historically are reluctant or unable to participate in research trials due to the demands made of them to attend research visits. For instance, research suggests that the vast amount of research findings are based on studies that have included participants in close proximity to specialised centres, men and those who have fewer comorbidities. This leaves a gap in research for the majority of the population in the UK to whom research findings are being applied. This study hopes to not only encourage participation of such patients but to assess the extent to which conduct of trials can be made more efficient and hence affordable.

Outputs:

The research agenda, plan of investigation and monitoring of the execution of the ATEMPT trial is overseen by a trial steering committee. The trial steering committee is made up of professional members including a Professor of Ageing and Stroke Medicine, a Professor of Cardiology, a GP plus two PPI members, individuals who are able to contribute to the wider public perspective. The main trial results from the ATEMPT Pilot Study are expected in 2022 with a publication towards the end of that year.

The results aim to be disseminated widely, including presentation at relevant conferences such as the European Society of Cardiology annual meeting and publication in an open-access, high-impact medical journal such as the European Journal of Cardiology. Further academic papers (including a protocol paper and results of remote recruitment and management of the trial) will be published in open-access, high impact, peer-reviewed journals and on the trial website.

A non-technical summary of the main study findings will be provided to participants and other interested groups and published on the study website (https://atempt.wrh.ox.ac.uk/).

All outputs will be aggregated with small number suppression applied (as per the HES analysis Guide).

The findings from this pilot study aim to be used to inform and plan an adequately powered major, multi-national Randomised Controlled Trial to start late 2022/early 2023. Additionally, the experience gained from utilising IT-enabled systems to remotely recruit and monitor participants will be evaluated with a view to expanding the use of the software to manage other research trials within the department and wider University. The online system for the ATEMPT trial has been developed in conjunction with members of the public aged 65 years or older in order to ensure that it is as simple and easy to use as possible.

Processing:

METHODOLOGY
1. The University of Oxford will send NHS Digital a cohort of approximately 221 consented individual records via Secure Electronic File Transfer service (SEFT). These specific identifiers will be provided according to when the study participant was consented:
> Cohort participant consented between 19 December 2020 to 24 May 2021 will provide the NHS Number only (approx 67 records), along with Study ID, date of consent and withdrawal date (if applicable).
> Cohort participant consented between 02 July 2021 to present will provide name, postcode, and date of birth (approx 154 records), along with Study ID, date of consent and withdrawal date (if applicable).

This information will be requested in one dissemination of five years and 3 months of data (2017 up to end June 2022).

2. NHS Digital will use the cohort identifiers to identify and extract relevant data from Hospital Episode Statistics (HES) Admitted Patient Care (APC) and Civil Registrations (Deaths) Secondary Care Cut. NHS Digital will then remove all identifiers, leaving the Study ID.

3. NHS Digital will disseminate the record-level pseudonymised files to the University of Oxford via SEFT.

The record-level pseudonymised data from NHS Digital will be stored in the ATEMPT study database and not shared with any other organisations. The Personal Identifiable Data (PID) data the study collected from participants will be stored separately to the study data and the NHS Digital record level pseudonymised data within the study database. Whilst record-level pseudonymised data will be provided by NHS Digital, while the University of Oxford hold the identifiers for the cohort, the data is considered by NHS Digital to be potentially identifiable.

The study team makes every effort to ensure the record-level data from NHS Digital will remain pseudonymised, including using the Pseudo-Study ID to link with the ATEMPT study data. However, the Study Team point out that individuals would be re-identified if it was in the participant's interest, for example, in the case of an adverse event and for safety monitoring purposes. Re-identification would be on an individual basis and data shared only with the study participant.

Data is stored securely by the University of Oxford in a high compliance system (HCS), managed and owned by the University of Oxford, suitable for storing personal and special category data, and data can only be accessed by the ATEMPT Study Team members, who are substantive members of the University of Oxford plus one consultant under an appropriate contract with the University of Oxford, who have authorisation to access the data for the purposes described and have been appropriately trained in data protection and confidentiality.

Statistical analysis of the data will be performed using an appropriate statistical package. This will be carried out either directly in person or remotely via a University of Oxford owned remote device connected to the University's HCS, which requires a username, password and secure two-factor authenticator (Virtual Private Network or VPN). All data analysis will be conducted within the confines of the University’s secure server, and will not be downloaded to remote devices for storage or processing.

The record-level data released by NHS Digital will not be shared with any other organisation or used for any other purpose other than those stated in this agreement. NHS Digital data is not being matched or linked to publicly available data, nor being linked to other data sets held by University of Oxford which are not directly related to the ATEMPT study. The data received from NHS Digital will only be linked to data in the ATEMPT study database.

VIRTUS Holdco Ltd do not access data held under this agreement as they only supply the building for storage of back-up tapes. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. They are therefore not considered a Data Processor.

HES DISCLOSURE CONTROL / SMALL NUMBER SUPPRESSION
In order to protect patient confidentiality, when presenting results calculated from HES record level data, outputs will contain only aggregate level data with small numbers suppressed in line with HES Analysis Guide. When publishing HES data, you must make sure that:
· National-level figures only may be presented unrounded, without small number suppression
· cell values from 1 to 7 (inclusive) are suppressed at a sub-national level to prevent possible identification of individuals from small counts within the table.
· Zeros (0) do not need to be suppressed.
· All other counts will be rounded to the nearest 5.
Data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

MR1460 - OxValve - Survival following a diagnosis of Valvular Heart Disease in a primary care population (OxValve-Survive) — DARS-NIC-135294-P7L0F

Opt outs honoured: No - consent provided by participants of research study, No - data flow is not identifiable, Identifiable, Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (NHS Trust)

Sensitive: Sensitive

When:DSA runs 2019-07-23 — 2022-07-22 2018.10 — 2022.09.

Access method: One-Off, Ongoing

Data-controller type: OXFORD UNIVERSITY HOSPITALS NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. Civil Registration - Deaths
  5. Demographics
  6. Civil Registrations of Death

Objectives:

The main function of the heart is to pump blood around the body. There are four main valves in the heart which ensure the blood travels in the right direction. If the valves become narrowed or leaky, this can mean the heart functions less efficiently.

Valvular heart disease (VHD) occurs when one or more valves does not form properly before birth (congenital) or if they are damaged (acquired) during life. In the developing world, infections such as rheumatic fever are still prevalent and can cause valve damage. In the UK and other developed countries, the most common cause of VHD is degeneration - wear and tear - over time.

When the heart valves don’t work properly, the heart muscle may need to work harder to cope. If valves become narrowed (stenosis), more pressure is needed to force the blood through them, and if they become leaky (regurgitation), more blood flows back into the heart chamber and needs to be pumped out again.

Over time, VHD can cause the heart to stop working properly. Some people may experience symptoms such as breathlessness, chest pain or passing out, while for others, problems with their valves will not cause them any difficulties. The treatment for severe VHD is surgery but this has risks. Identifying people who need to have an operation to deal with symptoms, or prevent future problems, and are well enough to have the procedure, is important.

The burden of VHD in the community population was poorly understood and therefore a cardiology team at the Oxford University Hospital NHS Foundation Trust embarked on finding out how common VHD was in the older population. In 2009, the team started the OxValve study with the aim of screening people age 65 years and over from primary care to determine how many participants had VHD. The study received ethical approval from the Southampton and South West Hampshire Research Ethics Committee (REC ref. no. 09/H0502/58) under consent versions 1 to 7 which was used to recruit 4,009 participants in total between 2009 and May 2016. Each participant underwent detailed examination including echocardiography to establish the presence, and severity, of VHD.

The findings of the first 2,500 participants were reported in the European Heart Journal in 2016. VHD was found in 1,269 participants (51% of the cohort). Most VHD was mild with only 159 participants having a new diagnosis of clinically significant disease (12.5% of those with VHD, 6.4% of the OxValve cohort). The cohort is currently undergoing a 5 year follow up where participants are asked if they are willing to be rescreened.

The long-term outlook for people with VHD is not fully understood. It is not known how long people with VHD, detected at screening, live for and whether they die from heart-related problems or something else. The OxValve-Survive study aims to report the survival rates of people in the OxValve cohort with and without VHD. The study will provide estimates of one, five and ten year survival, and the cause of death.

The cardiology team at Oxford University Hospital NHS Foundation Trust are in working partnership with the Nuffield Department of Primary Care Health Sciences (NDPCHS) at the University of Oxford working with the primary care team at NDPCHS which is leading on the primary care theme of the OxValve programme. Only individuals at the NDPCHS will do the data linkage and analysis for the data received from NHS Digital. The reason for this is that NDPCHS have expertise in survival analysis and experience with mortality linkage. The original OxValve team (at Oxford University Hospitals NHS Foundation Trust) will not be involved in processing the data for this purpose and will have no access to the data in its raw form. Oxford University Hospitals NHS Foundation Trust will only have access to aggregated reports with small numbers suppressed in line with the HES Analysis Guide.

Yielded Benefits:

The findings have been presented at the British Society of Cardiology conference. The OxValve study (at NDPCHS) are currently drafting a manuscript for submission to a peer-reviewed journal.

Expected Benefits:

The number of people with VHD in the community population of the UK was previously unknown. OxValve has given reliable prevalence estimates and is following participants up. However, the number of people in the cohort who have died, and their cause of death, remains unknown. This is important information to understand the natural trajectory of the disease and whether VHD found at screening is associated with higher mortality, or not.

A better understanding of prognosis could help inform patients, clinicians and commissioners. The limited data on survival for people with VHD can make discussions on outlook between patients and clinicians more challenging. Accurate mortality data linked to a well-phenotyped cohort could improve clinicians understanding of likely survival rates and causes of death for people with VHD. Commissioners of healthcare are also likely to be interested in the findings to allow them to provide appropriate surgical and palliative care services for this population.

Identification of risk factors for death in people with VHD may allow targeted treatment of modifiable risk factors. The findings from the study are likely to be relevant to other European countries where the prevalence VHD and risk factors for death are likely to be comparable.

Further scientific benefits include the contribution of the project to future systematic reviews and meta-analyses of risk factors for the prognosis of VHD. The findings of the study may also lead to future randomized controlled trials of treatments, and of interventions aimed to target risk factors, to improve the prognosis of these patients.

Outputs:

The OxValve-Survive study will report the one and five year survival rates of participants with and without VHD, and report the most common causes of death.

The findings will be presented at a relevant conference such as the Annual Scientific Meeting of the Society for Academic Primary Care or the British Cardiovascular Society Annual Conference. The choice of conference will depend on the timing of completion of the statistical analyses and deadline for submission of the abstract. Dissemination of the findings of the project at either of these meetings, will inform frontline clinicians that interact with these patients on a daily basis. This project will help to provide an evidence base to inform decisions that are likely to improve the quality of care for patients with VHD in the UK and similar countries.

The findings of this project will also be published in a peer reviewed scientific journal approximately one year after receiving the mortality data i.e., before the end of 2019. Target journals will include the British Medical Journal, European Heart Journal and the British Journal of General Practice. Depending on obtaining the necessary funding, the aim will be to publish open-access.

The findings of this project will also be disseminated through the OxValve study website, the Nuffield Department of Primary Care Health Sciences website, and through relevant social media channels. Through these platforms, clinicians, academics, media, patients, and the public will be reached.


All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. ONS disclosure rules will be followed.

Processing:

The original OxValve study dataset is held at Oxford University Hospital NHS Foundation Trust. A copy of the full OxValve dataset is also held by NDPCHS at the University of Oxford and this dataset will be linked with the civil registration mortality data via NHS Digital. This contains the data of individuals who consented to participate in the study from 2009 onwards. Individuals recruited in the first year using consent materials version 1.1 did not give sufficient consent for their personal data to be shared with a body such as NHS Digital for the purpose of accessing their mortality data and no data will be requested about these individuals unless they provided additional consent subsequently.

The primary care team at NDPCHS based at University of Oxford will send NHS Digital; name, date of birth, NHS number, and the pseudonymised study ID for all participants who gave sufficiently informed consent. NHS Digital will match and flag the cohort and will return Mortality data (including Date and Cause of death) linked to the study ID. This data will be linked into the main OxValve study database at NDPCHS which only contains the clinical data (i.e. information collected at the screening appointment such as outcomes of ECGs, blood tests and self-reporting information). OxValve identifiers are stored separately. The data will not be linked with any other data and only the linkages described above are permitted under this Agreement.

The data will be stored on a restricted access network drive, with access restricted by password to the authorised user of the data only. Both the PC and network drive are on a secure part of the main University network with EAL4+ compliant perimeter firewalls. The wider University network is monitored and secured by the University OxCERT team. The local network and PCs are operated under the departments Information Security and Information Governance policies.

The data will be used exclusively for the purposes of the study specified hereby at University of Oxford only. The data will not be made accessible to any other 3rd parties, including Oxford University Hospitals NHS Foundation Trust.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract - i.e. employees, agents and contractors of the Data Recipient who may have access to that data).

The Data will only be used for the purposes described in this Agreement.

MR779: ASCEND (A Study of Cardiovascular Events iN Diabetes) — DARS-NIC-302994-C2Q2Y

Opt outs honoured: No - consent provided by participants of research study, Identifiable, Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), Informed Patient consent to permit the receipt, processing and release of data by NHS Digital, Health and Social Care Act 2012 – s261(2)(c)

Purposes: No, Yes (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2019-10-05 — 2022-10-04 2017.09 — 2022.09.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. Hospital Episode Statistics Critical Care
  5. Hospital Episode Statistics Outpatients
  6. Hospital Episode Statistics Accident and Emergency
  7. Hospital Episode Statistics Admitted Patient Care
  8. Civil Registration - Deaths
  9. Demographics
  10. Cancer Registration Data
  11. Emergency Care Data Set (ECDS)
  12. HES-ID to MPS-ID HES Accident and Emergency
  13. HES-ID to MPS-ID HES Admitted Patient Care
  14. HES-ID to MPS-ID HES Outpatients
  15. National Diabetes Audit
  16. Hospital Episode Statistics Accident and Emergency (HES A and E)
  17. Hospital Episode Statistics Admitted Patient Care (HES APC)
  18. Hospital Episode Statistics Critical Care (HES Critical Care)
  19. Hospital Episode Statistics Outpatients (HES OP)
  20. Civil Registrations of Death

Objectives:

The aim of the British Heart Foundation (BHF)-funded ‘A Study of Cardiovascular Events iN Diabetes’ (ASCEND) randomised trial is to determine reliably whether aspirin (100mg daily or matching placebo) and/or supplementation with omega-3 fatty acids (1g capsule or matching placebo) safely prevents cardiovascular events and deaths in patients with diabetes, who do not already have clinically manifest arterial disease. Although aspirin is recommended for people with arterial disease, since it also causes bleeding, the balance of benefits and possible harms are not clear in this group with diabetes.

The study is funded by the British Heart Foundation and the packaged study treatment is provided free of charge by Bayer Pharma AG [100mg aspirin/matching placebo] and Abbott Products Operations AG [1g omega-3 capsule/matching placebo].

15,480 participants were randomized between June 2005 and July 2011 and median follow-up is currently ~4 years with a planned duration of at least 7 years. All serious adverse events (SAEs) are to be recorded among the randomised participants to allow detailed analyses of both the safety and efficacy of trial treatments. In order to reliably record and code reported SAEs during follow-up, as outlined within the study protocol, the team require clinical information about diagnoses and operations, including dates of occurrence. Access to electronic central records of hospital episodes will allow this complete and unbiased follow-up and appropriate intention-to-treat analyses.

The sensitive identifiable fields are required to ensure robust linkage to ensure that the records pertain to the correct participant, which is vital for the accuracy of the study data. The HES data will also provide additional confirmation of some participant reported events. The HES data relevant to the trial outcomes (heart attacks, strokes etc.) will be further followed up via GPs for additional information, and will be reviewed by study clinicians (who remain blind to treatment allocation) with the aim of confirming or refuting events.

Yielded Benefits:

For the on-treatment phase of the ASCEND study, the analyses have shown conclusively that aspirin reduces the risk of vascular events in primary prevention, as it does in people who already have cardiovascular disease, but these benefits are largely counter-balanced by the number of major bleeds caused by aspirin. This is an important finding with implications for many millions of people who have diabetes but have not yet had cardiovascular events. Previous clinical guidelines have varied in their recommendations about the use of aspirin for primary prevention because of a previous lack of clear evidence. The results of ASCEND now provide much needed clarity. The findings are likely to be widely incorporated into future guidelines for the prevention of cardiovascular events in people with diabetes both nationally and internationally. The World Health Organisation statistics detail 422 million people with diabetes in 2014, a rise of almost 400% since 1980. The prevalence of diabetes in the UK and global population is expected to continue to rise.

Expected Benefits:

ASCEND is a high profile trial (already widely referred to in international journals) whose results have the possibility of influencing national and international guidelines for the use of anti-platelet therapy in this very large patient group (currently at least 3M in the UK and over 300M worldwide). Access to the data requested will allow a complete and unbiased analysis of the benefits and hazards of allocation to aspirin and to omega-3 fish oils in the ASCEND study, thus enhancing the reliability of the study findings. The study results are expected in 2018 but any changes to treatment guidelines as a result of the ASCEND trial may take several years to emerge.

Outputs:

The main outputs from the research will be in the form of scientific reports of the results of the trial. If ASCEND can reliably demonstrate that aspirin and/or omega-3 FA safely reduce the risk of cardiovascular events, cancers and deaths in diabetic patients, without pre-existing occlusive arterial disease, this would be relevant to some hundreds of millions of people world-wide currently not receiving such therapy, and could save tens of thousands of lives each year. On the other hand, if the risks of serious bleeding outweigh or are similar in magnitude to the cardiovascular benefits in this group, then these risks could be avoided by the very large number of diabetic patients who are currently being treated with aspirin.

The results will be presented during 2018 at international scientific meetings and published in a prominent peer-reviewed medical journal within about a year. The arrangements are not yet finalised but the international meeting is likely to be one of: American Diabetes Association, European Society of Cardiology or the American Heart Association. The journal is likely to be The Lancet but again not finalised at this stage. In addition the BHF will play a role in the advertising and promotion of the results. The results are likely to be incorporated into an individual participant data meta-analysis of similar trials run by the Anti-thrombotic Trialists Collaboration.

All outputs will consist of aggregate data only with small numbers suppressed in line with HES analysis guide.

In addition to this the two pharmaceutical companies providing the medication and matching placebo for the study and some funds to cover the costs of packaging the treatment have an interest in seeing these treatments (aspirin and fish oils) properly evaluated in a large well run randomised trial. They will receive the same publically available results. If the study results show benefits for diabetic patients, the pharmaceutical companies may wish to use tabular data from the study to seek approval from the regulatory agencies for the marketing of these treatments to this group. The data provided would be aggregated with small numbers suppressed in line with the HES Analysis Guide.

If a submission is made to a regulatory agency, the Clinical Trial Service Unit at Oxford University would provide relevant information in the form of tabulations of numbers (for example: number of participants randomized, experiencing serious or other adverse events) with no individuals being identifiable in any submission. There would be considerable overlap between any tabulations provided to the companies and the published results, however the companies might ask for specific detail that were not considered necessary to publish. No participants will be identifiable in any information provided and the pharmaceutical companies do not have any influence in the research or the results. All data provided to the companies would be aggregate with small numbers suppressed in line with the HES Analysis Guide.

Processing:

Information is collected routinely of the ~15,480 participants who were randomised between June 2005 and July 2011 by postal questionnaire however, over time, some participants stop returning questionnaires and this is more likely among people who have also stopped their study treatments. To minimise bias all participants need to be followed-up irrespective of whether they have been taking their study treatments and all events included in intention-to-treat analyses.

In order to comply with the EU Clinical trials directive, the data received will be transferred into the trial database by the person registered to receive data. The ASCEND trial database is study specific and is stored on an ASCEND specific server. The identifiable HSCIC data is stored in an encrypted TrueCrypt container to which access is granted on a "need to know" basis i.e. the level of access will depend on the staff role. All such access will be granted on the instruction of the Information Asset Owner for ASCEND. Access is routinely reviewed and revoked when the team member leaves ASCEND. The complete supplied HES data will need to be retained for 15 years (as per the ASCEND Protocol, section 2.4.6) as the team need to be able to trace all study medical events (some of which may be identified from HES data) back to source data to comply with the European Medicines Agency guidelines for good clinical practice and have this readily available during any Medicines and Healthcare Products Regulatory Agency (MHRA) inspection.

The ASCEND study team shall not, except as may be strictly necessary for carrying out the project, provide or otherwise make available the HES or ONS data to any third party or allow use of it or them by or on behalf of any third party, in whole or in part, whether by way of sale, resale, loan, transfer, hire or any other form of exploitation. This statement is intended to cover the situation where there may be a need to write to an individual participant’s GP for further clarification about a medical event or death to support the adjudication coding process undertaken by the study medical team. The event/death may have been supplied by ONS/HSICIC although the source of the event would not be supplied to the GP. Clinical trials involving Investigational Medicinal Products (IMPs) are legally required to comply with GCP which is regulated by the MHRA so this phrase also covers MHRA Good Clinical Practice inspections, during which an inspector may see an individual record with a medical event or death recorded which may have been supplied by ONS/HSCIC.

Cause of death data from the Office for National Statistics has been processed by NHS Digital through a separate Data Sharing Agreement and will be assessed separately when the agreement is due for review in March 2017.

Epidemiological and health services research using routine NHS data: work programme of the Unit of Health-Care Epidemiology, Oxford University — DARS-NIC-315419-F3W7K

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2019-10-01 — 2022-09-30 2017.09 — 2022.09.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Office for National Statistics Mortality Data (linkable to HES)
  3. Civil Registration - Deaths
  4. HES:Civil Registration (Deaths) bridge
  5. Civil Registration (Deaths) - Secondary Care Cut
  6. HES-ID to MPS-ID HES Admitted Patient Care
  7. Civil Registrations of Death - Secondary Care Cut
  8. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The Unit of Health-Care Epidemiology (UHCE) was founded in 1963 as a research Unit that, among other activities, undertook research using routinely collected hospital admissions data and mortality data. The Unit’s overall aims, currently, are to undertake epidemiological and health services research, in particular by using routine NHS statistical data and by undertaking studies that use cohort methodologies.

Pseudonymised data is required to support the following work projects :-

1. Trends in admission rates in hospital specialties; trends in admission rates for individual diseases and operations

UHCE is undertaking research into trends in most hospital specialities, and for many individual diseases, distinguishing the extent to which increases or decreases have occurred; distinguishing between episode-based rates, multiple episodes per person, and person-based rates; assessing the extent to which changes represent or go beyond demographic changes in the resident population; profiling changes in the clinical content of specialties’ work and in lengths of stay (including the use of day case care); and, involving clinicians, attempting to explain the trends.

UHCE is also undertaking studies of trends in the use of hospital care by particular demographic groups including children, adolescents, and the elderly. In addition to the study of individual diseases, UHCE will also include studies of medical problems defined by behaviour and aetiology (e.g. self-poisoning in teenagers and young adults; accidental injury), where appropriate studying age and cohort effects as well as period effects.

The overall aim is to undertake a comprehensive study of trends in hospital admission rates in England from the 1960s to the present. The study will serve two main purposes. First, it will provide a detailed understanding of factors underpinning the long-term growth in hospital admission rates in the NHS: hospital admission rates in England have risen seemingly inexorably for decades. Second, it will provide epidemiological insights into trends in incidence and prevalence of diseases that warrant hospital care.


2. Geographical variation in hospital admission rates across England

UHCE have used the data to analyse the distribution of hospital admission rates across England. Where admission rates for a condition vary – particularly for chronic conditions like asthma and diabetes – linked data are invaluable in distinguishing whether the variation is attributable to differences in the number of individuals admitted or in the scale of multiple admissions per person. The geographical units would vary according to the topic (and in particular according to the incidence/prevalence of the condition). For example, local authority level would be appropriate for common conditions such as myocardial infarction, asthma and diabetes; county or regional level would be appropriate for less common conditions such as multiple sclerosis, motor neurone disease or haemophilia. UHCE intend to update the ‘atlases’ of disease across England.


3. Mortality rates for each diagnosis and operation

This data can be used to develop ‘a science of prognosis’. The aim is to study mortality rates following admission for each diagnosis and operation. UHCE will focus on diseases and operations for which there is likely to be interest in long-term trends, using the benefit of the five-decade runs of data. For example, in studies in the former Oxford region UHCE have shown substantial declines in 30-day and 90-day mortality after emergency admission for myocardial infarction and stroke.


4. Natural history of disease: disease associations

The aim is to use patient pathways within the data to investigate associations between diseases and, where relevant, between operations and diseases, to determine the likelihood that, given one clinical condition, other conditions may follow. The work programme will quantify known disease associations accurately; will test hypotheses about suspected associations; and will generate hypotheses about possible hitherto unrecognised relationships between diseases. Associations between diseases may indicate shared genetic susceptibility, e.g. leukaemia and other cancers in people with Down’s syndrome. Clinical conditions may be associated because one may predispose to the other, e.g. ulcerative colitis and large bowel cancer, benign and malignant breast disease.


5. Maternal, obstetric and perinatal factors and subsequent disease

One area of work currently proposed is a study of maternal and perinatal factors and subsequent asthma in the child; others include the study of perinatal factors and cerebral palsy and congenital malformations. The maternal and perinatal factors include, for example, mother’s history of disease, mother’s smoking in pregnancy, child’s birth weight, gestational age, and number of siblings. Similar studies have been done by UHCE on diabetes mellitus and on congenital conditions in the past.

Work is underway to analyse characteristics of the pregnancy and its ‘child’ outcomes for women with a range of diseases (e.g.mothers with diabetes); and to analyse the maternal and pregnancy characteristics of children with a range of diseases (e.g. maternal and pregnancy characteristics of children who develop diabetes, bronchiolitis, cancer, and other child outcomes).


Yielded Benefits:

This database and the research which uses it significantly contributes to the body of evidence and knowledge available which leads to changes in treatment, care and policies which are of benefit to the patient and the health care system. By way of further illustration, the following provides a small sample of publications and the impact they have had. 1. Seagroatt V, Goldacre MJ. Crohn's disease, ulcerative colitis, and measles vaccine in an English population, 1979-1998. J Epidemiol Community Health. 2003 Nov;57(11):883-7 Key point: This study directly influenced official NHS public engagement policy in relation to the measles vaccine. Detail: Publication of this study was followed immediately by a NHS Immunisation Information press release (11 Dec 2003), which stated: “A new study has confirmed that the introduction of measles vaccine in this country played no part in causing Crohn’s Disease and Ulcerative Colitis. The theory that measles vaccine was linked to bowel disease and then autism depended on a belief that measles virus damaged the bowel. This study adds to the available evidence that says that this is not the case.” 2. Martin NG, Iro MA, Sadarangani M, Goldacre R, Pollard AJ, Goldacre MJ. Hospital admissions for viral meningitis in children in England over five decades: a population-based observational study. Lancet Infect Dis. 2016;16:1279-87 Key point: The study was reported directly to Public Health England for use as evidence in communication with the general public concerning the need to maintain the highest possible MMR vaccination levels. Detail: This study of 50-year trends in hospital admission rates for viral meningitis in childhood documented the impact of MMR on viral meningitis and an upsurge in the 2000s when MMR coverage dropped following the Andrew Wakefield scandal; it also documented trends in several other viral aetiologies. 3. Seminog OO, Goldacre MJ. Risk of pneumonia and pneumococcal disease in people hospitalized with diabetes mellitus: English record-linkage studies. Diabet Med. 2013 Dec;30(12):1412-9. doi: 10.1111/dme.12260. Epub 2013 Jul 24 Key point: This study directly informs the position statements of high-profile diabetes charities in the UK. Detail: Following publication of this study, position statement from Diabetes UK: “All people with diabetes over the age of two years should be offered the pneumococcal vaccine.” 4. Goldacre RR. Associations between birthweight, gestational age at birth and subsequent type 1 diabetes in children under 12: a retrospective cohort study in England, 1998-2012. Diabetologia. 2018;61(3):616-625 Key point: High birthweight for gestational age and low gestational age at birth were both found to be significantly associated with subsequent type 1 diabetes. Detail: These findings demonstrated the potential role of gestational and early life environmental risk factors in the pathogenesis of type 1 diabetes, including the potential roles of insulin sensitivity and gut microbiota. 5. Goldacre MJ, Maisonneuve JJ. Mortality from meningococcal disease by day of the week: English national linked database study. J Public Health (Oxf). 2013 Sep;35(3):413-21. doi: 10.1093/pubmed/fdt004. Epub 2013 Feb 1 Key point: This study directly informed the legal debate about the 7-day NHS and the "weekend effect". Detail: This study featured in various mainstream news outlets at the time and was referenced in the High Court judicial review case between NHS junior doctors, the British Medical Association and the Secretary of State for Health in relation to the new NHS contract for junior doctors. The study was described by Mr Justice Green as a "trenchant" piece of evidence. 6. Dharmasena A, Hall N, Goldacre R, Goldacre MJ. Time trends in ophthalmia neonatorum and dacryocystitis of the newborn in England, 2000-2011: database study. Sex Transm Infect. 2015;91:342-5 Key point: This study demonstrated to Public Health England that linked hospital data are the best available data for routinely monitoring the national incidence of newborn conjunctivitis. Detail: The annual figures for this notifiable disease that were reported during the study period under statutory health protection regulations drastically underestimated the actual occurrence of this disease among individuals in hospital (only 1 in 20 cases were reported to Public Health England). 7. Mukhtar TK, Yeates DR, Goldacre MJ. Breast cancer mortality trends in England and the assessment of the effectiveness of mammography screening: population-based study. J R Soc Med. 2013 Jun;106(6):234-42 Key point: This study directly informed the public debate about breast cancer screening. Detail: Publication of this study was immediately followed by an NHS news release, which stated: “There is a great deal of information on both the pros and cons of screening…This study provides additional valuable population data to inform the breast cancer screening debate.” 8. Hallifax RJ, Goldacre R, Landray MJ. Trends in the Incidence and Recurrence of Inpatient-Treated Spontaneous Pneumothorax, 1968-2016. JAMA. 2018;320(14):1471-1480. doi:10.1001/jama.2018.14299. Key point: This study is immediately expected to enable general practitioners and respiratory specialists to make more informed prognoses about the risk of pneumothorax recurrence within specified time periods based on the patient’s comorbidity profile and demographic characteristics. Detail: The study of pneumothorax incidence and recurrence in England has put the current rates of this disease into a 50-year historical context as a matter of public record. The linked national data enabled precise estimations of the likelihood of recurrence based on patient characteristics such as age, sex and the presence of comorbid chronic lung disease.

Expected Benefits:

UHCE’s database and research is unique. Nowhere else can research use hospital data from 1963 to present day to provide such a complete picture of the progress of secondary care and cover such a large percentage of the history of the NHS.

With 50 years’ worth of hospital data, research into areas such as hospital trends, mortality rates, disease history and maternal disease links, that could take years to complete, can be achieved extremely efficiently. This means UHCE can react extremely swiftly to issues arising for the health care system or from patients. For example, in the questions over the ‘week-end effect’, UHCE was able to publish on mortality rates for meningococcal meningitis. There are few other diseases which can act as such a good marker to show the difference between expert treatment or no or suboptimal treatment. The study showed no evidence of an adverse day of the week effect.

This database and the research which uses it significantly contributes to the body of evidence and knowledge available which leads to changes in treatment, care and policies which are of benefit to the patient and the health care system.

There are many examples of benefits; the following provides just a small sample;

1.Hospital Trends:
One of the overall aims is to undertake a comprehensive study of trends in hospitals from the 1960s to now. This will provide policy makers with an understanding of the factors that have led to the seemingly inexorable rise in hospital admissions over the decades, the trends in which diseases are being treated and the changes in mortality rates from these diseases. This programme of research will form a body of publications which will be of benefit to clinicians and policy makers who want to understand impacts such as the use of prevention versus treatment in particular conditions.

There are hundreds of publications using this data for this aim; the following are just a few some examples;
I. Publications on hospital admissions for children
Using this unique database research was able to look at the long term trends in hospital admissions for children with various conditions. In particular to document the beneficial impact of the MMR vaccination when uptake was high, and the negative impacts when MMR coverage dropped. This research was reported into NICE as well as the Department of Health’s Joint Committee on Vaccination and Immunisation in order to add to the information used in polices to reduce disease rates.

II. Public health finding for the Department of Transport
This research looked at the hospital admission rates over time compared with falling police reported incidents. UHCE were able to report back to the Department that the hospital data showed no such fall and advised the department and the Chief Medical Officers that there was a need for closer collaboration with official statistics in order to fully understand the numbers of injuries and fatalities on the roads and so improve road safety.

III. Public health finding on Rickets
UHCE was able to show that admission rates in 2011 were higher than at any time since the 1960s. This publication adds to the evidence and knowledge used by public health policy makers in advising the general public in how to avoid this preventable disease.

2. Geographical Variation:
Another aim is to study geographical variation in hospital rates, some cases to study of the epidemiology of a particular disease or to investigate potential clinical variations in treatment and care. Again, these outputs cover a large amount of time and so are of benefit in adding robust research to the evidence base in these areas for the improvement of patient outcomes. An example of a past publication, which was aimed at ENT surgeons, was about the huge variation in tonsillectomy rates. As this was unlikely to be warranted by epidemiology it was more likely to be variation between clinicians. NIHR went on to use this research as an example of variation which could be used by commissioners and clinicians to identify cost savings.

3. Mortality rates:
There have been many publications on mortality rates with many aimed at clinicians. For example, a study showing the elevated mortality rate following admission for anorexia nervosa adding to the evidence that this can be a life threatening disorder and this was published in journals specifically aimed at clinicians who treat these disorders.
Being able to provide accurate knowledge mortality risk provides benefits in terms of (i) its value in informing clinicians, public health and the public about success (or lack of it) in health-care performance, and (ii) its value in informing clinicians about the likely course of illness and risk in their individual patients.

4. Disease:
It is crucial that clinicians, researchers, patients and public-health policymakers are made aware of important associations between diseases in order to prevent multi-morbidity and to reduce "years of life lost" and "years lost due to disability" through identification of at-risk groups of the population. This crucial database and associated research provides this opportunity. The following are examples of recent UHCE studies in this area;
- Significantly increased risk of primary malignancies in people with non-melanoma skin cancers, particularly the young
- Severe under-diagnosis and under-treatment of cataract and other sight disorders in people with dementia
- Significantly increased risk of dementia in people with obesity
- Significantly increased risk of autoimmune disease in people with vitamin D deficiency, and vice versa
- Significantly increased risk of biliary tract and liver complications in people with polycystic kidney disease

By way of further illustration, the following provides a small sample of publications and the impact they have had.

1. Seagroatt V, Goldacre MJ. Crohn's disease, ulcerative colitis, and measles vaccine in an English population, 1979-1998. J Epidemiol Community Health. 2003 Nov;57(11):883-7
Key point: This study directly influenced official NHS public engagement policy in relation to the measles vaccine.
Detail: Publication of this study was followed immediately by a NHS Immunisation Information press release (11 Dec 2003), which stated: “A new study has confirmed that the introduction of measles vaccine in this country played no part in causing Crohn’s Disease and Ulcerative Colitis. The theory that measles vaccine was linked to bowel disease and then autism depended on a belief that measles virus damaged the bowel. This study adds to the available evidence that says that this is not the case.”

2. Seminog OO, Goldacre MJ. Risk of pneumonia and pneumococcal disease in people hospitalized with diabetes mellitus: English record-linkage studies. Diabet Med. 2013 Dec;30(12):1412-9. doi: 10.1111/dme.12260. Epub 2013 Jul 24
Key point: This study directly informs the position statements of high-profile diabetes charities in the UK.
Detail: Following publication of this study, position statement from Diabetes UK: “All people with diabetes over the age of two years should be offered the pneumococcal vaccine.”

3. Goldacre MJ, Maisonneuve JJ. Mortality from meningococcal disease by day of the week: English national linked database study. J Public Health (Oxf). 2013 Sep;35(3):413-21. doi: 10.1093/pubmed/fdt004. Epub 2013 Feb 1
Key point: This study directly informed the legal debate about the 7-day NHS and the "weekend effect".
Detail: This study featured in various mainstream news outlets at the time and was referenced in the High Court judicial review case between NHS junior doctors, the British Medical Association and the Secretary of State for Health in relation to the new NHS contract for junior doctors. The study was described by Mr Justice Green as a "trenchant" piece of evidence.

4. Mukhtar TK, Yeates DR, Goldacre MJ. Breast cancer mortality trends in England and the assessment of the effectiveness of mammography screening: population-based study. J R Soc Med. 2013 Jun;106(6):234-42
Key point: This study directly informed the public debate about breast cancer screening.
Detail: Publication of this study was immediately followed by an NHS news release, which stated: “There is a great deal of information on both the pros and cons of screening…This study provides additional valuable population data to inform the breast cancer screening debate.”









Outputs:

All outputs will be aggregated analysis, with suppression in accordance with the HES analysis guide. Such data will appear within research papers, academic journals and conference presentations. The presentation format of that data may vary – from individual tabulation, through to geographical presentation through the Atlases mentioned previously.

Such analysis is derived from data provided by UHCE, who receive requests for aggregated, non-sensitive, non-identifiable tabulated data in fulfilment of its research work programme (as detailed above). These tabulations require statistical analysis prior to being provided to researchers, whilst equivalent in format to those provided by NHS Digital (hence could not be provided directly).

UHCE does not solicit requests for tabulations through any web advertising or promotional activity. It does welcome collaborative research work with academic researchers on topics that can be covered by its existing research themes already described in this document. For researchers outside the UHCE but working with the UHCE, the UHCE will only provide aggregated tabulations, not individual-level records. It does not and will not provide UHCE tabulations on request to people outside the UHCE for any purpose other than research.

UHCE have a long track record in published work in the programme of work described in this document. More information can be found here https://www.uhce.ox.ac.uk/uhce/publications.php

The Unit was, historically, very closely associated with the Regional tier of the NHS and with the Department of Health. Although the UHCE is part of Oxford University, it was based on the Oxford RHA’s site from 1963 until the reorganisation of Regional Health Authorities in the mid-1990s. In particular, the UHCE worked closely with the Oxford RHA on medical statistics, record linkage (including the Oxford Record Linkage Study (ORLS), and health services research. From 1998-2005, the Unit had strong service links with the Department of Health’s National Centre for Health Outcomes Development (NCHOD) - the Unit Director, directed the work programme of the Oxford site of NCHOD. As part of the NCHOD work, the DH commissioned the UHCE to construct and analyse English national record-liked HES files, with HES-to-HES linkage and HES-to-mortality linkage, along the lines of the Oxford Record Linkage Study.

The Unit Director was also Co-founder and Scientific Director of the South East England Public Health Observatory from its inception in 2000 until 2005. The UHCE has run a continuous work programme of rolling research, notably using hospital statistics, mortality data, and record linkage, from 1963 to the present. It is part of the University of Oxford’s Department of Public Health (now, as from 2013, the Nuffield Department of Population Health).

Processing:

Only substantive employees of the University of Oxford will have access to the data and only for the purposes described in this document. The University of Oxford will amend this agreement if the requested data is needed for research which does not fall under the themes described here.

Background of the datasets used in these projects:
UHCE undertakes research on hospital statistics, and on mortality, using four different datasets. These are the :-
a. Oxford Record Linkage Study, phase 1 (1963-1998) (ORLS1);
b. the Oxford Record Linkage Study, phase 2 (1989/90-2013/14) (ORLS2);
c. the Hospital In-patient Enquiry for England (1968-1985) (HIPE);
d. Hospital Episode Statistics (HES) for England, 1989/90-2013/14 (provided by NHS Digital)

These four datasets are not linked to one another as individual-level records.

These four datasets constitutes the longest run of hospital data in England. The UHCE dataset for the ORLS spanning 1963-2013/14 is the longest run of hospital data in England at regional level and is the only long-running dataset in England with record linkage going back decades (to 1963).

UHCE holds ONS data which was provided directly by ONS. This will be the first time that UHCE are requesting ONS data from NHS Digital.

In the development and use of large datasets of routine health data, UHCE undertakes original research; it collaborates with others on research projects; and it provides support as required to the NHS, DH and their information functions.

Processing:
NHS Digital will supply pseudonymised HES and the ONS (including month and year of death) data to UHCE via a secure file transfer system.

The data are held in individual SQL databases, subject to individual user control. The HES and ONS national data are processed on receipt so as to have the same database configuration, in order to ‘look like’ the hospital and mortality data in the ORLS. This is done to facilitate the running of the same software across all the databases.

The datasets are all de-identified and are held securely within the UHCE and no individual-level records are ever provided to anyone outside the UHCE.

The datasets are not linked to one another as individual-level records. When there is a need to construct studies based on data that span the time frame of the years covered by the different datasets – typically in, for example, studies of hospital admissions across five decades – the UHCE software packages are invoked to run the analyses within each dataset to produce aggregated statistical results. At the stage of the aggregated statistics, UHCE software templates are used to bring together the tabular results that span individual results from within each dataset – e.g. electronic tables for admission rates in the years 1968-1985, 1989-1998, 1989/90-2013/14 are brought together – into combined tables for the whole period 1968-2013/14.

UHCE have suites of software, developed over many years in the UHCE, which can be used to run the trend analyses in a highly automated way. Researchers do not need to ‘see’ individual level records in order to ‘queue and run’ the software. The UHCE has developed a ‘front end’ to the analytical software such that, for each new run, the member of staff uses a menu. The menu gives the operator a choice of selecting the ICD code and diagnostic code (or equivalent for operations), the required age groups, selection of gender, selection of people, the calendar or financial years required, whether to select the primary diagnosis or all diagnostic fields, whether to select all cases or just electives or just emergencies, and so on. A suite of templates and data manipulation tools then analyses the data in the four files separately (ORLS1, ORLS2, HIPE, HES 1989/90-2013/14) automatically. When the results in each separate dataset have run, another suite of templates and data manipulation tools automatically ‘pulls together’ the results from each set of electronic tables and combines them into tables and graphs giving (seemingly) continuous runs of trends

Similarly, in studies that require data for the full length of the ORLS (1963-2013/14), UHCE software packages are invoked to run the analyses within ORLS1 and, separately, within ORLS2 to produce aggregated statistical results based on the data within each of the two datasets separately. At the stage of the production of the aggregated statistics, UHCE software templates are used to bring together the tabular results that span individual results e.g. electronic tables for admission rates in the years 1963-1998 and for 1999-2013/14 – into a combined table for the whole period 1963-2013/14.

Outputs are therefore all aggregated data, suppressed in line with the HES analysis guide. No record level data may be extracted from the server.

Access to the data is controlled through specific user access controls. Each user has to complete a staff declaration form which ensures that the user is aware of their obligations in relation to the data (eg: to not attempt any re-identification, to use the data solely for the purposes of the individual project).

The information systems used by the Unit of Health-Care Epidemiology are secure and comply with the principles outlined in BS7799 (The Code of Practice for Information Security Management).

All the datasets proposed for use in these studies are pseudonymised. They include no direct identifiers, no NHS number, and the smallest geographical unit associated with each record is that of the person’s Local Authority area of residence (of which there are currently 354 in England).

UHCE have no requirement to re-identify the individuals within the datasets and will make no attempt to re-identify.

UHCE will not share any record level data and all outputs will be aggregated with small numbers suppressed in line with the HES analysis guide.

UHCE will not link the data provided with the dataset described, or any other datasets.

The processing of ONS data is in accordance with standard ONS terms and conditions.

MR1055 - HPS2-THRIVE Treatment of HDL to Reduce the Incidence of Vascular Events — DARS-NIC-147885-0TV66

Opt outs honoured: Y, Identifiable, Yes (Section 251 NHS Act 2006, Consent (Reasonable Expectation))

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No, Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2020-01-01 — 2020-09-30 2016.04 — 2022.08.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration
  4. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  5. Hospital Episode Statistics Admitted Patient Care
  6. Mental Health and Learning Disabilities Data Set
  7. Mental Health Minimum Data Set
  8. Mental Health Services Data Set
  9. MRIS - Flagging Current Status Report
  10. MRIS - Members and Postings Report
  11. Cancer Registration Data
  12. Civil Registration - Deaths
  13. Demographics
  14. Hospital Episode Statistics Admitted Patient Care (HES APC)
  15. Mental Health and Learning Disabilities Data Set (MHLDDS)
  16. Mental Health Minimum Data Set (MHMDS)
  17. Mental Health Services Data Set (MHSDS)
  18. Civil Registrations of Death

Objectives:

Background Large-scale randomized trial to access the clinical effects of a combined daily tablet of niacin 2g plus MK-0524 40 mg (MK-0524A) on the risk of heart attack or coronary death, stroke, or the need for atrial bypass procedures in people with a history of circulatory problems. Aims The study will include 20,000 patients aged 50-80 years, 7,500 from around the UK plus (at least 7,500 from China and 5,000 from Scandinavia) with a history of circulatory problem.

Yielded Benefits:

In any future application, the applicant will be required to provide details of the actual benefits achieved as a result of the study.

Expected Benefits:

In any future application, the applicant will be required to provide details of the expected benefits resulting from the study.

Outputs:

No new outputs will be produced under this Data Sharing Agreement.

In any future application, the applicant will be required to provide details of the outputs that were produced and disseminated by the study as well as details of any future outputs planned.

Processing:

Under this Agreement, the data may be securely stored but not otherwise processed. No new data will be provided by NHS Digital under this Agreement.

The study data, including data provided by NHS Digital under previous agreements, are currently held by the University of Oxford. Under this interim extension all devices containing data will be securely locked away in a locked cabinet at the University of Oxford storage address specified in this Agreement.

The following provides background on the processing activities undertaken for the original study:

Identifying data was shared with ONS to carry out the linkage between the study data and civil registration data. Participants records were ‘flagged’ with the Office for National Statistics (ONS). ONS notified the study team at the University of Oxford of participants’ deaths (date and cause) and cancer events when they occurred. The ‘flagging for long-term follow up’ service transferred from ONS to the HSCIC in 2008. Data was last supplied in September 2016.

MR706 - SEARCH: Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine — DARS-NIC-148341-TC6TD

Opt outs honoured: Y, Identifiable, Anonymised - ICO Code Compliant, Yes (Consent (Reasonable Expectation), Section 251 NHS Act 2006)

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c), National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No, Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2020-01-01 — 2020-09-30 2016.04 — 2022.08.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Members and Postings Report
  3. MRIS - Cohort Event Notification Report
  4. MRIS - Scottish NHS / Registration
  5. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  6. Hospital Episode Statistics Admitted Patient Care
  7. Mental Health and Learning Disabilities Data Set
  8. Mental Health Minimum Data Set
  9. Mental Health Services Data Set
  10. MRIS - Flagging Current Status Report
  11. Cancer Registration Data
  12. Civil Registration - Deaths
  13. Demographics
  14. Hospital Episode Statistics Admitted Patient Care (HES APC)
  15. Mental Health and Learning Disabilities Data Set (MHLDDS)
  16. Mental Health Minimum Data Set (MHMDS)
  17. Mental Health Services Data Set (MHSDS)
  18. Civil Registrations of Death

Objectives:

The data supplied will be used only for the approved medical research project MR706 - SEARCH: STUDY OF THE EFFECTIVENESS OF ADDITIONAL REDUCTIONS IN CHOLESTEROL AND HOMOCYSTEINE

Yielded Benefits:

In any future application, the applicant will be required to provide details of the actual benefits achieved as a result of the study

Expected Benefits:

In any future application, the applicant will be required to provide details of the expected benefits resulting from the study.

Outputs:

No new outputs will be produced under this Data Sharing Agreement.

In any future application, the applicant will be required to provide details of the outputs that were produced and disseminated by the study as well as details of any future outputs planned.

Processing:

Under this Agreement, the data may be securely stored but not otherwise processed. No new data will be provided by NHS Digital under this Agreement.

The study data, including data provided by NHS Digital under previous agreements, are currently held by the University of Oxford. Under this interim extension all devices containing data will be securely locked away in a locked cabinet at the University of Oxford storage address specified in this Agreement.

The following provides background on the processing activities undertaken for the original study:

Identifying data was shared with ONS to carry out the linkage between the study data and civil registration data. Participants records were ‘flagged’ with the Office for National Statistics (ONS). ONS notified the study team at the University of Oxford of participants’ deaths (date and cause) and cancer events when they occurred. The ‘flagging for long-term follow up’ service transferred from ONS to the HSCIC in 2008. Data was last supplied in September 2016.

MR542 - MRC/BHF HEART PROTECTION STUDY — DARS-NIC-148069-ZB4GM

Opt outs honoured: Y, Identifiable, Yes (Consent (Reasonable Expectation), Section 251 NHS Act 2006)

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(2)(c), , National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2019-12-01 — 2020-03-31 2016.04 — 2022.08.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Scottish NHS / Registration
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Hospital Episode Statistics Outpatients
  7. MRIS - Flagging Current Status Report
  8. MRIS - Members and Postings Report
  9. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  10. Cancer Registration Data
  11. Civil Registration - Deaths
  12. Demographics
  13. Mental Health and Learning Disabilities Data Set
  14. Mental Health Minimum Data Set
  15. Mental Health Services Data Set
  16. Hospital Episode Statistics Accident and Emergency (HES A and E)
  17. Hospital Episode Statistics Admitted Patient Care (HES APC)
  18. Hospital Episode Statistics Outpatients (HES OP)
  19. Civil Registrations of Death
  20. Mental Health and Learning Disabilities Data Set (MHLDDS)
  21. Mental Health Minimum Data Set (MHMDS)
  22. Mental Health Services Data Set (MHSDS)

Objectives:

The data supplied will be used only for the approved medical research project MR542 - MRC/BHF HEART PROTECTION STUDY

Yielded Benefits:

The HPS demonstrated that lowering Low Density Lipoprotein (LDL) cholesterol with statins reduces vascular morbidity and mortality, and, as a result of the results published in 2002, such medications are now widely prescribed. For various reasons it has not been possible to analyse HES data supplied previously. Initially, the data were more complex than had been anticipated (particularly given the relatively morbid population included in HPS) and it was difficult to decipher incident (i.e. new) events from prevalent disease. Then, problems with data flow from death registries meant it was not possible to censor the study population. Both these problems have now been overcome, but analysis of the data we currently hold without updating with more recent years would invite speculation from reviewers and readers as to why we had not included all available data.

Expected Benefits:

Millions of people at increased risk of heart disease in the UK and around the world are already taking statins. HPS has shown that a much wider range of patients can gain worthwhile benefits, and following these results statin use increased substantially. Reliable evidence about the long-term effects of cholesterol-lowering with statins is therefore necessary. Extended follow-up of the large numbers of participants in HPS can provide substantially more information about any long-term benefits or hazards of about 5 years of statin treatment than can the other statin trials that have been conducted. Importantly, reassurance about the long-term safety of statins should help to maintain long-term compliance, and so realise the full potential benefit of treatment. Moreover, evidence about the effects on major vascular events after the end of the scheduled treatment period is needed to assess the full cost-effectiveness of about 5 years of statin therapy. Extended follow-up of the surviving participants in HPS will also allow assessment of any delayed effects of the antioxidant vitamin regimen studied.

Outputs:

Several major publications describing the long-term safety and efficacy of 5 years lipid-lowering therapy with simvastatin and, separately, antioxidant vitamins are planned. Details will be provided in a future application to NHS Digital.

All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

Under this Agreement, the data may be retained and processed by the University of Oxford for the purposes of original study but no new data will be provided by NHS Digital.

The following provides background on the processing activities undertaken for the original study:

All HPS study participants are already flagged with NHS Digital, therefore no transfer of data from Clinical Trial Service Unit (CTSU), University of Oxford to NHS Digital is required. Consent was obtained from participants for the main HPS study for the long-term follow-up of HPS participants in 2011.

CTSU has successfully acquired, analysed and appropriately stored data from HES for the HPS and other studies, and is an approved data safe haven which meets the highest standards for data protection.

The identifiable data, already held, is stored in an encrypted TrueCrypt container, to which access is granted on a “need to know basis”, i.e. the level of access will depend on the staff role. All such access will be granted on the instruction of the Information Asset Owner for HPS. Access is routinely reviewed and revoked when the team member ceases to work on HPS. Only personnel involved in the long-term follow-up study for HPS (processing and analysing data) will have access to this data. CTSU has a Corporate Level Security Policy that has been fully adopted by management and will apply fully to the long-term follow-up study.

The HPS study team shall not make available the NHS Digital data to any third party or allow use of it by them or on behalf of any third party, in whole or in part, whether by way of sale, resale, loan, transfer, hire or any other form of exploitation. No data will be accessed outside of the UK.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

The Data will only be used for the purposes described in this agreement.

EMPA-KIDNEY (The Study of Heart and Kidney Protection With Empagliflozin) — DARS-NIC-449860-L0D6W

Opt outs honoured: Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Sensitive

When:DSA runs 2022-05-24 — 2023-05-23 2022.06 — 2022.07.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Demographics

Yielded Benefits:

This is a new Data Sharing Agreement. No data has yet been disseminated and there are as yet no yielded benefits.

Outputs:

The results of this research would be presented at relevant scientific meetings (e.g., the World Congress in Nephrology), in peer-reviewed journals, and as such should influence clinical practice. The main results paper will be submitted to the world’s leading medical journals (e.g., the Lancet and the New England Journal of Medicine (NEJM)). Lay summaries of important results will be provided on www.empakidney.org following appropriate review by ethics committees (where relevant). The REC committee includes lay members. If time allows (there are very short timelines between getting results and needing to distribute these), the PPI group will be involved in reviewing these.

The NDPH contributes widely to health policy, particularly in the area of vascular risk prevention. It contributes to debate with academic papers, conference participation, lectures to the public and advice to government (including NHS Digital).

The EMPA-KIDNEY study team will share outputs via the following listed channels:

- Study website https://www.empakidney.org/
- Open lectures and talks
- Posters
- Press/media engagement and other public promotion of the research (e.g. via the Nuffield Department of Population Health website (https://www.ndph.ox.ac.uk/), or Twitter account (@oxford_ndph).

The University of Oxford aims to issue the first main publication of results by end 2022.

Participants are kept informed about the study via newsletters, Participant Information Leaflets and the study website https://www.empakidney.org/.

All outputs will only contain results in highly aggregated format and as statistical summaries and measures of association. Reports will be in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

Processing:

All organisations party to this Agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by ‘Personnel’ (as defined within the Data Sharing Framework Contract i.e., employees, agents and contractors of the Data Recipient who may have access to that data).

The list of identifiers that the EMPA-KIDNEY study team at the University of Oxford will send to NHS Digital are as follows:

• Study ID (pseudonymised)
• NHS Number
• Date of birth
• Postcode
• Surname
• Forename
• Sex

The data product the EMPA-KIDNEY study team have requested are as follows:

• Demographics (Study ID, Fact of death, Formal Date of Death, Informal Date of Death, NHS Number, Date of birth)

The data being requested includes historic data starting from 1st February 2019 to present to be received by the EMPA-KIDNEY study team as soon as possible, and then one additional drop of the latest data to be received on 11th July 2022.

NHS Digital will return the study ID and the following identifiers to the EMPA-KIDNEY study team for the purposes of validating linkage. This enables the study team to ensure that the linkage process is robust and accurate. These identifiers will not be used for analysis:

• NHS Number
• Date of Birth

The University of Oxford will retain the data until sites have confirmed any deaths that the EMPA-KIDNEY study team report to them. Identifiers received from NHSD will be destroyed after validation of deaths. NDPH will not be destroying the identifiers held directly from participants.

The data will be transferred from NHS Digital to the Data Controller’s (NDPH, University of Oxford) NHS DSP Toolkit compliant environment via the secure electronic transfer system (SEFT). All data will be transferred, handled and processed in agreement with the NHS Digital Data Sharing Framework Contract, and will be subject to Fair Processing requirements. No NHS Digital data will be transferred to BI.

The data received from NHS Digital will be used as follows:

• Information on deaths will be cross-checked with the trial database. If ‘new’ deaths are identified (i.e., deaths not previously reported by the site), the fact and date of death will be reported to sites to assist them with identifying deceased participants and find medical records relating to their death held locally.

• NHS Digital death data will not be entered into the trial database or used for analysis. In the event that an unreported death is identified, the site responsible for the participant will be prompted to check their records (which include access to the NHS Spine) and to report the death into the study database via the web-based system as they would do routinely for deaths reported in other ways (e.g., via a relative or other healthcare provider).

The EMPA-KIDNEY study team request linkage only to the dataset that contains relevant information on deaths, minimised to the cohort recruited during the period of 1st February 2019 to 11th July 2022. Filters can not be applied to the Demographic data set to minimise data from 1st February 2019, however the University of Oxford is only requesting details of deaths for participants who consented after that date. No other data fields are required for participants who are still alive.

All processing of data will be performed within the Nuffield Department of Population Health at the University of Oxford within an NHS DSP Toolkit compliant environment. No identifiable data will be shared other than with associated researchers working on this project, all of whom are substantive employees of the Nuffield Department of Population Health at the University of Oxford.

Access to patient identifiable information is protected by the appropriate authentication procedures (user IDs and passwords). Authentication is only given to personnel with a legitimate need to access the required data. NDPH has a Corporate Level Security Policy that has been fully adopted by management and will apply fully to this study.

Researchers will not link the NHS Digital death data to other datasets.

As part of the consent form, participants explicitly agree to the collection, storage, processing, transfer and use of their personal data as explained in the EMPA-KIDNEY Participant Information Leaflet.

NDPH researchers are experienced in handling confidential and participant sensitive data and have appropriate training in information governance. The NDPH servers are protected against unauthorised external access by an appropriate strength firewall.

All information is stored securely by the University of Oxford and is kept confidential. Access to the computer database is by unique combinations of usernames and passwords and only authorised study personnel can access information about participants. All authorised study personnel are substantive employees of the University of Oxford. The building is secure with authorised swipe card access only. There will be no attempts made to identify participants in any study reports.

EMPA-KIDNEY participants have consented for their personal data (i.e., their name, address, date of birth and medical information) to be accessed by the EMPA-KIDNEY study team, and for these details to be stored securely within the Nuffield Department of Population Health at the University of Oxford.

As detailed in the study documentation, participants give consent for their data to be shared with other parties including central registries, BI, and regulatory authorities. This data sharing will not include the NHS Digital data being requested in this Agreement.

MBRRACE-UK - Delivering the National Maternal, Newborn and Infant Clinical Outcome Review Programme - National Surveillance of Perinatal Deaths — DARS-NIC-359651-H3R1P

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251 NHS Act 2006, , )

Legal basis: Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'., , Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2017-04-01 — 2020-03-31 2017.06 — 2022.07.

Access method: Ongoing

Data-controller type: HEALTHCARE QUALITY IMPROVEMENT PARTNERSHIP (HQIP), HEALTHCARE QUALITY IMPROVEMENT PARTNERSHIP (HQIP), NHS ENGLAND (QUARRY HOUSE)

Sublicensing allowed: No

Datasets:

  1. Birth Notification Data

Objectives:

Purpose 1: Processing for MBRRACE-UK purposes

The Maternal, Newborn and Infant Clinical Outcome Review Programme (MNI-CORP) is a national programme, delivered by the MBRRACE-UK collaboration, which aims to systematically assess quality and stimulate improvement in safety and effectiveness of maternal, newborn and infant healthcare by enabling clinicians, commissioners and policy makers to learn from adverse events and good practice. The purpose of the programme is to (1) monitor, through population surveillance, the frequency of deaths in relation to maternal, perinatal and infant mortality (2) review clinical practice and assess quality of care for women and babies who have died and those who are seriously ill (mortality and morbidity) through confidential enquiries, with the aim of identify factors that can be attributed to suboptimal clinical care, and also examples of good practice.

MBRRACE-UK received from the HSCIC data extracts from the NHS Numbers for Babies (NN4B) dataset (2013 & 2014 births) and the Personal Demographic Services (PDS) dataset (2015, 2016 and 2017 births to date) and needs to retain these copies (to carry out time trend analyses) and also requires further extracts of equivalent annual data going forward to 2021. MBRRACE-UK will link these dataset with statutory birth, stillbirth and infant death notification data supplied under a separate Data Access Agreement with University of Oxford from the Office for National Statistics in order that essential additional data items are available on an individual level, most importantly gestational age at birth and ethnicity (these are not available in the ONS data and can only be obtained from NN4B/PDS data). This linkage process generates the denominator data for the calculation of 'crude' and 'stabilised & adjusted' perinatal mortality rates; individual level data are required for these calculations. The numerator data come from clinical data about perinatal deaths collected directly by MBRRACE-UK from NHS Trusts and Health Boards. The two additional variables derived from PDS data are essential to enable 'adjusted' perinatal mortality rates to be calculated for commissioning and service delivery organisations down to individual hospital level which take into account the risk profile of the population served by those commissioning and service delivery organisations. The risk profile includes high risk pregnancies which are defined by gestational age at birth and the ethnicity of the population served, as well as other risk factors for example maternal age. This enables 'fairer' comparisons of mortality rates between hospitals and organisations which deal with 'high risk' cases, for example tertiary referral centres which have a higher proportion of preterm births compared with smaller 'district general hospital' type hospitals which would refer high risk pregnancies (for example those at risk of preterm birth) to tertiary hospitals. 'Crude' comparisons which fail to take the risk profile of the different patient populations into account lead to spurious conclusions concerning relative mortality rates and variations in outcomes. Data are required at an individual identifiable level to enable both the linkage and adjusted analyses to be performed.

The linkage to the linked ONS/PDS data also allows MBRRACE-UK to identify deaths and missing information which have not been notified directly to MBRRACE-UK and using this information MBRRACE-UK is able to chase up missing cases to collect the relevant clinical information.

Purpose 2: Processing for NNAP purposes

MBRRACE-UK will produce aggregated data with small number suppression, in line with HES analysis guide, and supply it to the National Neonatal Audit Programme (NNAP) based at the Chelsea and Westminster Hospital who are acting as data processors for the National Neonatal Audit Programme under contract to HQIP and the Royal College of Paediatrics and Child Health. This activity is separate to the primary purpose for MBRRACE-UK receiving and processing the data. However, as a consequence of the processing activities involved in MBRRACE-UK’s primary purpose, MBRRACE-UK will produce a dataset that, with minimal additional processing, would meet NNAP’s requirements and thus negate the need for NNAP to duplicate this complex data processing. Further details relating to the use of the aggregated data in support of the aims of NNAP can be found in the latest NNAP Annual Report published on the NNAP website.

http://www.rcpch.ac.uk/improving-child-health/quality-improvement-and-clinical-audit/national-neonatal-audit-programme-nn-3

Yielded Benefits:

The national perinatal mortality surveillance is conducted by MBRRACE-UK in the context that the UK has one of the highest rates of perinatal death (deaths around the time of births which include late fetal losses, stillbirths and neonatal deaths) and infant deaths (deaths from birth to one year of age) in Europe. Recent figures published in the Lancet places the UK 20th out of 28 for highest stillbirth rates in Europe and it has been estimated that had the UK had a similar neonatal mortality rate to the rate in Sweden, in 2013 1,000 fewer babies would have died. The following provides some examples (not an exhaustive list) of the benefits arising from the findings of the MBRRACE-UK programme. 1. The impact of MBRRACE-UK reports on national policy and practice In March 2015 Dr Bill Kirkup published his report of the investigation of perinatal and maternal deaths in the Universities Hospitals of Morecambe Bay – the ‘Morecambe Bay Inquiry’ Report (1). As part of the recommendations of that report it was noted that good information on pregnancy outcomes (including deaths) is a key driver for improvements in the quality of care provided for pregnant women and newborn babies. This is the role of the MBRRACE-UK programme. The findings of the high rates of perinatal deaths in England, the variation between trusts and the results of the Kirkup enquiry resulted in an independent national maternity services review which was launched in March 2015. The findings of this review were published as the ‘Better Birth’ Report in 2016 and MBRRACE-UK surveillance and confidential enquiry findings were highly cited throughout the report as evidence of the need for changes to maternity services to improve the care provided and as a consequence the outcomes for mothers and babies. It was against this background that in November 2015 the Secretary of State for Health announced additional funding for maternity services and the national ambition to reduce the maternal and perinatal mortality rate by half by 2030; this was subsequently redefined in 2016 to achieve this ambition by 2025, with a 20% reduction by 2020. This ambition was re-iterated in the NHS five year forward plan in 2018. MBRRACE-UK provides the mechanism by which the achievement of the ambition can be monitored. It is the role of MBRRACE-UK to monitor progress towards the national ambition and to identify Trusts which are failing to achieve adequate progress. For the MBRRACE-UK second national perinatal surveillance report (published in May 2016), Ben Gummer, the then Parliamentary Under-Secretary of State for Care Quality wrote in his Foreword to the report: “I want to pay tribute to the remarkable academic achievement that is MBRRACE-UK and underline the influence it is now having on the formulation of policy and impact on services. By providing a consistent and robust evidence base on which to take decisions, MBRRACE-UK is already saving lives.” In the 2018 maternal mortality report, reiterated in the 2019 report, we identified the continuing ethnic inequalities in maternal death (there are similar inequalities in perinatal deaths) where women who are Black are five times more likely to die and women who are Asian are over twice as likely to die as a maternal death than their white counterparts. Disseminating this information via our technical report, lay reports and selected information via twitter has led to an enormous amount of policy activity by NHS England/Improvement; the Department of Health and Social Care; the Cabinet office; and groups of individual Black women who have set up their own campaigns, for example the first Black Women’s Maternal Health Awareness Week was run this year organised by the Five X More campaign. 2. The impact of MBRRACE-UK findings on national guidelines and clinical toolkits: Along with NICE, the Royal College of Obstetricians and Gynaecologist (RCOG) are responsible for producing national guidance for care during pregnancy, labour, birth and postpartum. A number of guidelines have been developed or updated as a consequence of MBRRACE-UK findings. For example, two updated RCOG ‘Green-Top Guidelines’ were released with direct relevance to findings reported in the 2014 and 2017 maternal reports: (i) Prevention and management of post-partum haemorrhage (GTG 52) (RCOG 2016a); and (ii) Blood transfusion in obstetrics (GTG 47) (RCOG 2015)). In response to findings of high rates of maternal deaths from sepsis from our reports the UK Sepsis Trust released six new clinical toolkits specifically for women in pregnancy. Tools are available for out of hours/telephone triage, community midwives, pre-hospital/ambulance services, general practice, emergency departments and acute medical units, as well as acute hospital inpatients. Following the publication of the first MBRRACE-UK national perinatal surveillance report in June 2015 NHS England launched the ‘Saving Babies’ Lives Care Bundle’ in March 2016 which was aimed specifically at ensuring Trusts put in place a series of key actions to prevent stillbirths which will also have an impact on neonatal and infant morbidity. The identification in the MBRRACE-UK reports that the majority of perinatal deaths occur in preterm births, when version two of the Care Bundle was released in March 2019, it included a new action and target aimed at the prevention of pre-term birth. As a consequence of the continuing unwarranted variation in perinatal mortality rates between Trusts and the poor quality of local reviews identified in the MBRRACE-UK perinatal confidential enquiries, in 2017 the Department of Health and Social Care commissioned MBRRACE-UK, via the Healthcare Quality Improvement Programme, to develop a national Perinatal Mortality Review Tool (PMRT). Launched in January 2018 the PMRT supports Trusts to carry our robust, systematic reviews of their local perinatal deaths ensuring that every stage of the care of the mother and baby is reviewed from pre-conception through to bereavement and follow-up care. Over 10,000 perinatal deaths have now been through the process of local review using the PMRT, there has been a demonstrable improvement in the quality of reviews conducted and demonstrable improvements in care have been instituted in Trusts as a consequence of their local review findings. Furthermore, as a consequence of the report which is produced following each review bereaved parents are provided with a clearer explanation of why their baby died and any relevant advice and information regarding the care of any future pregnancies they may plan. 3. The impact of MBRRACE-UK findings on service delivery: MBRRACE-UK surveillance data demonstrated that over 60% of all maternal deaths are as a consequence of pregnancy exacerbated medical complications. As a consequence of the MBRRACE-UK findings, in 2017, NHS England committed to developing 12 maternal medicine networks in England. These have now been established with funding to support both training and new posts to develop a hub and spoke model to ensure that pregnant women with medical complications in pregnancy are able to receive consultant level care from obstetric physicians all around the country. 4. The impact of MBRRACE-UK findings on the activities of the regulator: MBRRACE-UK has an ongoing relationship to provide maternal and perinatal data to the Care Quality Commission. This key mortality information is included in CQC inspection packs to support their regulatory activities and visits to inspect maternity and neonatal services. 5. Impacts of MBRRACE-UK findings on local activities in trusts: The first report of national perinatal mortality surveillance by MBRRACE-UK for deaths in 2013, reported for the first time ‘stabilised and adjusted’ perinatal mortality rates for individuals Trusts which enables appropriate comparison of mortality rates across health care organisations, taking into account the fact that some hospitals provide care for high risk women and hospitals care of vastly different numbers of pregnant women each year. This analysis has enabled MBRRACE -UK to not only report the national perinatal mortality rate but also to identify variation in death rates between Trusts. Using comparisons by level of care provided, MBRRACE-UK has identified those Trusts with higher than average mortality rates and published the findings using a traffic light, RAG rating system. For those Trusts with ‘red’ and ‘amber’ mortality rates it is recommended that in addition to reviewing all their perinatal deaths individually using the PMRT they explore system level issues with the delivery of care to identify potentially preventable causes of death. The purpose being to enable them to put actions in place to prevent such deaths in the future. Evidence of action in individual units came from the submission of abstracts to the MBRRACE-UK conference in May 2016 where the second national MBRRACE-UK report was launched. For example, on the back of their review, one small district general hospital introduced a new referral form for antenatal booking to enable risk factors for stillbirths to be clearly identified, so that timely consultant review can be arranged if required and any women meeting the NICE criteria for risk of gestational diabetes have an appropriately timed glucose tolerance test arranged at their dating scan appointment (which ensures that the test is not missed). As a consequence of these and other actions this hospital had seen a reduction in the number of stillbirths over the previous 12 months. (1) Kirkup B. The Report of the Morecambe Bay Inquiry. March 2015. The Stationery Office, London. 2015. [https://www.gov.uk/government/publications/morecambe-bay-investigation-report]

Expected Benefits:

Expected measurable benefits to health and/or social care including target date:
Target date: annual ongoing benefits as below with an end date of 30th September 2021

There are two overarching goals of the MBRRACE-UK programme:

(1) To improve care provided to women during pregnancy and the care provided to their babies following birth; and
(2) To reduce the rate of late fetal losses, stillbirths and infant deaths.

The MNI-CORP programme is commissioned by HQIP on behalf of NHS England. MBRRACE-UK delivers the programme and is responsible for conducting national surveillance of late fetal losses, stillbirths and infant deaths to contribute to national learning to reduce these rates. The MBRRACE-UK team does not have direct responsibility for carrying out any actions which follow from this national learning; our role is to produce the findings and ensure appropriate dissemination to the bodies responsible for changing practice to ensure that the national perinatal mortality rate reduces over time.

The surveillance is conducted in the context that the UK has one of the highest rates of perinatal death (deaths around the time of births which include late fetal losses, stillbirths and neonatal deaths) and infant deaths (deaths from birth to one year of age) in Europe. Recent figures published in the Lancet places the UK 20th out of 28 for highest stillbirth rates in Europe and it has been estimated that had the UK had a similar neonatal mortality rate to the rate in Sweden, in 2013 1,000 fewer babies would have died.

In March 2015 Bill Kirkup published his report of the investigation of perinatal and maternal deaths in the Universities Hospitals of Morecambe Bay – the ‘Morecambe Bay Enquiry’ Report (1). As part of the recommendations of that report it was noted that good information on pregnancy outcomes (including deaths) is a key driver for improvements in the quality of care provided for pregnant women and newborn babies. This is the role of the MBRRACE-UK programme.

The first report of national perinatal mortality surveillance by MBRRACE-UK for deaths in 2013, reported for the first time ‘stabilised and adjusted’ perinatal mortality rates for individuals Trusts which enabled appropriate comparison of mortality rates across health care organisations, taking into account the fact that some hospitals provide care for high risk women and hospitals care of vastly different numbers of pregnant women each year. This analysis has enabled MBRRACE -UK to not only report the national perinatal mortality rate but also to identify variation in death rates between Trusts. Using comparisons by level of care provided, MBRRACE-UK has identified those Trusts with higher than average mortality rates and published the findings using a traffic light system. For those Trusts with ‘red’ and ‘amber’ mortality rates it has been recommended that they review all their perinatal deaths individually to identify potentially preventable causes of death to enable them to put actions in place to prevent such deaths in the future. Evidence of action in individual units has come from the submission of abstracts to the MBRRACE-UK conference in May 2016 where the second national MBRRACE-UK report was launched.

For example, on the back of their review, one small district general hospital has introduced a new referral form for antenatal booking to enable risk factors for stillbirths to be clearly identified, so that timely consultant review can be arranged if required and any women meeting the NICE criteria for risk of gestational diabetes have an appropriately timed glucose tolerance test arranged at their dating scan appointment (which ensures that the test is not missed). As a consequence of these and other actions this hospital has seen a reduction in the number of stillbirths in the past 12 months.

There has also been action at national level. Following the publication of the first MBRRACE-UK national report in June 2015 NHS England launched the ‘Saving Babies’ Lives Care Bundle in March 2016 which is aimed specifically at ensuring Trusts put in place a series of key actions to prevent stillbirths which will also have an impact on neonatal and infant morbidity.

It is against this background that on the 13th November 2015 the Secretary of State for Health announced additional funding for maternity services and the national ambition to reduce the perinatal mortality rate by half by 2030 with a 20% reduction by 2020. It is the role of MBRRACE-UK to monitor progress towards this ambition and to identify Trusts which are failing to achieve progress. For MBRRACE-UK’s second national report (published in May 2016), Ben Gummer, the then Parliamentary Under-Secretary of State for Care Quality wrote in his Foreword to the report: “I want to pay tribute to the remarkable academic achievement that is MBRRACE-UK and underline the influence it is now having on the formulation of policy and impact on services. By providing a consistent and robust evidence base on which to take decisions, MBRRACE-UK is already saving lives.”

(1) Kirkup B. The Report of the Morecambe Bay Investigation. March 2015. The Stationery Office, London. 2015. [https://www.gov.uk/government/publications/morecambe-bay-investigation-report]

Outputs:

Purpose 1: MBRRACE-UK outputs

1A. Data processing by MBRRACE-UK of the 2013 birth notification data has resulted in findings which have been included in Trust level reports which were issued to Trusts/Health Boards in autumn 2015. Findings were also reported in peer-reviewed scientific outputs reporting the methods and results from the analyses. Findings were also published and findings which were published in the 'Perinatal Mortality Surveillance Report - UK Perinatal Deaths for births from January to December 2013' issued on 10th June 2014:
[https://www.npeu.ox.ac.uk/downloads/files/mbrrace-uk/reports/MBRRACE-UK%20Perinatal%20Surveillance%20Report%202013.pdf].

1B. Data processing by MBRRACE-UK of the 2014 birth notification data has resulted in findings which were included in the 'Perinatal Mortality Surveillance Report - UK Perinatal Deaths for births from January to December 2014' which was been issued on 17th May 2016.; this was accompanied by further relevant scientific reports of methodological developments and further in-depth analyses. The data were also be used to generate Trust/Health Board Level reports for issue to Trusts/Health Boards a week before the public release of the national report..

1C. Data processing by MBRRACE-UK of the 2015 birth notification data has resulted in findings which have been included in the 'Perinatal Mortality Surveillance Report - UK Perinatal Deaths for births from January to December 2015' which will be issued on 22nd June 2017. As the third set of analyses we have been able to conduct this report also includes for the first time trend data using the 2013 and 2014 data. The national report will be accompanied by scientific reports of relevant methodological developments and further in-depth analyses. The data are also being used to generate Trust/Health Board Level reports for issue to Trusts/Health Boards on 15th June 2017.

1D. Data processing by MBRRACE-UK of the 2016 birth notification data is underway in preparation for the production of the national report, local Trust/Health Board level reports and scientific peer-reviewed papers. The national and local reports will be issued in May 2018. As well as reporting the 2016 data these outputs will also include trend data incorporating the 2013, 2014 and 2015 data.

1E. Future data processing by MBRRACE-UK will continue for the 2017, 2018 , 2019, 2020 and 2021 births as per the format above with the outputs being the national annual report (including time trend data), the local reporting and scientific peer-reviewed papers. The national and local reports will be issued from May 2019 onwards.



Purpose 2: Outputs for NNAP

2A. Data processing by MBRRACE-UK of the 2013 and 2014 birth notification data to generate aggregated tables of live births by gestational age by hospital by year will carried out to enable NNAP to further analyse the audit measure published in the NNAP report for 2013 and 2014 reports.

2B. Data processing by MBRRACE-UK of the 2015 birth notification data to generate aggregated tables of live births by gestational age by hospital by year will be used in the production of audit measures for the measures generated by NNAP in the 2015 report.

2C. Data processing by MBRRACE-UK of the 2016 birth notification data to generate aggregated tables of live births by gestational age by hospital by year will be used in the production of audit measure for the NNAP report for 2016 which will be issued by NNAP in October 2017.

2D. Data processing by MBRRACE-UK of the 2017, 2018 , 2019, 2020 and 2021 births as per the format above will continue to support the NNAP outputs.

All national outputs (for Purposes 1 and 2) are aggregated with small numbers suppressed adhering to the HES analysis guide. The local MBRRACE-UK Trust/Health Board level reports do not involve small number suppression because we are giving the Trusts/Health Boards back information about the cases they were responsible for caring for and reporting to us so they already know how many cases there are together with all their individual clinical characteristics.

Processing:

Purpose 1: Processing for MBRRACE-UK purposes

The birth notification data are stored on the National Perinatal Epidemiology Unit (NPEU) secure high compliance servers which are used solely for MBRRACE-UK data processing activities.

The servers are accessed only at the National Perinatal Epidemiology Unit (NPEU), University of Oxford. Processing of ONS and NN4B/PDS birth notification identifiable data occurs in the high compliance area.

The birth notification data are linked to the ONS births and stillbirths data in order to add the key variables gestational age and ethnicity to the ONS births/stillbirths information. Once a combined ONS/NN4B or PDS birth notification dataset has been generated and cleaned the clinical data on late fetal losses (in utero deaths 22-23 completed weeks' gestation), stillbirths (in utero deaths 24+ weeks' gestation) and neonatal deaths (0-27 days after birth) collected by MBRRACE-UK are linked to the combined ONS/birth notification dataset.

The identifiable dataset is stored only on the secure NPEU servers. Cleaning and analysis take place in two locations. The MBRRACE team based at University of Oxford link and clean the perinatal data and generate items such as index of multiple deprivation (IMD) using the postcode data.

An extract dataset containing a limited number of identifiers (referred to here as partial identifiers) is transferred to the MBRRACE-UK analysts at the University of Leicester using the University of Oxford secure electronic data transfer mechanism Oxfile. This dataset extract has all identifiers other than the babies dates of birth and dates of death removed. These identifiable data items remain as they are required to generate date-dependent variables by the MBRRACE-UK analysts at the University of Leicester during the course of analysis. All other Identifiable data items are removed before transfer (e.g. name, address, postcode, NHS number etc).

Purpose 2: Processing for NNAP purposes

MBRRACE-UK will produce aggregated data with small number suppression, in line with HES analysis guide, and supply it to the National Neonatal Audit Programme (NNAP).

Models of Resilience – Covid-19 and Non-Covid-19 Contexts — DARS-NIC-378657-B8F3K

Opt outs honoured: Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information',

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-06-24 — 2024-06-23 2021.12 — 2022.06.

Access method: Ongoing

Data-controller type: UNIVERSITY OF BIRMINGHAM

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. Emergency Care Data Set (ECDS)
  3. HES:Civil Registration (Deaths) bridge
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Hospital Episode Statistics Critical Care
  7. Hospital Episode Statistics Outpatients
  8. Civil Registrations of Death - Secondary Care Cut
  9. Hospital Episode Statistics Accident and Emergency (HES A and E)
  10. Hospital Episode Statistics Admitted Patient Care (HES APC)
  11. Hospital Episode Statistics Critical Care (HES Critical Care)
  12. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The University of Birmingham is requesting data from NHS Digital in order to help them to determine the impact of hospital-level variation in organisational and clinical approaches to acute care delivery at the hospital/community interface during waves of COVID 19 (e.g., prescribing strategies, staff redeployment, integrated community care planning, etc.) on indicators of healthcare resilience such as (a) operational outcomes (e.g. acute care flow, discharge rates), (b) clinical outcomes for COVID-19 related conditions (e.g. mortality, readmission, rates of pulmonary embolism), and (c) clinical outcomes for non-COVID-19 health conditions (e.g. rates of new onset heart failure, stroke). The data requested will make it possible to study five comparative periods of analysis: (i) Pre-COVID-19, no winter pressures, (ii) Pre-COVID-19, winter pressures, (iii) COVID-19 outbreak peaks, (iv) Post-COVID-19 peaks, and (v) Concurrence of COVID-19 and winter pressures (season 2020-2021).

The surge of COVID-19 has had a profound impact on the management and delivery of acute healthcare. To tackle the epidemic, trusts have redesigned organisational models with changes in processes of assessment and care delivery, redeployment of staff, new pathways of care, and different prescribing strategies. These changes have been implemented to provide a rapid increase in acute care assessment and treatment capacity across a system of care for patients with COVID-19-related symptoms, whilst also trying to maintain delivery of care for patients with non-COVID-19 healthcare needs.

The purpose of this agreement is to determine the optimal design of the acute care interface with the community, by correlating hospital-level care delivery approaches elicited by the Society for Acute Medicine Benchmarking Audit data (SAMBA) and hospital and patient outcomes from HES data before, during, and after the COVID-19 periods.

The data requested will support the achievement of the aim of the project through the construction and analysis of indicators of hospital and healthcare resilience, which is defined as (1) the ability to deliver acute care for COVID-19, and (2) the ability to provide standard care for non-COVID-19-related conditions that can present with acute complications. Examples of resilience indicators include readmission rates, length of stay, mortality, intensive care unit admission rates, number of specialist visits, number of elective and emergency hospital admissions (for COVID-19), rates of heart failure (for non-COVID-19-related conditions).
The datasets from NHS Digital will allow the University of Oxford (University of Birmingham's sole Data Processor) to construct indicators of hospital resilience for COVID-19, and non-COVID-19-related conditions that can evolve and develop complications that require acute care (such as, e.g., heart failure, stroke, cancer) by:
- Following patients across different types of health services that they use before, during, and after COVID-19 outbreak periods;
- Accounting for multiple episodes of hospital attendance/admission and study readmissions for COVID-19 and non-COVID-19-related symptoms;
- Estimate out-of-hospital mortality for patients using data from the Civil Registry (Deaths) - Secondary Cut.
The requested data (years 2018 to 2021) will allow the data processor (University of Oxford) to study five comparative periods of analysis:
(i) Pre-COVID-19, no winter pressures
(ii) Pre-COVID-19, winter pressures
(iii) COVID-19 outbreak peaks
(iv) Post-COVID-19 peaks
(v) Possible interactions between COVID-19 and winter pressures (season 2020-2021).
Due to the novel setting and disease that this project studies, and the as yet unknown COVID-19 and non-COVID-19-related medical complications that the current pandemic may cause, there is a major exploratory element to this study. The uncertainty related to the object of investigation requires access to multiple sources of data such as HES critical care, A&E, Outpatients and Inpatients, emergency care (ECDS), as well as the civil registry of deaths (secondary cut). The data processor, the University of Oxford, will use the pseudonymised code provided by NHS Digital to follow patients across the different NHS Digital products that are requested in this agreement.
The University of Oxford will use the hospital code in HES to complement the analysis with information at the hospital and catchment area level from publicly available datasets and the Society for Acute Medicine's SAMBA survey of practice, which provides information regarding the size and staffing organisation of each acute medical department in the UK, alongside strategies for care delivery as well as methods of interaction with community care providers. In particular, the project will use SAMBA data from the 2018 and 2019 Winter version, and the 2020 COVID-SAMBA survey.
The Society for Acute Medicine (SAM) is the national representative organisation for acute health care staff. Formed in 2000, the Society now has over 1000 affiliates, the majority of which are doctors training or specialising in acute medicine. SAM delivers annual SAMBA audits to assess acute medicine approaches and the sharing of good practices. These are England-wide surveys at the hospital level and, in the UK, they are recognised by the Healthcare Quality Improvement Partnership.

The study that is subject of this agreement is part of a broader project, which has three operational tiers:
(i) The first part includes literature reviews, engagement with stakeholders and a survey of healthcare delivery practices of UK acute medicine units at the hospital level, based on the Society for Acute Medicine Benchmarking Audit (SAMBA). This part of the project will not use NHS Digital data.
The Principle Investigator (PI) of the overall project is an active member of the SAM (Society for Acute Medicine) network, has delivered three previous national surveys through the SAMBA network, and has published peer reviewed papers analysing key points from previous audits.
(ii) The second part of the project is the empirical analysis of hospital resilience based on the NHS Digital data that the University of Birmingham (Data Controller) is requesting in this agreement. This part will rely on developing quantitative econometric analyses of indicators of healthcare resilience for COVID-19 and non-COVID-19 diseases with acute complications constructed from the HES data. Examples of indicators of healthcare resilience include mortality rates, readmission rates, rates of pulmonary embolism, average length of stay in intensive care units, rates of new onset heart failure or stroke, rates of A&E attendances and emergency admissions for heart attack and stroke/transient ischaemic attack (TI), during and after the first COVID-19 wave.
Hospitals will be grouped by common approaches to organisation of care from the SAMBA survey (see (i) above). The trust/hospital/deliverer-level variables that describe care delivery approaches elicited from SAMBA will constitute the main explanatory variables. The analyses will control also for patients' demographics and comorbidities, and data on pre-COVID-19 organisational practices and healthcare needs of the patients and of the population in the trust/hospital/deliverer catchment area. The analysis will deliver aggregate-level results that do not identify individuals, and the publications will not identify hospitals. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide.
(iii) The third part of the overall project will develop a qualitative study to learn about healthcare seeking behaviour among patients with non-COVID-19 severe disease. For example, this part of the project will develop focus groups to understand the reasons behind the reorganisation/postponement and delay of diagnoses (e.g., for cancer-related screenings and the screening and treatment of heart failure). This final part of the project will also include qualitative work based on site visits (or remote interviews) in well performing systems of care, to understand how novel structures and organisational contexts were successfully implemented and embedded. This part of the study will not involve analyses of NHS Digital data nor any linkage to NHS Digital data.

The analysis will control for underlying health conditions, healthcare needs, and characteristics of the population in acute care units and in their catchment areas. Information on different care delivery approaches at the hospital level will be elicited from a national survey of organisation and delivery of acute care, the Society for Acute Medicine Benchmarking Audit (SAMBA). The SAMBA dataset is described below. Importantly, SAMBA contains information at the hospital level and does not entail patient-level linkages.

The findings from this programme of research will enable policy makers within the Department of Health and Social Care and NHS England to determine how best hospitals and community systems should organise and deliver care during and after waves of COVID-19.

The GDPR legal basis for processing data for this research comes under Article 6(1)E – "task in the public interest" The data processing will provide evidence to help (a) policymakers make evidence-based policy decisions, (b) hospital managers to develop evidence-based decisions on the organisation of acute medical services, and (c) acute care clinicians to understand which practices have improved the resilience of acute care services.
The public interest that justifies the processing of this data relates to the improvements that can be made to health-care provision within the NHS as a result of the findings. The University of Birmingham is proposing to process data under point (j) of Article 9(2).

The 2020 version of SAMBA for COVID-19 (COVID-SAMBA) collects hospital-level information about variations in organisational and care delivery approaches during the COVID-19 outbreak, such as the degree of integration across acute/community healthcare providers (e.g., discussion of guidelines and common planning for the referral and management of patients with ambulance services, primary care providers, and care homes), novel care pathways (e.g. prescription and patient screening strategies, staff redeployment), and novel structures/systems of care (e.g. home-based hospitalisation).
With regards to the analysis for which the University of Birmingham is requesting access to NHS Digital data, the SAMBA surveys will provide information at the hospital level on care delivery approaches. SAMBA data will be linked to HES data using hospital site codes and not at the patient level.

After the onset of the current pandemic, the Department for Health and Social Care (DHSC) asked the project team to analyse pressures as a consequence of COVID-19, as this is an overwhelming national priority in acute care. In particular, following the research focus commissioned by the DHSC, this project defines the concept of hospital resilience as the ability to meet the acute healthcare needs of the population during COVID-19. The researchers will assess which organisational and care delivery practices are associated with improved healthcare delivery performance.

Other parts of the study, which are not based on the requested data and do not include data analysis, involve literature reviews, consultations with stakeholders, health professionals and patients, and qualitative work in a sample of acute hospitals. The University of Birmingham’s data processor, the University of Oxford, will process the NHS Digital data received and conduct a quantitative analysis for this project.

The University of Birmingham holds the main NIHR research contract for the overall study and has entered into an honorary contract with the Chief Investigator (CI). The CI will formulate hypotheses to be tested and help to interpret the findings of the overall study. Hence, the University of Birmingham is the Data Controller. It will not, however, be involved in processing the data. In its capacity as the University of Birmingham’s data processor, the University Oxford team will hold and process the NHS Digital data. The University of Birmingham are determining the means and purpose of the processing of the personal data and the University of Oxford are providing their expertise as the data processor but have no role in determining the means and purpose of the processing. The University of Warwick, where the CI now resides, will not be involved in any decisions about the data nor the analysis of the data.

The University of Leicester employs the researchers undertaking the qualitative component of the wider study, that is the third part of the study as described above. The University of Leicester team will not access, process nor control the data.

Department for Health and Social Care (DHSC) has no role in the conduct of the study. It is providing the funding (through the NIHR) and will receive the outputs. It is not involved in deciding which analyses should or should not be conducted.
The overall project, including the collection of SAMBA data and the cross-mapping of SAMBA with HES data, received ethical approval.

Since the study is not an evaluation of a specific intervention, the research approach is not based on a distinction between treated and control groups. Rather, the empirical design relies on correlations between hospital-level care delivery approaches and health outcomes. More specifically, the analysis will correlate indicators based on patient clinical information, mortality data from civil death registry with hospital-level indicators that identify relevant elements in the organisation of acute care delivery during and after COVID-19 outbreaks, elicited from the SAMBA survey.
As the data processor on behalf of the University of Birmingham, Oxford will inform the analysis using data from all attendances at A&E specialist or outpatient clinics or admissions between January 2018 and September 2021. This project requires information on all patients attending/admitted to the hospital, with information on the referral status, the cause of attendance/admission, inpatient/outpatient visits and outcomes, the length of stay, and the clinical health outcome for each episode/service use. The analysis will be conducted with pseudonymised data and no individual patient data will be released.
The purpose of this project is to understand which acute care delivery approaches developed and implemented before, during and after COVID-19 outbreaks translate into better acute care and health outcomes for the population, and to identify the practices best able to make hospitals more resilient when there is an outbreak of a disease such as COVID-19 or the winter flu. Combining SAMBA and HES data will allow the data processor, the University of Oxford, to achieve this aim. While SAMBA contains all the information relating to the processes of care implemented by English hospitals, HES data make it possible to investigate how these processes affect patients and hospitals.
This project requires data from the following data sets:
Emergency Care Data Set (ECDS)
Hospital Episode Statistics Accident and Emergency (HESA&E), non sensitive data
Hospital Episode Statistics Admitted Patient Care (HESAP), non sensitive data
Hospital Episode Statistics Critical Care (HESCC), non sensitive data
Hospital Episode Statistics Outpatients (HESO)
HES: Civil Registration (Deaths) - Secondary Care Cut link
The ECDS data requested is not currently within the TRE dat offering and thus this request can not at this point in time be fulfilled by the NHS Digital TRE service.

Using the pseudonymised identifiers provided by NHS Digital to bridge the products requested, the data analysis will connect patient's admission episodes across the HES products (inpatient, outpatient, critical care) and with (i) readmissions, and (ii) out-of hospital mortality (through the Death Civil Registry). As the data processor, the University of Oxford will analyse this information also in conjunction with hospital-level care delivery approaches from the Survey of Acute Medicine Benchmark Audit (COVID-SAMBA and 2019, 2020 SAMBA - please see point 4 of this section and the attached documents for a description) and with aggregate metrics of general health and population characteristics in the acute department's catchment area from publicly available data sources.

Due to the as yet unexplored and as yet unknown context of a novel disease outbreak, and because this project studies how COVID-19-related care as well as non-COVID-19-related conditions relate to different healthcare provision approaches, information on all symptoms and causes of hospital admission is necessary. As features of acute illness are often non-specific (e.g. confusion, generalised functional decline, reduced mobility among older adults), the project requires all available health information without restriction to specific conditions. In addition, there is no guidance yet as to which groups of patients have had fewer admissions due to COVID-19 and its overall effect on hospitals' ability to deliver care: therefore looking at all hospital admissions is the most inclusive and correct approach.
Hospital Episode Statistics Accident and Emergency data will allow the data processor, the University of Oxford, to identify whether patients that attend A&E are discharged or admitted, and to classify the cause of attendance (COVID or non-COVID related).
HES A&E (and the ECDS, once a code will be developed), HES-Outpatient, HES-Inpatient, HES-Critical Care will make it possible to:
- Follow patients that attend A&E/ the hospital/ trust in the subsequent stage (i.e., inpatient / outpatient / discharged), record their process of admission and outcome (e.g., length of stay and clinical health outcome);
- Control for the utilisation of primary care before and after an acute illness that requires A&E attendance or
inpatient/outpatient admission;
- Construct and correlate indicators of acute care resilience with organisational changes and care delivery practices during and after COVID-19 outbreaks (from the SAMBA hospital-level data).
Civil Registration Deaths - Secondary Cut will allow the data processor at the University of Oxford to link attending/admitted patients with out-of-hospital mortality outcomes.
In particular, the University of Birmingham is requesting the following groups of variables:
- Admissions - Period of care (e.g., method, source, date, waiting time) to control for different circumstances and procedures of admission in the analysis of the correlation between hospital-level acute care delivery approaches and average health outcomes, and group patients’ health outcomes by heterogeneous characteristics;
- Augmented/critical care period variables, with information such as time, period, outcome, source, discharge, status, intensive care, high dependency of patient’s admission episodes, to construct outcomes for the analysis (e.g., average time in intensive care, mortality, probability of high dependency case), controlling for further clinical and admission characteristics;
- Clinical information with date of operation, cause of admission, primary and additional diagnosis codes, operation status, and durations, to control for these elements in the analysis, construct health outcomes by specific circumstances/causes/etc. of admission, and duration of the episode(s);
- Clinical information regarding patient classification and consultant/treatment specialty, and Practitioner/Referring organisation codes, to categorise patients’ health outcomes according to specific treatment groups either by own classification or consultant specialty or practitioner;
- Diagnosis codes and Alcohol Attributable Fraction;
- Discharge dates and methods (and flags), to control for length of admissions and cross-validate precision of the duration, and study time lags between readiness for discharge and actual discharge, and their trends before, during, and after peaks of acute care activity;
- Episodes and spells (Period of care) data, such as dates, durations, types, ward types, and Patient Pathway information, to form groups of similar episodes and to control for such characteristics in the analysis of the correlation between care delivery approaches and health, mortality, and readmission outcomes;
- Geographical codes (e.g., CCG, area, region, site code of GP practice, treatment, residence areas, ONS electoral ward codes), Healthcare resource groups (HRG), Organisation codes/information, and Socio-economic indicators (location-based IMD indexes), to control for/group health outcomes by locations, and associate health outcomes to other local-level information from publicly available data at the trust/catchment area level;
- Patient demographic data, to group patients by categories or control for patient characteristics in the empirical analysis of the correlation between hospital-level care delivery approaches and indicators of health care resilience from patient health outcomes;
- System Data to verify validity of assignment of patient/CDS/SUS codes.
The specification of a COVID-19 diagnosis for patients will be based on the ICD-10 code.
This project only requires pseudonymised data, because the analysis will follow patients in the different services/units. The analysis requires patient level records to analyse health outcomes by different patterns of use of the healthcare services and to be able to group/control for demographic characteristics, waiting times, diagnosis, procedures, etc. Furthermore, patient record data will allow the empirical estimations to follow patients/episodes of care across the various NHS Digital products such as, e.g., deaths registry data, outpatients, etc., to measure healthcare outcomes, before, during and after the pandemic. The University of Birmingham does not request any identifiable or "high risk" variable, and the estimation outcomes and findings of this project will be produced solely in aggregate form, with small numbers suppressed in line with the HES analysis guide.

Nonetheless, the results of the quantitative analyses will only be included in the study outputs and communicated at an aggregate level. There will be no way to identify individual or critically small/selected groups of people from the results of the study all outputs will be aggregated in line with the HES analysis guide. The estimations will only deliver coefficients of correlation between care delivery practices and aggregate categories of health outcomes and indicators (e.g., total A&E admissions, mortality rate, total admissions in cardiology, ICU admissions, average length of stay by non-identifiable demographic characteristics such as age groups).
This data request is limited to the years between 2018 and 2021 inclusive.
This will allow the University of Birmingham to study five comparative periods of analysis:
Pre-COVID-19, no winter pressures (2018-2019, spring-summer)
Pre-COVID-19, winter pressures (2018-2019, winter)
COVID-19 outbreak peaks (2019-2020 winter and spring)
Post-COVID-19 peaks (e.g., July-August 2020)
Possible interactions between COVID-19 and winter pressures in the winter season of 2020-2021.

The quantitative analysis will compare the outcomes of patients in different hospitals and acute care units across England and, as such, it needs data concerning all English hospitals.
There exists no possibility other than via HES to construct and analyse variables that are based on following patients across different units of care, multiple episodes of admission, and out-of-hospital mortality at the hospital/acute care unit aggregate level. This project requires patient-level information also to account for patients' demographic characteristics, and prevalence of as yet not know preconditions and co-morbidities in the reference population that may contribute to determining the success and failure of hospital/acute care unit care delivery organisational approaches and practices in terms of both COVID-19 and non-COVID-19 related care.

The University of Birmingham has minimised the request in the time dimension. In particular, the required data is limited to the years between 2018 and 2021, ending with the release of September 2021.
Due to the exploratory nature of the project and as yet unknown consequences of COVID-19 and care delivery approaches during the current pandemic, the request is not restricted to specific health conditions and causes of admission. The aim of this proposal makes it necessary to request and explore individual-level data because this study is the first of its kind, and the context of the COVID-19 pandemic is as yet unexplored. This analysis will request and explore all possible conditions, causes of admission, and demographic characteristics. It is not possible to pre-aggregate and request health outcomes at the hospital level. This is required to understand the pathways of each individual in the use of the health system, in response to the COVID-19 pandemic, and to group outcomes by (or control for) demographic characteristics, waiting times, diagnosis, and procedures in the analysis.

The ethnic category variable is requested because there is evidence that people from BAME communities are the most affected by the COVID-19 pandemic and the analysis needs to control for this factor. This project requires only pseudonymised data and the request does not include any identifiable or "high risk" variable. The results of the quantitative analyses will only be communicated and included in the study outputs at an aggregate level, further suppressing critically small/selected groups of people.

The request is further minimised by excluding data concerning maternity and psychiatry.

The University of Birmingham is the sole Data Controller. University of Birmingham are determining the means and purpose of the processing of the personal data and the University of Oxford are providing their expertise as the data processor but have no role in determining the means and purpose of the processing. The University of Oxford operates under specific protocols for processing of data directed by the University of Birmingham.

The University of Warwick is not carrying out joint data controllership activities, in light of the Chief Investigator holding an honorary contract with the University of Birmingham, but being a substantive employee of the University of Warwick. The University of Birmingham will remain the only Data Controller, according to its original contract with DSHC and NIHR . University of Leicester employs the researchers undertaking the qualitative component of the wider study. They are not involved in the NHS Digital data processing.

The Department of Health and Social Care (DHSC) is the commissioner of this project. DHSC has no direct influence over the analysis performed and will have access to a final report of the findings but not the data used. The project funder is National Institute fir Health Research (NIHR).

Expected Benefits:

The anticipated evidence produced by this research is hoped will be directly relevant to
a) patients and NHS beneficiaries,
b) policymakers, planners and decision-makers, and
c) health providers, managers and practitioners.

The study hopes to produce findings on what changes in the organisational and cultural approach of hospitals are associated with better coping with Covid-19. These are hoped will be relevant for the development of policy, the organization of NHS acute medical services and the management of patients with COVID-19 and of patients with other conditions during national or local increases in numbers of COVID-19 patients. The dissemination of the anticipated study findings is hoped will enable the NHS to take measures to improve patient care based on evidence gathered on the topics studied.

The project and its dissemination strategy is designed to rapidly inform the Funder (DHSC) and engage in ongoing debates and policy reviews. The University of Birmingham and its project partners have worked with the Patient and Public Involvement (PPI) panel group, study Steering Group and the Funder to agree an engagement and dissemination plan at the start of the project, with activities running throughout its course. The anticipated outputs from the study are centred on informing policy and acute service provision. The University of Birmingham and its team will use its varied professional networks and professional social media presence to raise awareness of the outcomes of this study and maximise engagement with its findings. The external stakeholder group of this project, comprising representatives from the Royal College of Emergency Medicine, the Society for Acute Medicine, NHS Providers and Care England, will consider the anticipated research findings and where it is hoped these will inform potential service improvement or further resources that could help service provision.

It is hoped that the study will identify which organisational and healthcare delivery approaches minimise the impact of COVID-19 in the community, and will identify which practices support the ability to deliver routine care in COVID-19 times. The two focuses of this research project benefit the public interest because they may lead to improved health outcomes, via adoption by the healthcare community. It is hoped that the short-term findings on clinical strategies and organisational approaches associated with high performance in “peak 1” of COVID-19 will inform policy for acute hospitals and acute community providers for any subsequent outbreaks, whether these outbreaks are national or more localised.

Medium term benefits are hoped will be the identification of strategies to maintain ‘business as usual’ healthcare for both acute non-COVID-19 illnesses and serious longer term disease.
Both sets of results will be available to policymakers and health care providers and their guidelines will benefit the public interest and the community.

This project has been solicited by the Department of Health and Social Care (DHSC), to understand how the current pandemic is affecting the delivery of acute care and the delivery of routine care for conditions that may develop into acute complications. By investigating which care delivery approaches entail a better performance for patients with COVID-19 as well as non-COVID-19-related conditions, this project it is hoped will be able to directly inform policy and treatment for future waves of COVID-19, and similar pervasive public health emergencies, and to inform development of new standards of care delivery. The anticipated project outputs and results are hoped will directly feed into policymakers’ decisions. The project team will share the results also with the academic, scientific, and general communities with help from professional networks and by drawing on the team’s personal networks.

The project dissemination plan includes the following list of activities and tentative timeline:

AUTUMN – WINTER 2021:
- Main interim report (draft stage): findings of the COVID-19 related research analysis
- PPI panel meeting
- External Stakeholder Group meeting

WINTER 2021:
- Journal article, first draft: results of the COVID-19 SAMBA questionnaire findings
- Journal article, first draft: results of the quantitative analysis of hospital resilience

SPRING 2022:
- External Stakeholder Group meeting: presentation of the interim results
- PPI panel meeting: presentation of the interim results
- Workshop: presentation of the interim results
- Paper articles: submission to scientific/academic journals

AUTUMN 2022-SPRING 2023
- Final analyses and report writing
- External Stakeholder Group meeting, with a press release
- PPI panel meeting
- End of project dissemination meeting with a press release.
- Press coverage: blog articles, social media-based dissemination activities

It is hoped that with the help of Funder, advisers and stakeholders this project will make findings available to DHSC, NHS England, NHS Acute Trusts and professional organisations so that they can use them to inform their decision-making. The plans for dissemination are set out above.

The University of Birmingham hopes that the findings of this project should lead to improved decision-making by policy-makers, NHS managers and clinicians. While it is not certain in advance of conducting the study what specific decisions will be made as a result, the findings of this project will lead to improvements in the organization and management of acute medical care that will in turn lead to improved quality of care for patients and improved outcomes.

There is potential for large numbers of patients with acute medical conditions to benefit from improvements to their care based on evidence provided by this study. There is also potential for efficiency if improved care leads to better outcomes, including fewer emergency re-admissions and fewer patients experiencing deterioration of their condition resulting in need for more intense and costly treatment. The benefits will accrue to NHS acute services and ultimately to patients.

The benefits could be monitored through future surveys and future analyses of HES and other data sets, if DHSC decides to conduct or fund such monitoring. The University of Birmingham envisages that benefits will start to accrue soon after dissemination of the findings. This may depend on the specifics of the findings and on decisions by DHSC and NHS managers and clinicians informed by the findings.

The study does not support a PhD/post graduate research study.

Outputs:

Outputs from the study will include:
(a) Tables of HES-based information aggregated at hospital level with any small numbers suppressed (if there are any), such as number of admissions in period t of patients with condition X;
(b) Correlation or regression coefficients from analyses of hospital level data, such as correlation between operational practice X and proportion of patients with COVID-19 who died within 28 days; and
(c) Possibly a composite resilience index for each hospital calculated as a weighted sum of some of the hospital level data.
The project team will disseminate the research findings to patients, clinicians, professional bodies, and policy makers, and publish the aggregate results of the study in academic journals. The project will produce reports for the Department of Health and Social Care and communicate findings through webinars and conference presentations.


The results of this study will consist of the coefficient of correlation (or effect size) between an organisational or healthcare delivery practice and aggregate outcomes such as:

i. Operational outcomes (e.g. acute care flow, discharge rates);

ii. Indicators of healthcare resilience based on clinical outcomes for patients with COVID-19, e.g.:
• Mortality rates,
• Readmission rates,
• Rates of pulmonary embolism,
• Probability of readmission for suspected COVID-19,
• Average length of stay in intensive care unit;

iii. Indicators of healthcare resilience based on clinical outcomes for patients who do not have COVID-19, e.g:
• Rates of new onset heart failure or stroke,
• Total numbers and rates of A&E attendances and emergency admissions for heart attack and stroke/TI, during and after the first COVID-19 wave.

The researchers will not publish any disaggregated data or information about single individuals or critically small and identifiable groups of individuals. The University of Oxford will ensure that discrete variables cannot be used (either alone or in combination) to identify an individual. Tabulations and summaries of outcomes that may contain very small sample numbers in some cells will not be reported. Tables and other outputs will not be published in a form where the level of geography would threaten the confidentiality of the data.

The project team will disseminate the research findings to patients, clinicians, professional bodies and policy makers, as well as publish in academic journals. The evidence produced by this research will be directly relevant to:

A. Policymakers, planners and decision-makers;
B. Health providers, managers and practitioners.

The dissemination activities are designed with the goal of informing and supporting health and care policy through developing evidence that is crafted and presented with the policy user in mind, rigorous and authoritative, and timely.
The dissemination activities will include a one-day conference for key stakeholders at the end of the project, seeking their responses to study results. The project will ensure that a range of relevant organisations are included at the conference, such as professional societies, CCGs, service users, and carers. This event will be press released.

The project will inform practice at local and national level, leveraging the national roles of co-applicants and collaborators to ensure a wide dissemination to policy makers, relevant Policy Research Units, and professional societies. The project will disseminate findings of hypotheses of health system resilience through practice networks, professional societies and ALBs.
The project will raise public awareness by producing lay summaries of the results in accessible formats, including through webinars and blog entries, which will ensure a broad dissemination thanks to the extensive resonance of the network of universities and stakeholders involved in the project.

The project team will leverage the national roles and visibility of its co-applicants, collaborators, and funding partner to ensure a wide dissemination of the products of the research to policy makers in ALBs (NHS Improvement, Getting It Right First Time, Health Education England) as well as relevant Policy Research Units (Commissioning, Older people and Frailty) and professional societies (British Geriatrics Society, Society for Acute Medicine). The project team will disseminate the findings of this exploration of best practices in acute care delivery and health system resilience in COVID-19 times through practice networks, professional societies and ALBs (Arms Length Bodies)

To raise public awareness, this project will produce a summary of the results in an accessible format with the help of the PPI Panel and distribute it to a range of stakeholders, e.g. the NHS, commissioning groups, policymakers and service users. The University of Birmingham and its data processor, the University of Oxford, will ensure that the research is synthesised and communicated in a meaningful and clear way, such that the results of this study can be employed by all beneficiaries in practice to deliver real healthcare benefits.

The results and outputs of this project do not involve the development of tools, technologies, algorithms, or any similar instruments that may entail issues related to data and knowledge ownership, management, rights, and access.

Target date for the preliminary analysis of acute care delivery during COVID-19 outbreaks: late Summer 2021
Target date for the preliminary analysis of acute care delivery during COVID-19 outbreaks and winter pressures (possibly occurring in winter 2020-2021): Autumn 2021 - Winter 2021/22
Target date for the final analyses, report writing, end of project dissemination meeting, and press release/press coverage: Winter 2022-Spring 2023.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract, i.e.: employees, agents and contractors of the Data Recipient who may have access to that data)”

No data is flowing into NHS Digital. The analysis will employ the requested data solely in its pseudonymised form. The ethnicity data flowing is not identifiable but is a sensitive field. The research team will not publish any individual-level or identifiable information, with patient level HES data used solely to produce a range of aggregate variables for each hospital and to produce information for healthcare resilience indicators. The results of the analyses will be disseminated in aggregate form (e.g., the mean value of a clinical outcome and its standard deviation), with small numbers suppressed in line with the HES analysis guide and the publications will not identify hospitals by name.

The organisation responsible for data processing is the University of Oxford. As the University of Birmingham’s data processor, the University of Oxford will analyse the correlation between hospital-level care delivery approach variables (from the COVID-SAMBA data) and HES-based indicators of healthcare resilience (e.g. readmission rates, mortality for COVID-19 related symptoms, length of stay in Intensive Care Units (ICU), etc.).

This project will develop a dashboard of indicators at the hospital level including information relating to care delivery approaches (from the SAMBA survey) and aggregate baseline acute health and frailty outcomes. These measures will be correlated with healthcare resilience indicators from the NHS Digital data using site-specific codes (i.e., hospital identifiers). As the University of Birmingham’s data processor, the University of Oxford will process that data. It will not link the requested NHS Digital data with any other data at individual patient level. The analysis will be based solely on information at hospital level derived from the requested HES data and hospital level information from other data sources, in particular with SAMBA survey data, but this will not involve any linkage at patient level.

HES data will be used exclusively in its pseudonymised form. The analysis will follow patients across the requested NHS Digital datasets using only pseudonymised codes, such as the Encrypted HESID. The data will not be linked to any other data about individual patients.

As the University of Birmingham’s data processor, the University of Oxford will add to the analyses at hospital level the SAMBA data, which is collected at hospital level from a survey of clinical practice, without any patient data. The project will use COVID-SAMBA and 2019, 2020 regular SAMBA datasets. In addition, the project will add site-level information from a range of publicly available data for hospitals, Commissioners of Health and Social Care and local authorities, using site codes.
The datasets are:
- Office for National Statistics (ONS) data (population aged 65+, IMD of area, rurality index), https://www.ons.gov.uk/;
- NHS workforce statistics (hospital staff, community staff, primary care staff) by CCG, https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics ;
- NHS Digital NHS Outcome Framework (https://digital.nhs.uk/data-and-information/publications/statistical/nhs-outcomes-framework) ;
- Adult Social Care Outcome Framework measures https://digital.nhs.uk/data-and-information/publications/statistical/adult-social-care-outcomes-framework-ascof ;
- Skills for Care data on adult social care staff by LA, https://www.skillsforcare.org.uk/adult-social-care-workforce-data/Workforce-intelligence/publications/Data-and-publications.aspx;
Care Quality Commission (CQC) data on ratings of hospitals, https://www.cqc.org.uk/about-us/transparency/using-cqc-data; and
- NHS England SitRep data on hospital performance, closures, and bed pressures, https://www.england.nhs.uk/statistics/statistical-work-areas/winter-daily-sitreps/ .

The datasets include only variables that are aggregated at the hospital level or catchment areas and there is no identification of any patient.

There is no requirement for the study to re-identify individuals for this project and the University of Birmingham and its data processor, the University of Oxford, confirm that no attempts will be made to re-identify individuals.

As the University of Birmingham’s data processor, the University of Oxford will process the data received. This team resides at the University of Oxford, Nuffield Department of Primary Care Health Sciences (NDPCHS). All researchers and staff at the University of Oxford follow specific protocols for the protection and confidentiality of the data. All team members are also subject to training on these requirements initial upon start at the Department and annually thereafter. This team will retain the data on a secure, network server and limit access to only those researchers approved to access it via an encrypted remote desktop application.

QResearch-Oxford Data Linkage Project — DARS-NIC-240279-Y2V2N

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: Yes (Academic)

Sensitive: Non Sensitive, and Non-Sensitive, and Sensitive

When:DSA runs 2019-02-01 — 2020-09-25 2019.07 — 2022.06.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Outpatients
  5. Civil Registration - Deaths
  6. Emergency Care Data Set (ECDS)
  7. Civil Registration (Deaths) - Secondary Care Cut
  8. HES-ID to MPS-ID HES Accident and Emergency
  9. HES-ID to MPS-ID HES Admitted Patient Care
  10. HES-ID to MPS-ID HES Outpatients
  11. Civil Registrations of Death - Secondary Care Cut
  12. Hospital Episode Statistics Accident and Emergency (HES A and E)
  13. Hospital Episode Statistics Admitted Patient Care (HES APC)
  14. Hospital Episode Statistics Critical Care (HES Critical Care)
  15. Hospital Episode Statistics Outpatients (HES OP)
  16. Civil Registrations of Death

Objectives:

QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients.

QResearch has also been used to develop and validate risk prediction algorithms such as QRISK2. QRISK2 can be used by clinicians to calculate an individual’s risk of a heart attack or stroke taking account of their individual risk factors such as age, sex, ethnicity, clinical values and diagnoses. The research describing the derivation and validation of QRISK2 has been published in the BMJ (2008) and the software implementing QRISK2 is available as open and closed source software. QRISK2 is recommended by NICE as the risk score for use in its guidance on lipid modification (2014) and it is also recommended for use in the NHS Health Check.

The HES and mortality data are requested to link to the existing QResearch database so that it can be used for medical research. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.

QResearch is a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities are being transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.

The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.

In addition to coded data from the GP electronic record, the QResearch database also contains the linked cause of death derived from the death certificate data which was originally supplied directly by the Office of National Statistics (ONS) but NHS Digital has since assumed ownership for this data, and cancer registration data supplied directly by Public Health England, following approval by Trent MREC and Secretary of State for Health. From October 2018, new mortality data (referred to as Civil Registration data) will be supplied by NHS Digital. Any mortality originally supplied by ONS data will be considered data supplied by NHS Digital and only NHS Digital can approve ongoing access to this data and its use for specific purposes.

The data linkages for QResearch were extended in 2011 to include additional health information from secondary care including HES. The additional linked HES data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines. For example, QResearch data linked to HES data is being used to undertake an assessment of different types of direct anticoagulant medication which is prescribed in primary care to reduce risk of stroke and manage thrombosis. Adverse effects from anticoagulants include major haemorrhage which can be life threatening or life ending. Haemorrhage can affect the brain, gastrointestinal tract, urinary track or other parts of the body. The primary care data provides information on the prescriptions issued and the linked HES data provides information on haemorrhage which is serious enough to require hospital admission. Another example is a recent study looking at safety of the oral contraceptive pill. The primary care data provide information on exposure to the medication and the linked HES data provides information on thrombosis. These are just two examples of projects which can only be done using the linked data. The results help identify and quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.

The patient level data linked to QResearch is only accessed by academics employed by University of Oxford, as well as authorised individuals employed by the University of Nottingham. In all cases, data can only be accessed on site at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication but not handling the data) may be employed by other UK universities. The HES and mortality data stay on site at the University of Oxford and are only handled by University of Oxford staff, the data processor contracted to the University of Oxford (Dancing House Consulting) and authorised researchers employed by the University of Nottingham. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data. In addition, the external researcher may initiate a project but the University of Oxford has sole autonomy for determining the purposes for which the HES and/or civil registration data will be processed and analysis will be done by University of Oxford staff with the data located at the University of Oxford. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.

Only University of Oxford staff, their data processor, Dancing House Consulting, and authorised researchers employed by the University of Nottingham will have access to HES and/or mortality record level data and external researchers will only have access to tabular outputs. Record level data are not shared with researchers outside of the University of Oxford. Small numbers are suppressed in line with the HES Analysis Guide.

Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal.

All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.

Yielded Benefits:

The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice. The research is ongoing with target dates for individual projects rather than one overall target date. A complete list of research papers using QResearch database is published at http://www.qresearch.org/SitePages/publications.aspx There are many benefits arising from this research - some examples are listed here. QRISK, which the applicant continues to develop and enhance, has become the preferred cardiovascular risk assessment tool used across the NHS. It is implemented in every GP practice, used extensively in NHS Health Checks, and a variant is used for the NHS Choices 'Heart Age' tool. It was recognised as an outstanding impact case study in the Research Assessment Exercise in 2014. Use of QRISK2 has led to targeted interventions to reduce CVD risk as shown in NHS health check research published in BMJ Open in 2016. In response to nationally identified NHS needs, the applicant has developed & updated a suite of risk prediction tools to identify patients at high risk of an adverse condition for intervention to improve outcomes. Many of these tools are recommended in policy, NICE guidelines & widely implemented in clinical practice. Examples include three risk assessment tools known as QFracture, QDiabetes and QAdmissions. 1. The QFracture tool which assesses risk of fracture has been recommended in NICE guidelines on fracture prevention (August 2012). It is the preferred tool in the NICE quality standard (2016) & SIGN in Scotland. It was included as a quality indicator within the GP Quality and Outcomes Framework in 2013/4. The tool is integrated in over 4,300 GP practices. It helps identify patients who have a high risk of fracture who can then be offered interventions to reduce their risk of a fracture. This is especially important for elderly patients for whom a osteoporotic fracture can be life changing or life limiting. 2. QDiabetes is recommended by 2012 NICE guidance on diabetes prevention & used to identify patients for the national Diabetes Prevention Programme for interventions to reduce risk of type 2 diabetes. Interventions to reduce diabetes risk will have benefits for the individual patient who may otherwise have developed diabetes. Reducing the incidence of diabetes is also likely to have a wider benefit for the health system given the high work load and costs associated with caring for people with diabetes. 3. Research on identifying patients at high risk of emergency hospital admissions led to the development of the QAdmissions risk assessment tool. This tool is recommended by 2016 NICE guidance on co-morbidity & is used to delivery NHS England's unplanned admissions Designated Enhanced Service by identifying those at high risk of emergency admissions for targeted interventions to reduce their risk of having an emergency admission. This is important since emergency admissions are distressing for the patient and their families but also put strain on the NHS. It will also help GP practices to identify frail older patients as required by the new changes to the 2017 GP contract. Research into the early diagnosis of cancer was awarded the 2012 Royal College of General Practitioners (RCGP) paper of the year category. It also led to implementation of new risk assessment tool in over 4300 GP practices to improve early diagnosis of cancer, in partnership with Macmillan Cancer Support. It also led to professor Hippisley-Cox becoming an expert witness to the All Party Parliamentary Group on Pancreatic Cancer (2012, 2017) which produced high profile recommendations on how to improve early diagnosis of pancreatic cancer. Patients who are diagnosed with cancer at an earlier stage have a better chance of accessing treatment which is capable of improving survival and quality of life. Research on the safety of antidepressants has been published in the British Medical Journal and research on how it could be used to improve prescribing decisions for antidepressants in primary care is supported as part of the new NIHR funded Biomedical Research Centre in Nottingham (2017-2022).

Expected Benefits:

The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice. The research is ongoing with target dates for individual projects rather than one overall target date.

A complete list of research papers using QResearch database is published at http://www.qresearch.org/SitePages/publications.aspx

Research arising for the QResearch database including the linked data has been used to inform national policy. For example, research findings have been included in NICE guidelines on fragility fracture, diabetes, suspected cancer and lipid modification. Research findings have informed the NHS Health Checks programme and Department of Health guidelines on health checks.

Examples of research include assessment of the safety of antidepressant drugs and novel anticoagulants; investigation of potential links between diabetes drugs and cancer; quantification of the risk of thrombosis associated with various types of the oral contraceptive pill.

Outputs:

The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.

Examples of conferences include the annual academic conference for the Society of Academic Primary Care ( next due July 2018) and the NIHR School for Primary Care Research (Sept 2017); international conferences such as the North American Primary Care Research Group (NAPCRG- US Nov 2018); the biannual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch Sept 2017, Feb 2018, Sept 2018 etc); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre (Dec 2017).

Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines. For example,
• The results of recent research on cardiovascular risk in people with severe mental health illness were shared in August 2017 with the guideline development group for guideline CG178 entitled “The Psychosis and schizophrenia in adults: prevention and management”.
• The results of work on unplanned admissions was shared with the NICE guideline on multi-morbidity [NG56] to inform its review and update process. This guideline was updated in September 2016 and can be found here. https://www.nice.org.uk/guidance/ng56

• Research on risk assessment for diabetes has fed into recently published NICE guideline on prevention of diabetes (PH38, September 2017) and the update to the “NHS Health Checks best Practice Guidance” published by Public Health England in February 2017.

• The recent research to update QRISK3 was shared at meetings with the British Heart Foundation and Public Health England as well with the National Directors of Cardiovascular Disease (Huon Grey and Matt Kearney) and at meetings of Expert Scientific Advisory Group which oversees the NHS Health Checks program and which is chaired by John Newton (PHE). QRISK3 will be implemented in the NHS from 2018 and will continue to be updated on regular basis to ensure that the tool gives the most accurate estimation of risk possible.

• Planned research on the risks and benefits of HRT are targeted at the recent NICE guideline on HRT at the next review in 2019. Patient representatives have been involved with the development of the research question and the grant application and will advise on the research as it progresses.

The results of two studies published in the BMJ which described enhanced methods to estimate risk of bleeding (QBleed) and stroke (QStroke) have informed NICE's decision to update their guidelines on atrial fibrillation [CG180]. For more details please see the information published on NICE's website in Sept 2017 on assessment of bleeding and stroke risk https://www.nice.org.uk/guidance/cg180/resources/surveillance-report-2017--atrial-fibrillation-management-2014-nice-guideline-cg180-4597399263/chapter/Surveillance-decision

Results have been shared with the Parliamentary Enquiry into Pancreatic Cancer (JHC attended as an expert witness in 2017).
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.

The results tables within the papers will only contain statistical information with cell counts of > 5, being suppressed in line with the ICO code on anonymisation. Outputs will contain aggregate level data with small numbers suppressed in line with the HES analysis guide.

No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.

Processing:

The data has been stored on secure servers at the University of Nottingham. These servers hold only the QResearch data and are not connected to any other servers within the University of Nottingham. Data on the servers are backed up on tapes which also contain only QResearch data.

This Data Sharing Agreement grants permission for controllership of this data to transfer to the University of Oxford and, as determined by the University of Oxford, for the servers and back up tapes to be physically transported to a server room in the University of Oxford. The servers and back up tapes will be transported separately by secure couriers. Once relocated within the University of Oxford, the University of Oxford will be the sole data controller for the data. From the point when the servers and back up tapes are removed from the University of Nottingham, the University of Nottingham will not hold any copies of the data.

Prior to the transfer of the QResearch database, a number of researchers substantively employed by the University of Nottingham were processing the data for the purposes of research projects under the controllership of the Principal Investigator who was then employed by the University of Nottingham but has since transferred to the University of Oxford. Under the continuing controllership of the Principal Investigator, these individuals will be permitted to continue to process the data to complete their research projects. The Principal Investigator may utilise substantive employees of the University of Nottingham in future research projects with an appropriate data processing agreement in place between the respective organisations and/or under honorary contracts or secondment agreements which NHS Digital has confirmed are acceptable before access is granted.

EMIS process the GP data from the original data controllers (GP practices) and sends it to the University of Oxford.

EMIS is not able to access or process any GP data once it is located at the University of Oxford.

EMIS is neither a data processor nor a data controller for the data provided by NHS Digital under this Agreement. EMIS is not able to access the HES data under any circumstances. EMIS has given permission for the GP data it supplies to be linked with the data from NHS Digital for purposes determined by the Principal Investigator at the University of Oxford.

Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the HES data. NHS Digital retains the salt key for this pseudonymisation, meaning that the University of Oxford are unable to re-identify the data but as described below they are able to link with GP data that was pseudonymised using the same Open Pseudonymiser tool. The University of Oxford will not be provided with a copy of the pseudonymisation salt.

NHS Digital provide the pseudonymised data to the University of Oxford which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the HES data. The data linkage is undertaken by an employee of the University of Oxford. No data items which would identify the data subjects are received by QResearch as the data is pseudonymised-at-source and at NHS Digital. Date of birth is rounded to year of birth before receipt by the University of Oxford.

The resulting data are then used for undertaking primary research. The linked data are only accessed by approved research staff with substantive contracts employed by University of Oxford, the contracted data processor (Dancing House Consulting) and authorised researchers employed by the University of Nottingham. Data is only processed on site on secure servers at the University of Oxford. No individual level data will be shared or stored outside the University of Oxford or supplied to any third party.

Applications for HES and/or civil registration data linked to QResearch GP data are restricted to academics employed by University of Oxford to undertake research. At least one member of the research team must be a medically qualified academic registered with the General Medical Council who signs the guarantee. Eligibility of applications is assessed according to the following criteria.
• You agree NOT to attempt to identify patient(s) or practice(s)?
• You undertake to provide a copy of the final report of the project and copies of any publications within one year of the project completion?
• You agree NOT to release the data to any third party including the funder, sponsor or other such body?
• You agree not to use the data for any other project except that which is expressly described in your protocol
• Do you have a statistician on the project team who has contributed to the design of the study and will advise on the analysis?
• Is the research a benefit to the UK Health and Social care system
All applications are reviewed by the QResearch Scientific Committee, which is overseen by the QResearch Advisory Board (which includes patients and general practice representatives). If an application does not meet the above criteria it would mean that application would be rejected and the data would not be shared. Details of the Scientific Committee and Advisory Board terms of reference and membership are published on the QResearch website, along with Advisory Board minutes.

Researchers originate a research question or hypothesis; write an outline protocol; and contact QResearch to discuss the feasibility of undertaking the study. If the study is feasible, QResearch will give a broad estimate of the costs of providing the analysis and will provide a letter to accompany any application for funding. The researcher then secures the necessary funding and completes the QResearch application form, including a detailed protocol and data specification. This application is sent for scientific review and feedback is given to the researcher. The researcher makes any necessary modifications to the protocol and approval is obtained, the researcher is given a timescale for the analysis. Once the researcher has the analysis, they have to approve it within one month of receipt.

As described in the section above, the QResearch database is also linked to mortality and cancer registration data. The database was first linked to ONS mortality data in 2007 and cancer data in 2011 (subsequently supplied by Public Health England since 2015). The data fields received from mortality data are: pseudonymised NHS number; year of birth, date of death; ICD10 cause of death. The cancer data includes pseudonymised NHS number; sex; year of birth; date of death; diagnosis date; cancer site and type; cancer stage and grade; cancer behaviour; cancer diagnosed only on death certificate; cancer treatment (surgery, hormone, chemotherapy, other).

In theory it would be possible to link additional datasets to the QResearch database though this would require consultation with the QResearch advisory board, the ethics committee, the confidentiality advisory group. It would also require amendment to the data sharing agreement with NHS Digital. There is no requirement to re-identify individuals from the data and no attempts will ever be made to do this.

The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake data linkage or analysis of the data.

All outputs are restricted to aggregate data with small numbers supressed in line with the HES Analysis Guide.
Regular reviews against the ICO code on anonymisation (2012) will be undertaken to ensure that the data remain anonymised and all appropriate controls are in place to minimise any risk of re-identification.

The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

MR360 - Early Breast Cancer Trialists' Collaborative Group — DARS-NIC-148204-7B1XT

Opt outs honoured: Y, Identifiable, Yes (Does not include the flow of confidential data, , Section 251 NHS Act 2006, )

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2019-11-01 — 2020-03-31 2016.04 — 2022.06.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report
  4. MRIS - Members and Postings Report
  5. Cancer Registration Data
  6. Civil Registration - Deaths
  7. Demographics
  8. Civil Registrations of Death

Objectives:

The data supplied by the NHSIC to Clinical Trial Service Unit (CTSU) will be used only for the approved Medical Research project MR360.

Yielded Benefits:

The first collaboration produced clear evidence of a modest but real effect, in at least some women, of adjuvant hormonal and cytotoxic therapy on five year mortality, and gave statistically stable evaluations of the effects of treatment on recurrence free survival in different types of patient. The results of this first cycle of the overview have already altered routine clinical practice in the UK and elsewhere.

Expected Benefits:

In any future application, the applicant will be required to provide details of the expected benefits resulting from the study.

Outputs:

No new outputs will be produced under this Data Sharing Agreement.

In any future application, the applicant will be required to provide details of the outputs that were produced and disseminated by the study as well as details of any future outputs planned.

Processing:

Under this Agreement, the data may be securely stored but not otherwise processed. No new data will be provided by NHS Digital under this Agreement.

The study data, including data provided by NHS Digital under previous agreements, are currently held by the University of Oxford. Under this interim extension all devices containing data will be securely locked away in a locked cabinet at the University of Oxford storage address specified in this Agreement.

The following provides background on the processing activities undertaken for the original study:

Identifying data on approximately 2,850 patients from 14 clinical trials was shared with ONS to carry out the linkage between the study data and civil registration data. Participants records were ‘flagged’ with the Office for National Statistics (ONS). ONS notified the study team at the University of Oxford of participants’ deaths (date and cause) and cancer events when they occurred. The ‘flagging for long-term follow up’ service transferred from ONS to the HSCIC in 2008. Data was last supplied in November 2016.

Revision Hip and Knee Replacements: Evaluation of Clinical, Psychological and Surgical Outcomes — DARS-NIC-380650-K4F6X

Opt outs honoured: Anonymised - ICO Code Compliant, Yes, No (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(2)(b)(ii); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-05-12 — 2024-05-05 2022.05 — 2022.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. HES:Civil Registration (Deaths) bridge
  3. Hospital Episode Statistics Admitted Patient Care
  4. Patient Reported Outcome Measures (Linkable to HES)
  5. Civil Registrations of Death - Secondary Care Cut
  6. Hospital Episode Statistics Admitted Patient Care (HES APC)

Expected Benefits:

It is hoped that the expected measurable benefits to health and or social care will be:
1. Patients potentially better informed to participate in the decision to undergo revision hip and knee replacement. Patients will hopefully be informed about the observed joint function, quality of life, short-term complications, longer-term outcomes and variations in care following revision joint replacement. It is hoped patients will be informed via social media engagement, public reports and patient engagement work as listed in the section above. The rationale for a broad, multifaceted engagement strategy is to reach as wide a patient audience as possible. Patients undergoing revision joint replacement are heterogenous and whilst many are avidly engaged with social media, many prefer more traditional engagement activities.

2. It is hoped that surgeons will be supported to change practice to deliver evidence-based surgery. Surgeons will hopefully be informed of the findings via journal publications, conference presentations, a DPhil thesis, and communication through social media and the British Orthopaedic Association (BOA). Target dates have been listed in the output strategy above. A broad engagement strategy has been chosen in order to reach as many surgeons and researchers practicing in this are from across the country. For example, the BOA is in regular communication with more than 5,000 members across the UK.

3. It is hoped that NHS commissioners will be supported to promote evidence-based surgery and appropriate resource allocation. There are approximately 13000 revision joint replacement procedures performed each year in the UK. The hypothesis for this work is that some types of procedures or procedures for certain groups of patients may be found to be of limited benefit and as such may be candidates for decommissioning. This may lead to cost savings, and may also mean that patients do not experience procedures with high risks of adverse events for limited or no benefit. This information will be available to care providers and commissioners via outputs 1/2/5 above.

4. It is hoped that there will be improvement in patient reported outcome measures (PROMs) following revision surgery. The NHS routinely collects data on the patient perspective following revision hip and knee replacement as part of the NHS PROMs programme. If this proposal is successful and changes are implemented into practice, then one might expect improvement in PROMs over time.

Outputs:

The specific outputs from the proposed work can be classified as:
1. Peer reviewed journal publications. This will include original articles on patient-reported outcomes, short term complications and longer term outcomes from revision hip and knee replacements. These publications will be Open Access. A first publication is target at 9 months from data receipt, with further publications at 6 monthly intervals.

2. Conference presentations: The proposed work will be presented at national and international conferences. This is to engage a broad spectrum of surgeons and allied health professionals who practice in or around revision joint replacement surgery across the UK. The British Association of Surgeons of the Knee (BASK) and British Orthopaedic Association (BOA) Annual Meetings in 2021 are targets for presentation of the proposed work.

3. Clinical practice guidelines. The University of Oxford study team work closely with BASK and the BOA and have been involved in recent work to produce British Orthopaedic Association Standards (BOASts) which are guidelines on the management of orthopaedic conditions. The evidence generated from the proposed work will be used to update the BOASTs for revision joint replacement. The target date for this is 2023.

4. Social media engagement with patients, surgeons, researchers and allied health professionals. The NDORMs Twitter account (@ndorms) will be used to announce study progress and results. This is intended to engage patients, surgeons and researchers with research outputs as they are produced.

5. Public reports: A project summary will be provided on the NDORMs study webpage. The proposed work will also produce a summary for the National Joint Registry Annual Report. This first summary is intended for the NJR 19th Annual report in 2022.

6. DPhil thesis. This is intended to be published Open Access in 2023 to provide information on outcomes following revision knee replacement. The DPhil student is funded by the Royal College of Surgeons (RCS). The RCS will have no access to the data and no role in its analysis or interpretation.

7. Patients engagement work: The University of Oxford has already started to involve patients and the public with this proposal and will continue this over the duration of the proposal. The Patient and Public Involvement Strategy for this proposal is as follows:
(i) Establishing key priorities for research on revision joint replacement.
See previous references to James Lind Alliance Priority Setting Partnership work.

(ii) Establishing patient views on data linkage without consent
This has been tested in 4 patients known to the research team (mix of men, women, different ages, from various parts of the country who have had a known problem with an implant after surgery). Patients were chosen as a convenience sample by the Nuffield Orthopaedic Centre, Oxford. 4/4 patients agreed that it would not be practical to contact all patients to obtain consent due to the size of the population. The feedback from all had a common theme: the potential benefits from new information on revision knee replacement meant that it was important for this research to be performed. They were satisfied that opt-out mechanisms were in place should patients wish to do so.

(iii) Establishing patient views on the programme of revision knee replacement research proposed
Telephone interviews have been performed with four patients to describe the proposed research programme. They provided very positive feedback. Some quotes were:
-“This will provide very useful information”
-“I think it can help others in the future, and it seems like a very good idea”
-“I have been through several operations on my knee and there isn’t much information out there for patients like me.”
-“I actually had very good information from Oxford when undergoing my revision knee replacement, and I really wish that information on revision knee replacement was rather better known around the country.”

(iv) Extended PPI work
The University of Oxford is recruiting a PPI group of 8-12 patients who are awaiting or have undergone revision knee replacement. The University of Oxford will explore with this group:
-What factors are important to patients in the decision to undergo revision knee replacement?
-How should outcomes following revision knee replacement be evaluated in a way that is meaningful to patients?
This group is currently being recruited.

(v) Revision arthroplasty study website
This is being set-up. Lay summaries of research will be available on this website.

(vi) Open days
These were held regularly, but are currently paused due to Covid-19, but will be reinstated in-person or virtually within the first year of the project.

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

All organisations party to this agreement must comply with the data sharing framework contract requirements, including those regarding the use (and purposes of that use) by “personnel” (as defined within the data sharing framework contract i.e. employees, agents and contractors of the data recipient who may have access to that data).

STAGE 1
(Data Flow 1) On behalf of HQIP, Northgate Information Solutions will transfer the following identifying fields into NHS Digital from the National Joint Registry (NJR):
- NHS number
- Date of Birth
- Gender
- Postcode
- NJR unique identifier (which will be designated the Study ID)

The NJR unique identifier is provided for each eligible patient from the NJR for the purpose of linkage to NHS Digital held datasets.

NHS Digital will create the study cohort:
(i) NHS Digital will link the NJR cohort to HES using sensitive identifiers
(ii) NHS Digital will identify a cohort of patients with a hip or knee replacement (University of Oxford will send in the operation codes) from HES APC (1998/99 to 2019/20)
(iii) NHS Digital will identify all hip and knee replacement procedures on PROMs
(iv) NHS Digital will combine the cohorts from (i), (ii) and (iii), and extract:
-All HES APC episodes for these patients from 1998/99 to 2019/20
-Civil Registrations (Mortality) for these patients

(Data Flow 2) NHS Digital will transfer to the Big Health Data Group (BHDG) at University of Oxford, pseudonymised records of eligible individuals from HES APC, Civil Registration (deaths) and PROMS. The only pseudonym to be returned is the Study ID.

(Data Flow 3) Northgate Information Solutions will transfer to the Big Health Data Group (BHDG) at University of Oxford, pseudonymised records of eligible individuals from the NJR. These records will include:
- Study ID
- Age at surgery in years
- Gender.

(Data Flow 4) On receipt of the NJR/HQIP and NHS Digital data sets, the BHDG will be responsible for linking the two data sources. The resulting pseudonymised record-level data-set will be linked, stored and analysed within the BHDG's Data Security Protection Toolkit (DSPT) compliant environment.

STAGE 2
(Data Flow 5)
When the Master Person Service (MPS - an enhanced person-matching algorithm that increases the number of linkable records where incomplete records have been submitted.) is available for PROMs, NHS Digital will repeat linkage of the NJR to PROMs based on the sensitive identifiers provided in Data Flow 1 (which will have been retained). NHS Digital will supply the PROMS_SERIAL_NO (a unique ID in PROMS) and Study_ID to BHDG in University of Oxford as a separate linkage file.

The University of Oxford is requesting HES APC data for patients found in the NJR cohort AND patients with hip and knee joint replacements not found in the NJR cohort.

There will be no linkage to any publicly available data. There will be no attempt to re-identify individuals from this data-set, and results will be presented at aggregate level with small numbers suppressed in line with the HES Analysis Guide. The linked data set containing data from NHS Digital will not be accessed by the NJR/HQIP. Only substantive employees of the data controller will have access to the data under this agreement. These employees of the data controller have been appropriately trained in data protection and confidentiality. Within the University of Oxford Big Health Data Group, data is held in the Secure Computing Room, which is access controlled. Data are encrypted and stored in a safe. Data are accessed on non-networked computers.

DATA MINIMISATION:
>Datasets
1. NJR-HES-PROMs-Civil Registrations (Mortality) are requested. It is not possible to reduce the number of datasets because: (i) incomplete case ascertainment of NJR (ii) further granularity is required on patient comorbidities and complications (pre-existing and new) from HES and PROMs datasets (iii) data on mortality is required as a competing risk for implant (re)revision
2. The purpose can be achieved in a less intrusive way by using pseudonymised data.

>Years
3. The proposed study will investigate trends over time in modern arthroplasty practices. As such, data from 1998 is appropriate.

>Filtering
4. The proposed study will investigate geographic trends so geographic narrowing is inappropriate.
5. The proposed study will investigate the effect of patient demographics, so filtering is inappropriate - except data for those under 18 is not required.
6. The proposed study requests data on patients who have received a hip or knee joint replacement and requests filtering on this criterion (using OPCS codes supplied and linkage to the NJR dataset)

>Episodes
7. The study is investigating both the effect of pre-existing comorbidity on outcomes and the development of new comorbidity both in the short-term and long-term (up to 15 years). As such, episodes 5 years prior to joint replacement and all subsequent episodes are relevant.
8. Elective episodes are relevant as they may represent further surgery to the joint replacement or a new comorbidity
9. Maternity and neonatal episodes are not required and have not been requested.
10. Timeframe - Data from 1998 is requested to appropriately investigate trends overtime in modern arthroplasty practices.

Fields
11. Fields have been selected to provide predictors and outcomes of interest. This includes ETHNOS as ethnicity may be a predictor of outcome.
12. Full date of death has not been requested, but month/year has been requested instead to prevent intrusion

Cohorts
13/14. Linkage to NJR cohort is requested AND/OR matching with a pre-specified list of hip and knee replacement OPCS codes

There will be no data linkage undertaken with NHS digital data provided under this agreement that is not already noted in the agreement.

MR1231 - The 3C Study (Campath, Calcineurin inhibitor reduction and Chronic allograft nephropathy) — DARS-NIC-388486-D9M5N

Opt outs honoured: No - consent provided by participants of research study, No - data flow is not identifiable, Identifiable, Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-02-01 — 2022-01-31 2017.09 — 2022.05.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Outpatients
  6. MRIS - Scottish NHS / Registration
  7. Civil Registration - Deaths
  8. Cancer Registration Data
  9. Demographics
  10. MRIS - Flagging Current Status Report
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Outpatients (HES OP)
  13. Civil Registrations of Death

Objectives:

Despite improvements in short-term outcomes in kidney transplantation (e.g. acute rejection rates) there has been no improvement in long-term outcomes (e.g. transplant survival at 5 – 20 years after transplantation). One reason for this is that drugs (in particular, calcineurin inhibitors or “CNIs”) used to prevent rejection in fact damage the transplant in the long-term and are major reasons for its ultimate failure. The 3C Study is investigating two strategies that may allow the use of CNIs to be minimised or removed completely. Nearly all kidney transplant trials to date have been both too small and in particular too short-term to detect any clinically meaningful treatment effects in outcomes that matter to patients i.e. long-term function and survival of the transplant. The 3C study therefore aims to study a large enough group of patients for long enough to detect such treatment effects.

In order to detect the long-term effects of the study treatments cost-effectively, the Clinical Trial Service Unit at University of Oxford (CTSU) prospectively planned to flag all participants with available NHS registries (including ONS and HES) to capture information on relevant outcomes. The outcomes that are covered by the registries relevant to this, and previous, applications include cause-specific mortality, site-specific cancer and hospitalisation (which includes infections which are a particularly important outcome for kidney transplant recipients).

The 3C Study is investigating two possible strategies that could improve the lifespan of kidney transplants: firstly, is Campath-based induction treatment superior to standard basiliximab-based treatment; secondly, is sirolimus-based maintenance treatment superior to standard tacrolimusbased treatment. Although the primary outcomes of the study are relatively short-term, there is considerable scientific interest in the long-term (i.e. 5 – 20 year) outcomes from this study. In particular, there is uncertainty about the safety of such treatments with malignancy being one complication of transplantation that often occurs late.

The data from NHS Digital (formerly known as Health and Social Care Information Centre) will therefore be crucial to the ability of the trial to provide uniquely reliable information on the short- and long-term effects of different immunosuppression strategies in kidney transplantation.

Yielded Benefits:

Two major publications have informed practice nationally and internationally. In particular, the first Lancet publication has increased the use of alemtuzumab (one of the tested interventions) nationally.

Expected Benefits:

The results of analyses based on the data received from NHS Digital would be considered by guidelines writers and agencies such as NICE when updating recommendations for the care of transplant recipients. They would be unique in the field of kidney transplantation in that they would provide unbiased, randomised assessment of the long-term effects of different immunosuppression strategies. They are highly likely to affect the management of future patients receiving kidney transplants.

Future Benefits

Once the outputs from this study are publicly available they will be included in systematic reviews of immunosuppression in kidney transplantation, including those conducted by organisations such as the National Institute for Health and Care Excellent (NICE). Such reviews would consider the outputs from this trial along with those from other trials in the same area and make clinical practice recommendations based on the totality of the evidence.

NICE have recently published guidance on this topic and therefore it will be automatically reviewed in the next few years. The 3C study have not engaged directly with NICE but expect that the outputs from the trial will be considered when the topic area is updated.

The benefits will depend crucially on the results of the analyses. The published outputs have indicated that alemtuzumab-based induction therapy reduces the risk of rejection compared to current standard treatment with no early complications. This has led some kidney transplant units to adopt alemtuzumab as their standard of care, whereas others are waiting for the longer-term results (and NICE appraisal) to decide whether to change. The avoidance of rejection is of benefit to patients as rejection may reduce the longevity of the transplant. It is also costly so strategies to reduce rejection can save money.

If analysis of long-term outcomes (which requires linkage with NHS Digital) shows that one or other of the treatments being tested in the 3C Study improves transplant survival this would be a substantial benefit to patients (who experience longer and better quality of life with a functioning transplant compared to being on dialysis), providers (because it is substantially less-expensive to care for patients with a functioning transplant than patients on dialysis) and the wider public (because if transplants last longer then the demand for re-transplantation will fall and thus reduce pressure on the waiting list).

Outputs:

Outputs to date

The 3C Study has already had one publication in the Lancet (Volume 384, No. 9955, p1684–1690, 8 November 2014). The data requested here would be used to generate presentations at international medical conferences and subsequent publications in high-impact medical journals.

Future Outputs

Another publication is in preparation (and an abstract has been accepted for presentation at an international conference). The University of Oxford hope to publish a further output in a major medical journal later this year. Further outputs will follow in later years.

The long-term follow-up is also likely to be of substantial interest and would be presented and published in the future. For example, the protocol-specified 5 year follow-up will be conducted in 2017/18, but later analyses will also be of substantial interest.

Participants will be followed (where possible) for their lifetime and beyond the period of questionnaire-based follow-up. The outputs from this trial will not be limited to the assessments specified in the protocol.

The results of the 3C Study will be published in general and renal/transplant journals. The early results were published in The Lancet and the first results using these data will also be published in a high-impact medical journal. In addition, they will be presented at national and international meetings such as the British Transplantation Society, the Renal Association, the European Society of Transplantation, the American Transplant Congress and the Transplantation Society. The outputs will also be shared with all appropriate bodies such as NICE and the Cochrane Centre.

The University of Oxford will send a plain English summary of the results to all surviving participants (and publish the same on the University website) at the time of any publication of the relevant output. The University of Oxford will ensure the results are available in open-access format so anyone with an interest can access the outputs.

All outputs will contain only data that is aggregated with small numbers supressed in line with the HES Analysis Guide.

The commercial sponsors provided funding for the trial and will receive copies of any outputs prior to their public dissemination. They have the right to comment on these outputs but they cannot require or mandate any changes. For the avoidance of doubt, the principal investigators (all employed by the university) have the final decision regarding any outputs.

Processing:

The 3C study includes 800 patients aged over 18 years who were listed for kidney transplantation.

Participants were recruited around the time of transplantation when informed consent was sought (see attached consent form which includes long-term follow-up through NHS registries) and they were randomised into the study prior to receiving their transplant. They have then been followed-up alongside their routine clinical care for one year with study clinic visits. In addition, data will be collected on adverse events they have experienced, current medication, laboratory results, quality of life and healthcare usage through annual mailed questionnaires.

The approved study protocol also specifies that all participants will be “flagged” with NHS registries such as the Medical Research Information Service and Hospital Episode Statistics. CTSU would seek information on cause-specific mortality, cancer diagnoses and hospital admissions on an annual basis. Any relevant information supplied may be verified with the participant’s managing doctors and then used in the study analyses.

In addition, information on mortality would be used to prevent the coordinating centre from contacting any participant known to be dead.

The study has already provided a list of participant identifiers along with a unique study participant ID number to NHS Digital. The flow of information back from NHS Digital can therefore be anonymised.

Update

Data is only accessed by substantive employees of the university within the Clinical Trial Service Unit, Nuffield Department of Population Health, the University of Oxford.

The data provided by NHS Digital is reviewed by clinicians using bespoke programs. Events of interest that are identified are then entered into the study database using a separate program which records the details of interest (namely, nature, date and outcome of event). The analysis is then run on the study database.

Data will be processed in line with ONS terms and conditions

MR1247 - Evaluating the age extension of the NHS Breast Screening Programme — DARS-NIC-147931-DT25Y

Opt outs honoured: Yes - patient objections upheld, Identifiable, Anonymised - ICO Code Compliant, Yes, No (Section 251, Section 251 NHS Act 2006, , )

Legal basis: Section 251 approval is in place for the flow of identifiable data, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(7), , National Health Service Act 2006 - s251 - 'Control of patient information'.; Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.; National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2011-05-25 — 2026-05-24 2016.05 — 2022.03.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. MRIS - Members and Postings Report
  5. Hospital Episode Statistics Admitted Patient Care
  6. MRIS - Scottish NHS / Registration
  7. Demographics
  8. Cancer Registration Data
  9. Civil Registration - Deaths
  10. MRIS - Personal Demographics Service
  11. MRIS - Bespoke
  12. HES-ID to MPS-ID HES Admitted Patient Care
  13. Hospital Episode Statistics Admitted Patient Care (HES APC)
  14. Civil Registrations of Death

Objectives:

Evaluating the age extension of the NHS Breast Screening Programme - Age Extension (AgeX) trial

The NHS Breast Screening Programme (NHSBSP) routinely invites women aged 50-70 years to come for three-yearly screening. Because of uncertainty about the effects of screening outside this age range, an England-wide cluster-randomised trial is under way to assess the risks and benefits of additional invitations for screening at ages 47 to 49 and, separately, after age 70 (currently 71-73).

Random allocation of small clusters of participants is used to determine (in a 50:50 ratio) which women are offered one additional screening invitation before age 50 and which are not, and which women are offered additional screening after age 70 and which are not. This trial will involve about 71 of the 81 breast screening units in England and will randomize at least two million women aged 47-49 and one million aged 71-73 to be invited for additional screening. Women will be followed up by routine electronic linkage to NHS and ONS mortality records to assess short-term and long-term effects of the additional screening on: patterns of investigation, detection and treatment of breast lesions; breast cancer incidence; breast cancer mortality; hospital admissions; and overall mortality.

The principal outcome for screening before age 50 and, separately, after age 70 will be breast cancer mortality, eventually subdivided by 5-year time periods (0-4, 5-9, 10-14 years, etc.) since random allocation. Subsidiary analyses will assess effects on other outcomes. The main results are expected in the mid-2020s.

When the main results of the trial are available results will be reported to the Advisory Committee on Breast Cancer Screening which oversees the NHSBSP and reports to government ministers. The NHSBSP sets the standards for the screening units and monitors performance through a national quality assurance network.

The data will be pseudonymised before analysis and will not be shared with any third parties.

The reason for this trial is to provide definitive evidence on an issue that has been the subject of controversy for over 15 years – specifically whether the harms of breast screening outweigh the benefits. Previous trials have evidenced the benefits without sufficiently evidencing the potential harms. Numerous claims and counter claims have been made and continue to be made emanating from such sources as patient groups, publications, scientific literature and in the media. The nature of the claims varies over time. For example, it may, at different times, be claimed that harms include increases in operations, hospital stays, related health episodes of specific types, etc. Such claims are typically varied with a lack of definitive supporting evidence.

This trial aims to amass and analyse sufficient evidence that will enable the final reports to provide definitive unequivocal evidence. Where such claims of harm have been made, the trial will investigate the validity and tailor the content of planned reports to provide responses. Due to the potentially wide variety of possible harms which might be attributed to screening, the trial requires wide ranging hospital episode data not restricted to specific types of episode.

The number of women involved in the trial is necessarily large in order to ensure that the findings of the trial are unequivocal. The sample size has been reviewed by numerous committees and accepted as being appropriate.

In terms of the number of years of data required, it is necessary to have data over such a long period because firstly, the screening programme currently offers women 7 screenings between the ages of 50 and 70 and the trial is investigating the effect of adding just one screen to the existing 7 routinely offered. Therefore the screening period is already more than 20 years and effects of screening are likely to become visible at least 10 years after the first screening. Secondly, it is essential for the trial design to have information on morbidity prior to randomization. It is well established that women with prior co-morbidities are less likely to attend for screening, when invited. It is stated explicitly in the protocol that groups of women will be excluded from the main analyses where there are known (on the basis of information collected before randomization) to be unlikely to accept an invitation for screening. It is therefore a fundamental requirement that the trial analyses HES data going back as far as is possible.

Yielded Benefits:

No benefits to date the study is due to be completed in the mid-2020s

Expected Benefits:

The aim of the trial is to assess reliably the risks and benefits of additional NHS invitations for breast screening before age 50 and, separately, after age 70. The results are expected to help determine future NHS policy on age at breast screening. The main results are not expected before the mid-2020s but interim results will be reviewed and the trial end date may be brought forward if the evidence is clear before then.

Public Health England will use the findings to inform government and influence policy. This will affect breast screening policy in the UK and influence policy in much of the rest of the world for decades after the results have published.

The benefits of interim analyses are that these will be used to monitor the protection and safety of trial participants in addition to feeding into the wider trial aims.

Outputs:

The primary output of the study will be findings disseminated by publications in peer-reviewed open-access journals and presented at medical conferences to academics, NHS national policy makers and on the web. The results are expected to help determine future NHS policy on breast cancer screening outside the 50-70 age group. The main results are not expected before the mid-2020s.

Outputs will include only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

As with any clinical trial, it is necessary to gather evidence incrementally throughout the course of the trial in order to check that the trial itself is not exposing participants to increased risk of harm or, conversely, to check for evidence of benefits. The trial end date is not fixed and may be brought forward if at any stage, sufficient evidence is obtained.

The outputs from the interim analyses will be annual reports to the Data Ethics and Monitoring Committee who may also request additional analyses within the scope of the clinical trial’s objectives if they identify the need. The Data Ethics and Monitoring Committee report any concerns about the trial or findings to the trial management group.

Processing:

To date, identifiable details have been supplied to NHS Digital (formerly known as HSCIC) in separate batches for flagging on NHS Digital's MIDAS system and NHS Digital has supplied periodic updates on deaths, cancers and exits from NHS registration. Approximately 2 million cohort members have been flagged to date with the number to rise to 2.5m.

Further batches of trial participants will be supplied as new participants are randomised into the trial on an ongoing basis.

In addition to the ongoing supply of notifications, NHS Digital will perform further linkage to Hospital Episode Statistics data and supply the linked data to the CEU.

All data received from NHS Digital is linked with trial participants’ records from the National Breast Screening System and PHE cancer outcome datasets.

The purpose for collecting the data is to assess the short-term and long-term effects of the additional screening on: patterns of investigation; detection and treatment of breast lesions; breast cancer incidence; breast cancer mortality; hospital admissions, and overall mortality.

The data will be held exclusively at the Cancer Epidemiology Unit (CEU) at the University of Oxford and used solely for the purpose of this project.

All data (core trial data, including personal details, flagging information, deaths, cancers, externally linked data, some admin data) is held in a central database. Several internal ID's exist so that data can be appropriately structured. No statistical analyses of the data are performed directly on this database. Direct access to the entire database is limited to the database manager. Access to personal identifiers is required to process externally linked data, and to prepare datasets for external linkage to HES, ONS, Cancer Registration, NHS Registration and NHS Screening records.

Pseudonymised analysis data is extracted from the database, either as database views or via text files. When accessing views directly, appropriate roles exist to limit access to the required view(s) only. Analysis datasets will have no individual level identifiers but may have a group identifier e.g. year recruited or site recruited. Analysis datasets will never contain any personally identifiable data e.g. name, NHS number etc. ONLY linkage datasets will require this information. If required year and month of birth will be used instead of full date of birth or death.

How general practice team composition and climate relate to quality, effectiveness and human resource costs: a mixed methods study in England. — DARS-NIC-344271-Q5X0S

Opt outs honoured: Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-08-17 — 2024-08-16 2022.02 — 2022.02.

Access method: One-Off

Data-controller type: ROYAL COLLEGE OF GENERAL PRACTITIONERS, UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Accident and Emergency (HES A and E)
  4. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

Background:
The British National Health Service (NHS) is a primary care led system with general practitioners (GPs) being the first point of contact for citizens with non-emergency health care needs. GPs have traditionally worked in practices, led by partners (or a sole partner), employing a team of staff (nurses, care assistants, receptionists, managers) and liaising with other community services. They coordinate care for local people who register with their practice. The sector is currently facing financial and other pressures that threaten the patient experience. Increases in the number of older people, conditions related to social or economic determinations of health , rising expectations of access and quality of medical care of the general public and transfer of some tasks previously undertaken in hospitals to primary care have added significantly to the general practice workload. Simultaneously, recruitment and retention problems have reduced the number of GPs per capita, and shortages of primary and community nurses have exacerbated staffing problems. The number of qualifying doctors choosing general practice has gradually declined over the last decade, whilst increasing numbers of GPs have left practice, with many opting to work abroad.

Concerns about recruitment and retention have coincided with a period of rapid change in the organisation of general practice. Over time, practices have become larger and incorporated a wider range of staff. In September 2016, the BMA reported 7,613 GP practices in England, a decline of 8% since 2006.
Recently, new organisational forms (e.g. ‘super-practices’, federations, and integrated models of primary and community-based care), and different ownership and contractual models (e.g. Alternative Provider Medical Services) of general practice have developed. In this challenging and changing situation, research is required to produce evidence that will enable primary care commissioners and GP practice managers to make resource allocation decisions that will ensure the workforce is effectively and efficiently deployed, and high quality care is maintained. Whilst it is clear that practices are becoming increasingly multidisciplinary, with a wider range of staff involved in direct patient care representing more varied roles, identifying the optimal mix of professionals is complex. Historically workforce planning has been unidisciplinary, but promotion of workforce flexibilities for care delivery relies on a range of disciplines and requires a different approach to workforce planning.
Workforce is the largest single component of healthcare expenditure and the size and composition of the workforce affects performance and outcomes for patients. The ability of health care systems to provide safe, high-quality, effective, and patient-centred services depends on sufficient, well-motivated, and appropriately skilled personnel operating within service delivery models that optimise their performance.

Problems have been highlighted by the Health Foundation regarding national workforce policy in the English NHS concluding that “Workforce is a relatively neglected area of policy which is often pursued as an afterthought, with important clinical, operational and financial impacts on the front line”. However, a number of recent policy proposals (e.g. the NHS Five Year Forward View, and the GP Forward View) have specifically addressed general practice workforce issues. Moreover, developments driven locally by general practices, Clinical Commissioning Groups and community health service providers have led to changes in the practice organisation and structure. In addition, there have been a number of national reviews of the primary care workforce which have had an influence on policy and practice.
Evidence explaining why this research is needed now.

Aside from calling for increased investment and extended use of technology, recent workforce challenges in general practice have been approached in two ways: different ways of working (e.g. skill mix changes, task shifting, role substitution), and organisational changes. As a result, extended use of mid-level practitioners (advanced nurses, paramedics, pharmacists, physiotherapists) and the introduction of new roles (physician’s associates) is becoming more widespread. New collaborative forms of general practice and integrated models involving hospital-based specialists are also emerging (‘super practices’, networks and federations; and polyclinics and multispecialty providers, respectively).

Aim:
The overall aim of this study is to explore how team composition and climate affect quality of care, clinical outcomes (effectiveness) and human resource costs in England, in order to inform practice management and commissioning decisions. The workforce configurations in general practices are highly variable and there is a lack of evidence about what skill mixes and staff deployments generate the best outcomes for patients and savings for health care economies. In addition, evidence on how the micro-level team climate (trust, relationships, processes, etc) relates to quality is not strong.
Study objectives are:
1. Description of policy context; delivery models; practice level variability in skill mix and human resource costs for general practice
2. Exploration of the factors associated with practice performance in terms of quality of care, in particular, the role of skill mix and human resource costs
3. Exploration of impact of role substitution on practices costs and quality of care
4. Conduct patient level modelling of associations between skill mix and clinical effectiveness and implications for costs
5. Examination of how team working affects quality of care and effectiveness through focus groups with service users, a staff survey in a sample of GP practices and practice based case studies

To complete this research, the research team will obtain fully pseudonymised data from the Royal College of General Practice (RCGP) Research Surveillance Centre (RSC). In addition to the pseudonymised primary care medical records, the research team will need to link the RCGP RSC primary care data with secondary Hospital Episode Statistics (HES) Accident and Emergency records for the primary care data cohort.

These data are being requested by the University of Oxford in the performance of a task in the public interest Article 6(1)(e) (EU GDPR, "Lawfulness of processing") i.e. “processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller”; requested by the National Institute of Health Research to investigate work force and skill mix in GP primary care services, as part of their Health Services and Delivery Research (HS&DR) Programme which aims to produce rigorous, relevant evidence to improve the quality, accessibility and organisation of health services.
The processing required is in accordance with Article 9(2)(j) (EU GDPR, "Processing of special categories of personal data") with regards to the processing being necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law.

Patients who are cared for in general practice surgeries will interact with a wide range of staff, including administrative staff, nurses, healthcare assistants, physiotherapists as well as doctors. How the staff work together has a significant effect on how well a patient is treated and how their medical conditions are managed. Patients who become unwell often attend hospital Accident and Emergency departments and may be admitted for care to hospital as in-patients.
In order to measure how well or poorly general practices are managing the health of their patients, data about how often patients attend hospital accident and emergency departments due to serious medical conditions and how many of a practice’s patients become hospital inpatients. General practice's achievement are measured in the Quality Outcome Framework (QOF) against a scorecard of evidence-based indicators. These indicators span four domains: clinical, organisational, patient experience and additional services. In addition the study will use Emergency hospitalisations for ambulatory care sensitive conditions (ACSC) as the measure of effectiveness, and markers for performance globally as well as in the NHS (Tian Y, Dixon A, Gao H. Emergency hospital admissions for ambulatory care sensitive conditions: identifying the potential for reductions. The Kings Fund, Data Briefing 2012 & World Health Organisation. Assessing health services delivery performance with hospitalizations for ambulatory care sensitive conditions, working document WHO Europe, April 2016).

NHS Digital can provide the Hospital Episode Statistics about Accident and Emergency and Admitted Patient Care (in patient) data, to allow the research team to measure both the clinical and financial impact of the way general practice staff work together to provide patient care.

The study is part of a national program of research funded by the National Institute of Health Research related to Health Services and Delivery Research (HS&DR) Programme and more specifically to the aspect of workforce and skill mix in GP services. The study is part of an ongoing digest of NIHR funded projects (https://www.journalslibrary.nihr.ac.uk/hsdr/#/) aimed to provide evidence to help implement the response to the 2015 Roland Commission’s vision to provide challenging and fulfilling careers for health professionals while delivering a high standard of care.

This study requires secondary (the Hospital Episode Statistics) data and will form the basis of the quantitative assessment of clinical efficacy of primary care and the related cost of clinical outcomes of variation of care, when combined with patients’ medical records from primary care.

500 general practices, comprising the Royal College of General Practice (RCGP) Research Surveillance Centre (RSC) research network will be analysed. This analysis will use a multilevel logistic regression model for the likelihood of hospital admission testing for cross-level interactions between practice characteristics and skill mix profile and emergency admission into secondary care. The registered patients from practices will form the level 1 data, comprising data from secondary care:
• Hospital Episode Statistics Admitted Patient Care
• Hospital Episode Statistics Accident and Emergency
and primary care data from the RCGP RSC database for the year 2019 (1/1/2019 to 31/12/2019).
These data will provide both clinical outcome data and facilitate the calculation of the cost of secondary care.
The data will be pseudonymised and the primary care and secondary care data will be linked and combined with practice level data:
Workforce:
- Total FTE care staff per head of practice population
- Ratio of care staff FTE to total practice FTE
- Ratio of GP FTE to total practice FTE
- Proportion of care staff FTE that are temporary (locum, bank)/ mid level (physician’s associate, advanced nurse)
- Staff turnover, vacancies
Practice Characteristics:
- List size
- Age/ sex distribution of practice population (e.g. % over 75)
- Morbidity (clinical registers
- Region, urban / rural
- Index of multiple deprivations
- Contract type, practice payments
- Type of practice (traditional /new)
Quality indicators:
QOF clinical summary score
CQC inspection rating
GPPS patient experience indicators

When analysed, these data will be used to establish the practice level factors which are associated with patient outcomes / indicators of clinical effectiveness, controlling for patient demographic and comorbidity status, and other practice characteristics which may confound the relationship.

Hospital Episode Statistics about Accident and Emergency and Admitted Patient Care will form the basis of outcome measures of clinical effectiveness : Emergency hospitalisations for ambulatory care sensitive conditions (ACSC) will be the measure of effectiveness used in the analysis. Hospitalisations for ACSCs present a significant burden upon healthcare systems and adjusted rates are used as markers for performance globally as well as in the NHS . ASCS have been described as those conditions where it is possible, to a large extent, to prevent acute exacerbations and reduce the need for hospitalizations through strong primary health care-based services delivery, and are indicators used within the NHS Outcomes Framework (http://content.digital.nhs.uk/nhsof ). Whilst the levels of hospital admissions for select ACSC appear to be decreasing or stabilising over time, there remains wide variation in hospitalisation rates. ACSC are a suitable proxy for primary care clinical effectiveness, and individual conditions will be selected from the Kings Fund categorisation:
- Vaccine preventable: influenza and pneumonia
- Acute (dehydration and gastroenteritis, pyelonephritis, perforated or bleeding ulcer, cellulitis, pelvic inflammatory disease, ENT infections, dental conditions, convulsions/ epilepsy, gangrene)
- Chronic (asthma, congenital heart failure, diabetes complications, COPD, angina, iron deficiency anaemia, hypertension.

To establish the relevant factors determining the current performance of primary care, the most recent stable information has been sought, namely data for 2019.
Regional variation of both healthcare provision and outcomes is well established and to match the geographical spread of the primary care data sources, England wide hospital data will be required in creating a nationally representative dataset.
In order to obtain robust statistical models, hierarchical analyses require patient level will necessary to control for patient characteristics.
Variables have been defined to utilise minimal potential identifiers. Study variables have been specified in formats that will reduce the risk of any inadvertent identification through combinations of variables. Data requested has been limited to that directly relevant to the main outcomes of interest. The study team are not requesting NHS number; this will be pseudonymised using a non-reversible hashing algorithm.

The University of Oxford and the Royal College of General Practitioners are joint data controllers for this study. The University of Oxford is the sole Data Processor. The RCGP RSC has its secure data and analytics hub at University of Oxford, who will manage data governance, encryption and access. The RCGP, has an interest specifically in this study and the use of the data collected due to the nature of the study and therefore has engaged as a joint controller undertaking the relevant activities as a controller along side Oxford. More generally the RCGP does not act as controller of the data used in the research undertaken with the collected data. The RCGP research surveillance centre provides its network general practices with posters which are displayed in the public areas of the practices’ premises (poster provided), patients are informed of the use of their data for both surveillance and research and are provided with a patient leaflet (leaflet provided) and provided with the link to the RCGP RSC transparency statement https://clininf.eu/index.php/transparency-statement/, they are also informed of their right to opt out of their data being used for research and planning (https://digital.nhs.uk/services/national-data-opt-out).
The University of Oxford has a contract with the RCGP to provide this surveillance, quality improvement and research platform. The University of Oxford is identified as a processor of personal data for the Royal College of General Practitioners (RCGP).

Wellbeing (formally Apollo Medical Software Solutions), an approved third-party provider, has formal service agreements and service specifications with RCGP RSC and with individual participating GP practices to conduct data collection and secure web transfer. Each unique patient within the RCGP RSC database is pseudonymised at source before data is extracted from individual practices using a computer generated patient ID created by Wellbeing Software Solutions. This pseudonymisation of records includes production of a hashed NHS number using pseudonymisation algorithm (SHA-512).

The service RCGP use is their secure extraction service:
Wellbeing SQL is a data extraction software that will extract any and all data from the GP practice in a consented, appropriate manner. It allows practices to share information in an anonymised format to third party organisations, who may amalgamate data from multiple practices and apply business intelligence or risk stratification to assist the individual practice, local CCG or PCT. There are regular letters that go out to all practices, a monthly newsletter, and a weekly email.

The National Institute of Health Research have funded this research as part of the Health Services and Delivery Research (HS&DR) Programme (NIHR Funding award ID 17/08/34).


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Expected Benefits:

The findings will produce evidence about what skill mix configurations work best in primary care, and what opportunities exist for substitution of tasks between different health practitioners in order to reduce costs whilst maintaining or improving outcomes. The study will generate quantitative economic models, indicating key characteristics which drive costs and quality. These models will be corroborated by the detailed qualitative parts of the overall, complex, concurrent, parallel, multistage, mixed methods design, with embedded survey and intensive case studies, including patient surveys. The detailed technical economic models will then be interpreted by a working group comprising recommendations and ‘priority action points’. The targeted recommendations and ‘priority action points’ will be more readily interpretable by GP partners, managers and commissioners and will enable them to make staffing decisions based on comprehensive evidence based policy that will ensure that the limited available human resources can be deployed in a way that maximises patient benefit. Identifying efficient workforce configurations will enable more patients to be treated effectively at the same or lower costs. This will benefit the population who are service users, through improved access to more timely care, and tax payers (funders of the NHS).The findings will produce evidence about what skill mix configurations work best in primary care, and what opportunities exist for substitution of tasks between different health practitioners in order to reduce costs whilst maintaining or improving outcomes. The study will generate quantitative economic models, indicating key characteristics which drive costs and quality. These models will be corroborated by the detailed qualitative parts of the overall, complex, concurrent, parallel, multistage, mixed methods design, with embedded survey and intensive case studies, including patient surveys. The detailed technical economic models will then be interpreted by a working group comprising recommendations and ‘priority action points’. The targeted recommendations and ‘priority action points’ will be more readily interpretable by GP partners, managers and commissioners and will enable them to make staffing decisions based on comprehensive evidence based policy that will ensure that the limited available human resources can be deployed in a way that maximises patient benefit. Identifying efficient workforce configurations will enable more patients to be treated effectively at the same efficiently allocated. Overall this will contribute to the smooth running of the NHS in the future, and its sustainability.

The research will also provide information on the relative efficiency of new models of primary care, and whether new staff roles, and new ways of using existing staff, are associated with improvements in patient outcomes or savings in costs. Findings will also indicate how team working and relationships relate to patient outcomes and experiences and staff wellbeing and job satisfaction, providing further guidance about how to foster productive team working environments.
The identification of the changes in how health care is delivered in primary care in terms of roles, will allow for planning of the recruitment of the health care workforce across all specialties and with sufficient staff to provide sufficient training to do the work that is needed in primary care.

In line with the overall stated goals of the HS&DR Programme, this research will produce evidence on the workforce factors associated with quality care provided by general practice. This evidence will form the basis of evolving models of care, informing policy as to how the NHS might improve delivery of services , by developing statistical models that identify key GP workforce characteristics associated with both clinical effectiveness and cost of care. These models will be evaluated at various stages of the study to validate the models’ interpretations in the context of the qualitative evidence, building on the strengths of the mixed methods approach for investigating complex processes in health care. Practice workforce characteristic will be complied from the freely available NHS Digital Primary Care Workforce Minimum Dataset and supplemented by data from the Quality Outcome Framework process and data from the both the Royal College General Practitioners Research Surveillance Centre database and Workforce costs for practices will be examined in relation to the total payments received (NHS Digital Annual Payments Review). The cost of care data required for economic analysis will be estimated using a top down approach with national unit costs (Curtis L, Burns A. Unit Costs of Health and Social Care 2016, Personal Social Services Research Unit, University of Kent, Canterbury. 2016) applied to the direct FTE cost of each staff role by practice. Workforce costs for practices will be examined in relation to the total payments received (NHS Digital Annual Payments Review).

The statistical models based on the primary and secondary (Hospital Episode Statistics Accident and Emergency) care data, will identify the key characteristics of general practices organization and workforce that drive quality of care, facilitating the development of specific policies and planning for the NHS.
There is a detailed dissemination strategy to delivery throughout the project, led by a member of the research team, who is both a senior academic with expertise in Primary Care epidemiology and an active clinician). The plan will ensure results are shared and have impact. Input will be provided by part of the research team engaging directly with GP primary care practitioners, commissioners and service users to develop implementation recommendations, the Service User Panel and Study Steering Group.
To ensure that the outputs inform practice and thereby maximise benefit to patients and the NHS, the dissemination strategy will use a knowledge management framework, creating information at macro (health system), meso (health region/ locality) and micro (individual provider/ practice) levels.

The study outputs will have direct benefit to commissioners and strategic policy makers. Commissioning, policy development and implementation are complex processes. The outputs of this study will have the potential to contribute to these processes, ultimately improving the workforce effectiveness at the practice level through staffing policy and training.

Both economic analysis and investigation of the factors contributing to clinical effectiveness are major aspects of the proposed study. Findings will be disseminated to all stakeholders, both service users, clinicians and administrators at the 10 workshops and policy briefings and conferences. As one of the specified target audiences includes NHS England, there is potential to inform future policy at this level, in line with the NIHR’s remit. Such changes in policy regarding the future conformation of the GP practice workforce has the potential to improvement care quality outcomes and reduce cost of treatment throughout primary care , at the national level .
The benefits will be accrued at several levels of the primary healthcare system; the funder, the NIHR will gain insight into how workforce dynamics in general practice relate to clinical outcomes and from these insights commissioners will have statistical models which can form the basis of a workforce planning toolkit, to be utilized in the nationwide planning of primary healthcare services, to the advantage of the nation as a whole.

The study team will measure clinical effectiveness using adjusted hospitalization rates for ambulatory care sensitive conditions, which are well established markers for performance within the NHS. While the study will deliver its findings within 36 months and the project does not provide for implementation evaluation, the use of routinely used metrics will facilitate the on-going monitoring of clinical performance as defined within the research framework.
The epidemiological analysis of the primary care and linked HES data is expected to be completed within 27 months. The analysis will provide insight into the factors associated with clinical effectiveness However as the study employs a mixed methods approach, the synthesis of the quantitative epidemiological analysis with the qualitative patient and healthcare professional findings will be finalised and disseminated from month 34 to 36.

Specifically, the findings will produce evidence about what skill mix configurations work best in primary care, and what opportunities exist for substitution of tasks between different health practitioners in order to reduce costs whilst maintaining or improving outcomes. This will enable GP partners, managers and commissioners to make staffing decisions that will ensure that the limited available human resources can be deployed in a way that maximises patient benefit. Identifying efficient workforce configurations will enable more patients to be treated effectively at the same or lower costs. This will benefit the population who are service users, through improved access to more timely care, and tax payers (funders of the NHS), because the NHS budget will be more efficiently allocated. Overall this will contribute to the smooth running of the NHS in the future, and its sustainability.

The research will also provide information on the relative efficiency of new models of primary care, and whether new staff roles, and new ways of using existing staff, are associated with improvements in patient outcomes or savings in costs. Findings will also indicate how team working and relationships relate to patient outcomes and experiences and staff wellbeing and job satisfaction, providing further guidance about how to foster productive team working environments.

Outputs:

The study follows a mixed methods design. The findings from the quantitative analysis of the data from NHS Digital data - the hospital data (accident and emergency, in-patient data) and civil registration deaths, will be combined with the practice level database and patient level data from primary care in the RCGP RSC database and an economic regression qualitative using a synthesised using a convergent parallel mixed methods design (Cresswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Sage Publications, Inc., 2011).

To ensure that the outputs inform practice and thereby maximise benefit to patients and the NHS, the dissemination strategy will use a knowledge management framework (de Lusignan S, Pritchard K, Chan T. A knowledge-management model for clinical practice. Journal of Postgraduate Medicine 2002; 48(4): 297-303), creating information at macro (health system), meso (health region/ locality) and micro (individual provider/ practice) levels.

The knowledge translation literature indicates that new information is most effectively disseminated using multiple approaches and ideally face-to-face. In addition to maintaining a project website and giving written and online feedback to study participants, activities will include:

Reports – a study report (planned delivery month 33) will be delivered to the funder, the NIHR. This report will detail all the methods, results and conclusions, including patient and public involvement.

Patient and public involvement has been formalised by the establishment of a Service User Panel (SUP), a form of public patient involvement (PI) advisory group. It will comprise 10 members recruited from different types of practices (traditional and new models, in varied socio-economic-ethnic areas in Kent and Surrey) and will meet four times per year to provide the perspective of patients and the public on issues within the research. The advisory group will be asked to assist with preparing information sheets for participants, focus group topics, patient survey questions, statements for the implementation guideline development process and dissemination materials for lay audiences. The SUP will receive training for their role and full information about the project at the first meeting and will be involved in the knowledge transfer process. Members will be reimbursed for their attendance at meetings, and contributing to research activity, for reasonable travel expenses and time commitments at National Standards for Public Involvement rates.

The NIHR study report the format will conform to the guidance given by the NIHR.

Submissions to peer reviewed journals – research papers will be submitted to lead journals relating to health service and delivery research, health economics and primary care medicine. These research papers will be written and submitted within the year following the end of the study.

Presentations - ten interactive workshops across England on implementation of good practice recommendations developed. These workshops will involve Commissioners and NHS managers- GPs, GP consortia, and other primary care providers- Dept. of Health, NHS Digital, National Institute for Care Excellence (NICE), Care Quality Commission, Health Education England - Royal College General Practitioners (RCGP), Royal College Nursing (RCN), British Medical Association and its Local Medical Committees; other groups dependent on skill mix e.g. Faculty of Physicians Associates (FPA), Royal College of Physicians

Conferences

Patient/public guide (developed with input from the SUP – to help patients and public appraise the pros and cons of skill mix in primary care; targeted at practices PPI group members; lay members of CCGs/STPs; national patient groups/charities

Press releases and policy briefings disseminated through links with key organisations

Social media (LinkedIn® & Twitter®) with associated infographics at key milestones

Massive open online course (MOOC) Webinar, video (YouTube®), multimedia evidence summaries

All data reported in the study outputs will be at the aggregate level with small number suppressed in line with HES analysis guide for quantitative analyses.

Implementation recommendations:
Qualitative research, comprising findings from all aspects of the work will be brought together in a consensus forming process involving GPs, professionals, commissioners and service users in order to produce implementation recommendations that are relevant and workable throughout the NHS .
The consensus forming process will employ the Nominal Group Technique (McMillan, S. S., King, M., & Tully, M. P. 2016. How to use the nominal group and Delphi techniques. International journal of clinical pharmacy, 38(3), 655–662. https://doi.org/10.1007/s11096-016-0257-x) to synthesise findings and elicit consensus among experts on implementation recommendations. The method facilitates the generation of ideas in relation to problems, solutions, or both, and is based on the premise that accurate and reliable assessment is best achieved by consulting a panel of experts and accepting group consensus. Development sessions attended by the members of the research team, Service User Panel and Professionals and Commissioners Panel will establish key learning from the research and identify Knowledge Transfer Topics. Consensus-building workshops with commissioners, healthcare professionals in general practice and service-user representatives (experts) will to consider the Knowledge Transfer Topics and will develop recommendations and ‘priority action points’ that will support practice management and commissioning decisions related to the GP workforce composition and team functioning.
Stakeholders will be recruited to the to the Nominal Group Technique based workshops, so as to ensure that there is good geographical coverage and that different types of practices and a variety of socio-economic and ethnic areas are represented.
The recruitment use two processes
1) an open invitation to commissioners, GPs, other professionals in General Practice and service users) will be publicised via the project website, social media and targeted communications.
2) partnerships will be formed with national networks, such as the Clinical Research Networks, the Primary Care Collaboratives, RCGP and other influential groups, to support recruitment.
These two approaches are expected to be supplemented by the snowballing technique and will thus increase participation rates, with members of the team and individuals who have participated in the research during the two-year period circulating invitations to their contacts. A nominal financial recruitment incentive will be offered to each stakeholder taking part in the workshop, and participant travel expenses will be reimbursed.

a) Patient and carer focus groups
b) Survey of team members in a representative sample of general practices
c) Case studies in 12 general practices

The recommendations from the Nominal Group Technique will inform short term staffing decisions and longer term training plans at practice, regional and national levels.
They will be disseminated through multiple means including interactive workshops, policy briefings and presentations to the relevant audiences.

The workshops lead by a senior primary care researcher will initiate discussion on how to implement good practice recommendations with Commissioners and NHS managers (e.g. Clinical Commissioning Groups, Sustainability and Transformation Plan areas, NHS England), GPs, GP consortia, and other primary care providers, external statutory organisations (e.g. Dept. of Health, NHS Digital, National Institute for Care Excellence (NICE), Care Quality Commission, Health Education England), external non-statutory bodies: Royal College General Practitioners (RCGP), Royal College Nursing (RCN), British Medical Association and its Local Medical Committees; other groups dependent on skill mix e.g. Faculty of Physicians Associates (FPA), Royal College of Physicians.

Policy briefings and press releases will be disseminated to Commissioners and NHS managers (e.g. Clinical Commissioning Groups, Sustainability and Transformation Plan areas, NHS England), through links with key organizations: external statutory organisations (e.g. Dept. of Health, NHS Digital, National Institute for Care Excellence (NICE), Care Quality Commission, Health Education England), external non-statutory bodies: Royal College General Practitioners (RCGP), Royal College Nursing (RCN), British Medical Association and its Local Medical Committees; other groups dependent on skill mix e.g. Faculty of Physicians Associates (FPA), Royal College of Physicians, academia, especially primary care academia through RCGP, conferences and Society of Academic Primary Care (SAPC). The briefing documents will be high level summaries of the key findings of the study, written in a less technical style.

Publications are planned; to report the clinical efficiency and economic evaluation findings, in conjunction with the qualitative research findings - within a year of the end of the study in journals such as the Health Services and Delivery Research (ISSN: 2050-4357).

Statistical mixed effects models of clinical effectiveness and estimation of care costs (and savings) at the practice level and more widely at higher levels of the NHS will be published in the appropriate journals to share findings with the various audiences identified by the dissemination team.

Due to information governance restriction patient and staff level data will not be shared. However all findings will be shared, and publishing in journals offering open access will be sought, aggregated with small numbers suppressed in line with HES analysis guide.

All reporting and wider dissemination activities are scheduled to be completed by the end of September 2021.

Outputs will be produced that meet the needs of six key audiences:

• Commissioners and NHS managers (e.g. Clinical Commissioning Groups, Sustainability and Transformation Plan areas, NHS England). They will be involved in ten interactive workshops based on implementation of good practice recommendations. Press releases and policy briefing will be targeted at this group along with peer reviewed journal articles.
The benefits of the evidence the study will produce are closely aligned with the aims of the funder, NIHR HS&DR to produce evidence on the quality, accessibility and organisation of health services and how the NHS might improve delivery of service. Use of the NHS Digital data will enable the development of statistical models exploring the key determinates of health outcomes of primary care and associated costs, allowing for the inclusion of both individual patient characteristics and the characteristics of their specific GP surgery’s health care teams characteristics responsible for delivery their care.

• GPs, GP consortia, and other primary care providers. They will be involved in ten interactive workshops based on implementation of good practice recommendations and will also benefit from social media, webinar and peer reviewed journal articles. The study findings and their implications will be presented in the workshops and it is hoped that the interaction between the researchers and the GP community will mean that findings and implications of the research can be explored in a supportive and collaborative environment.

• Patients and the public. A Patient/public guide (developed with input from the patient advisory group) will be provided– to help patients and public appraise the pros and cons of skill mix in primary care. Specifically practices PPI group members; lay members of CCGs/STPs; national patient groups/charities will be made aware of this resource. Additionally, social media, webinar and peer reviewed journal articles will be available to further inform.
The benefits of public involvement is an intrinsic part of citizenship, public accountability and transparency and can lead to empowering people who use health and social care services, providing a route to influencing change and improvement in issues which concern people most.

• External statutory organisations (e.g. Dept. of Health, NHS Digital, National Institute for Care Excellence (NICE), Care Quality Commission, Health Education England). These bodies will be invited to attend ten interactive workshops based on implementation of good practice recommendations. Press releases and policy briefing will be targeted at this group and they will also benefit from social media, webinar and peer reviewed journal articles.
This audience, while varied in its make-up, have broad remits in terms of planning and providing health services and also the generation of process and outcome data and its synthesis. By presenting new models of the interaction of GP team composition and climate and outcomes of quality and effectiveness of care, costs, the aim is to refine the co-ordination of the work of these organisations.

• External non-statutory bodies: Royal College General Practitioners (RCGP), Royal College Nursing (RCN), British Medical Association and its Local Medical Committees; other groups dependent on skill mix e.g. Faculty of Physicians Associates (FPA), Royal College of Physicians. These bodies will be invited to attend ten interactive workshops based on implementation of good practice recommendations. Press releases and policy briefing will be targeted at this group and they will also benefit from social media, webinar and peer reviewed journal articles. It is essential that the bodies representing the GP workforce are appraised of the findings of the study, if policy change is to be effective. Engaging with the representatives of the workers delivering change and explaining the study findings and their implications, can only help to initiate change within the body of healthcare professionals required, in response to the new insights the study findings will provide. It is expected that findings will include the degree of variation within primary care, of modes of multi-disciplinary working and it is thus essential the professional bodies can contribute to any strategic planning at the earliest opportunity while be appraised of the complexity of the totality of the system. These workshops will ensure the new findings can be explained in an interactive fashion ensuring details can be explored in a supportive and collaborative environment.

• Academia, especially primary care academia through RCGP, conferences and Society of Academic Primary Care (SAPC) . Academic researchers and societies will be key targets for peer reviewed journal articles generated by the study team and they will also have available the widely disseminated press releases and policy briefings, social media and webinar. The dissemination of research findings and the subsequent discourse is a well established aspect of modern science and will be key to establishing the validity of the findings, promoted the evidence and to initiate further work in the field.

• Ten interactive workshops across England on implementation of good practice recommendations developed as part of the study. The audiences will comprise: commissioners and NHS managers (e.g. Clinical Commissioning Groups, Sustainability and Transformation Plan areas, NHS England), GPs, GP consortia, and other primary care providers , external statutory organisations (e.g. Dept. of Health, NHS Digital, National Institute for Care Excellence (NICE), Care Quality Commission, Health Education England), and external non-statutory bodies (e.g. Royal College General Practitioners, Royal College Nursing, British Medical Association and its local medical committees; other groups dependent on skill mix e.g. Faculty of Physicians Associates, Royal College of Physicians)
• Patient/public guide (developed with input from the SUP)– to help patients and public appraise the pros and cons of skill mix in primary care; targeted at practices PPI group members; lay members of CCGs/STPs; national patient groups/charities (skill mix to deliver quality)
• Press releases and policy briefings disseminated through links with key organisations : commissioners and NHS managers, external statutory organisations, external non-statutory bodies, academia
• Social media (Linked in & Twitter) with associated infographics at key milestones (All)
• Massive open online course (MOOC) webinar, video (Youtube), multimedia evidence summaries
• Publications, including full NIHR report, articles for professional and academic journals, conference presentations

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by "Personnel" (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

The RCGP RSC is based at the University of Oxford. The University of Oxford has a contract with the RCGP to provide this surveillance, quality improvement and research platform. The University of Oxford is identified as a processor of personal data for the Royal College of General Practitioners (RCGP).

The hashing of identifiable data for the Clinical Informatics and outcomes Research Group at the University of Oxford is conducted by the Salt Service of the University of Oxford Central IT team, so that the holder of the pseudonymised data is separated from the service that holds the non-reversible hash key. This avoids pseudonymised data becoming identifiable data.

The SALT methodology rationale will be as follows
1) Wellbeing Software Solutions, an approved third-party provider, has formal service agreements and service specifications with RCGP Research Surveillance Centre and with individual participating GP practices to conduct data collection and secure web transfer.
2) Each unique patient within the RCGP RSC databank is pseudonymised at source before data is extracted from individual practices using a computer-generated patient identifier created by Wellbeing Software Solutions.
3) This pseudonymisation of records includes production of a hashed NHS number using pseudonymisation algorithm (SHA-512).
4) An encryption salt is held by a designated staff member of the University of Oxford Medical Science Division who is not a member of the ORCHID staff.
5) When a data linkage is required to the data extracted by Wellbeing Software Solutions in the RGCP RSC databank, the encryption salt holder sends the encryption salt to the data provider (in this case it is NHS Digital)
6) NHS Digital will hash personal identifiers (in the data requested by ORCHID) using a hashing algorithm. NHS Digital will use the same pseudonymisation algorithm (SHA-512)
7) To make this key unique, an encryption salt is added at the end of the NHS number (e.g. NHS number= 12345678 ; SALT (held by someone other than ORCHID staff) = bob. So, hashing would take place using the SHA2-512 algorithm by 12345678bob = return pseudonymised data.
The encryption salt is one-way.
8) The member of staff who holds the encryption SALT is not a member of the research team working on the data provided by the RGCP or NHS Digital.
9) Therefore the researchers do not have the means available to ‘un-hash’ the data provided.

NHS Digital will hash their NHS numbers using the same pseudonymisation algorithm (SHA-512). NHS Digital will undertake data linkage via the hashed NHS numbers in both sets of data. This process has been used for previous projects linking different sets of data, and the linkage has been successful. Records for each study participant containing information from HES, together with hashed NHS numbers will be sent to the University of Oxford.

There will be no subsequent flows of data from the University of Oxford.

In this agreement the University of Oxford will act as a joint data controller with the RCGP. University of Oxford will process the data.

University of Oxford have requested 41 fields of the dataset HES APC, including the codes present in

General practices within the RCGP RSC network have been involved in disease surveillance for over 50 years. Over this period practices have had feedback about their data quality and many practices have been computerised since the late 1990s, allowing long-term outcomes to be studied.
Wellbeing Software Solutions has formal service agreements and service specifications with RCGP RSC and with individual participating GP practices to conduct data collection and secure web transfer.

Each unique patient within the RCGP RSC database is pseudonymised at source before data is extracted from individual practices using a computer generated patient ID created by Wellbeing Software Solutions. This pseudonymistion of records includes production of a hashed NHS number using pseudonymisation algorithm (SHA-512).

Pseudonymised record-level HES data will be processed and stored at the University of Oxford. Patient level databases are held in the database server within the Research Group's secure network. The Research Group's dedicated secure network is sited behind a firewall within the University's network. It is a standalone, independent network, all in-bounded connections are block, but out-bounded connections are allowed. All staff members of the research group working within the team base work from secure workstations or secure laptops with encrypted drive. Only substantive employees of the University of Oxford will have access to the data and only for the purposes described in this document. The data will be used solely for the study titled "How general practice team composition and climate relate to quality, effectiveness and human resource costs: a mixed methods study in England".

The University of Oxford will send the hashed NHS numbers to NHS Digital. The following flow of hashed NHS numbers will be undertaken.

University of Oxford will identify the study patients for the cohort above from primary care records in the RCGP RSC practices and send the hashed NHS numbers of the cohort under study to NHS Digital to link to HES/ Civil registration data. No other GP data will be sent to NHS Digital.

The process of linkage is as follows:
• NHS Digital will hash their NHS numbers using the same pseudonymisation algorithm (SHA-512) as used by the RCGP.
• NHS Digital will undertake data linkage via the hashed NHS numbers in both sets of data. This process has been used for previous projects linking different sets of data, and the linkage has been successful
• NHS digital extract all HES records for which there are matched primary care records
• NHS digital will send the extract of HES records with the hashed NHS number to the University of Oxford
• University of Oxford will link the HES records together with GP data from the primary care records from RCGP RSC practices with the same hashed NHS numbers
Records for each study participant will when fully linked contain information from HES together with information from RCGP RSC primary care practices.

Each unique patient within the RCGP RSC database is pseudonymised at source before data is extracted from individual practices using a computer-generated patient ID. The University of Oxford holds no identifiable data and only hashed NHS number. Combining/ linking data from University of Oxford for this project will not lead to or increase the risk of pseudonymised data becoming identifiable data. Linkage of two non-confidential datasets does not create a confidential dataset.

Only pseudonymised data with direct patient identifiers removed will be used. The research team will not seek individual patient identifiers; where required, data linkage will be achieved through ‘hashing’ algorithms to generate non-identifiable, unique IDs from identifiable data; as a further protection, non-reversible, pseudonymised ID numbers held be database organisations will be converted to unique study IDs, the keys to which will not be accessible to the research team; and, when using these data small numbers in reporting will be suppressed and the presentation of data that can potentially be used to reveal identities will be avoided. Data extracts and aggregate analyses will be pseudonymised/anonymised as described.

All data processing is carried out by staff with contracts with the University of Oxford. All staff have received Information Governance training on an annual basis and have all passed the NHS Information Governance on-line test for the current year.

Access to the data will limited to researchers with substantive employee contracts with the University of Oxford. All researchers will be required to complete training and sign the relevant agreements to be able to access the data on the University of Oxford secure environment. No individual-level study data can leave this environment, and all aggregated results data is reviewed prior to export.

All data transferred from the secure, “safe haven” computing environment, undergoes a statistical control process, where the aggregated data are assessed in order that no patients can be identified by inference, such as reporting rare diseases or operations conducted at a specific time or location. Practice level identification is also avoided by ensuring the granularity of the analyses reported are at the highest level possible while providing meaningful scientific insights.

The Research Group has conducted a risk assessment of the physical security of the offices and servers where patient level data is kept. The Research Group of Department of Clinical and Experimental Medicine at the University of Oxford has worked with routinely collected healthcare data in a number of research and evaluation projects over the last 15 years. The Research Group works within the Research and Information Governance team at the University of Oxford.

No data is stored outside of the secure computer system hosted at the University of Oxford.

All outputs will be scrutinized by a lead senior academic with the Service User Panel and the Study Steering Group before the output is disseminated. The study has a set up a working group, the dissemination of findings team specifically for this purpose.

National Core Studies - Data and Connectivity: COVID-19 Vaccines Pharmacovigilance (DaC-VaP) — DARS-NIC-431355-B1L8W

Opt outs honoured: Anonymised - ICO Code Compliant, Identifiable, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 - s261(5)(d)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-07-13 — 2022-07-12 2021.10 — 2021.12.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  5. COVID-19 Vaccination Adverse Reactions
  6. COVID-19 Vaccination Status
  7. Diagnostic Imaging Dataset
  8. Emergency Care Data Set (ECDS)
  9. Mental Health Services Data Set
  10. MSDS (Maternity Services Data Set)
  11. Secondary Uses Service Payment By Results Accident & Emergency
  12. Secondary Uses Service Payment By Results Episodes
  13. Secondary Uses Service Payment By Results Outpatients
  14. Secondary Uses Service Payment By Results Spells
  15. MSDS (Maternity Services Data Set) v1.5
  16. Civil Registrations of Death - Secondary Care Cut
  17. COVID-19 Second Generation Surveillance System (SGSS)
  18. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  19. Diagnostic Imaging Data Set (DID)
  20. Maternity Services Data Set (MSDS) v1.5
  21. Mental Health Services Data Set (MHSDS)

Objectives:

OVERALL AIM
This application is part of the urgent public health study that is funded by HDRUK to investigate the pharmacovigilance of the COVID-19 vaccine.

The purpose of this application is to link data held by NHS digital to support the University of Oxford to conduct observational epidemiological studies that inform the national public health response to COVID-19 and importantly the COVID-19 vaccine. The RCGP RSC dataset includes individual patient level up-to-date primary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care. For example, the database contains the following variables for each patient (where present)

Specifically, the objectives are to: measure variation in vaccine uptake in relation to a)population characteristics; b) assess vaccine effectiveness (VE) against infection, transmission, severe outcomes, and deaths; and c) identify the risk of adverse events following immunisation (AEIs).

The “outcome” measures of vaccine effectiveness are:
- Incidences of vaccine preventable disease (VPD) – e.g. COVID-19, influenza etc.
- Hospital admission – usually within 28 days of suffering from the index VPD
- Intensive care admission
- Death, again usually within 28 days of the index date of the VPD

Real time information will be provided to Public Health England (PHE), and through them to the Joint Committee for Immunisation and Vaccination (JCVI) and SAGE. The requirement varies with the stage and impact of any VPD.

The study involves COVID-19 vaccine pharmacovigilance across England, Wales, Scotland and Northern Ireland, where each of the nations do their own analyses within their secure environment. The University of Edinburgh will liaise with each of the analyses leads in the four nations.

The study team are requesting to utilise all the datasets coming into the University of Oxford secure environment as part of the MAINROUTE (DARS-NIC-381683-R6R6K) agreement.
The datasets included are:
- Detailed demographic and risk factor data.
- COVID-19 appointments: information on whether or not a virology swab was taken and the outcome of the swab
- Non-COVID-19 appointments
- Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE
- Vaccination status: date of vaccination, type of vaccination
- Co-morbid conditions
- Medication which may be associated with better or adverse outcomes.
- Test results
- Referrals made
- A&E visits
- Inpatient appointments, including critical care
- Secondary Uses Services Payment by Results (SUS)

Datasets that are requested to flow under this agreement are:

- COVID-19 Second Generation Surveillance System (SGSS) – (Pillar 1)
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar2)
- Civil Registrations (Deaths)– Secondary Care Cut
- Mortality data
- Maternity Services Data Set (MSDS)
- Covid-19 vaccination status and adverse effects following vaccination

The impact of infection on pregnancy (including the need for intervention), and the impact of infection on infants in the months of life (if their mother is not immune) are really important. For example, if mothers are not immune to RSV or influenza – then there are no antibodies crossing the placenta to protect the young infant. Hence vaccine uptake in pregnancy and linkage to infant outcomes are a very important part of our academic work. Capturing vaccine exposure in pregnancy is important. As no trial to date has included pregnant women this type of study is the only opportunity to explore safety and effectiveness in pregnant women and their babies.


The same pseudonymisation algorithm will be applied to all data involved in this study (and any other studies) so the researchers can draw scientific conclusions for a study population.

The University of Oxford is the sole data controller for processing the data that is mentioned within this agreement. Some of the data which will be accessed under this agreement will be data which is already in the hands of University of Oxford under a different agreement DARS-NIC-381683 for which University of Oxford operate as a Data processor for on behalf of Pubic Health England (PHE) and the Royal Collage of GPs (RCGP). This agreement will also flow new data sets (such as the COVID-19 vaccine data) which are not currently held by the Data Controller and these datasets are only for use by the Data Controller (University of Oxford) for the purposes set out in this data sharing agreement.

University of Oxford – The University are a data processor for the surveillance activities it undertakes for PHE In addition to surveillance, there is an agreement between PHE and Oxford to use the data collected for surveillance activities for further research studies, for which University of Oxford will be the data controller. the work being undertaking under this agreement falls in the further research area which is under University of Oxford control.

University of Edinburgh - This study is part of urgent public health studies and funded by the HDRUK Data and National Connectivity Studies, Rapid Funding Call. Edinburgh applied for the funding on behalf of University of Oxford as they are managing the home nations response. Each of the home nations are taking control of the work within their regions and so for England the University of Oxford are the sole Data controller with their own ethical approval in place for this work. The University of Edinburgh do not make any decisions about the means by which the personal data are being processed under this agreement.

The data will only be processed by University of Oxford. Analysts at University of Edinburgh may gain access to outputs which will be aggerated with small numbers suppressed in line with the HES analysis guide.

The GDPR Lawful basis for processing the requested data under this agreement are;

Article 6(1)(e) (Public Task processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller).

Article 9(2)(j) (processing is necessary for reasons of public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interest of the data subject.

Only the named Data Controller and Processor have permission to access the record level data provided under this agreement. The size of the cohort is 25,428,392 individuals.

The DaCVAP and AstraZeneca (AZ) programme of work are different analyses over different time periods, using different datasets and range of data sources. The results will hopefully be compatible and comparable but quite different. Achieving similar results from different lines of enquiry is an important part of science – particularly in epidemiologal studies like this where we identify associations, rather than measure direct causation.

The main difference between the DaCVAP study and the AZ programme is the nature of the analysis. The DaCVAP study looks to report the relative risk (how much more likely the event is), whereas the AZ analysis will provide the rate of these events. The former may say that an event is two or three times more common, the latter is that the rate in the unvaccinated group is 1 per million and that in the vaccinated group 2 pear million (these rates are just illustrative and not based on analysis).

In greater detail: The AZ approach is that of a cohort study and therefore utilises the entire RCGP ORCHID cohort and eventually, in phase 2, the wider English cohort available in NHS Digital. The DaCVAp study is a nested case control study and, for each case utilises up to 10 controls (people in RCGP ORCHID who have not, by index date - event date of the case - have not experienced an event of interest). Post-hoc confirmatory analysis will be carried out via a self-controlled case series model. The cohort, AZ approach, allows for the calculation of incidence rates (absolute risk) as well as relative risk, furthermore it allows for a wider set of modelling techniques, namely AI ensemble and compartmental, mathematical modelling to be implemented. There are additionally differences in the variables, time periods and matching approaches between the studies. DaCVAP also coordinates a distributed analysis across the four UK devolved nations, whereas our AZ study is just with English data.

Expected Benefits:

Analyses conducted under HDRUK-funded DaC-VaP will lead to a better understanding of the characteristics of patients being tested for COVID-19 and the associations between demographics, comorbidity and medications on the likelihood of developing COVID-19 post COVID-19 vaccinations and subsequent complications, if any. The major benefit of this study is to see differences between various demographic characteristics, especially ethnicity.

Moreover, the study will establish the safety of COVID-19 vaccination and its effectiveness to reduce COVID-19 infections. Analyses will also establish if COVID-19 vaccine will have an impact on other flu-like illness. All these will benefit public health and will inform them about the benefits of the COVID-19 vaccine.

Furthermore, the study will support COVID-19 vaccine surveillance.

Outputs:

Specific Outputs for this study are:
• To measure the outcome of the COVID-19 vaccine
• To look at risk of COVID-19 infections, hospitalisations and deaths post vaccination
• To track the impact of COVID-19 vaccination in terms of visual descriptions (dashboards) of the number and rates of patients vaccinated.
• Subgroups of data will be identified to enable display of vaccination by GP practice, region, age group, gender, and ethnicity
• Furthermore, ability to track number of patients receiving one or both of the COVID-19 vaccine dose, vaccine brand and categorise by age group, gender, ethnicity.
• To establish differences in the vaccine effectiveness between the different brands of vaccine and different doses

A protocol of the study design will be published (already submitted to The Lancet) as well as publish papers in international journals. A number of publications are expected across each of the 4 nations as well as one for the harmonised analyses

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Patient and Public Involvement and Engagement (PPIE) members have been involved since the beginning of this project. Research proposal for the Wales analysis has also been reviewed by members of the public. Their contribution includes defining research questions, interpretation, and dissemination of study findings. The PPI and study team did a survey around vaccine emotions due to the high media discussion around the Oxford –AZ COVID-19 safety. The PPI group showed high confidence in vaccine safety and felt strongly that the media attention could be a political issue (EU and UK). They do have concerns about the long term impact of COVID-19 (Long COVID). The PPI have further raised the potential need of COVID-19 booster doses and also making the vaccine mandatory for work and/or travel.

Processing:

Flows of data:
- Data are extracted from practices that are members of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network by Wellbeing. The University of Oxford subcontracts with Wellbeing to do this as part its contractual responsibilities.
- The University of Oxford will provide NHS digital with a list of pseudonymised NHS numbers and pseudonymised date of birth for the cohort monthly.
- NHS Digital will link the cohort to the requested datasets and send pseudonymised linked datasets securely back to University of Oxford.
- University of Oxford will store the data on the secure network.
- University of Oxford will process and aggregate pseudonymised data to produce approved reports for surveillance (as part of the National surveillance process); and for the purpose of COVID-19 vaccine pharmacovigilance and quality improvement.

No identifiable data items will be passed into or out of NHS Digital

SALTING METHODLOGY:
The University of Oxford will follow a salting method in a manner that all the data will be non-identifiable. The process is as follows:
1. An encryption salt is held by a designated staff member of the University of Oxford Medical Science Division who is not a member of the ORCHID staff.
2. When a data linkage is required, the encryption salt holder sends the encryption salt to the data provider (NHS D)
3. The data provider will hash personal identifiers (in the data requested by ORCHID) using a hashing algorithm
4. The hashing algorithm is SHA2-512.
5. To make this key unique, an encryption salt is added at the end of the NHS number (e.g. NHS number= 12345678 ; SALT (held by someone other than ORCHID staff) = bob. So, hashing would take place using the SHA2-512 alogrithm by 12345678bob = return pseudonymised data)

The data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.
Data will only be accessed by individuals within the University of Oxford who have authorisation that are substantive employees of University of Oxford. The authorisation process includes: (1) Contractual requirement to follow IG principles; (2) Using the email registered with Human Resources to complete IG training and to return the certificate; (3) Staff email is authorised by the IT department for one year to access the secure network and staff computers are configured to allow this; (4) At any point the project managers or Head can have access to the secure network turned off. There is special authorisation to have access to the main database.

The additional linkages will be added to the data that the University of Oxford already receives from the RCGP RSC network practices and PHE reference laboratories.

This process for previous projects linking different sets of data, and the linkage has been successful, provided both parties use the same pseudonymisation algorithm (SHA-512).

There will be no requirement nor attempt to re-identify individuals from the data. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

The use of national data is needed as the University of Oxford are a national surveillance centre and the cohort are from across England and Wales.

The use of pseudonymised NHS numbers are essential as the request to link to the data that the University of Oxford already received from the RCGP RSC network general practices and PHE reference laboratories.

NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

R15 - The Platform Randomised trial of INterventions against COVID-19 in older peoPLE (PRINCIPLE) trial — DARS-NIC-373132-D3Y7P

Opt outs honoured: No - consent provided by participants of research study, Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2020-11-10 — 2021-10-09 2020.11 — 2021.10.

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Secondary Uses Service Payment By Results Spells
  2. Secondary Uses Service Payment By Results Episodes
  3. Secondary Uses Service Payment By Results Outpatients
  4. COVID-19 Hospitalization in England Surveillance System
  5. Secondary Uses Service Payment By Results Accident & Emergency
  6. Civil Registration - Deaths
  7. Secondary Uses Service Payment By Results Accident & Emergency
  8. Civil Registrations of Death

Objectives:

University of Oxford are running the Platform Randomised trial of INterventions against COVID-19 In older people (PRINCIPLE) Trial.

COVID-19 disproportionately affects people over 50 years old with comorbidities and those over 65 years old. The infection causes considerable morbidity and mortality in this population group in particular, and is having a devastating effect on people's health, and society in the UK and internationally. So far, there are no specific treatments for COVID-19 that have been proven in rigorous clinical trials to be effective. Most cases are being managed in the community. It is essential that we urgently identify interventions that may favourably modify progression of the infection. An ideal intervention would be one that is safe, with few side-effects, helps prevent disease progression, and can be administered in the community using existing NHS processes and capability.

Setting up a bespoke randomised controlled trial for each potential intervention that might become available will be inefficient. The University of Oxford therefore propose establishing a platform, randomised controlled trial in primary care that can be rapidly deployed to evaluate low risk interventions for high risk people. Using an efficient open clinical trial design, with procedures embedded in existing health service structures and capabilities as afar as possible, our trial aims to give a rapid answer about the effectiveness of trial treatments in modifying the disease course. The goal is to prevent disease progression such that affected individuals will recover sooner, but critically, avoid the need for hospital admission. The platform trial will be flexible in that it will operate under a master protocol that will allow the addition of further interventions into the trial while the trial is already in progress, should such suitable interventions become available for this kind of evaluation.(5) This means that a new trial does not need to be started afresh each time an additional suitable intervention becomes available, and it also means that existing controls can be used efficiently to give rapid answers about the effectiveness of new interventions. This is particularly important as new candidate interventions are being considered on a regular basis.

The trial will be implemented in the first instance by the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) general practices. This is one of Europe's oldest sentinel systems. RCGP RSC has produced a weekly report of influenza, respiratory and other infections in primary care for over 50 years. RCGP RSC works closely with Public Health England (PHE). More information at: www.rcgp.org.uk/rsc. The RCGP RSC Network has over 500 practices, including 100 practices currently swabbing patients with suspected COVID-19 in partnership with Public Health England (PHE).

Trial aspects will be managed by the UK Clinical Research Collaboration Registered University of Oxford Primary Care and Vaccines Clinical Trials Unit.

The trail participants will be consented into the trial and will be made up of Patients ≥50-64 years with comorbidities, and aged ≥65 with or without comorbidity presenting within 7 days since onset of symptoms with a new continuous cough and/or high temperature during time of prevalent COVID-19 infections. Recruitment has commenced and at the time of writing approx. 1500 patients are enrolled. A sperate agreement is in place with NHS Digital to aid and boost recruitment of individuals into the trial.

Primary objective of PRINCIPLE - To assess effectiveness of trial treatments in reducing the need for hospital admission or death, for patients aged ≥50 years with comorbidity, and aged ≥65 with or without comorbidity and suspected COVID-19 infection during time of prevalent COVID-19 infections.

Primary objective outcome measures: Hospital admission or mortality related to suspected COVID-19.

Secondary objectives - To explore whether trial treatment reduces
1) Duration of severe symptoms
2) Time taken to self-report recovery
3) Contacts with the health services
4) Consumption of antibiotics
5) Hospital assessment without admission
6) Oxygen administration
7) Intensive Care Unit admission
8) Mechanical ventilation
9) To determine if effects are specific to those with the infections syndrome but who test positive for COVID-19
10) Duration of hospital admission

Secondary objective outcome measures:
1-2 Patient report on day they feel to have recovered (Daily online symptoms score. Telephone call or text day 7, 14 and 28 if data not being received online).
3. Contacts with health services reported by patients and captured by reports of patients linked medical records where the practice is a member of RSC (GP notes review through RCGP RSC network after 28 days and linkages to HES/ECDS/SUS/CHESS)
4. Bi-weekly reports from participants primary care medical records (RSC)
5-8 and 10 patient report/carer report/linked medical records from primary care and hospital care (HES/SUS/MORTALITY/CHESS/RSC data linkage after 28 days where patients have been assessed in hospital)
9. Swab results for COVID-19 will indicate an “Intention to Treat Infected” group within the overall cohort for sub analysis from national laboratory infrastructure (RSC/SGSS)

Organisations Involved
The University of Oxford are the sole data controller for this request.

Public Health England (PHE)

For the purposes of the PRINCILE trial PHE do not hold any data controllership responsibilities. However members of the statistical team are based at PHE working under the direction of the data controller and are therefore lusted as data processors.

Royal Collage of General Practitioners (RCGP)

For the purpose of the PRINCIPLE trial RCGP are not a data controller. The RCGP are joint data controllers for the RCGP RSC collected data which is being linked to the cohort data. The RCGP play no role in determining the means by which or the purpose for which the data will be processed for the PRINCIPLE trial.

University of Surrey

The RCGP Research Surveillance Centre (RCGP RSC) was based at the University of Surrey, but is in the process of transferring to Oxford. Data being shared for the PRINCIPLE trial will be sent to Surrey so that the data they hold on the RCGP RSC for the consented cohort can be linked to the PRINCIPLE cohort. The RCGP RSC is a growing network of over 1200 GP surgeries based in England. University of Surrey are data processors

The University of Surrey acts as Data Processor on behalf of the Data Controller for the PRINCIPLE trial (Oxford). An existing secure network at the University of Oxford is progressively housing the RCGP RSC data. This data collection is known as (ORCHID secure). This process is underway and due to be completed by early 2021, at which stage all these data will all be held on the Oxford secure network.

University of Surrey are a joint data processor they currently host the RCGP RSC Database for which the trial participants data will also be linked with. The RCGP RSC dataset includes individual patient level up-to-date primary and secondary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care. For example, the database contains the following variables for each patient (where present):

• Detailed demographic and risk factor data.
• COVID-19 appointments: including information on whether or not a virology swab was taken and the outcome of the swab
• Non-COVID-19 appointments.
• Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE
• Vaccination status: date of vaccination, type of vaccination
• Co-morbid conditions
• Medication which may be associated with better or adverse outcomes.
• Test results
• Referrals made
• A & E visits
• Inpatient appointments, including critical care
• Outpatient appointments
• Mortality data (if applicable).

Expected Benefits:

There is an urgent need to report the data from the >1,500 patients who have joined the trial. The results may have immediate impact on management of suspected COVID-19 infection and this study rightly has urgent public health status.

The extracted data will identify whether there is undetected community transmission of COVID-19. For cases of COVID-19 PRINCIPLE will report the effectiveness of different treatments given early in the disease on health outcomes. For cases of COVID-19 the associations between demographics, comorbidity and medications on the likelihood of developing COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, death).

For PRINCIPLE linked data allows the primary and secondary objectives of the trial to be achieved as detailed in the previous "objective" section. It also allows validation and enhancement of sociodemographic and comorbidity data for PRINCIPLE participants

Outputs:

Specific outputs for this trial are:

PRINCIPLE will publish the results of each CTIMP investigated in open-access journals with summary reports made publicly available through the study website (https://www.principletrial.org/).

PRINCIPLE will work with patient and public representatives to ensure that such reports are communicated in an appropriate manner for a lay audience. The Investigators will be involved in reviewing drafts of the manuscripts, abstracts, press releases and any other publications arising from the study. Authors will acknowledge that the study was funded by UKRI/NIHR and any other funding that is secured. Authorship will be determined in accordance with the ICMJE guidelines and other contributors will be acknowledged. All outputs are expected to be submitted by the end of 2022.

Guidance how to manage cases of COVID-19 on presentation to primary care or other community services will be produced.




Processing:

Participants who have consented to the PRINCIPLE Trial will have their data initially linked with the primary care data which is already held as part of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network database.

• The University of Surrey, on behalf of the University of Oxford for the PRINCIPLE trial, will provide NHS Digital with a list of NHS numbers and date of births along with a unique Study ID for the PRINCIPLE cohort. A specific University of Surrey IT team within the IT department will share the identifiers with NHS D not the research team.

• NHS Digital will send back to University of Surrey the linked cohort data with SUS/CHESS and Mortality data included.

• University of Surrey will store the data on the secure network researchers will then analyse a pseudonymised data to produce outputs for the PRINCPLE Study.

The data is controlled and processed by a group of staff who are all based at the University of Surrey; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network. These same processes are replicated in Oxford.

The general practices in the RCGP RSC take virology swabs and serology samples to know if someone has a COVID-19 or a range of other infections, including influenza. These results are being passed back to the patients GP for their clinical care. However, a pseudonymised copy will go to the RCGP RSC. The COVID-19 status will be shared with the PRINCIPLE trial team (who also pseudonymise NHS number to allow this linkage).

NHS Digital data being accessed by a statistician in the USA will be aggregated with small numbers suppressed. All record level data will be held and stored within England and Wales.

RAPid Testing fOR Covid-19 (RAPTOR-C19). — DARS-NIC-396119-C8W3W

Opt outs honoured: Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-02-08 — 2022-02-07 2021.06 — 2021.06.

Access method: Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Civil Registrations of Death
  5. COVID-19 Second Generation Surveillance System (SGSS)

Objectives:

RAPID COMMUNITY POINT-OF-CARE TESTING FOR COVID-19 (RAPTOR-C19) is a study being run by the University of Oxford.

The NHS urgently needs quick, accurate rapid diagnostic tests to diagnose people with coronavirus or to confirm that people do not have the infection. Point-of-care Tests (POCTs) can be used in community settings where there is no easy access to a specialist laboratory. They provide quick results that allow people to get immediate advice about self-isolation and treatment, potentially blocking further spread of infection in the community. Companies are quickly developing new rapid diagnostic tests, but we do not know how well they work. Some tests give a result like a pregnancy test by using a drop of blood from a finger prick. Others use saliva, or a swab to collect a sample from the nose or throat.

Companies check tests work in their laboratories, but usually tests do not work as well when used in the field with real patients. Accurate rapid diagnostic tests are important so that people are not falsely reassured when they are infected, and are not wrongly diagnosed when they are not really infected.

The University team manages a national surveillance system with a network of community settings including GP practices from all over England that report directly to the Department of Health and Social Care about a wide range of infections. These GP practices have been testing for coronavirus since January 2020 with samples sent for laboratory tests. In this study, practices in the network will quickly compare new POCTs for coronavirus with laboratory tests so we can see how good the new tests are in a coordinated and efficient way. National COVID-19 Test centres may also support the research project.
There are currently no rapid diagnostic tests that have been evaluated as fit-for-purpose in NHS primary care that aim to identify whether adults are currently, or have been, infected by COVID-19. The UK and wider world is in the midst of the 2019 novel coronavirus (SARS-CoV-2) pandemic. Accurate diagnosis of infection, identification of immunity and monitoring the clinical progression of infection are of paramount importance to our response, and for all of these diagnostics are central. Widespread population testing has proven difficult in western countries and has been limited by test availability, diagnostic test sensitivity, human resources and long turnaround times (up to 72 hours). This has limited our ability to
control the spread of infection and to develop effective clinical pathways to enable early social isolation of infected patients, early treatment for those most at risk and early return to work for those with resolved infection and potential immunity.

POCTs can be used in the community where there is no easy access to a specialist laboratory, in locations such as NHS general practices. POCTs provide quick results that allow people to get immediate advice about self-isolation and treatment, potentially blocking further spread of infection in the community. In-context evaluation of POCTs in the community is important as test accuracy can vary based on the prevalence of disease in the population tested. “In-context” in this case means “in the clinical environment that the test is to be used”, for example, RAPTOR is evaluating tests for use in general practice, in general practice. This is an important process as tests perform differently (either more or less accurate) when used in different settings / environments / contexts.

The severity of the COVID-19 disease in the community is much lower than in hospital patients. Symptomatic acutely unwell hospitalised patient are likely to have higher viral loads that are easier to detect, and may be undergoing invasive procedures to collect samples from the lower respiratory tract, that have a higher yield. Testing only severe patients introduces spectrum bias, and biases the results to overestimate test performance. It is important to diagnose hospital patients, but from a public health point of view the most concerning patients are ambulatory outpatients, who may spread the virus much further in the community if falsely reassured. Evaluations of COVID-19 POCTs are therefore required in each clinical setting. Community based POCTs may lead to additional public health impacts such as reducing onward household transmission of COVID-19, improving surveillance of NHS and social care staff, accurate prevalence estimates, and understanding of COVID-19 transmission dynamics in the population.

RAPTOR-C19 will provide the community testbed to the COVID-19 National DiagnOstic Research and Evaluation Platform (CONDOR). It should be noted that CONDOR will not contain any NHS Digital data.

RAPID COMMUNITY POINT-OF-CARE TESTING FOR COVID-19 (RAPTOR-C19)

Aim - to assess the diagnostic accuracy of multiple current and emerging point-of-care tests (POCTs) for active or past COVID-19 infection in the community setting.

It is estimated that up to 10,000 patients will be recruited for the trial (approximately 1,500 patients per test).

Specific objectives - RAPTOR-C19 will incorporate a series of prospective observational parallel diagnostic accuracy studies of COVID-19 POCTs against laboratory and composite reference standards in patients with suspected current or past COVID-19 attending RCGP RSC general practices. Because the current reference tests are imperfect, the RAPTOR-C19 protocol allows “standard” and “enhanced” diagnostic accuracy studies for active and past infection:

• Standard diagnostic accuracy of POCTs for active COVID-19 infection with reference to Public Health England (PHE) reference standard virology testing.
• Standard diagnostic accuracy of POCTs for past COVID-19 infection with reference to PHE reference standard serology testing.
• Enhanced diagnostic accuracy of POCTs for active COVID-19 infection assessed against a composite reference standard using multiple tests data, linked Electronic Health Records (EHR) data, and patient reported outcomes data
• Enhanced diagnostic accuracy of POCTs for past COVID-19 infection assessed against a composite reference standard using multiple tests data, linked EHRs, and patient reported outcomes data

Composite reference standards - An assumption of standard diagnostic accuracy studies is that the reference standard is infallible. This constrains the performance of the index test to the performance of the reference standard and assumes every time the tests get different results the reference is correct and the index is incorrect. In reality, the reference standard is unlikely to be perfect, so we will undertake further analyses using composite reference standards. Composite reference standard 1 will be designed to minimise false negatives (FNs), and composite reference standard 2 will be designed minimise false positives (FPs).

For example, for POCTs for current infection:
1. A positive composite reference standard to minimise the impact of a FN PHE reference test result for current infection at visit one / increase sensitivity will also include:
i. paired PHE antibody testing suggesting active infection at visit one (positive Immunoglobulin G (IgM)) and past infection at visit two (positive Immunoglobulin G (IgG)), or
ii. EHRs showing a confirmed COVID-19 diagnosis (in another setting), such as a 111 contact, COVID-19 hospital related admission or death in the following 28 days, or
iii. a positive household contacts within 14 days identified via RCGP-RSC

2. A positive composite reference standard to minimise the impact of a FP PHE reference test result for current infection at visit one / increase specificity will also include:
i. at least two positive PHE reference tests for current infection, or
ii. paired PHE antibody testing suggesting active infection: visit one (positive for IgM) and visit two (positive IgG), or
iii. linked EHRs showing a 111 contact for COVID-19, COVID-19 hospital admission, or death

For POCTs for past infection:
1. A positive composite reference standard to minimise the impact of a FN PHE reference test result for past infection at visit one / increase sensitivity will also include:
i. positive visit two IgG positive PHE antibody tests, or
ii. linked EHRs showing a confirmed past COVID-19 diagnosis (in another setting), such as positive PHE test for active COVID-19 infection, a 111 contact for COVID-19, hospital COVID-19 related admission, or
iii. a previous household COVID-19 contact identified via RCGP-RSC

2. A positive composite reference standard to minimise the impact of a FP PHE reference test result for past infection at visit one / increase specificity will also include:
i. Paired PHE serology: visit one (positive IgG) and visit two (positive IgG), or
ii. linked EHRs showing a 111 contact for COVID-19, COVID-19 hospital admission

Linkage to the CHESS, SGSS, and the Civil Registration of Deaths datasets will allow clinical information about SARS-CoV-2 to be captured from outside of the primary care setting prior to and following the date of RAPTOR point of care test evaluation. These data will allow the RAPTOR team to construct a composite reference standard to identify occasions where the laboratory SARS-CoV-2 test used as the primary reference standard is likely to have been a false negative or false positive. Civil Registration of Deaths is required as CHESS only captures COVID death in hospital and there is the need to identify any COVID mortality (in hospital and in the community) within 28 days of recruitment to use in the composite reference standard.

Organisations Involved

The University of Oxford are the sole data controller for this request.

Public Health England (PHE)

For the purposes of the RAPTOR trial PHE do not hold any data controllership responsibilities. However members of the statistical team are based at PHE working under the direction of the data controller and are therefore listed as data processors.

Royal Collage of General Practitioners (RCGP)

For the purpose of the RAPTOR trial RCGP are not a data controller. The RCGP are joint data controllers for the RCGP RSC collected data which is being linked to the cohort data. The RCGP play no role in determining the means by which or the purpose for which the data will be processed for the RAPTOR trial.

University of Surrey

The RCGP Research Surveillance Centre (RCGP RSC) was based at the University of Surrey, but is in the process of transferring to Oxford. Data being shared for the RAPTOR trial will be sent to Surrey so that the data they hold on the RCGP RSC for the consented cohort can be linked to the RAPTOR cohort. The RCGP RSC is a growing network of over 1200 GP surgeries based in England. University of Surrey are data processors

The University of Surrey acts as Data Processor on behalf of the Data Controller for the RAPTOR trial (Oxford). An existing secure network at the University of Oxford is progressively housing the RCGP RSC data. This data collection is known as (ORCHID secure). This process is underway and due to be completed by early 2021, at which stage all these data will all be held on the Oxford secure network. Further information relating to ORCHID can be found here: JPH - The Oxford Royal College of General Practitioners Clinical Informatics Digital Hub: Protocol to Develop Extended COVID-19 Surveillance and Trial Platforms | de Lusignan | JMIR Public Health and Surveillance

University of Surrey currently host the RCGP RSC Database for which the trial participants data will also be linked with. The RCGP RSC dataset includes individual patient level up-to-date primary and secondary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care. For example, the database contains the following variables for each patient (where present):

• Detailed demographic and risk factor data.
• COVID-19 appointments: including information on whether or not a virology swab was taken and the outcome of the swab
• Non-COVID-19 appointments.
• Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE
• Vaccination status: date of vaccination, type of vaccination
• Co-morbid conditions
• Medication which may be associated with better or adverse outcomes.
• Test results
• Referrals made
• A & E visits
• Inpatient appointments, including critical care
• Outpatient appointments
• Mortality data (if applicable).

The RAPTOR-C19 study (IRAS ref 284320) was approved by the North West - Liverpool Central Research Ethics Committee (ref 20/NW/0282) on June 10th 2020. Participants give individual patient consent for RAPTOR-C19 to access their medical records data.

Note that current funding for the study is until June 2021, but the end date could be later than this depending on whether test continue to require evaluation and further funding is secured.

Expected Benefits:

For RAPTOR-C19 linked data allows the development of an enhanced reference standard to overcome imperfections in laboratory tests for COVID-19, thereby providing more accurate estimates of diagnostic accuracy. It also allows validation and enhancement of sociodemographic and comorbidity data for RAPTOR-C19 participants.

Outputs:

Specific outputs for this trial are:

• RAPTOR-C19 will publish the results of each POCT evaluation in open-access journals, the protocol on the study website (https://www.condor-platform.org/condor_workstreams/raptor) and registries, and summary reports which can be made publicly available through e.g. the websites of the study and of the NIHR Community Healthcare MIC (https://www.community.healthcare.mic.nihr.ac.uk/). RAPTOR-C19 will work with patient and public representatives to ensure that such reports are communicated in an appropriate manner for a lay audience. The Investigators will be involved in reviewing drafts of the manuscripts, abstracts, press releases and any other publications arising from the study. Authors will acknowledge that the study was funded by UKRI-MRC and any other funding that is secured. Authorship will be determined in accordance with the ICMJE guidelines and other contributors will be acknowledged. All outputs are expected to be submitted by the end of 2021.

Findings from this trial will contribute to the main outputs of the RCGP RSC.

The NIHR Community Healthcare MedTech and In vitro Diagnostics Co-operative and CONDOR have PPI groups feeding into the RAPTOR-C19 study. They have contributed to the development of the RAPTOR-C19 protocol, have commented on the relevance and acceptability of research questions and methods, they have assisted in the development of patient facing materials, and continue to advise on the public dissemination of results.

Processing:

Participants who have consented to the RAPTOR Trial will have their data initially linked with the primary care data which is already held as part of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network database.

• The University of Surrey, on behalf of the University of Oxford for the RAPTOR trial, will provide NHS Digital with a list of NHS numbers and date of births along with a unique Study ID for the RAPTOR cohort. A specific University of Surrey IT team within the IT department will share the identifiers with NHS D not the research team.

• NHS Digital will send back to University of Surrey the linked cohort data.

• University of Surrey will store the data on the secure network researchers will then analyse a pseudonymised data to produce outputs for the RAPTOR Study.

The data is controlled and processed by a group of staff who are all based at the University of Surrey; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network. These same processes are replicated in Oxford.

The general practices in the RCGP RSC take virology swabs and serology samples to know if someone has a COVID-19 or a range of other infections, including influenza. These results are being passed back to the patients GP for their clinical care. However, a pseudonymised copy will go to the RCGP RSC. The COVID-19 status will be shared with the RAPTOR trial team (who also pseudonymise NHS number to allow this linkage).

All record level data will be held and stored within England and Wales.

ATEMPT: Antihypertensive Treatment in Elderly Multimorbid Patients (Pilot Study) — DARS-NIC-311182-F5W4X

Opt outs honoured: Identifiable, Yes (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-04-29 — 2022-04-28 2021.06 — 2021.06.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Demographics

Expected Benefits:

This data application will enable an eligible group of potential participants to be identified and recruited into the ATEMPT clinical trial (pilot).

It is hoped that the results from the ATEMPT trial will be relevant for the management of hypertension in older people with multimorbidity or polypharmacy, a patient group for whom currently the treatment effects of pharmacological blood pressure (BP) reduction remains uncertain. These patients have been underrepresented or excluded in previous research, thus, leaving an important gap in our understanding of treatment effects. One key reason for the existing gap in evidence is the challenge of recruiting sufficiently large numbers of older and multimorbid patients into clinical trials. This pilot study aims to address this challenge by recruiting approximately 200 participants aged 65 or over who have at least 3 long-term health conditions or are taking at least 5 medications in addition to any being taken to manage blood pressure.

The findings from this pilot study are intended to provide the feasibility required to inform the planning of a larger, multi-national, home-based study to assess the effect of treatment changes on patient-important outcomes. This trial could impact the treatment regime of millions of patients in the UK.

The study is designed to minimise the burden of participation to patients. There is no need for clinic attendance. Participation and follow-up will take place at home using a bespoke IT system with much of the data collected remotely.

The experience of using bespoke IT-enabled systems to remotely recruit and monitor participants will, if shown to be effective, be considered for use in the recruitment and study management of other research trials. This could provide the means for other researchers to not only streamline research processes but also include participants in trials who historically are reluctant or unable to participate in research trials due to the demands made of them to attend research visits. For instance, research suggests that the vast amount of research findings are based on studies that have included participants in close proximity to specialised centres, men, and those who have fewer comorbidities. This leaves a gap in research for the majority of the population in the UK to whom research findings are being applied. This study should not only encourage participation of such patients but will assess the extent to which conduct of trials can be made more efficient and hence affordable.

Outputs:

The research agenda, plan of investigation and monitoring of the execution of the ATEMPT trial is overseen by a trial steering committee. Members of a patient group, or individuals able to contribute to the wider public perspective are involved in the committee and review public-facing outputs.

The main trial results from the ATEMPT Pilot Study are expected in 2022 with a publication towards the end of that year.

The results will be disseminated widely, including presentation at relevant conferences such as the European Society of Cardiology annual meeting and publication in an open-access, high-impact medical journal such as the European Journal of Cardiology. Further academic papers (including a protocol paper and results of remote recruitment and management of the trial) will be published in open-access, high impact, peer-reviewed journals and on the trial website.

A non-technical summary of the main study findings will be provided to participants and other interested groups and published on the study website.

The data will be de-identified at the end of the study and only anonymised results will be used in publications.

The findings from this pilot study will be used to inform and plan an adequately powered major, multinational Randomised Controlled Trial. Additionally, the experience gained from utilising IT-enabled systems to remotely recruit and monitor participants will be evaluated with a view to expanding the use of the software to manage other research trials within the department and wider University. The online system for the ATEMPT trial has been developed in conjunction with members of the public aged 65 years or older in order to ensure that it is as simple and easy to use as possible.

Processing:

NHS digital will extract the details of patients that are aged 65 and older, residing in the Thames Valley (specific postcodes provided) with at least 3 co-morbidities diagnosed in the 5 years prior to the search, excluding those with a previous diagnosis of heart failure (ICD code I50). Patients who are deceased would be excluded. This information will be requested in up to 2 batches based on subsets of postcodes. If enrolment targets are met before the second batch of demographics data have been disseminated, the University of Oxford will inform NHS Digital as soon as possible, halting the production and dissemination of personal details which are no longer required.

NHS Digital will provide to the University of Oxford a file containing the following identifying data for potentially eligible participants within the specified postcodes for a given request: Name, address, gender, and date of birth. The University will then undertake further work to ascertain eligibility. The data will be stored in the ATEMPT pre-screening database.

The University will write to the potential participants inviting them to take part in the trial. In order to efficiently print and mail invitation letters, batches of personal data (name and address) will be sent securely to CFH Docmail Ltd who will process these data in order to print and post the invitation letters. The letters will be sent out gradually in smaller batches across one month, enabling the study team to stop sending letters as soon as there are enough participants enrolled on the study. Data is retained by CFH Docmail for one month after an invitation letter is mailed to a participant at which point the data is deleted. The University of Oxford may send one reminder to those who have not responded within that timescale.

The potential participants will be able to use a website address provided in the letter to find out more about the trial and a unique access code to log in and register their interest. The name and address of those consenting to take part will be copied to the ATEMPT study database at the point of e-consent and then participant name, address and other identifiable information will be collected directly from the participant.

At the end of enrolment the University of Oxford will delete data of all individuals not taking part in the trial. Participants who have provided their consent for taking part in the trial will have their identifiable information sent to NHS Digital under a new data sharing agreement to obtain Hospital Episode Statistics data for participants (safety data). Given identifiable information will be provided under consent for the subsequent agreement, all NHS Digital data provided using s251 approval for this original agreement will be deleted at the end of enrolment.

The ATEMPT pre-screening database and ATEMPT study database are not linked to any other databases. Data is stored securely by the University of Oxford in a high compliance system (HCS) suitable for storing personal and special category data, and data can only be accessed by study team members with a level of access based on their role. The HCS is accessed remotely on a university device and requires a unique username and password and two factor authentication. Data is not being matched to publicly available data. The data received from NHS Digital will only be accessed by individuals in the ATEMPT Study Team who have authorisation to access the data for the purposes described, all of whom are substantive employees of the University of Oxford and have been appropriately trained in data protection and confidentiality.

Where names and addresses are transferred to CFH Docmail to print and mail invitation letters, only CFH Docmail staff with appropriate authorisation and who need to access the data in order to fulfil their job role are allowed access to the data. These staff are all appropriately trained in data protection and confidentiality. They are required to sign a confidentiality agreement on an annual basis and are subject to refresher training and updates relating to data protection, GDPR and confidentiality on a regular basis, at least once a year. Data access at CFH Docmail is arranged by Security Group membership and Access Control Lists. Only restricted authorised production personnel can access compiled files. CFH Docmail have two employed data protection officers who would be responsible for the investigation and response to any data breach.

During the pandemic, where feasible, CFH staff are currently working from home and accessing their infrastructure via a secure VPN. The VPN is configured to provide users with the same access that they would have if they were on the cabled CFH Docmail internal network and is secured on an individual basis, with two factor authentication being required.

Comparing COVID-19 Vaccine Schedule Combinations – Stage 2 (Com-COV2) — DARS-NIC-448303-Z7H5R

Opt outs honoured: No - consent provided by participants of research study, Identifiable (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2021-03-23 — 2022-03-22 2021.04 — 2021.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Permission to Contact

Objectives:

This Data Sharing Agreement authorises the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials. The data will be processed on behalf of the data controller, University of Oxford, by NHS Digital as a data processor for the purpose of supporting recruitment to participate in a COVID-19 vaccine trial being run by University of Oxford.

The following provides background to the Permission to Contact (PtC) Service:

NHS Digital has agreed to work in partnership with the National Institute of Health Research (NIHR) to build and host a first of type online Permission to Contact (PtC) Service on nhs.uk where members of the public can register their details and give their permission to be contacted by researchers working on NIHR approved UK coronavirus vaccine trials about participating in those trials. This PtC Service, which is called “Sign Up to be Contacted about Coronavirus Vaccine Studies” on the nhs.uk website was launched as a national service on 20th July 2020.

This Service enables participants to:
• Provide permission for NHS Digital to share an individual’s details provided through the Service with the researchers undertaking COVID-19 UK vaccine trials for the purposes of researchers contacting that individual about taking part in those trials.
• Provide their permission to be contacted by NHS Digital about progress and outcomes from CV19 vaccine studies and in relation to the development of the PtC Service, including to inform them of opportunities to participate in other types of health research.

The data collected from individuals who sign up includes sufficient information to achieve the following purposes:
• Matching potentially eligible participants to eligibility criteria provided by the vaccine trials for their specific studies. This data will comprise of age, sex, geographic locations, type of employment, and a number health question e.g. about whether they have long-term health conditions.
• Providing relevant details of potentially eligible participants which have been obtained through the Service to researchers. This will allow the researchers to contact the participants with a view to discussing their taking part in a trial and if so, to obtain their further permission to take part in the trial.
• NHS Digital will provide access to the information obtained from individuals through the Service via the existing Data Access Request Service (DARS) process available to researchers working on UK COVID-19 vaccine trials sponsored by the National Institute of Health Research. The Service will only provide researchers with the data collected directly from individuals themselves through the Service.

The contact details will be used to invite potentially eligible individuals to undertake an eligibility assessment and, if eligible, to give informed consent to participate in this trial. NHS Digital, as data processor acting on behalf of University of Oxford, will be sending the email to eligible participants.

This request relates specifically to a vaccine trial. A single-blind, randomised, phase II UK multi-centre study to determine reactogenicity and immunogenicity of heterologous prime/boost COVID-19 vaccine schedules – Stage 2.

A total of 1050 (+ up to additional 10%) participants will be enrolled, all of whom will have had their first dose of COVID-19 vaccine in the community as part of the NHS vaccine roll-out programme. Participants will be evenly divided by prime vaccine type (525 per prime vaccine group). Within these vaccine groups participants will be enrolled into either an Immunology cohort who will have more visits (n=150 in total, 75 from each vaccine prime group) or a General cohort (n=900 in total, 450 from each vaccine prime group).

The initial mailout will aim for around four / five times the number of potential participants to be recruited and therefore the estimate is for around 5,250 individuals to be contacted.

Although this application relates to the combining of commercial vaccines, there is no commercial element specifically attached to this application itself, the purpose of which is about influencing national vaccine rollout strategy. Although results may influences the manner in which the commercial firms role out vaccines in future, this application relates solely to an academic exercise.

Although AstraZeneca, Pfeizer, Moderna and Novavax have allowed the use of their vaccines for this trial, they have no responsibilities as to how the trial is conducted and therefore University of Oxford remain the sole Data Controller for this application.

Expected Benefits:

The primary benefit of using the data will be to recruit participants for the clinical study/trial in a manner which:
• Enables individuals to volunteer in advance to participate in COVID-19 vaccine trials as an alternative to other potentially more intrusive mechanisms, e.g. sharing data with researchers about individuals under section 251 consents or COPI notices, which although lawful is initially less transparent.
• Allows researchers to identify a suitable cohort and recruit them quickly into the vaccine trials – thus reducing the overall time to recruit into the trials and to accelerate the delivery of an effective vaccine to treat individuals to manage the COVID-19 outbreak and to save lives.
• Reduces burden on research staff in identifying and contacting potential clinical trial participants.
• Supports the Vaccines Taskforce objectives to drive forward, expedite and coordinate efforts to research and then produce a coronavirus vaccine and make sure one is made available to the public as quickly as possible.

Outputs:

The information from NHS Digital will be used to facilitate contact with individuals who are potentially eligible and who have indicated willingness to potentially participate in studies/trials of COVID-19 vaccines.

This is expected to result in individuals entering the trials screening process with a view to them participating in the trial with fully informed consent.

The main results from this trial are expected to inform development of a safe and effective multiple vaccine combination against COVID 19.

Processing:

NHS Digital will extract a list of patients meeting the following criteria, where that criteria can be ascertained using the PtC registry:

INCLUSION CRITERIA:

- Participant is willing and able to give written informed consent for participation in the trial
- Male or Female, aged 50 years or above and in good health as determined by a trial clinician
Participants may have well controlled or mild-moderate comorbidity
- Has received one dose of the prime/boost schedules being studied via the UK COVID-19 vaccination
programme at a timing to allow boost dose given in the trial to fall between D56-84 post-prime.
Evidence of this will be gathered from medical history and/or medical records.
- Female participants of childbearing potential must be willing to ensure that they or their partner use
effective contraception from enrolment continuously until 3 months after
boost immunisation. See Section 12.13.1 for definition of child bearing potential
- In the Investigator’s opinion, is able and willing to comply with all trial requirements
- Willing to allow their General Practitioner and consultant, if appropriate, to be notified of
participation in the trial
- Willing to allow investigators to discuss the volunteer’s medical history with their General
Practitioner and access all medical records when relevant to study procedures
- Agreement to refrain from blood donation during the course of the study

EXCLUSION CRITERIA:

Receipt of any vaccine (licensed or investigational) within 30 days before enrolment (one week for licensed seasonal influenza vaccine or pneumococcal vaccine)
- Previous receipt of two or more COVID-19 vaccine doses
- Prior or planned receipt of an investigational or licensed vaccine or product likely to impact on interpretation of the trial data (e.g. Adenovirus vectored vaccines other than ChAdOx1 nCoV-19)
- Administration of immunoglobulins and/or any blood products within the three months preceding the planned administration of the vaccines
- Any confirmed or suspected immunosuppressive or immunodeficient state; asplenia; recurrent severe infections and use of immunosuppressant medication within the past 6 months, except topical steroids or short-term oral steroids (course lasting ≤14 days)
- History of anaphylaxis, allergic disease or reactions likely to be exacerbated by any component of study vaccines (e.g. hypersensitivity to the active substance or any of the SmPC-listed ingredients of
the Pfizer vaccine), as specified in the UK Immunisation ‘Green Book’ COVID-19 vaccine chapter 14a
- Pregnancy, lactation or willingness/intention to become pregnant within 3 months post boost vaccine
- Malignancy requiring receipt of immunosuppressive chemotherapy or radiotherapy for treatment of solid organ cancer/haematological malignancy within the 6 months prior to enrolment. (Presence of
basal cell carcinoma of the skin, cervical carcinoma in situ, prostate cancer under observation and breast cancer on hormone therapy as secondary prophylaxis are not excluded).
- Bleeding disorder (e.g. factor deficiency, coagulopathy or platelet disorder), or prior history of significant bleeding or bruising following IM injections or venepuncture
- Continuous use of anticoagulants, such as coumarins and related anticoagulants (i.e. warfarin) or novel oral anticoagulants (i.e. apixaban, rivaroxaban, dabigatran and edoxaban)
- Suspected or known current alcohol or drug dependency
- Any other significant disease, disorder or finding which may significantly increase the risk to the volunteer because of participation in the study, affect the ability of the volunteer to participate in the study or impair interpretation of the study data
- Severe and/or uncontrolled cardiovascular disease, respiratory disease, gastrointestinal disease, liver disease, renal disease, rheumatological disease, endocrine disorder and neurological illness (mild/moderate well controlled comorbidities are allowed)
- History of active or previous auto-immune neurological disorders (e.g. multiple sclerosis, Guillain-Barre syndrome, transverse myelitis). Bell’s palsy will not be an exclusion criterion
- History of laboratory confirmed COVID-19 prior to enrolment (e.g. history of SARS-CoV-2 detection by PCR or antibody to SARS-CoV-2)
- Significant renal or hepatic impairment
- Scheduled elective surgery requiring overnight admission and/or general anaesthetic during the trial
- Participant with life expectancy of less than 6 months
- Participants who have participated in another research trial involving an investigational product in the past 12 weeks
- Insufficient level of English language to undertake all study requirements in opinion of the Investigators.

The inclusion and exclusion criteria noted above is based on the information provided by cohort members on the permission to contact dataset (where it can be obtained from this dataset), and is not collected from other NHS data sources. Some of the above inclusion and exclusion criteria will be used by the sites during the Screening phase.

NHS Digital will identify all individuals within the PtC dataset meeting the relevant criteria and will extract their names, email addresses and postcodes.

It is not known in advance how many individuals meeting the above criteria will have records in the PtC dataset. The number may be amended and the process may be repeated depending on the level of response. In the event of the trial not achieving a suitable balance in recruited participants, such as an uneven ratio of males to females, subsequent mail outs may restrict the required criteria to a greater degree than previously, for example, only requesting details for male participants as opposed to both males and females. This could encompass any part of the criteria, such as age, gender, ethnicity or location and various others, depending on how the recruitment progresses.

NHS Digital will write to the individuals in the subset inviting them to participate within the trial using ethically approved text provided by University of Oxford. The email will remind the individuals of the background of the permission to contact programme and give them the opportunity to state that they do not wish to be contacted again. The email will also direct volunteers to NIHR’s Be Part of Research website to access study information and regional contact information. Individuals will not be contacted multiple times under this Agreement and NHS Digital will record the fact that the individuals have been contacted to ensure compliance with the maximum number of contacts outlined as part of consent. Furthermore, in order to ensure that NHS Digital are able to update the register with which participants are registered with an active trial, and therefore prevent them from being invited to any further trials, NHS Digital will be provided with regular updates of those registered participants who have consented. This sharing of information is built into the Permission to Contact signing up information and will also be added to the trial consent and participant information.

Individual trial recruitment sites will supply NHS Digital with details of those who have signed up to take part in their trial so that NHS Digital can suitably capture this information within the Permission To Contact registry. All data that flows to NHS Digital in this context falls under the controllership of the data controller, regardless of whether they themselves are specifically involved in the processing of that data as it flows to NHS Digital. For this agreement there may be flows from each individual site. Once the data is received at NHS Digital then NHS Digital become controller for that data in their existing role as controller of the Permission To Contact Registry.

Due to the nature of trial recruitment sites, they often only become confirmed as sites very close to recruitment, and so NHS Digital will leave the responsibility with the lead site / data controller to appointment data processors themselves under their own due diligence. This practice aligns with their obligations under GDPR as a data controller and the emphasis will be on the lead site / data controller to appoint appropriate data processors on their behalf. Ordinarily NHS Digital would carry out these checks, but attempting to do so for this service would cause unnecessary delay to the initial application, as well as potentially multiple and costly amendments thereafter. Therefore all recruitment sites / data processors and their processing activities will be covered under a suitable processing agreement between themselves and the lead site / data controller which does not require NHS Digital’s inclusion. Specific details of recruitment sites, such as key contact, location, will therefore not be made known to NHS Digital unless there is a specific reason to do so.

No other processing of the data will take place and the data will not be linked with information from any other sources.

University of Oxford will not have access to any of the data being disseminated by NHS Digital under this agreement.

Comparing COVID-19 Vaccine Schedule Combinations (Com-COV) — DARS-NIC-428459-V7Q8M

Opt outs honoured: No - consent provided by participants of research study, Identifiable (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2021-01-29 — 2022-01-28 2021.01 — 2021.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Permission to Contact

Objectives:

This Data Sharing Agreement authorises the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials. The data will be processed on behalf of the data controller, University of Oxford, by NHS Digital as a data processor for the purpose of supporting recruitment to participate in a COVID-19 vaccine trial being run by University of Oxford.

The following provides background to the Permission to Contact (PtC) Service:

NHS Digital has agreed to work in partnership with the National Institute of Health Research (NIHR) to build and host a first of type online Permission to Contact (PtC) Service on nhs.uk where members of the public can register their details and give their permission to be contacted by researchers working on NIHR approved UK coronavirus vaccine trials about participating in those trials. This PtC Service, which is called “Sign Up to be Contacted about Coronavirus Vaccine Studies” on the nhs.uk website was launched as a national service on 20th July 2020.

This Service enables participants to:
• Provide permission for NHS Digital to share an individual’s details provided through the Service with the researchers undertaking COVID-19 UK vaccine trials for the purposes of researchers contacting that individual about taking part in those trials.
• Provide their permission to be contacted by NHS Digital about progress and outcomes from CV19 vaccine studies and in relation to the development of the PtC Service, including to inform them of opportunities to participate in other types of health research.

The data collected from individuals who sign up includes sufficient information to achieve the following purposes:
• Matching potentially eligible participants to eligibility criteria provided by the vaccine trials for their specific studies. This data will comprise of age, sex, geographic locations, type of employment, and a number health question e.g. about whether they have long-term health conditions.
• Providing relevant details of potentially eligible participants which have been obtained through the Service to researchers. This will allow the researchers to contact the participants with a view to discussing their taking part in a trial and if so, to obtain their further permission to take part in the trial.
• NHS Digital will provide access to the information obtained from individuals through the Service via the existing Data Access Request Service (DARS) process available to researchers working on UK COVID-19 vaccine trials sponsored by the National Institute of Health Research. The Service will only provide researchers with the data collected directly from individuals themselves through the Service.

The contact details will be used to invite potentially eligible individuals to undertake an eligibility assessment and, if eligible, to give informed consent to participate in this trial. NHS Digital, as data processor acting on behalf of University of Oxford, will be sending the email to eligible participants.

This request relates specifically to a vaccine trial. This is a Single-blind, randomised prime-boost vaccine administration study comparing COVID19 vaccine schedule combinations.

On the 2nd December 2020 the MHRA granted emergency authorisation for a vaccine against COVID-19, ‘COVID-19 mRNA Vaccine BNT162b2’. This was the first in what are expected to be multiple vaccines approved for use against COVID-19. . Most of these are expected to be approved as a two-dose regimen, using the same vaccine for both the initial (prime) and subsequent (boost) dose. There are likely to be significant logistical challenges immunising large portions of the population. There would be significant advantages to having flexible immunisation programmes whereby the second vaccine dose is not necessarily the same as the first dose. Accordingly, this study will determine the safety as well as the immune responses to a variety of combinations of prime/boost schedules for candidate COVID-19 vaccines that are potentially to be deployed in the UK. The vaccines to be studied in this protocol will primarily be determined by those made available to the Department of Health and Social Care (DHSC) for population use, but for the purposes of this articular application, the aim is to recruit participants to test combinations of ChAdOx1 nCOV-19 (developed by University of Oxford / AstraZeneca) and BNT162b2 (developed by Pfeizer).

The primary objective is to determine whether the immune response in COVID seronegative participants to immunisation with heterologous prime/boost COVID-19 vaccines regimens (boosted at D28) is non-inferior to that observed following immunisation with approved homologous prime-boost regimens (boosted at D28).

The secondary objectives are to determine whether the immune response in COVID seronegative participants to immunisation with heterologous prime/boost COVID-19 vaccines regimens across all dosing intervals is non-inferior to that observed following immunisation with approved homologous prime-boost regimens, and the reactogenicity and safety of heterologous & homologous prime/boost schedules of COVID-19 vaccines.

The aim for this application is to recruit a total of total of 820 participants, consisting of an Immunology cohort receiving their booster vaccine dose after 28 days (n=100) and a General cohort (n=720). Half of the general cohort participants (N-360) will receive their booster vaccine after 28 days, and half will receive their booster vaccine after 84 days.

Within the immunology cohort participants will be randomised 1:1:1:1 to the following arms receiving their booster vaccine dose after 28 days:
• Prime ChAdOx1 nCOV-19, Boost ChAdOx1 nCOV-19
• Prime ChAdOx1 nCOV-19, Boost BNT162b2
• Prime BNT162b2, Boost BNT162b2
• Prime BNT162b2, Boost ChAdOx1 nCOV-
Within the general cohort participants will be randomised 1:1:1:1:1:1:1:1 to the following arms:
• Prime ChAdOx1 nCOV-19, Boost ChAdOx1 nCOV-19 28 day boost
• Prime ChAdOx1 nCOV-19, Boost BNT162b2 28 day boost
• Prime BNT162b2, Boost BNT162b2 28 day boost
• Prime BNT162b2, Boost ChAdOx1 nCOV-19 28 day boost
• Prime ChAdOx1 nCOV-19, Boost ChAdOx1 nCOV-19 84 day boost
• Prime ChAdOx1 nCOV-19, Boost BNT162b2 84 day boost
• Prime BNT162b2, Boost BNT162b2 84 day boost
• Prime BNT162b2, Boost ChAdOx1 nCOV-19 84 day boost

There will therefore be a sum total of 205 participants receiving each different permutation of vaccine, 25 of whom will be in the Immunology cohort with booster vaccine dose after 28 days, 90 in the General Cohort with booster vaccine dose after 28 days and 90 in the General Cohort with booster vaccine dose after 84 days.

The initial mailout will aim for around four / five times the number of potential participants to be recruited and therefore the estimate is for around 4,100 individuals to be contacted.

Although this application relates to the combining of two commercial vaccines, there is no commercial element specifically attached to this application itself, the purpose of which is about influencing national vaccine rollout strategy. Although results may influences the manner in which the commercial firms role out vaccines in future, this application relates solely to an academic exercise.

Although AstraZeneca and Pfeizer have allowed the use of their vaccines for this trial, they have no responsibilities as to how the trial is conducted and therefore University of Oxford remain the sole Data Controller for this application.

Expected Benefits:

The primary benefit of using the data will be to recruit participants for the clinical study/trial in a manner which:
• Enables individuals to volunteer in advance to participate in COVID-19 vaccine trials as an alternative to other potentially more intrusive mechanisms, e.g. sharing data with researchers about individuals under section 251 consents or COPI notices, which although lawful is initially less transparent.
• Allows researchers to identify a suitable cohort and recruit them quickly into the vaccine trials – thus reducing the overall time to recruit into the trials and to accelerate the delivery of an effective vaccine to treat individuals to manage the COVID-19 outbreak and to save lives.
• Reduces burden on research staff in identifying and contacting potential clinical trial participants.
• Supports the Vaccines Taskforce objectives to drive forward, expedite and coordinate efforts to research and then produce a coronavirus vaccine and make sure one is made available to the public as quickly as possible.

Outputs:

The information from NHS Digital will be used to facilitate contact with individuals who are potentially eligible and who have indicated willingness to potentially participate in studies/trials of COVID-19 vaccines.

This is expected to result in individuals entering the trials screening process with a view to them participating in the trial with fully informed consent.

The main results from this trial are expected to inform development of a safe and effective multiple vaccine combination against COVID 19.

Processing:

NHS Digital will extract a list of patients meeting the following criteria, where that criteria can be ascertained using the PtC registry:

INCLUSION CRITERIA:

1. Participant is willing and able to give written informed consent for participation in the trial.

2. Male or Female, aged 50 years or above and in good health as determined by a trial clinician Participants may have well
controlled or mild-moderate comorbidity.

3. Female participants of childbearing potential must be willing to ensure that they or their partner use effective contraception from 1 month prior to first immunisation continuously until 3 months after boost immunisation.

4. In the Investigator’s opinion, is able and willing to comply with all trial requirements.

5. Willing to allow their General Practitioner and consultant, if appropriate, to be notified of participation in the trial.

6. Willing to allow investigators to discuss the volunteer’s medical history with their General Practitioner and access all medical records when relevant to study procedures.

7. Agreement to refrain from blood donation during the course of the study

EXCLUSION CRITERIA:

The participant may not enter the trial if ANY of the following apply:

1. Receipt of any vaccine (licensed or investigational) other than the study intervention within 30 days before and after each study vaccination (one week for licensed seasonal influenza vaccine or pneumococcal vaccine).

2. Prior or planned receipt of an investigational or licensed vaccine or product likely to impact on interpretation of the trial data (e.g. Adenovirus vectored vaccines, any coronavirus vaccines).

3. Administration of immunoglobulins and/or any blood products within the three months preceding the planned administration of the vaccines.

4. Any confirmed or suspected immunosuppressive or immunodeficient state; asplenia; recurrent severe infections and use of immunosuppressant medication within the past 6 months, except topical steroids or short-term oral steroids (course lasting ≤14 days).

5. History of allergic disease or reactions likely to be exacerbated by any component of the study vaccines.

6. Any history of angioedema.

7. Any history of anaphylaxis or if they have been advised to carry an adrenaline auto-injector.

8. Pregnancy, lactation or willingness/intention to become pregnant within 3 months post boost vaccine.

9. Current diagnosis of or treatment for cancer (except basal cell carcinoma of the skin and cervical carcinoma in situ).

10. History of serious psychiatric condition likely to affect participation in the study.

11. Bleeding disorder (e.g. factor deficiency, coagulopathy or platelet disorder), or prior history of significant bleeding or bruising following IM injections or venepuncture.

12. Continuous use of anticoagulants, such as coumarins and related anticoagulants (i.e. warfarin) or novel oral anticoagulants (i.e. apixaban, rivaroxaban, dabigatran and edoxaban).

13. Suspected or known current alcohol or drug dependency.

14. Any other significant disease, disorder or finding which may significantly increase the risk to the volunteer because of
participation in the study, affect the ability of the volunteer to participate in the study or impair interpretation of the study data.

15. Severe and/or uncontrolled cardiovascular disease, respiratory disease, gastrointestinal disease, liver disease, renal disease, endocrine disorder and neurological illness (mild/moderate well .controlled comorbidities are allowed).

16. History of active or previous auto-immune neurological disorders (e.g. multiple sclerosis, GuillainBarre syndrome, transverse myelitis). Bell’s palsy will not be an exclusion criterion

17. History of laboratory confirmed COVID-19 prior to enrolment (history of SARS-CoV-2 detection by PCR or antibody to
SARS-CoV-2).

18. Significant renal or hepatic impairment.

19. Scheduled elective surgery during the trial.

20. Participant with life expectancy of less than 6 months.

21. Participants who have participated in another research trial involving an investigational product in the past 12 weeks.

22. Insufficient level of English language to undertake all study requirements in opinion of the Investigators.

The inclusion and exclusion criteria noted above is based on the information provided by cohort members on the permission to contact dataset (where it can be obtained from this dataset), and is not collected from other NHS data sources. Some of the above inclusion and exclusion criteria will be used by the sites during the Screening phase.

NHS Digital will identify all individuals within the PtC dataset meeting the relevant criteria and will extract their names, email addresses and postcodes.

It is not known in advance how many individuals meeting the above criteria will have records in the PtC dataset. The number may be amended and the process may be repeated depending on the level of response. In the event of the trial not achieving a suitable balance in recruited participants, such as an uneven ratio of males to females, subsequent mail outs may restrict the required criteria to a greater degree than previously, for example, only requesting details for male participants as opposed to both males and females. This could encompass any part of the criteria, such as age, gender, ethnicity or location and various others, depending on how the recruitment progresses.

NHS Digital will write to the individuals in the subset inviting them to participate within the trial using ethically approved text provided by University of Oxford. The email will remind the individuals of the background of the permission to contact programme and give them the opportunity to state that they do not wish to be contacted again. The email will also direct volunteers to NIHR’s Be Part of Research website to access study information and regional contact information. Individuals will not be contacted multiple times under this Agreement and NHS Digital will record the fact that the individuals have been contacted to ensure compliance with the maximum number of contacts outlined as part of consent. Furthermore, in order to ensure that NHS Digital are able to update the register with which participants are registered with an active trial, and therefore prevent them from being invited to any further trials, NHS Digital will be provided with regular updates of those registered participants who have consented. This sharing of information is built into the Permission to Contact signing up information and will also be added to the trial consent and participant information (see supporting document SD3 for further details).

Individual trial recruitment sites will supply NHS Digital with details of those who have signed up to take part in their trial so that NHS Digital can suitably capture this information within the Permission To Contact registry. All data that flows to NHS Digital in this context falls under the controllership of the data controller, regardless of whether they themselves are specifically involved in the processing of that data as it flows to NHS Digital. For this agreement there may be flows from each individual site. Once the data is received at NHS Digital then NHS Digital become controller for that data in their existing role as controller of the Permission To Contact Registry.

Due to the nature of trial recruitment sites, they often only become confirmed as sites very close to recruitment, and so NHS Digital will leave the responsibility with the lead site / data controller to appointment data processors themselves under their own due diligence. This practice aligns with their obligations under GDPR as a data controller and the emphasis will be on the lead site / data controller to appoint appropriate data processors on their behalf. Ordinarily NHS Digital would carry out these checks, but attempting to do so for this service would cause unnecessary delay to the initial application, as well as potentially multiple and costly amendments thereafter. Therefore all recruitment sites / data processors and their processing activities will be covered under a suitable processing agreement between themselves and the lead site / data controller which does not require NHS Digital’s inclusion. Specific details of recruitment sites, such as key contact, location, will therefore not be made known to NHS Digital unless there is a specific reason to do so.

No other processing of the data will take place and the data will not be linked with information from any other sources.

University of Oxford will not have access to any of the data being disseminated by NHS Digital under this agreement.

D27 - QResearch COVID-19 linkage — DARS-NIC-375354-G8V1H

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: Yes (Academic)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2020-04-27 — 2020-09-25 2020.06 — 2021.03.

Access method: Ongoing

Data-controller type: INTENSIVE CARE NATIONAL AUDIT & RESEARCH CENTRE (ICNARC), UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Outpatients
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Admitted Patient Care
  4. Civil Registration - Deaths
  5. Civil Registration (Deaths) - Secondary Care Cut
  6. Hospital Episode Statistics Accident and Emergency
  7. Civil Registrations of Death - Secondary Care Cut
  8. Hospital Episode Statistics Accident and Emergency (HES A and E)
  9. Hospital Episode Statistics Admitted Patient Care (HES APC)
  10. Hospital Episode Statistics Critical Care (HES Critical Care)
  11. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

This agreement specifically relates to QResearch COVID-19 research projects undertaken in relation to the 2020 pandemic. Under this agreement QResearch data linked to NHS Digital data will be used solely for the purpose of research into COVID-19. Other QResearch uses of NHS Digital data are covered by a separate data sharing agreement (DARS-NIC-240279-Y2V2N).

QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients.

Monthly HES and mortality data are requested to link to the existing QResearch database so that it can be used for medical research. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.

QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data, with ICNARC as joint data controller for COVID-19 projects using linked ICNARC data.

In addition to coded data from the GP electronic record, the QResearch database also contains the linked cause of death derived from the death certificate data which was originally supplied directly by the Office of National Statistics (ONS) but NHS Digital has since assumed ownership for this data, and cancer registration data supplied directly by Public Health England, following approval by Trent MREC and Secretary of State for Health. From October 2018, new mortality data (referred to as Civil Registration data) will be supplied by NHS Digital. Any mortality originally supplied by ONS data will be considered data supplied by NHS Digital and only NHS Digital can approve ongoing access to this data and its use for specific purposes.

The data linkages for QResearch were extended in 2011 to include additional health information from secondary care including HES. This agreement grants additional permission to link NHS Digital data to an existing database of data from 185 intensive care units nationally which has the clinical data for all those admitted with severe disease requiring ventilation. This is known as the Intensive Care National Audit and Research Centre (ICNARC) database and the COVID-19 testing database held by PHE. ICNARC will be a joint data controller (along with the University of Oxford) solely for COVID-19 projects involving the use of this linked data, and not for any other purposes.

The patient level data linked to QResearch is only accessed by academics employed by University of Oxford, as well as authorised individuals employed by the University of Nottingham. In all cases, data can only be accessed on site at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication but not handling the data) may be employed by other UK universities. The HES and mortality data stay on site at the University of Oxford and are only handled by University of Oxford staff, ICNARC where they are a joint data controller, the data processor contracted to the University of Oxford (Dancing House Consulting) and authorised researchers employed by the University of Nottingham. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data. In addition, the external researcher may initiate a COVID-19 project but the University of Oxford has sole autonomy for determining the purposes for which the HES and/or civil registration data will be processed and analysis will be done by University of Oxford staff with the data located at the University of Oxford. The sole exception to this is for COVID-19 projects using linked ICNARC data, for which ICNARC will be a joint data controller. For these COVID-19 projects ICNARC statisticians will have a role in the design, analysis and interpretation of the data under the overall direction of University of Oxford, and they will be co-authors on arising papers. ICNARC will not be involved in any decisions regarding other research projects which do not use their ITU data. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.

Only University of Oxford staff, their data processor, Dancing House Consulting, ICNARC, and authorised researchers employed by the University of Nottingham will have access to HES and/or mortality record level data. External researchers will only have access to tabular outputs that are aggregate with small numbers suppressed in line with the HES Analysis Guide. Record level data are not shared with researchers outside of the University of Oxford.

Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal.

All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.

Expected Benefits:

This data linkage with ICNARC seeks to match against data on prior long-term medication and chronic disease, information contained in the QResearch databases derived from the de-identified general practice health records. The aim is to provide useful knowledge that patients, GPs and intensive care doctors can use to reduce the risk of severe COVID-19 infection within this pandemic.

Specifically it will help research to understand whether drugs commonly taken for chronic conditions such as hypertension or diabetes may exacerbate or reduce the severity of COVID-19 disease. It is hoped this study will be able to identify alternative drugs for patients with chronic conditions, as well as possible drugs to treat COVID-19; and recognise high-risk patients in primary care.

Around 14 percent of the adult population in England take anti-hypertensive medications, and around five percent receive medication to treat diabetes. The prevalence increases with age, making usage particularly common in those at risk of for severe COVID-19 infections. In many cases drugs from a different class could be used instead. If these drugs are increasing the risk of severe infection, they represent one of the few modifiable risk factors for severe COVID-19 infection. Medical and research communities need rapid large-scale accumulation of data on the outcomes of patients who develop COVID-19 infection whilst taking these drugs to allow appropriate risk assessment and clinical decision making for these patient groups. Other drugs in common use in primary care patients are believed to have anti-viral activity to COVID-19, such as hydroxychloroquine, used in rheumatoid arthritis, and lopinavir-ritonavir, used in the treatment of HIV.

There are also immune-suppressive therapies that may either increase the risk of severe illness by preventing the body’s response to infection, or attenuate the hyperinflammation syndrome associated with COVID-19 disease, so preventing severe disease.

The incidence of severe disease in patient groups taking these medications urgently needs to be established to guide both their management and investigation of COVID-19 treatment strategies.

ICNARC is already providing up-to-date information on the admission characteristics and outcomes of all patients with severe COVID-19 infection treated on an ICU in England, Wales and Northern Ireland.

Outputs:

The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.

Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.

Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.

The results tables within the papers will only contain statistical information with cell counts of > 5, being suppressed in line with the ICO code on anonymisation. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.

No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.

Processing:

Under this agreement, primary care data from The Phoenix Partnership (TPP) from general practices which use the SytmOne computer system will be included in QResearch linkage in addition to data from EMIS practices. This will give a larger population of patients and enable more coverage of patients who subsequently go into hospital and onto Intensive Treatment Unit (ITU) with and without COVID-19.

EMIS and TPP process the GP data from the original data controllers (GP practices) and sends it to the University of Oxford. EMIS and TPP are not able to access or process any GP data once it is located at the University of Oxford.

EMIS and TPP are neither a data processor nor a data controller for the data provided by NHS Digital under this Agreement. EMIS and TPP are not able to access the HES data under any circumstances. EMIS and TPP have given permission for the GP data it supplies to be linked with the data from NHS Digital for purposes determined by the Principal Investigator at the University of Oxford.

Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the HES data. NHS Digital retains the salt key for this pseudonymisation, meaning that the University of Oxford are unable to re-identify the data but as described below they are able to link with GP data that was pseudonymised using the same Open Pseudonymiser tool. The University of Oxford will not be provided with a copy of the pseudonymisation salt.

NHS Digital provide the pseudonymised data to the University of Oxford which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the HES data. The data linkage is undertaken by an employee of the University of Oxford. No data items which would identify the data subjects are received by QResearch as the data is pseudonymised-at-source and at NHS Digital. Date of birth is rounded to year of birth before receipt by the University of Oxford.

The resulting data are then used for undertaking primary research relating to COVID-19. The linked data are only accessed by approved research staff with substantive contracts employed by University of Oxford, the contracted data processor (Dancing House Consulting), ICNARC (for COVID-19 projects using linked ICNARC data) and authorised researchers employed by the University of Nottingham. Data is only processed on site on secure servers at the University of Oxford. No individual level data will be shared or stored outside the University of Oxford or supplied to any third party.

Applications for HES and/or civil registration data linked to QResearch GP data are restricted to academics employed by University of Oxford to undertake research. At least one member of the research team must be a medically qualified academic registered with the General Medical Council who signs the guarantee. Eligibility of applications is assessed according to the following criteria.
• You agree NOT to attempt to identify patient(s) or practice(s)?
• You undertake to provide a copy of the final report of the project and copies of any publications within one year of the project completion?
• You agree NOT to release the data to any third party including the funder, sponsor or other such body?
• You agree not to use the data for any other project except that which is expressly described in your protocol
• Do you have a statistician on the project team who has contributed to the design of the study and will advise on the analysis?
• Is the research a benefit to the UK Health and Social care system
All applications are reviewed by the QResearch Scientific Committee, which is overseen by the QResearch Advisory Board (which includes patients and general practice representatives). If an application does not meet the above criteria it would mean that application would be rejected and the data would not be shared. Details of the Scientific Committee and Advisory Board terms of reference and membership are published on the QResearch website, along with Advisory Board minutes.

Researchers originate a research question or hypothesis; write an outline protocol; and contact QResearch to discuss the feasibility of undertaking the study. If the study is feasible, QResearch will give a broad estimate of the costs of providing the analysis and will provide a letter to accompany any application for funding. The researcher then secures the necessary funding and completes the QResearch application form, including a detailed protocol and data specification. This application is sent for scientific review and feedback is given to the researcher. The researcher makes any necessary modifications to the protocol and approval is obtained, the researcher is given a timescale for the analysis. Once the researcher has the analysis, they have to approve it within one month of receipt.

As described in the section above, the QResearch database is also linked to mortality and cancer registration data. The database was first linked to ONS mortality data in 2007 and cancer data in 2011 (subsequently supplied by Public Health England since 2015). The data fields received from mortality data are: pseudonymised NHS number; year of birth, date of death; ICD10 cause of death. The cancer data includes pseudonymised NHS number; sex; year of birth; date of death; diagnosis date; cancer site and type; cancer stage and grade; cancer behaviour; cancer diagnosed only on death certificate; cancer treatment (surgery, hormone, chemotherapy, other).

To enable COVID-19 research, data will also be linked to the ICNARC database and the COVID-19 testing database held by PHE. No other data linkage is permitted without further amendment to the data sharing agreement with NHS Digital. There is no requirement to re-identify individuals from the data and no attempts will ever be made to do this.

The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake data linkage or analysis of the data.

All outputs are restricted to aggregate data with small numbers supressed in line with the HES Analysis Guide.

Regular reviews against the ICO code on anonymisation (2012) will be undertaken to ensure that the data remain anonymised and all appropriate controls are in place to minimise any risk of re-identification.

The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement, and the specific QResearch projects outlined within the data sharing agreement DARS-NIC-240279-Y2V2N while that agreement remains active.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

MR1164 - The Asymptomatic Carotid Surgery Trial (ACST-2) — DARS-NIC-10123-M5K5H

Opt outs honoured: No - consent provided by participants of research study, Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-04-21 — 2022-01-20 2019.06 — 2021.03.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. Demographics
  5. Civil Registration - Deaths
  6. MRIS - Members and Postings Report
  7. Civil Registrations of Death

Objectives:

The University of Oxford requires identifiable data for the Asymptomatic Carotid Surgery Trial (ACST-2); a large international multi-centre randomized clinical trial comparing carotid endarterectomy (CEA) and carotid artery stenting (CAS) for stroke prevention. ACST-2 is designed to reliably compare the long-term stroke risk of all patients randomized to CEA with those randomised to CAS. To do this, all patients are followed-up 1 month after the procedure by an independent neurologist (to record any procedural complications) and thereafter, follow-up is achieved via annual questionnaires, supplemented with cause-specific civil registry mortality data. The UK is the only country to benefit from mortality data sourced from NHS Digital but all other countries in the trial use the same patient information and questionnaires. The University of Oxford is the coordinating centre and collate all of the data for this international study.

The proposed data processing is in line with Article 6(1)(e) ‘processing is necessary for the performance of a task carried out in the public interest’. Identifiable record level data from central registries provide reports of fatal stroke and also inter-current mortality to allow appropriate censoring of the trial population. This data is the smallest amount that is necessary to answer the research question.

ACST-2 will complete recruitment in 2019-2020 with a major report (describing initial hazards of surgery and 4-year follow-up) envisaged in 2021. Follow-up (exclusively questionnaire and registry-based) will continue until 2025 (thereby ensuring a minimum follow-up of 5 years), with a final report in 2025-2026 (median follow-up of ~10 years). The data are reviewed annually by the independent Data Monitoring Committee (iDMC), comprising expert members who are independent of the trial. Their role is to ensure trial participants are not exposed to excess hazards due to their participation in ACST-2 by reviewing unblinded aggregate trial data. Unblinded aggregate data is pooled data that is broken down by treatment allocation only, prepared as a report and given to the iDMC. The iDMC does not have access to patient level data. Public interest is in line with Article 9(2)(j) ‘processing is necessary for scientific or historical research purposes’.

The trial was started in 2008 at St George’s Hospital, London before moving to the University of Oxford in 2011. The University of Oxford is now the sole data controller. St George’s University of London has no ongoing involvement with the project and is not accessing any data. The trial was supported by the NIHR HTA and the BUPA Foundation and more recently secure long-term funding has been provided by The Nuffield Department of Population Health. It will be the largest trial of a carotid procedure ever completed. Currently 3134 participants have been recruited across 33 countries with the aim of recruiting 3600 by the end of 2019 / early 2020.

England is currently the second largest recruiting country, with 426 patients randomised from 23 hospitals to date. The results of the first ACST trial (which compared CEA with medical therapy) changed clinical practice worldwide and the University of Oxford expects the results of ACST-2 to be similarly impactful in the UK and beyond.

The MRIS Cohort Event Notification and Cause of Death data that is provided by NHS Digital is critical in informing the study’s endpoints from both a safety and efficacy viewpoint as well as the primary short term and long-term objectives.

The information requested from NHS Digital is to help the trial achieve its primary goals, namely the comparison of the peri-procedural risks (myocardial infarction [MI], stroke and death within 30 days of procedure and the longer term objective of preventing stroke, especially disabling of fatal strokes over the period of follow up (with major reports at 4 and 10 years median follow-up). NHS Digital data is also used to help avoid contacting the relatives of recently deceased patients, which would be intrusive and cause significant additional distress.

The study will compare:
1) Peri-procedural risks (myocardial infarction [MI], stroke and death;
2) Long-term (>5 years) prevention of stroke, particularly disabling or fatal stroke.
3) Procedural and stroke-related healthcare costs and;
4) Evaluate quality of life.

The University of Oxford will be the sole data processor. The only organisation that will have access to the data that is supplied by NHS Digital will be the designated personnel at the Clinical Trials Service Unit and the ACST-2 data team within the University of Oxford.

The information provided by participants at the point of consent and randomisation as well as each year when they return their annual questionnaires will be used to describe the risks and benefits of not only the surgical procedures but the co-morbidities and the long term use of medications to prevent future strokes.

Yielded Benefits:

The trial is ongoing.

Expected Benefits:

The ACST-2 study is a stroke prevention study enrolling asymptomatic patients across over 20 countries in Europe (including the UK), North and South America and Asia, to compare the early safety and long-term efficacy of CEA v CAS. There are approximately 200,000 procedures in Europe and US per annum, half of which are CEA, half CAS.

ACST-2 will be the largest-ever trial of a vascular surgical procedure and its results will be impactful. If it shows that CEA is superior to CAS, large numbers of patients (who currently undergo CAS) may switch to CEA. Alternatively, if the long-term results of both procedures are comparable, patients and doctors have a choice, and many patients may prefer a less invasive stent over surgery. If CEA is better than CAS, 100,000 patients will directly benefit by avoiding an inferior procedure. If CEA is equal in outcome to CAS then it becomes a patient/doctor choice and many patients may chose minimally invasive option of CAS. Either way, results may be impactful worldwide and will likely change practice.

The impact of ACST-2 will be tracked via national registries of vascular procedures. Practice changed worldwide following ACST-1 within 1-2 years, and University of Oxford expect ACST-2 to be similarly impactful once the 10-year follow up is reported in 2025-26.

Outputs:

Interim unblinded results are provided to the independent Data Monitoring Committee (DMC) annually. These reports are prepared by the trial statistician and data manager. There are no patient identifiable information contained in the report, which is circulated by email. This committee can advise the Trial Steering Committee (TSC) if there is proof ‘beyond reasonable doubt’ that one procedure is better than the other. In such circumstances, the TSC may choose to end the trial prematurely.

Two major reports are planned: It is expected that ACST-2 will complete recruitment in 2019-2020 with a major report (describing initial hazards of surgery and 4-year follow-up) envisaged in 2021. Follow-up (exclusively questionnaire and registry-based) will continue until 2025 (thereby ensuring a minimum follow-up of 5 years), with a final report in 2025-2026 (median follow-up of ~10 years).

Until then, the Principal Investigators will give trial updates at meetings of various learned societies aimed at raising the trial profile and encouraging recruitment. No patient identifiable data will be shared in such talks.

The ACST-2 have been involved in disseminating information to the general public through various means including engaging with Social Media, the UK Stroke Forum and the Oxford Biomedical Research Centre. The ACST-2 Trial Steering Committee includes lay members who have been present at the meetings throughout the course of the trial. The University of Oxford will continue to use these platforms not only to share information about the trial, but the disease process and Stroke.

All published outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

It is necessary for the University of Oxford to use NHS Digital to follow-up patients and receive information relating to mortality so that the study has access to the correct survival status and cause of death of trial participants. The data supplied by NHS Digital will be used with other sources of event information to assess the long-term safety and efficacy of CEA and CAS.

a) The Clinical Trials Service Unit (CTSU) securely transfers a file of identifying information including NHS Number, Date of Birth and Postcode plus Unique Study ID to NHS Digital.
b) NHS Digital will flag the participants and link the data to Civil Registry mortality data.
c) NHS Digital will return identifiable linked data in MRIS Cohort Event Notification reports and Cause of Death reports. The data will include the Unique Study ID, date of birth, gender, name, NHS number, date and cause of death of those participants who have died.
d) CTSU stores the data on a server based at CTSU, which can be only accessed by CTSU staff at the University of Oxford.
e) CTSU will extract a subset of the data containing/comprised of those patients who have died and make this available to the ACST-2 Trial Manager within the same organisation via encrypted email.

Data will only be accessed by individuals within CTSU and ACST-2 who have authorisation from the data controller to access the data for the purpose(s) described, all of whom are substantive employees of University of Oxford.

The data will be linked at record level with the trial data.

All contact data for the participants are held securely on a database at the Clinical Trials Service Unit and all paper documents are kept in a locked office to which only ACST-2 staff have access.

The patient-identifiable data will not be made available to any third parties, including the worldwide collaborators.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract - i.e. employees, agents and contractors of the Data Recipient who may have access to that data).

The Data will only be used for the purposes described in this Agreement.

No data will be shared with third parties.

For clarification, NHS Digital will not supply information about cancer notifications for the purpose of this trial.

MR1134 - The Oxford Monitoring System for Attempted Suicide: Mortality following Deliberate Self-harm — DARS-NIC-147957-4444C

Opt outs honoured: Yes - patient objections upheld, Y, Anonymised - ICO Code Compliant, Identifiable, Yes (Section 251, Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(7), Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(7); Other-Section 251, Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.; Other-Section 251, Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.; National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2018-08-01 — 2021-07-31 2019.02 — 2021.03.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration
  4. MRIS - Flagging Current Status Report
  5. MRIS - Members and Postings Report
  6. Demographics
  7. Civil Registration - Deaths
  8. MRIS - Bespoke
  9. Civil Registrations of Death

Objectives:

The data supplied by the NHSIC to University of Oxford will be used only for the approved Medical Research Project MR1134.

Yielded Benefits:

Conducted studies which are relevant to clinical services and policy on suicide prevention. One example is how through linking episodes of self-harm with suicide as an outcome, researchers are able to identify clinically important risk factors for suicide following an episode of self-harm (non-fatal self-harm being the most important risk factor for suicide). For example, in children and adolescents is has been shown that risk of future suicide is strongest in boys, and in those with multiple episodes of previous self-harm and where certain specific methods of self-harm have been used. In adults it has been shown that a particularly high risk of subsequent suicide in people who have a history of multiple episodes of self-harm. Another example concerns the relationship between clinical management and subsequent suicide. Thus, the researchers have been able to investigate whether receipt of a psycho-social assessment while a person is in hospital following self-harm decreases the risk of future suicide. A further example has been to show that a measure of suicidal intent (that is of an individual’s apparent wish to die that is associated with an episode of self-harm) is related to short-term risk of future suicide, but not longer-term risk. This is relevant to clinical practice because suicidal intent is often measured by clinicians using a specific scale (the one used in this research).

Expected Benefits:

Every year there are about 200,000 presentations to general hospitals following self-harm in England and Wales. Self-harm is the strongest risk factor for completed suicide and is associated with non-suicidal premature death. It is also associated with considerable healthcare costs. Self-harm has been highlighted as a priority area for research in the National Suicide Prevention Strategy for England and especially in the 2017 update of the strategy.

Understanding risk factors for mortality following self-harm and how clinical services may mitigate these risks are key to reducing premature mortality. Data from this study are expected to improve understanding of specific risk factors for premature mortality and specific causes of death following presentation to hospital for self-harm. In addition, this study is expected to improve the University of Oxford’s understanding of how specific aspects of in-hospital care for patients who self-harm are related to risk of premature death. This will help design better services for patients who self-harm which aim to reduce distress, self-harm repetition and mortality.

The study is part of a collaboration between three centres: University of Oxford, University of Manchester, and Derbyshire Healthcare NHS Foundation Trust. Sharing of de-identified data between these three centres is important for maintaining this collaboration.

Findings from this study inform the research community as well as policy and decision-making bodies who translate the findings into healthcare benefits.

Outputs:

The Oxford monitoring System and the Multicentre Study of Self-harm in England (including data from the monitoring systems for self-harm at University of Manchester and Derbyshire Healthcare NHS Foundation Trust) are ongoing projects. Outputs from these projects are multiple. The aims of this study are to identify risk factors for specific causes of death (including suicide) in persons who present to hospital after self-harm and to investigate how specific aspects of in-hospital care relate to mortality following self-harm. Specific outputs planned for the next 18 months include:

* Identifying overall risk factors for suicide and other specific causes of death following self-harm.
* Identifying risk factors for suicide and non-suicidal premature mortality in children and adolescents
* Specific method of self-harm as a risk for suicide and premature death
* Epidemiology of paracetamol poisoning and risk of death by suicide.

* The findings from these studies will be published in peer-reviewed journals aimed at clinicians and researchers.
* Periodic reports for the Department of Health (the study funding body) and the National Suicide Prevention Strategy for England Advisory Group to communicate progress and key findings will be produced.
* Findings are published on the website of the Multicentre Study of Self Harm in England where information is summarized in layman's terms.

Other outputs include:
* Presentations of research findings at scientific meetings aimed at clinicians, researchers and stakeholders
* Presentations at meetings with clinical staff involved in delivering care for individuals who self-harm
* Meetings with policy making and regulatory bodies such as the National Institute for Health and Care Excellence (NICE), Medicines and Healthcare products Regulatory Agency (MHRA), National Suicide Prevention Strategy working group

In all the above, data will be presented in an aggregate format and individuals cannot be identified.

The University of Oxford has previously produced a large number of studies on mortality following self-harm using the linked data from NHS Digital.
All outputs including lay summaries of publications are accessible on the Centre for Suicide Research webpages on the University of Oxford’s website:
http://cebmh.warne.ox.ac.uk/csr/recentpubs.html

Details of publications by the Multicentre Study are published on the Multicentre Study website: http://cebmh.warne.ox.ac.uk/csr/mcm/index.html

Processing:

For the purpose of the Oxford Monitoring System for Attempted Suicide, the University of Oxford collects information about every visit to the Accident and Emergency department of the John Radcliffe Hospital following self-harm.

The University of Oxford submits identifying details of study members (i.e. individuals included on the Oxford self-harm database) to NHS Digital so that the individuals can be ‘flagged’ for long-term follow up. NHS Digital then supplies notifications of study members’ deaths (date and cause) or exits from or re-entries into the NHS.

The University of Oxford has been receiving data on individuals recorded in the Oxford self-harm database from 1976 onwards. Reports on all flagged patients are supplied to the University of Oxford on an annual frequency.

On receipt of the data from NHS Digital, the University of Oxford cleans the data and links it with the existing data on self-harm by the same person. The data are stored on the High Compliance System (HCS) at the University of Oxford.

Data will be checked and processed on the HCS at the University of Oxford. After all identifiers are removed a de-identified version of the data using unique numerical identifiers will be merged into the Oxford Monitoring System study file for the purpose of local analysis.

Identifiers are needed as this is an ongoing study with multiple updates about mortality. Therefore, identifiers from NHS Digital are required in order to check the accuracy of specific records. For example, if a specific individual who has been updated on the University of Oxford’s records as deceased re-presents to hospital for self-harm after their recorded date of event, the University of Oxford will need to retrieve the original record from NHS Digital to check this record. The University of Oxford has considered the number of identifiers required and have minimised these to include only NHS number, date of birth, forename, surname and gender of patient for the purpose of record checks and verification.

As the Multicentre Coordinator, the University of Oxford receives deidentified data from the two other collaborating sites (University of Manchester and Derbyshire Health NHS Foundation Trust). On receipt of this data, the University of Oxford becomes the data controller. At the University of Oxford, the data is amalgamated to create a combined de-identified dataset consisting of data from all three sites. A copy of the combined pseudonymised dataset is transferred from University of Oxford to Derbyshire Healthcare NHS Foundation Trust and the University of Manchester. On receipt of their copies of the combined dataset, Derbyshire Healthcare NHS Foundation Trust and University of Manchester become the data controllers for their respective copies and will process the data in accordance with their respective Data Sharing Agreements with NHS Digital. The University of Oxford in turn is the data controller for its copy of the combined dataset.

The de-identified dataset is stored separately from the Oxford self-harm cohort and, while the combined cohort contains a copy of the Oxford self-harm cohort, the two datasets are not directly linked.

NHS Digital variables securely submitted by the collaborating sites to the study coordinator in Oxford are:
- Date of death
- Cause of death text (A to E)
- Date of registration
- ICD10 underlying cause
- ICD10 multiple cause codes (1 to 15)
- Event type

The same variables from the Oxford Monitoring System for Attempted Suicide are shared with the collaborating sites as described above.

No patient-identifiable data are shared between the three collaborating centres.

Both datasets are analysed by University of Oxford staff to identify risk factors for specific causes of death including suicide and other causes in persons who present to hospital after self-harm and to investigate how specific aspects of in-hospital care, may relate to subsequent mortality.

The University of Oxford must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract - i.e. employees, agents and contractors of the Data Recipient who may have access to that data).

Patient outcomes and NHS costs following primary hip and knee replacement surgery — DARS-NIC-172121-G0Z1H

Opt outs honoured: Yes - patient objections upheld, Anonymised - ICO Code Compliant, Yes (Section 251, Section 251 NHS Act 2006)

Legal basis: Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2018-07-01 — 2021-03-30 2018.06 — 2021.02.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Patient Reported Outcome Measures (Linkable to HES)
  3. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Oxford’s Big Health Data Group (BHDG) is undertaking two distinct research studies that require data from the National Joint Registry (NJR) linked with Hospital Episode Statistics (HES) and Patient Reported Outcome Measures (PROMs) data from NHS Digital.

The BHDG has received the linked data under a separate Data Sharing Agreement (ref: DARS-NIC-366845-Q1F0Q) for the purposes of two unrelated studies called STAR and ATLAS respectively. This data will be reused for two further studies called UKSAFE and UTMoST respectively.

Although the Principal Investigators of the studies are different, the same individuals (statisticians and a health economist), all of whom are substantive employees of the University of Oxford, will undertake both the UK SAFE and UTMoST studies at the Botnar Research Centre at the Nuffield Department of Orthopaedic, Rheumatology & Musculoskeletal Science (NDORMS).


UK SAFE- EVIDENCE BASED COST-EFFECTIVE HIP AND KNEE ARTHROPLASTY FOLLOW-UP CARE PATHWAYS

Most people now understand the need for a cost-effective NHS but seek reassurance that this will not reduce the standard of care. This is particularly true of older people, who are the group most likely to be affected by this research.

This research seeks to demonstrate that good aftercare is not necessarily expensive, in terms of time and money on the part of both the patient and hospital staff, and that individual patient-centred follow-up can better identify potential problems in a timely fashion, to the benefit of all concerned.

Total joint replacement provides considerable improvement in quality of life to people suffering with severe joint damage and in 2013 over 150,000 total hip and knee replacements were conducted. Due to increasing ageing and obesity in the UK population, this number is likely to increase each year. Sometimes, problems can develop with the replaced joint over time and a small percentage of people require further surgery. Because joint failure is not always associated with symptoms, follow-up care is provided to ensure that problems are identified as early as possible.

Providing this care for everyone in the years after their surgery is extremely expensive and the NHS is under increasing pressure to reduce its costs. Many hospitals have dramatically reduced the amount of follow-up provided and some provide no follow-up at all. There is very little research evidence to determine whether not providing follow-up care may be causing harm to people by missing the opportunity to pick up a problem with a replaced joint before serious damage occurs.

UK SAFE is a wider program of research funded by the NIHR Health Services & Delivery Research (HS&DR) and managed by the Leeds Biomedical Research Centre that covers four work packages. The principal investigator for the UK SAFE study is at University of Leeds. The investigator at the University of Oxford was a co-applicant on the grant and is leading work package 2a which will be using data from the NJR-HES-PROMS. This work package is being completed solely by the team at University of Oxford and University of Oxford is the sole data controller. The University of Oxford has sole autonomy for work package 2a and, as co-applicant on the grant, independently determined the purpose for and the means of data processing. Leeds BRC cannot direct or instruct any purpose or means for processing the data.

This study will enable University of Oxford’s BHDG to look at the routine data from the National Joint Registry on a large group of people admitted for revision surgery. Using PROMs data, the study will look at how people came to be admitted to hospital for revision surgery, their symptoms and previous hospital visits and then compare for those who needed emergency surgery, with those who have more timely revision surgery. This will help to understand when people are most likely to develop problems with their joint replacement and to identify whether some are more likely to develop problems than others.

It is vital that a decision to stop providing follow-up is not made just to save costs to the NHS, but is based on precise evidence, which includes understanding potential benefit and harm to people.

This NIHR funded project is designed to address how to improve productivity within NHS services; ensure that the right care is delivered in the right settings; develop new, innovative ways of developing health care and allocating spending more rationally. The areas of research that the study will address will help identify mechanisms to close the NHS funding gap whilst ensuring that the interests of patients remain protected and that the standard of service provision is not compromised as prioritised by Monitor.
At the end of the study an expert panel, including individuals who have undergone joint replacement, will consider the results to develop guidelines about how follow-up should be conducted to ensure no harm is caused.


UTMoST- RISK-BENEFIT AND COSTS OF UNICOMPARTMENTAL (COMPARED TO TOTAL) KNEE REPLACEMENT FOR PATIENTS WITH MULTIPLE CO-MORBIDITIES: A NON-RANDOMISED STUDY, AND DIFFERENT NOVEL APPROACHES TO MINIMISE CONFOUNDING

Surgical randomised controlled trials (RCTs) are the gold standard in research methodology. However, despite recent evidence suggesting that surgical RCTs are both safe and useful, they remain uncommon for a number of reasons, including costs, time, ethical concerns, surgeon equipoise, and feasibility.

Non-randomised studies relying on routinely collected data, could offer an efficient alternative for the comparative assessment of surgical interventions in the National Health Service (NHS). In addition, these studies offer results which are potentially generalizable to the whole population of real world NHS patients (regardless of comorbidities or age) including patients who would have been excluded in RCTs, and they can be conducted at a much lower cost as well as within a shorter time. However, observational studies are limited by confounding and related bias due to the non-random allocation of treatment alternatives.

The University of Oxford is currently running a larger and very expansive randomised controlled trial called TOPKAT. This NIHR HTA-funded RCT is a multi-centre randomised trial to measure the clinical effectiveness and cost effectiveness of total and partial knee replacement for medial compartmental osteoarthritis. Because of its restrictive eligibility criteria, the TOPKAT RCT involves comparatively healthier participants rather than patients with more severe comorbidities. For this reason, it is unclear whether the findings of TOPKAT will have external validity to the large number of patients with multiple co-morbidities (1 in 6 according to National Joint Registry data).

The University of Oxfords BHDG will replicate TOPKAT by analysing the association between unicompartmental knee replacement (UKR) (compared to total knee replacement (TKR)) and post-operative patient reported outcomes (PROMs) amongst participants in the National Joint Registry (NJR) eligible for TOPKAT (ASA grade <3 - ASA is the physical status classification system for assessing the fitness of patients before surgery) using different analytical methods and then test for a difference between the obtained estimates and TOPKAT. If validated, the same method will be used to study the benefits (PROMs), risks (revision, complications), mortality, costs and cost-effectiveness of UKR (compared to TKR) amongst NJR participants not eligible for TOPKAT (ASA 3+).

The UTMoST project will complement the results of the NIHR HTA-funded TOPKAT RCT, where unicompartmental (UKR) is compared to total knee replacement (TKR). The UTMoST will assess the risks, benefits and costs of these two alternative surgical procedures amongst the NHS patients with multiple co-morbidities who were not eligible for TOPKAT according to the listed inclusion/exclusion criteria. The study will have a clear impact on and benefits for the public and the NHS, as well as for clinical research funders including NIHR by providing information on the comparative risks, benefits (patient-reported outcomes), and cost of partial and total knee replacement for patients with multiple co-morbid conditions.

Expected Benefits:

UK SAFE- EVIDENCE BASED COST-EFFECTIVE HIP AND KNEE ARTHROPLASTY FOLLOW-UP CARE PATHWAYS

Upon completion, this research will have major immediate effect on national NHS planning and budgeting and patient well-being. The outputs will be evidence-based support for timing of follow- up and identification of the most cost-effective follow-up model. This fits directly within the NHS framework for improving outcomes from elective procedures. Rationalising current diversity of follow-up practices should enable substantial savings for the NHS. Novel follow-up strategies, such as creating a rapid access pathway after joint replacement for symptomatic patients will be examined. It is envisaged outputs to be readily applicable to the wider NHS, not only hip and knee but also other joint replacements.

The impact will be to reduce the burden on patients and the NHS in terms of outpatient visits and clinical tests that do not add benefit, while optimising detection of potential problems. From an NHS perspective, this work will provide NHS managers with economic and clinical information on arthroplasty follow-up to inform service planning and delivery, and the role of arthroplasty practitioners in this service; provide orthopaedic surgeons with guidance on follow-up, including patient and economic considerations of factors involved; produce arthroplasty follow-up guidelines for adoption by the relevant specialist societies and inclusion with information for their members. From a patient perspective, this work will help to inform patients about follow-up practice and empower them to make choices about future healthcare relating to their joint arthroplasty.

At the end of the project, a policy document will be created with support of the relevant societies, NHS England, CCGs and patient representation. It is anticipated that this will include a stratification algorithm to determine appropriate follow-up for an individual patient, taking into account, for example, implant type and patient factors, and that recommended follow-up pathways for hip may differ to those for knee. This advisory document will be disseminated to all stakeholders, including orthopaedic surgeons, arthroplasty surveillance professionals and NHS managers. With the committed support of these key organisations, the applicant anticipates that these guidelines will be positively received and that implementation will be widespread. It is the BDHG’s ambition that the recommended follow-up pathway/s defined by this programme of work will be adopted for all hip and knee replacement patients in the UK and internationally.

The target date is 36 months following receipt of data.


UTMoST- RISK-BENEFIT AND COSTS OF UNICOMPARTMENTAL (COMPARED TO TOTAL) KNEE REPLACEMENT FOR PATIENTS WITH MULTIPLE CO-MORBIDITIES: A NON-RANDOMISED STUDY, AND DIFFERENT NOVEL APPROACHES TO MINIMISE CONFOUNDING

This study will have clear impact on and benefits for both the public and the NHS, as well as for clinical research funders including NIHR by:

- Providing information on the comparative risks, benefit (patient-reported outcomes), and cost of partial and total knee replacement for patients with multiple co-morbid conditions.

If, as expected, UKR is safer, as effective, less costly, and thus more cost-effective than TKR for this specific patient group, it might become the first line surgical solution for severe knee arthritis in multi-morbid patients. The BDHG would then inform NICE and the Medicine and Healthcare products Regulatory Authority (MHRA) of the findings with the aim to impact on future guidelines for the treatment of severe knee arthritis. Depending on the study results, the BDHG would –if relevant- produce UK guidance documents and information leaflets for patients and health care professionals in both primary and secondary care involved in this area.

- Informing on the usefulness of efficient studies using routinely collected (non-randomised) data for the evaluation of surgical alternatives in the NHS to complement randomized studies.

If some or all of the proposed pharmaco-epidemiological analytical methods are able to replicate the findings from an ongoing surgical RCT, these could be used in the future to provide information on the comparative risk-benefit and cost-effectiveness of surgical options for patients typically under-represented in (or even excluded from) randomized studies. This would typically include a growing proportion of the UK population: the elderly and multi-morbid patients.

The target date is 48 months following access to data.

Outputs:

UK SAFE- EVIDENCE BASED COST-EFFECTIVE HIP AND KNEE ARTHROPLASTY FOLLOW-UP CARE PATHWAYS

This project will deliver the first research-supported, best-for-patient, joint-specific, cost-effective recommendations for follow-up care, providing a gold standard for clinical excellence, and follow-up advice for patients, surgeons, purchasers and health services. Value is not limited to the UK, but has massive global potential.

Nationally, the outputs, in the form of an executive summary statement of the agreed pathway/s will be disseminated through appropriate NHS Networks, the NHS England Elective Orthopaedics Sub- committee, the NHS Institute for Innovation and Improvement and professional societies. Dissemination will be key to developing a culture of ‘finding the best way of doing something and doing it everywhere’ to significantly reduce wastage of clinical resources and optimise NHS spend.

The University of Oxford have support of the British Hip Society (BHS), British Orthopaedic Association (BOA), British Association for Surgery of the Knee (BASK), NHS England, Arthroplasty Care Practitioners Association (ACPA), the National Joint Registry and three Leeds-based CCGs for this research and for dissemination activities. The University of Oxford will put forward the consensus statement to each society’s AGM for adoption as a resolution.

Internationally, dissemination platforms are already in place through the International Society of Arthroplasty Registers (ISAR) and the European Federation of National Associations of Orthopaedics and Traumatology (EFORT).

Overall findings and findings from individual work-packages will be disseminated through a variety of media. Abstracts will be submitted to major British and international orthopaedic conferences, and separate relevant meetings, including the Health Economics Study Group and Exploiting Existing Data for Health Research conference. The University of Oxford will look to present at the NIHR Methodology Conference and NHS Management conferences and events. Manuscripts will be submitted to appropriate peer-reviewed journals, including general medical, orthopaedic and management journals.

Patient dissemination will be supported through the Leeds Musculoskeletal Biomedical Research Unit (LMBRU) PPI forum and website and the strong ties with Arthritis Care of the applicant within the University. The BDHG will hold a PPI conference at the end of the study. The BDHG will encourage their PPI representatives to be involved in presentations, with support from research staff, and they will help to ensure conference material is appropriate. Working with the lay representatives, the BDHG will write a lay summary for publication in a patient publication such as The Patient or Inspire.

The target date is 36 months following receipt of data.


UTMoST- RISK-BENEFIT AND COSTS OF UNICOMPARTMENTAL (COMPARED TO TOTAL) KNEE REPLACEMENT FOR PATIENTS WITH MULTIPLE CO-MORBIDITIES: A NON-RANDOMISED STUDY, AND DIFFERENT NOVEL APPROACHES TO MINIMISE CONFOUNDING

The University of Oxford will write a thorough report of the research at the end of the project to be included in the NIHR HTA Journal. In addition, the University will publish at least two papers in national and/or international scientific journals to report key findings. In order to increase the impact and accessibility of the findings, they will be published in open access journal(s) when possible.

The results will also be presented at national (British Orthopaedic Association, British Society of Rheumatology, or similar) and international (American Association of Orthopaedic Surgeons, American College of Rheumatology, or similar) scientific conferences, preferably in the format of oral presentation/s.

The University of Oxford will discuss the results (including risk-benefit and cost-effectiveness evaluation/s) with relevant panels at NICE to make them available for future health technology assessments.

The University of Oxford will also disseminate the findings to the public. A Public Patient Involvement (PPI) co-investigator will help to design materials such as leaflets for this purpose, which will be distributed in key places/events like surgeries, hospitals and meetings organised by charities. The PI will present the results in meetings with both local and regional patient groups (such as the NJR patient Network), and charities such as National Rheumatoid Arthritis Society (NRAS) and Arthritis Care will be involved in this stage to ensure the public are reached in an effective and respectful way.

Finally, the results will be disseminated through the media when possible – local radio, charity magazines, etc.- following advice from departmental Outreach and Communications officers, as well as resources available through the Oxford NIHR Biomedical Research Centre network.

The target date is 36 months following access to data.

Processing:

NDORMS will forward to NHS Digital via a Secure Electronic File Transfer (SEFT) account from their current dataset and NHS Digital will reidentify them, apply objections and return a cleaned list of HESIDs. NDORMS can then extract only the data linked to those HESIDs thereby applying current patient objections.

The linked NJR, HES and PROMS datasets are held on a password protected University Computer on an encrypted drive at the Botnar Research Centre, Nuffield Department of Orthopaedic, Rheumatology & Musculoskeletal Science (NDORMS).

Access to the data is restricted to the statisticians and health economists who are substantive employees of the University of Oxford and based at the Botnar Research Centre and who will work collaboratively on both the UK SAFE and UTMoST studies.

The BHDG has received pseudonymised HES and PROMS data under a separate Data Sharing Agreement (ref: DARS-NIC-366845-Q1F0Q) which are linked with pseudnoymised NJR data for the purposes of two unrelated studies called STAR and ATLAS respectively. The BHDG will take copies of some that data to reuse them for the purposes of the UK SAFE and UTMoST studies respectively. The detailed process to do this is as follows:

1. From the dataset that the University of Oxford is authorised to hold and process under the Data Sharing Agreement DARS-NIC-366845-Q1F0Q, the encrypted HESIDs will be extracted and supplied to NHS Digital.
2. NHS Digital will decrypt those IDs to identify the individuals.
3. NHS Digital will apply national patient opt-outs to this list of patients. The data of any individual who has registered a Type 2 objection/patient opt out since the data was supplied under DARS-NIC-366845-Q1F0Q will be removed.
4. NHS Digital will remove all identifiers; re-encrypt the HESIDs using the same encryption key as for DARS-NIC-366845-Q1F0Q, and provide the list of HESIDs, as pseudo-anonymised data, to the University of Oxford.
5. The University of Oxford would extract a copy of the linked HES, PROMS and NJR supplied under DARS-NIC-366845-Q1F0Q for all individuals whose data is included in the output file from NHS Digital. The resulting dataset will be stored separately from the dataset supplied under DARS-NIC-366845-Q1F0Q with access controls ensuring the two will not be linked or accessed together.

The same fields of data are required for both studies and the knee replacement data will be used in both but the hip replacement data will only be used for UK SAFE. The data will be used exclusively for the purposes of the specified studies. The data will not be made accessible to any third parties. At the end of the studies, the data will be safely held in a password protected University Computer at the Botnar Research Centre, for further 5 years, and accessed only to answer questions arising from the publication and other publicity if required.

Only individuals, substantively employed by the University of Oxford, will have access to the data. The processing of the requested data will be carried out in the course of its legitimate activities by a University of Oxford research team which has a longstanding experience in orthopaedic outcomes.

All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide. No record level data falling under this agreement will be shared with any third-party.

All organisations party to this Agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract – i.e. employees, agents and contractors of the Data Recipient who may have access to that data.

RECOVERY Trial - Communications to Participants DSA — DARS-NIC-405749-N7T3M

Opt outs honoured: No - consent provided by participants of research study, Identifiable, No (Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2020-11-27 — 2023-11-26 2020.12 — 2021.01.

Access method: One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Demographics

Objectives:

BACKGROUND:
The RECOVERY trial, coordinated by Oxford University, is a national clinical trial aimed at identifying treatments that may be beneficial for people hospitalised with suspected or confirmed COVID-19 (Corona Virus).

In 2019 a novel coronavirus-induced disease (COVID-19) emerged in Wuhan, China. A month later the Chinese Center for Disease Control and Prevention identified a new betacoronavirus (SARS [Severe Acute Respiratory Syndrome] coronavirus 2, or SARS-CoV-2) as the aetiological (causing or contributing to the development of a disease or condition) agent. The clinical manifestations of COVID-19 range from asymptomatic infection or mild, transient symptoms to severe viral pneumonia with respiratory failure. As many patients do not progress to severe disease the overall case fatality rate per infected individual is low, but hospitals in areas with significant community transmission experienced a major increase in the number of hospitalized pneumonia patients, and the frequency of severe disease in hospitalised patients was recorded as high as 30%. The progression from prodrome (an early symptom indicating the onset of a disease or illness - in this case usually fever, fatigue and cough) to severe pneumonia requiring oxygen support or mechanical ventilation often takes one to two weeks after the onset of symptoms. The kinetics of viral replication in the respiratory tract are not well characterized, but this relatively slow progression provides a potential time window in which antiviral therapies could influence the course of disease. The RECOVERY Trial aims to compare several different treatments that may be useful for patients with COVID-19. These treatments have been recommended by the expert panel that advises the Chief Medical Officer (CMO) in England.

TRANSPARENCY/PARTICIPANTS:
The Health Research Authority (HRA) details in their Re