NHS Digital Data Release Register - reformatted

The University Of Manchester projects

1278 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

🚩 The University Of Manchester was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. The University Of Manchester 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.

Favourable Outcome of canceR Therapy (ODR1920_062) — DARS-NIC-656854-T9G6P

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2023-03-24 — 2024-06-28

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS National Radiotherapy Dataset (RTDS)
  3. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objectives:

Historical Data Processing

PHE sent the FORTY study team the requested dataset to process as follows:
1) Separate any patients with a death record within 5 years of treatment and
hold as cohort 2
2) Exclude any patients where the treatment intent is not radical using treatment intent codes, event codes and treatment details where intent is incomplete
3) Separate any patients with another treatment record within 5 years (indicating the first round of radical treatment did not cure the cancer) and hold as
cohort 3 using the same data codes as step 2 as well as event dates
4) Count the number of patients remaining, this is our patients cured cohort,
cohort 1
5) Stratify the main cohort (cohort 1) of patients remaining by treatment modality or modalities
Cohort 3 will be sent back to step 1 so patients who were cured with a second (or
more) round of radical treatment are still included in the analysis.
Once cohort 1 has been selected and separated by treatment modalities, the contribution that each modality has to the number of cured patients will be calculated.
For the secondary research questions, cohort 1 will also be separately stratified by:
a) Cancer site
b) Deprivation (IMD quintile)
c) Ethnic group
d) Age group

Yielded Benefits:

Abstracts at Scientific conferences

Expected Benefits:

Scientific publications and conference abstracts. Updating the available data and making it more rigorous and measurable

Outputs:

Journal Publications and conference presentations

Processing:

The data will be transferred from PHE to the UoM research data storage systems via the PHE secure file exchange. If a password is required for the data file then this will be provide via a separate communication line, such as telephone call.

The data will be loaded into a SQL database, held on a University of Manchester research data storage system which meets all requirements specified by any data-sharing agreement between Public Health England and the University of Manchester. The SQL database allows the data to be extracted, filtered and sorted without modifying the data offload from NCRAS, keeping the original data the same as originally received.


Typically for this type of research, we will assume that 'cure' means 5 years of survival following treatment, without tumour recurrence or progression.

The stages of processing the data to achieve the principle research question are as follows:

1) Remove any patients with a death record within 5 years of treatment and hold them separate

2) Remove any patients where the treatment intent is not radical

3) Remove any patients with another treatment record within 5 years (indicating the first round of radical treatment did not cure the cancer)

4) Count the number of patients remaining, this is our patients cured cohort

5) Separate the cohort of patients remaining by treatment modality or modalities

Once the final cohort of patients has been selected and separated by treatment modalities, the contribution that each modality has to the number of cured patients will be calculated.

For the secondary research questions, the main results from step 5 will be separately stratified by:

a) Cancer site

b) Deprivation (IMD quintile)

c) Ethnic group

d) Age group

Additionally, the patients that have a death record will be further analysed to identify any potential cohorts that died within 6 months of radical intent treatment.

Children and young people’s mental health, its changes over time and its relationship with the family ecosystem — DARS-NIC-632349-B5F8W

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2023-04-28 — 2026-04-27

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Mental Health of Children and Young People (MHCYP)

Objectives:

The University of Manchester requires access to NHS England data for the purpose of the following research project: “Children and young people’s mental health, its changes over time and its relationship with the family ecosystem”.

Aims of study…
1. Investigate trends, by demographic characteristics, in the proportion of CYP with: poor self- and parent-reported mental health; presenting to, and treated in, primary and secondary care for mental health problems.
2. Examine whether there are subgroups for which there are particularly large increases in the number presenting to services or the number with symptoms of distress.
3. Examine how gender and ethnicity affects the difference between the proportion with symptoms of distress (measured through survey) and the proportion who are seen in services.
4. Investigate whether the COVID-19 pandemic has affected the relationship rates of between mental health symptoms and treatment.
5. Investigate network effects between symptoms between children and their primary care giver

Intended analyses…
1. Investigate trends in mental health in CYP since 1999, by demographic subgroups of ethnicity and gender. These will be cross-referenced against analysis that is being undertaken by the research group of primary care data, using the Clinical Practice Research Datalink (CPRD). This process will not involve any direct linkage of data, rather the comparison will be of aggregated data and statistical measures (expected delivery date, July 2023).
2. Examine the link between a child’s mental health and that of the primary care giver. This will be examined using network models, where nodes on the network will represent mental health symptoms of the child or parent and the edges will represent partial correlations across nodes. This will provide a detailed mapping of the interdependencies of mental health between children and their primary care giver. (Expected delivery date, July 2025).
3. Investigate the specific effects of the pandemic on children’s mental health. University of Manchester will compare mental health of children before the pandemic (using the 2017 wave of data) with that measured on the same children during the pandemic (using the 2020 data). This will be examined as changes on a network, to represent the multifactorial process of change (expected delivery date, July 2027).

The following NHS England data will be accessed:
• Mental Health of Children and Young People (MHCYP) survey- 2017 and 2020.

The level of the data will be pseudonymised.

MHCYP is only available as a full dataset, thorough minimisation has taken place centrally prior to the dataset being made available. In addition, the full dataset is required in order to cover all England (national analyses) and to be able to control for a range of factors in the analyses, for example gender, ethnicity and parental age. A range of conditions will be examined in the analyses, for example phobia’s, body dysmorphia, attachment disorder and eating disorder.

The University of Manchester is the controller and 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, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.

The funding comes from multiple sources. Current joint funders include:
· The Royal Society– Funding is in place until 10/09/2027.
· Wellcome Trust – Funding is in place until 10/09/2027.
Funding to continue the work described will be sought on an ongoing basis.

Expected Benefits:

The findings of this research study are expected to contribute to evidence-based decision-making for policymakers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment, and experience of health care users relevant to the subject matter of the study.
The use of the data could also:
• Help the system to better understand the health and care needs of populations.
• Advance understanding of regional and national trends in health and social care needs.
• Support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).

The ultimate beneficiaries are expected to be children and young people themselves, by providing information that can feed into interventions to help improve their mental health. As part of the fellowship application process, a group of children and young people have been engaged with who have lived experience of mental illness. They fed back on aspects of the proposal and gave advice on how to communicate findings in a non-stigmatising way.
There are also benefits to survey participants. People took part in this survey for no reason other than that they wanted their views and experiences to inform research. They did not provide information as part of a service transaction, in order to access a diagnosis or treatment or services. They chose to take part in the survey to inform research.

It is hoped that through publication of findings in appropriate media, the findings of this research will 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.

As part of the fellowship, The University of Manchester will be engaging with a group of children and young people with experience of mental illness to talk about what the results mean to them, and how to best interpret the research in a way that resonates with them. The results that will be presented will be from aggregated data, as those published in journals will be. Their feedback will be invaluable in understanding whether the results will achieve their stated benefits. In addition, The University of Manchester will assess how many times the papers from the project will be referenced in academic papers, as a metric of how impactful the research is. Finally, there will be engagement throughout with key stakeholders, such as those who work in children and young people’s mental health services. The feedback from them shall be invaluable as to understanding whether they might have clinical benefit.

Outputs:

The expected outputs of the processing will be:
• Submissions to 2-3 peer reviewed journals, expected to be delivered within 3 years.
• Potentially presentations at specific conferences, however nothing has been confirmed yet.

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

The outputs will be communicated to relevant recipients through the following dissemination channels:
• Journals
• Social media
• Press release, using the University of Manchester’s press office. This is expected to be published in national press.
• Potentially webinars and posters

The first of these journals are expected to be published in July 2023, the second in July 2025 and the third in July 2027.

Processing:

No data will flow into NHS England for the purposes of this Agreement.

The MHCYP data is held on behalf of NHS England at the UK Data Service (UKDS) and UKDS are they are responsible for its dissemination under direction by NHS England. The data will contain no direct identifying data items. The data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.

The data will not be transferred to any other location.

The data will be stored on servers at the University of Manchester.

The data will be accessed onsite at the premises of the University of Manchester only.

The data will not leave England at any time.

Access is restricted to individuals within the University of Manchester who have authorisation from the Principal Investigator. All such individuals are substantive employees of the University of Manchester.

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

The data will not be linked with any other data.

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

Researchers from the University of Manchester will process the data for the purposes described above.

Evaluating prescribing safety indicators embedded in computerised clinical decision support software — DARS-NIC-253220-Q1X8H

Type of data: information not disclosed for TRE projects

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

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

Purposes: Yes (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-10-07 — 2024-10-06

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, UNIVERSITY OF NOTTINGHAM

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 Critical Care
  5. Civil Registrations of Death - Secondary Care Cut
  6. Hospital Episode Statistics Admitted Patient Care (HES APC)
  7. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

Medication errors in general practice are an important and expensive preventable cause of safety incidents, illness, hospitalisations and deaths. IT-based tools are increasingly being used to support general practitioners in their clinical decision-making including safer prescribing. The National Institute of Health Research (NIHR) are therefore funding a programme of work called: 'Avoiding patient harm through the application of prescribing safety indicators in English general practices (acronym: PRoTeCT)'. Using a series of linked work packages, the PRoTeCT Programme aims to evaluate two large-scale interventions in English general practices that employ prescribing safety indicators to reduce hazardous prescribing and avoidable harm to patients: the clinical decision support software OptimiseRx, and a pharmacist-led IT-based intervention (PINCER).

One of these tools, OptimiseRx, alerts the prescribers of potential errors during the medication prescription process. However, despite its potential to improve prescribing safety and patient outcomes, the effectiveness of OptimiseRx has never been quantified. Such investigation is of substantial public interest for two reasons. Firstly, to make recommendations to improve patient safety in the future. If OptimiseRx is effective in aiding health professionals to prescribe more safely, it is something to recommend. Secondly, to understand if it is cost-effective for the NHS. As part of the PRoTeCT programme, the University of Manchester is leading on the aim to evaluate the effects of OptimiseRx on potentially hazardous prescribing in primary care and serious harm outcomes, as well as to evaluate the cost-effectiveness of OptimiseRx on behalf of NHS England.

This study aims to assess the effect of the implementation of OptimiseRx on potentially hazardous prescribing and associated adverse outcomes including hospitalisation and death; and to evaluate the cost-effectiveness of OptimiseRx to NHS England. This study will derive generalisable insights into the effectiveness and cost-effectiveness of point-of-prescription decision support systems.

To fulfil these aims, Hospital Episode Statistics (HES) Admitted Patient Care (APC), HES Critical Care (CC), and Civil Registrations (Deaths) Secondary Care Cut pseudonymised patient-level data is requested from NHS Digital. This data will be linked to pseudonymised patient-level primary care patient data from the ResearchOne clinical research database. ResearchOne is a health and care research database developed by a company called TPP, which holds clinical and administrative data drawn from electronic patient records currently held on the TPP SystmOne clinical system. Patient-level data from all general practices that use the SystmOne live environment and contribute their data to ResearchOne will be used for the linkage, irrespective of whether the practice has installed OptimiseRx.

This data linkage of ResearchOne primary care and NHS Digital HES APC and deaths data will allow an assessment of whether 79 different identified prescribing safety indicators in OptimiseRx lead to reductions in hospitalisations and death from a range of associated serious harm outcomes including: gastrointestinal bleed, exacerbation of asthma, heart failure, stroke, myocardial infarction, acute coronary syndrome, venous thromboembolism, arrhythmia, acute kidney injury, pneumonia, fractures, and rhabdomyolysis. The HES APC and HES CC data also enables economic analysis of the OptimiseRx tool from the perspective of NHS England to estimate cost per hazardous prescribing event avoided, and cost per serious harm outcome avoided. All NHS Digital linked data will only be used for the OptimiseRx evaluation, not for any other part of the PRoTeCT programme.

OptimiseRx is implemented at the Clinical Commissioning Group (CCG) level. For each CCG that has rolled out OptimiseRx, the University of Manchester will obtain from First Databank (industry partner; vendor of OptimiseRx) the start date for each of their practices when OptimiseRx was activated. If any practice has stopped using OptimiseRx since, First Databank will also provide their stop date. Data from ResearchOne covers both practices that have implemented OptimiseRx and practices that have not.

The study will investigate how often potentially hazardous prescribing events occur in the 24 months before and the 12 months after the practice starts using the OptimiseRx system. For each month, rates of potentially hazardous prescribing events will be estimated and compared to what would be expected from the trends before OptimiseRx was implemented. Using data from GP practices that are in ResearchOne but have not implemented OptimiseRx will help in understanding the secular trends and seasonal patterns of prescribing, driven by other sources of confounding such as changes in policy. The linked HES and mortality data at the patient-level will further enable investigation of any changes in serious harm outcomes associated with hazardous prescribing.

Only data for England will be required due to the inclusion of only including English practices. OptimiseRx was first implemented in September 2013. Given interest in the risks of serious harm outcomes associated with hazardous prescribing in the 24 months before and 12 months after the practices have implemented OptimiseRx, linkage to HES and mortality data from 2011/12 is requested. Data has been requested up to 2019/2020 for all three datasets, enabling analysis of patients from practices which implemented OptimiseRx in early 2019. Only the variables that are needed for analysis in each dataset have been selected and no outpatient or A&E data have been requested. There are no alternative, less intrusive ways of achieving the purpose of this study without the patient-level data as described in this agreement.

There is no direct linkage system between ResearchOne and HES/ mortality data. In order to link the data, TPP will send the relevant patient identifiers to NHS Digital. Approval from the Confidentiality Advisory Group (CAG) for Section 251 support to process routinely collected, identifiable patient data without consent has been obtained. Specifically, to enable ResearchOne and NHS Digital to generate project-specific patient pseudonyms, for ResearchOne to transfer the patient pseudonyms, gender, and date of birth of the patients to NHS Digital, and for NHS Digital to share non-identifiable health data with the University of Manchester for data linkage and analysis.

The University of Manchester and the University of Nottingham are joint Data Controllers. Only the University of Manchester will process the requested NHS Digital data. The University of Manchester lead on the OptimiseRx evaluation work package. The wider PRoTeCT programme is managed by the Principal Investigator based at the University of Nottingham. As the PRoTeCT programme lead, the University of Nottingham is ultimately responsible for all major decisions made regarding the OptimiseRx evaluation work led by the University of Manchester.

The PRoTeCT programme is a collaboration between the University of Manchester, University of Nottingham, University of Dundee, and University of Edinburgh. The role of team members from the Universities of Dundee and Edinburgh is advisory for the OptimiseRx evaluation study. The Universities of Dundee and Edinburgh do not determine the purpose or the means of the processing. The funder of the study, NIHR, have no role in the study design nor data analysis. Neither TPP (vendor of ResearchOne data) nor First Databank (vendor of OptimiseRx) are joint data controllers as they have no roles in determining the purposes and means of the processing of the NHS Digital data.

The legal bases for processing these data are:
- 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";
- GDPR 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."

These legal bases for processing NHS Digital data apply to both the University of Manchester and the University of Nottingham.

The Universities of Manchester and Nottingham do not expect there to be any disadvantages or risks to individuals whose data will be used to understand patterns of potentially hazardous prescribing and associated outcomes. There will be no direct contact with patients and the research team will only have access to pseudonymised data. Only aggregated outputs will be shared with researchers outside of the University of Manchester and small numbers will be suppressed in line with the HES Analysis Guide.

Outputs:

The study team will work with a number of organisations such as NHS England, NHS Improvement, The Health Foundation, Academic Health Science Networks, the Royal College of General Practitioners, and others to disseminate research findings. The expected output would include:

- Dissemination through meetings, project summaries, seminars, webinars, policy briefings and open access journal publications (e.g. leading medical journals such as Plos Medicine).
- Production of evidence that will help the NHS make investment decisions concerning prescribing safety innovations in general practice.
- Presenting at national conferences aimed at health care planners, practitioners and policy makers.
- Articles in CCG newsletters where appropriate.
- Developing strong links with a wide range of patient safety communities, e.g. The Health Foundation Q Community: https://q.health.org.uk/, National Medication Safety Network, Patient Safety Collaboratives: https://www.england.nhs.uk/patientsafety/collaboratives/.

Results of the cost-effectiveness analysis will form part of these outputs.

The study team also have experienced patient and public involvement members aligned to this research project and programme management. They are involved in all aspects of the study design, progress and dissemination. Two patient members of the wider PRoTeCT Programme Grant Management Group attend monthly meetings. They will contribute to the final report and be involved in dissemination to relevant patient and public audiences. Wider involvement of patients and the public will be obtained by continuing engagement with the Research Users Group of the NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, and the Patient and Public Involvement Senate of the East Midlands Academic Health Science Network.

Only aggregated results will be disseminated, with small numbers suppressed in line with the HES Analysis Guide. The completion dates for the outputs would be:
Production of final report to the NIHR – by 28/02/2023.
Write open-access journal articles – by 28/02/2023.
Disseminate findings to policy makers, managers and clinical leaders through project summaries, seminars, webinars and policy briefings – by 28/02/2023.

Any foreground intellectual property (IP) generated as a result of the project is assigned to Notts Healthcare NHS Trust as per the NIHR collaboration agreement for the wider ProTeCT programme, and will not be held by the commercial companies that are involved (TPP and First DataBank). Each party within the programme is however granted an irrevocable, non-transferable, royalty-free right to use all arising IP generated in the course of the project for academic teaching, research purposes and for non-commercial clinical purposes.

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 steps described below will be used to enable linkage of data between First Databank (industry partner, vendor of OptimiseRx), TPP (industry partner, vendor of ResearchOne), and NHS Digital.

- The University of Manchester will generate two project-specific SALT strings. SALTs are strings of characters to be appended to the data that are being pseudonymised. SALT strings are a security tool for encrypting sensitive data. One of the SALT will be used to generate the general practice pseudonyms, and the other to generate the patient pseudonyms.

- The University of Manchester will transfer both SALTs to TPP and to First Databank.

- First DataBank will extract from their system the organisation data service (ODS) codes of the general practices, i.e. practices which have implemented OptimiseRx, together with their dates of implementation and end dates (if applicable).

- First DataBank will append one of the SALT strings to each ODS code and then apply a secure, one-way hash algorithm to create a ‘digest’ (practice pseudonym) – an alphanumeric string which cannot be reversed.

- First Databank will transfer the list of practice pseudonyms generated together with the OptimiseRx start/end dates to TPP.

- Using the same SALT that First Databank used, TPP will independently apply it to the ODS codes of the practices in the ResearchOne database. Since the same field (i.e. ODS code) and SALT are used by both First Databank and TPP, the same digest (practice pseudonym) can be understood by both parties. Therefore, comparison of the practice pseudonyms generated by TPP with those from First Databank will enable TPP to ascertain which practices have implemented OptimiseRx and are also contributing to ResearchOne.

- TPP will use the second SALT string to create a 'digest' for each patient (i.e. patient pseudonyms) in the ResearchOne database from their NHS number, and generate the primary care data extract. A bespoke study id for each patient (‘R1 IDs’) in the primary care data extract will also be generated.

- TPP will send the pseudonymised patient level primary care data extract to the University of Manchester, together with the R1 IDs. The SALT generated patient pseudonyms will not be included.

- TPP will transfer the SALT generated patient pseudonyms and R1 IDs to NHS Digital using the Secure Electronic File Transfer service (SEFT). The same file will also contain the patients' gender and date of birth. These variables are included to ensure accurate linkage between ResearchOne and HES/ mortality data.

- The University of Manchester will share the same SALT string that TPP used to generate the patient pseudonyms with NHS Digital. Using this SALT, NHS Digital will independently generate the patient pseudonyms in their database from the NHS numbers. These pseudonyms, together with the patients' gender and date of birth, will then be matched with the data from TPP. This will enable NHS Digital to ascertain those patients whose HES/ mortality data are required.

- NHS Digital will extract the HES/ mortality data for these patients for all data years requested.

- NHS Digital will transfer the pseudonymised patient level HES/ mortality data extracts containing the R1 IDs to the University of Manchester.

- Using the R1 IDs, the research team at the University of Manchester will link the primary care extract with the HES/ mortality data extracts. The merged file will then be used for analysis. There will be no requirement/attempt to re-identify individuals. There will be no subsequent flows of patient level data. NHS Digital data will only flow to the University of Manchester who will complete all data processing for the study.

Data storage and processing:

The research team at the University of Manchester will only have access to pseudonymised, record level data. All data received from TPP and NHS Digital will be stored in the University's Research Data Storage Service - an access restricted data share on the University network storage infrastructure, which is the recommended location for storing sensitive or critical University data. The storage infrastructure is hosted across two data centres for resilience and disaster recovery purposes.

The hardware in the data centres and the network infrastructure belong to the University of Manchester. Nothing is shared. There is dedicated space which is caged off. Dedicated University of Manchester staff manage the University infrastructure in both data centres. Physical access to the data centres is strictly limited to data centre staff and a limited number of authorised IT Services staff. The data centres are protected by physical and electronic access security systems, swipe card access in and out of the data centres and CCTV coverage.

Only authorised researchers at the University of Manchester employed for this study will have access to the data, and access control is managed via Active Directory groups and Unix groups. Users of the data have also all been trained in data protection and confidentiality and will adhere to the Data Protection Act 2018 when collecting, using, disclosing, retaining or disposing of personal data. The data will be processed on the University of Manchester interactive Computational Shared Facility (iCSF), which is a service designed specifically for interactive computationally-intensive work. The iCSF is only accessible on campus and exists on a private network. All data processing is done on the iCSF and no raw data will be transferred out. The workstations used for accessing the iCSF environment do not directly access the data. Access is via Virtual Desktop Infrastructure (VDI) technology to ensure the data is only processed within, and never leaves the virtual environment. No remnants of the data are ever stored on the user device through mechanisms such as temp files or browser caches.

A valid University of Manchester IT account is required to login to the iCSF. Account credentials are unique to each member of staff and only the account owner knows the password. All staff accessing the data will be substantive employees of the University of Manchester.

An interrupted time series (ITS) approach will be used to compare the incidence and prevalence of potentially hazardous prescribing events, and of associated serious harm outcomes. Practices are required to contribute at least 24 months of data to ResearchOne prior to their start date of OptimiseRx, and 12 months of data following their start date. The date of implementation of OptimiseRx will be considered as time point zero. For each month, the incidence and prevalence of potentially hazardous prescribing events as defined by the prescribing safety indicators will be estimated. The intervention effect will be calculated as the difference between the observed and predicted values, had the prior trends continued after OptimiseRx was turned on. Similarly, the patterns in serious adverse outcomes over the same time period will be estimated. Adverse events will be identified from primary care records using Read Clinical Terms Version 3 (CVT3) codes in ResearchOne, and ICD-10 codes from linked HES & mortality data. Practices contributing to ResearchOne that did not implement OptimiseRx will be used to explore and model secular trends in hazardous prescribing and serious harm outcomes, driven by other sources of confounding such as changes in policy. For the analysis of risks of serious harm outcomes associated with potentially hazardous prescribing, self-control case series method may also be explored. In this model, patients would act as their own control and risk of adverse outcomes during period of exposure to OptimiseRx intervention will be compared with the risk during all other observed time periods.

The economic evaluation will estimate, per practice, the difference in costs and outcomes generated for those practices implementing OptimiseRx, according to the proportion of hazardous prescribing detected and averted in a practice. The analysis will generate cost per hazardous prescribing event avoided, and estimates of cost per serious harm outcome avoided and hospitalisation avoided. Cost data will comprise the costs of providing OptimiseRX (from OptimiesRX data) and costs of hospitalisation (from NHS Digital HES data). Costs will be applied to hospital data via Healthcare Resource codes and the associated National Tariff price.

The data will be analysed using statistical packages. No record level data will be produced as an output at any stage; only aggregated results will be reported (with small numbers suppressed in line with the HES Analysis Guide). The outputs produced cannot be used to identify patients or sensitive information.

Investigating the relationship between Quality of Primary Care and Hospitalisation: A spatial whole population study for England — DARS-NIC-73469-F3B9N

Type of data: information not disclosed for TRE projects

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: Non-Sensitive

When:DSA runs 2019-01-01 — 2020-03-31

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

This work is being carried out as part of the National Institute of Health Research (NIHR) Fellowship Program. It offers 3 years full-time funding (or 4 or 5 years part-time) to undertake a PhD and is aimed at individuals, of outstanding potential, early in their research careers. It aims to fast-track them through a customised research training programme in an environment reflecting their individual talents and training needs. It is anticipated that successful applicants would become independent research leaders within 6 to 10 years of completing the award.

The data is required for a study that aims to investigate and quantify the relationships between recorded general practice performance, as measured in the Quality and Outcomes Framework (QOF), at population level in England and cause-specific hospitalisations. The purpose of this project is to investigate the effects of one of the largest Pay-for-performance (P4P) programmes worldwide which has attracted substantial attention from media, policy makers and the public. This study will explore whether there has been any effect on all cause and cause-specific hospitalisations (reductions/increases) due to financial incentives, targeted to improve the quality of services in Primary Care in the UK between 2006 and 2015. This analysis will be conducted longitudinally as well as cross-sectionally while controlling for factors that are known to have an effect on hospitalisations. The study will utilise HES Admitted Patient care data as it is deemed it to be the most robust and high-quality data on hospitalisations for the UK, which are available at the level of Lower Super Output Area (LSOA).

The QOF is one of the most comprehensive P4P schemes of its kind and it was introduced in 2004 to improve quality of Primary Care in the UK. However, doubts on its impact and value mean that the UK is now retreating from using incentives in primary care. There is a large amount of literature, which investigated the effects of the QOF in the UK but all research up to date was subject to methodological limitations which this research aims to overcome. The present analysis will be conducted at a low geographical level (LSOA) and will use geographically defined statistical models to avoid aggregation of data at bigger heterogeneous areas and populations. In relation to this, Manchester University has obtained relevant publicly available aggregate data on general practice quality of care and prevalence of relevant chronic diseases from NHS Digital. In addition HES data will be combined with publicly available aggregate information on deprivation (IMD score) and rurality sourced from the Office for National Statistics and which are available at the LSOA level.

This is a tabulation request with an unsuppressed small numbers specification which will support the validity of the findings. If suppression according to the standards and regulations of NHS Digital is applied, it will result in significantly large numbers of unused cells which will be treated as missing values thus rendering this analysis unreliable.

This study will constitute a substantial part of a PhD thesis at the University of Manchester which is due for submission in early 2019.

Yielded Benefits:

Not all of the data has been received under the previous agreement therefore the benefits have yet to be realised.

Outputs:

Output from the data provided by NHS Digital will form a substantial part of the PhD thesis which looks at the relationship between quality of care and patient outcomes under a large P4P scheme in English primary care. The thesis is due for submission in September 2019 at the University of Manchester. Outputs related to this project will be included in the final PhD thesis and will be submitted in the form of an original research study for international peer reviewed journals. The first output from the PhD which relates to suicides is currently under review with an international peer reviewed journal. This project has the possibility, of informing policy regarding the successes, failures and future of Pay-for-Performance implemented in Primary Care in the UK and worldwide. Potential journals for submission, but not limited to are: The British Medical Journal , The BMJ Quality and Safety and/or the British Journal of General Practice as these feature articles related to this research. Submission dates and subsequent publication will vary according to when the data will be obtained but submission is likely to occur between 3 to 4 months after the data is provided. The outputs from this research will be publicly available either via the PhD thesis or via any publication that might occur from this project.

The outputs related to HES data, in the form of an original study, will be initially seen by the PhD supervisory team. They will assess the validity of the results and there will be extended discussions about this research project throughout the duration of the project. Reviewers of the international peer reviewed journals mentioned previously will be able to see the outputs when the study is submitted (potentially in March-April 2019). As the PhD thesis will follow the alternative format (i.e. PhD by publication), the publication of the research findings is a prerequisite for the completion of the PhD. Furthermore, there is increased interest for studies that look at the relationship between quality of care and patient outcomes such as hospitalisations.

In addition, the committee that will assess the final PhD thesis will be able to see the output of this project in spring 2019. Finally, when publication to a peer reviewed journal is achieved the outputs will be accessed by the public as well as clinicians and academics and will be disseminated to decision makers via the SPCR, the University of Manchester and the Centre for Primary Care research and dissemination networks. The NIHR SPCR collects annual data on publications from all students and trainees associated with the School and advertises the respective publications on the SPCR website (wwww.spcr.nihr.ac.uk) although there are no publication requirements imposed by the SPCR. Furthermore, it is a requirement of the studentship, to update the NIHR SPCR for any publications that may occur during the studentship award. The outputs generated from this research will also be advertised at the University of Manchester website, where each of co-authors will include links of the HES relevant publications on their personal University of Manchester webpage (including the researcher named within the application). Finally, the outputs will be presented to the public via PPI groups such as the PRIMER patient and public involvement group based at the Centre for Primary Care at the University of Manchester and PPI activities incentivised and required from the NIHR SPCR.

All outputs will aggregated to small numbers suppressed in line with the HES analysis guidelines.

Processing:

The PhD looks separately at the relationships between financially incentivised quality of care and patient outcomes under a national P4P scheme in English primary care. The outcomes mentioned in this instance include hospitalisations, suicides and alcohol related deaths. These outcomes and the respective (i.e. Office for National Statistics (ONS) suicide and alcohol related deaths) datasets will not be linked, analysed or investigated together. There will be three different analyses that will constitute individual pieces of work and will be organised apart. The only instance in which analysis and results from the three different patient outcomes analyses will be present in the same document is for the sole purpose of the final PhD thesis. These data will be aggregated with small numbers suppressed.

The data will be stored in a locked encrypted server at the storage address specified at the University of Manchester. The data will only be accessed by the PHD Student as the authorised researcher, however responsibility for the processing of the data and outputs resulting from this remain with the University of Manchester. The data will be accessed locally at the Centre for Primary Care only and no remote access or transfer of the data to other locations (inside or outside the UK) will be possible.

The outputs produced from this project will be presented as aggregated information at a larger geographical level than the one used for the analysis. The data obtained from HES will be presented as aggregated at Regional level, which constitutes a much larger unit of the English geography, in order to eliminate any risk for identification. More specifically, the analysis will be conducted at the LSOA level but all outputs that refer to hospitalisation data will be presented at Regional level.

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).

Greater Manchester Rapid Service Evaluation Team: REVAL-GM. — DARS-NIC-671668-B4T0T

Type of data: information not disclosed for TRE projects

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(2)(a)

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2024-02-15 — 2027-02-14 2024.04 — 2024.08.

Access method: Ongoing

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Community Services Data Set (CSDS)
  2. Emergency Care Data Set (ECDS)
  3. Hospital Episode Statistics Accident and Emergency (HES A and E)
  4. Hospital Episode Statistics Admitted Patient Care (HES APC)
  5. Hospital Episode Statistics Outpatients (HES OP)
  6. Maternity Services Data Set (MSDS) v2

Objectives:

The University of Manchester requires access to NHS England data for the purpose of the following programme of work: Greater Manchester Rapid Service Evaluation Team: REVAL-GM.

The University of Manchester has secured National Institute for Health and Care Research (NIHR) funding to become a Rapid Evaluation Team.

The projects REVAL are asked to evaluate relate to topics that are deemed as high priority by health decision-makers at the NIHR Health and Social Care Delivery Research Programme (HSDR). REVAL conduct these rapid evaluations of health and care service innovations, in close partnership with those who deliver, manage and use these services. REVAL has a responsive role in evaluating projects and as a team REVAL do not choose topics for evaluation.

The following is a summary of the aims of the research programme provided by the University of Manchester:

"REVAL will use a systematic, collaborative approach based on careful planning and clear communication and will:
1. Evaluate innovations that are important to the NHS as identified by the funder, NIHR;
2. Evaluate these innovations using appropriate methods. This might involve talking to patients and staff, collecting new data and using data the NHS already collects;
3. Share findings as they emerge in ways that make sure they have impact and are useful to health service providers and patients.

"The pace of evaluation is crucial; rapid evaluation could speed up wider use of things that work and/or reduce the risk of using innovation that is not beneficial. [University of Manchester (UoM)] will use a systematic approach to ensure UoM are asking the right evaluation questions, that evaluations are grounded in what is already known and that the right people are involved. UoM will refine our approach as we learn new things.”

The following are current REVAL projects provided to illustrate the types of evaluations the data may be used for. The permitted use of the Data is not limited to these projects.

1. Rapid evaluation of the NHS recovery support programme.
2. Rapid evaluation of the enhanced maternity continuity of carer programme.
3. Rapid evaluation of a service delivery for people with type 1 diabetes and disordered eating.
4. Rapid evaluation of pilot hyperacute units to deliver acute sickle cell disease care.

Over the course of a 5-year period the NIHR will allocate the REVAL team approximately 10 innovations to evaluate. There will be a focus on rapid evaluation with some projects aiming to complete within 6 to 9 months. Others may have a slightly longer timeframe but the period from inception to analysis will be short to allow for timely formative feedback to the areas undertaking innovations.

The nature of the NIHR REVAL programme is that further projects, which are not named in this Agreement, will be identified at relatively short notice. Thus University of Manchester are requesting access to a wide range of datasets to cover likely needs across multiple projects over the period. The time that would be required to amend this Data Sharing Agreement for every specific use (once requested and therefore known) would not support the rapid response nature of this programme. Therefore, this Agreement approves the use of the data listed for rapid evaluations where the following criteria and conditions will apply:

• The data will only be used by the REVAL team to evaluate the following factors in relation to an innovation within the NHS:
o Does this innovation improve the way the NHS work?
o Does this innovation offer value for money compared with alternatives?
o Is this innovation acceptable to the people who use it?
o Which elements of the innovation are crucial to its success?
o Does this innovation improve access for those in most need?

• The innovations evaluated will be those considered as important to the NHS as informed by the NIHR and HSDR.
• Findings of all evaluations will be shared as they emerge in ways that maximise the impact and use to health service providers and patients such as reports, journal publications, news articles, Tweets, patient leaflets and so on.

All evaluations will conform to the following scope:

i. The subject of the evaluation will be a health or care provider (or group of health and/or care providers) which has/have adopted a new way of working which differs to those currently adopted by the health system - i.e. an 'innovation'. Health or care providers may be NHS Trusts, Local Authorities or, for example, a group of district nurses. The innovation may be the use of new technology or a financial and/or service model redesign.
ii. The evaluations will attempt to determine whether there were any detectable changes in the metrics over time that might be associated with the introduction of the innovation. This would be done using a variety of techniques such as comparing metrics for the health care provider(s) pre and post-introduction of the innovation or comparing the metrics of the health care provider(s) which adopted the innovation with health care provider(s) where the innovation is not in use.
iii. Alongside this quantitative analysis, local qualitative information gathering and analysis would be carried out to understand how and why the innovations might have had a particular impact in those local health economies.

The REVAL team and the Applicant Lead* will ensure that any proposed use of Data conforms to the agreed criteria above, with appropriate input from the University of Manchester Data Protection Officer (DPO).
* The Applicant Lead refers to a member of the University of Manchester REVAL team taking ownership of the individual programme of work.

The Data Access process will be as follows:
i. The REVAL team are assigned projects by NIHR.

ii. REVAL Co-leads (Professor of Applied Health Research and Senior Lecturer in Implementation science) discuss each project with key stakeholders, including any relevant datasets available to the team including routinely collected data (e.g. HES, CSDS, MSDS).

iii. An outline protocol is developed including research questions to address as part of the evaluation. Further discussions with key stakeholders and the wider REVAL team supports the development of the full protocol including methods to be used which may involve the use of routinely collected data.

iv. Projects are assigned to members of the REVAL team, who form a specific project team. The project team discuss the project on a weekly basis with the REVAL co-leads. As a part of this a separate protocol will be developed in conjunction with the wider REVAL team including statisticians and health economists clarifying the analysis plan and how the requested data will be used in the project.

v. Once the data analysis plan (protocol) has been developed the project team will complete a data request form to include:
a. Names of researchers who will access the Data.
b. A brief description and aim of the project.
c. What dataset(s) will be required and what justification for requiring these datasets.
d. Which data (informed by the data dictionary) will be required and what justification for requiring these. This might include;
- years of data
- size of case and control cohorts
- inclusion and exclusion criteria (e.g. use of specific diagnostic codes)
- approaches to matching, if being used

vi. The data request form will be reviewed by the HES applicant lead (Professor of Data Science & Health Services Research) and approved by the REVAL co-leads.

vii. Where appropriate REVAL researcher(s) will apply for ethical approval from the University of Manchester.

viii. Researchers will also be required to develop a Data Management Plan.

Prior to receipt of data the researcher(s) will be required to complete an information governance (IG) access form confirming the following University of Manchester training and policies have been completed:
· The University of Manchester’s Data Protection and Cyber Security training
· information security
· Minimum Controls Technical Security Standard
· Information security classification and secure information handling
· Data Protection
· Reporting data protection incidents
· Records Management
· Acceptable Use Policy
· Acceptable use of IT facilities and services – procedure for staff
· Authentication Technical Security Standards
· Password Technical Security Standards

Each REVAL project involving access to routinely collected data (e.g. HES, CSDS, MSDS) will therefore require a dedicated protocol, data request form (including data dictionary items) and data management plan. In addition, each researcher involved in the project will need to complete an IG access form.

The REVAL team aim to minimise any delays (such as the approval process for data access) as much as possible to maximise time spent on each evaluation, to maximise the efficient use of researcher time and to maximise cost-effective use of the NIHR grant. NIHR explicitly set up this programme to promote a much more rapid analysis of service innovations, that might more quickly benefit health care services nationally.

Once an evaluation topic has been received from NIHR and an initial scoping exercise performed, a detailed research protocol will be developed by the REVAL team. The protocol outlines the design and methods proposed to answer the research questions including the plans for data processing. The protocol will also include the approach to Patient Public Involvement (PPI), ethical issues, data and information management, risk management and dissemination of findings. The protocol will be reviewed by independent experts in healthcare organisations and management, and also by project stakeholders, NIHR and patient community involvement and engagement (PCIE) representatives. This stage of review will aim to advise the REVAL team of any shortcomings in the proposed work, or of additional research questions the REVAL team might find valuable to address. In developing the evaluation protocols the REVAL programme leads will ensure that any proposed use of the datasets conforms to the agreed scope above.

Each project lead will ensure that:
i. projects have clearly defined objectives and analysis plans;
ii. the use of the data is necessary and proportionate to the purpose of the project - this will include consideration of the necessity for the use of each dataset; the number of years of data; the size of any cohort or control cohorts derived from the data, and the inclusion and exclusion criteria (such as presence of specific diagnostic or procedure codes);
iii. appropriate safeguards are in place to protect confidentiality; minimising risks of re-identification and use of excessive data beyond necessity
iv. individual or individuals within the REVAL team who will carry out the project will be bound by an analysis plan detailing what data is permitted for use in the project and how it shall be processed. The REVAL team aim to start each project (or sub-project workstream, as appropriate) with an assessment of the data necessary for analysis, and the anticipated analytical steps.

REVAL does not choose which projects to evaluate but is responsive to requests from NIHR (HSDR to evaluate those projects they have identified as high priority. However, REVAL has an independent Stakeholder Advisory Group and input from Patient and Community Involvement and Engagement (PCIE).

The Stakeholder Advisory Group (SAG) draws together expert leaders from across relevant sectors, including NIHR, and an independent Chair.

The SAG will perform two main functions:
1) Programme oversight: the SAG will meet bi-annually to review programme and project progress, proposing amendments if appropriate. Recommendations will be recorded and shared with NIHR.
2) Ongoing advice and support: the SAG members will advise on: horizon scanning; current innovations and who to contact in relation to these; additional sites that are implementing innovations that REVAL-GM are evaluating; and on effective dissemination of findings.

PCIE is an effective and meaningful involvement of service users, carers, patients and members of the public who make for a stronger rapid evaluation team, with more focused research and more accessible and user-friendly outputs. The REVAL team worked with patient and public partners on the initial bid, and plans to involve people throughout, to ensure the work benefits from:
• Experience of health/social care services as a service user, patient, carer, or relative (including parents)
• Knowledge and understanding of patient, public and service user perspectives
• A unique voice to evaluations of innovation and service development in particular when evaluating involvement and engagement activity related to innovation
• Knowledge and understanding of successful involvement/engagement

REVAL places co-production at the heart of each evaluation. To facilitate rapid evaluation, University of Manchester work with a group of link PCIE team members to construct project-specific iterative PCIE co-production plans that reflect the project requirements. The plan outlines potential formats for engagement, such as workshops, individual discussion, or surveys, and consider stakeholder mix at sessions. University of Manchester draw on an established framework for experience-based co-design, using a combination of whole-group introductory and review events, and small group parallel co-production sessions to optimise study efficiency and timelines.

The team will develop introductory material with which to introduce the topic and work with PCIE link members to refine this as required. University of Manchester engage with relevant PCIE stakeholders at organisational and individual levels, using a combination of established networks and bespoke orientation events. University of Manchester plan to integrate findings into the project development work. University of Manchester will ensure ongoing evaluation of all PCIE activities using multiple methods including informal discussions, structured feedback and Equality, Diversity and Inclusion and auditing.

The number and frequency of uses of the data per typical year will be 2-3 depending on the specific details of the individual research projects and whether the use of data is necessary and proportionate to the purpose of the project.

The following NHS England data will be accessed:
• Hospital Episode Statistics (Admitted Patient Care, Accident & Emergency and Outpatients) - necessary because this data could be used to calculate relevant metrics that those implementing the technology would be seeking to affect, for example, readmissions to hospital for the targeted groups in those areas
• Emergency Care Data Set (ECDS) – necessary as this data could be used to calculate relevant metrics that those implementing the technology would be seeking to affect, for example, emergency care admissions for particular patient groups
• Community Services Data Set (CSDS) – necessary because this will enable the REVAL team to investigate areas such as displacement of activity where trusts may be successfully reducing re-attendances by displacing activity into other services such as community care
• Maternity Services Data Set V2 (MSDS) – necessary because one of the current projects relates to the enhanced maternity continuity of carer and access to this dataset would enable University of Manchester to look at whether teams are providing care to their intended target groups (lowest decile of deprivation) and in the longer term outcomes from the new service innovation.

Due to the variable nature of the evaluations, it is not possible to detail in advance exactly what data variables will be required. The University of Manchester has identified the above datasets as most likely to be required to support a rolling programme of evaluations and the broad range of variables per dataset reflects the need for a versatile dataset capable of use for multiple purposes.

The level of the data will be:
• Pseudonymised

The data will be minimised as follows:
• Limited to data between 2017/18 and the latest available year with annual refreshes as they become available.
• For each evaluation, the project lead and REVAL-GM team will ensure that the use of the data is necessary and proportionate to the purpose of the project.
• For each evaluation data will be minimised through selection of specific years and filtered by demographic characteristics and diagnostic codes as appropriate this will be documented in the Data access form

The University of Manchester have been broad in specifying data requirements as this approach will give University of Manchester the flexibility required to undertake rapid evaluations that come from the NIHR HSDR in a timely manner. University of Manchester will take a broad approach because they are not able to predict what evaluations the REVAL team will be allocated and what data will be required. All REVAL evaluations have a national focus.

The University of Manchester will not use the Data outside of a stringently controlled process. Data will be held centrally and the applicant co-lead will ensure that data for each individual project is necessary and proportionate for each individual project. Access to Data will be minimised by the applicant co-lead so that individual researchers will only access data relevant to each specific project.

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

NIHR Health Services and Delivery Research (HS&DR) programme has commissioned University of Manchester to undertake the work. NIHR does not specify what data are required to deliver the work nor how the data shall be processed to achieve that purpose. Such decisions are taken by University of Manchester.

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, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care. Specifically, the data will be used to track changes in health care activity over time enabling analysis of the impact of changes in health services; conduct detailed analysis of particular health events used to identify particular issues with quality of care; and study variation between hospitals or areas in use of services which can identify populations where there are gaps in care, and also areas delivering high quality care from which the NHS can learn more widely.

The funding is provided by NIHR HS&DR Programme. The funding is specifically for the work described.

Data will only be accessed by substantive employees of the University of Manchester.

Expected Benefits:

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

The use of the Data could:
• help the 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.
• advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as obesity and diabetes.
• 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 which need to be addressed.
• support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).

The benefits of the work may be seen in terms of decisions made by healthcare commissioners and providers when thinking about the types of services needed to deliver benefits to patients, as well as by policymakers.

The wider REVAL team have extensive networks in health and care sectors and an impressive track record in co-producing meaningful research findings and using a wide range of approaches to reach and influence decision-makers.

The REVAL team will develop a communications and dissemination strategy for the overall programme as well as for each project.

REVAL will aim to produce evidence on how to organise and deliver services to best meet patient need. The evidence produced will facilitate improvements in the organisation and provision of health services, which will ultimately result in improved access and outcomes for patients.

It is hoped that through the publication of findings in appropriate media, the findings of this research will 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.

There are several benefits that are expected to arise from the University of Manchester’s use of the Data and that expectation is supported by work University of Manchester has previously undertaken.

Informing service delivery in the NHS:
REVAL evaluations are co-produced with the health and care system. Having access to timely HES and other datasets will facilitate rapid evaluations of changes in service delivery in health and social care, enabling evaluations to inform local, regional, and national commissioning decisions and adaptations in service delivery, thus helping to inform service delivery in the NHS.

Informing policy:
As a team REVAL have a strong track record of sharing learning and insights throughout the research process and in ways that have directly informed the commissioning and delivery of services, locally, regionally and nationally. The team will harness the strategic communications expertise at the University of Manchester and work with Policy@Manchester. They have established working relationships with local, national, and international policy-relevant organisations, and have successfully delivered meaningful policy engagement, including high- level civil service briefings, select committee appearances, combined/regional authority workshops, and bespoke Westminster events.

The University of Manchester REVAL team are also active members of the Northern Health Science Alliance which brings together ten universities, ten research-intensive NHS Trusts and four Academic Health Science Networks and is mandated to improve the health and wealth of the North of England. The team harness this network to help ensure University of Manchester are maximising the potential for regional and national policy impact.

Gaining insights into equity in health and social care:
Record-level data enables evaluations to understand the impacts of changes on protected characteristics, informing whether service changes facilitate adherence to the public sector Equality Act (and NHS Constitution). It allows the team to evaluate equity in access across various population groups, and equity in completing the intervention, if relevant. In addition, record-level data enables a more accurate evaluation of service changes, enabling adjustment for potentially confounding factors, that in their absence, may result in the advocation of service changes that reinforce or widen inequalities in health and care.

Outputs:

The REVAL team are committed to disseminating evaluation findings to appropriate evidence users in a timely way, and in a format that meets user needs. All varied outputs will be shaped around the audience, what they need to know and how quickly they need to know it.

For formative work, feedback will often only go to policy and organisational staff in the form of reports, meeting papers and presentations. Timeliness is of the essence to ensure that results can be used to inform service delivery and decisions around this.

The University of Manchester will also ensure they feedback to PCIE representatives in appropriate ways. Across projects the University of Manchester have conducted previously, they have done this using more innovative approaches, such as animations and workshops, as well as using more standard approaches including infographics and lay summaries.

To cascade more summative findings to appropriate audiences, the University of Manchester will write and submit journal papers. All work will be logged on the Open Science Framework to ensure transparency.

Work will also be presented at national and international academic conferences. Where relevant, the REVAL team will work with the University of Manchester Communication and Marketing Team.

REVAL has a communications and dissemination strategy which will be used for each project:
o Identify the audience: who needs to know?
o Set a timetable for dissemination outputs: weeks, not months
o Decide on appropriate media and form of communications
o Ensure written outputs are as simple as possible
o Provide context and other evidence relevant to each evaluation.

The REVAL team also have a webpage (https://sites.manchester.ac.uk/reval/) devoted to the REVAL programme.

The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.

The outputs may be communicated to relevant recipients through the following dissemination channels:
• Journals
• Workshops involving, for example, PCIE representatives
• Webinars open to interested parties
• Social media
• Public reports
• Direct bilateral engagement with NIHR and NHS-England stakeholders
• Briefing documents provided to NIHR and NHS-England stakeholders
• Co-hosted events where appropriate
• Open source frameworks
• Public events such as national and international conferences
• Posters at national and international conferences
• Patient Information leaflets
• Press/media engagement
• Public promotion of the research
• Participant newsletters
• Reports aimed at participants/patients

In terms of target output dates, these will be set for each project as part of the project planning and milestone setting. Some requirements will be rapid, with feedback being required in terms of weeks, and some outputs will be expected nearer to the project's end.

Processing:

No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).

NHS England will provide the relevant records from the HES, ECDS, CSDS and MSDS datasets to University of Manchester, The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be re-identified.

The Data will not be transferred to any other location.

The applicant co-lead at the University of Manchester will extract a subset of the Data comprised of relevant data and make this available to other researchers within the REVAL team within the University of Manchester where appropriate.

The Data will be stored on servers at University of Manchester and Equinix. Equinix only provides the building and cooling and does not have access to the Data.

University of Manchester uses off-site back-up services provided by Ascendas Reit.

The Data will be accessed by authorised personnel via remote access.

The Controller must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.

For remote access:
- Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;
- Access controls granting users the minimum level of access required are in place;
- Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;
- Multifactor authentication (MFA) is required for remote access;
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
- All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.

The above applies in addition to any condition set out elsewhere within the DSA.

The data will not leave England at any time.

Access is restricted to substantive employees of the University of Manchester who have authorisation from the REVAL programme leads and the applicant leads.

The Principal investigator will always be a University of Manchester substantive employee. Access is restricted to individuals within the REVAL team of The University of Manchester who have authorisation from the REVAL programme leads and applicant lead.

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

The Data will not be linked with any other data.

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

Long term follow-up of the UK NCRI RAPID clinical trial in early stage Hodgkin lymphoma with a particular focus on overall survival and late consequences of treatment. — DARS-NIC-597255-J9M1H

Type of data: information not disclosed for TRE projects

Opt outs honoured: Identifiable, Yes (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 - s261(5)(d); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2024-05-06 — 2026-05-05 2024.06 — 2024.06.

Access method: One-Off

Data-controller type: THE CHRISTIE NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. Cancer Registration Data
  2. Civil Registrations of Death

Objectives:

The Christie NHS Foundation Trust requires access to NHS England Data for the purpose of the following Clinical trial:
A Randomised Phase III Trial to Determine the Role of FDGPET Imaging in Clinical Stages IA/IIA Hodgkin's Disease (FDG-PET Study): RAPID

The following is a summary of the aims of the research project provided on behalf of The Christie NHS Foundation Trust:
Hodgkin lymphoma is a potentially curable cancer with a median age at presentation of 29 years. Standard of care treatment for early-stage disease comprises 3-4 cycles of chemotherapy (CT) followed by radiotherapy (RT) to previously involved sites of disease. This is highly effective, but radiotherapy is known to increase the risk of second cancers and cardiovascular disease both of which undermine the quality and duration of survival. In an effort to reduce the incidence of RT related second cancers and cardiovascular disease, the UK NCRI RAPID trial compared standard treatment (CT+RT) with an experimental approach using CT alone in patients who became PET scan negative after 3 cycles of CT. Results published in the New England Journal of Medicine 2015 showed that disease control in PET negative trial participants receiving CT alone was almost as good as for those receiving CT+RT.

The initial results of RAPID are very encouraging but the second cancer and cardiovascular complications of RT take several years to manifest, and it is for this reason that long term follow up of the trial population is required. This point was also made by an editorial accompanying the New England Journal of Medicine publication. Unfortunately, many trial participants have now been discharged from hospital care and trial sites have re-focused their limited resources on other projects so new ways of accessing second cancer, cardiovascular and survival data for the RAPID population are required. It is for this reason that the study team are applying to NHS England to access Cancer Registry (for survival and 2nd cancer data) and, in parallel, the National Institute for Cardiovascular Outcomes Research (NICOR) for cardiovascular outcomes data (which is not a part of this Data Sharing Agreement). This proposal was submitted to Health Data Research UK (HDRUK) in 2020 and selected as a national exemplar.

The following NHS England Data will be accessed:
RAPID compared two different types of treatment, chemotherapy alone or chemotherapy plus radiotherapy and is now in long term follow up until 2028. In order to inform future treatment approaches nationally and internationally the study wishes to compare the late consequences of the two different treatments used in RAPID on survival, and incidence of cardiovascular disease, second cancers and other complications.

• Civil Registrations of Death – necessary because vital status is required to determine the impact of the different treatments used in the RAPID trial on survival. Cause of death is required to determine the impact of different treatments on cause of death. This request will provide information about RAPID trial patient survival, date and cause of death.

• Cancer Registration – necessary because the site of a second cancer and its date of diagnosis will determine whether or not it was likely to be related to radiotherapy. This request will provide information about all cancer diagnoses in the cohort so that a comparison of incidence and cancer type between the two treatment arms in RAPID can be performed.

DATA MINIMISATION
• Limited to data for a study cohort of approximately 500 patients who met the inclusion criteria (aged between 16 and 65 years who were recruited to the RAPID trial 2003-2010)
• Limited to data being provided once in approximately early 2024. Latest available at the time of dissemination.

The Christie NHS Foundation Trust is the research sponsor and the Controller as the organisation responsible for ensuring that the data will only be processed for the purpose described above. The Christie NHS Foundation Trust have no access to NHS England Data listed in this DSA.

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.

The study has support under section 251 of the NHS Act 2006 to enable the common law duty of confidentiality to be temporarily lifted so that confidential patient information can be processed without consent. National Data Opt-Outs will be upheld for the cohort.

This processing is in the public interest because the study hopes to change the management of early-stage Hodgkin lymphoma with its findings, reducing the duration and cost of treatment for Hodgkin lymphoma itself and reduce costs associated with managing these late consequences of treatment allowing re-deployment of resources elsewhere in the healthcare economy both in the UK and internationally.

The funding is provided by a private donor affiliated with The University of Manchester. The funding is specifically for the study's outcomes data analysis as described above. The Controller has confirmed to NHS England that the donor has no say in the way the study has been conducted or its purpose, no access to NHS England data, and has no ability to suppress the reporting of outcomes from this study.

University College London (UCL) is a Processor acting under the instructions of The Christie NHS Foundation Trust.

Expected Benefits:

If, because of this study, it is found that a reduced incidence of second cancers and cardiovascular disease in patients who did not receive RT in the RAPID trial this is hoped to be a major benefit for patients and hopes to change the management of early-stage Hodgkin lymphoma both in the UK and internationally.

These changes also hope to reduce the duration and cost of treatment for Hodgkin lymphoma itself and reduce costs associated with managing these late consequences of treatment allowing re-deployment of resources elsewhere in the healthcare economy.

Outputs:

The expected outputs of the processing will be:
• A report of findings for dissemination to trial participants and offer to present results at Lymphoma Action's annual conference where previously there has been great interest in the late consequences of treatment for curable lymphomas.
• Presentations at relevant international meetings in 2024 (selected from American Society of Clinical Oncology, May 2024, European Haematology Association meeting June 2024, International Symposium on Hodgkin lymphoma, October 2024, American Society of Haematology, December 2024).
• A paper describing results will be submitted to relevant high impact journals (New England Journal of Medicine (NEJM), Journal of Clinical Oncology, Lancet) in 2024/2025.

The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.

The outputs will be communicated to relevant recipients through the following dissemination channels:
• Article in Lymphoma Action magazine “Lymphoma Matters” 2024
• UCL and CRUK Cancer Trials Centre website, 2024
• HDR-UK website, 2024

Processing:

The UCL Cancer Trials Centre will securely transfer a cohort of approximately 500 individuals from the RAPID Trial to NHS England. The cohort file will consist of a specific set of individual participant's identifiers (specifically NHS Number, Date of Birth, and a unique person ID called a Study ID) in order for NHS England to link and extract the required data files from their data sets.

NHS England will then remove all RAPID participants who have registered a National Data Opt-Out.

NHS England Data will securely transfer the relevant record-level Data files with the study ID remaining for linkage purposes from the Civil Registrations of Death and Cancer Registration Data datasets to UCL Cancer Trials Centre.

NHS England Data will be securely stored on UCL servers for data analysis by authorised personnel. Access to confidential patient identifiable record-level NHS England Data is restricted to employees or agents of University College London. All personnel accessing the data have been appropriately trained in data protection and confidentiality.

The Data will be accessed by authorised personnel via remote access. The Data will remain on the servers at UCL at all times. Access is restricted to employees or agents of UCL authorised by the study Chair.

The Controller must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.

Remote access will only be from secure locations situated within the territory of use stated within this DSA;
- Access controls granting users the minimum level of access required are in place;
- Remote access is only via secure connections to protect data;
- Multifactor authentication (MFA) is required for remote access;
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
- All remote access is undertaken within the scope of the organisation’s Data Security Protection Toolkit (or other security arrangements as per this agreement) and complies with the organisation’s remote access policy.

The above applies in addition to any condition set out elsewhere within the DSA.

The NHS England Data will be linked with cardiovascular outcomes data from the National Institute for Cardiovascular Outcomes Research (NICOR) requested under another Data Sharing Agreement between the Controller and NICOR.

The NHS England Data will not leave England at any time.

MR727 - Toxicity from anti-TNF therapy — DARS-NIC-148353-G88Q7

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable, 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, Health and Social Care Act 2012 – s261(2)(c)

Purposes: Yes (Academic)

Sensitive: Non Sensitive, and Sensitive

When:DSA runs 2019-08-01 — 2022-03-31 2016.04 — 2024.06.

Access method: Ongoing, One-Off

Data-controller type: BRITISH SOCIETY FOR RHEUMATOLOGY, THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Cause of Death Report
  3. MRIS - Scottish NHS / Registration
  4. MRIS - Members and Postings Report
  5. MRIS - Personal Demographics Service
  6. Demographics
  7. Cancer Registration Data
  8. Civil Registration - Deaths
  9. MRIS - Flagging Current Status Report
  10. Civil Registrations of Death

Objectives:

The study aims to test the hypothesis that biologic therapy (Anti-TNF) in patients with rheumatic diseases increases the risk of malignancy, important co-morbidity and severe infection. Primary endpoints include malignancy, infection requiring hospitalisation, serious co-morbiditity and death. Subsidary hypotheses include (1) To test whether an increased risk is related to dose/duration of therapy, (2) there are identifiable disease characteristics that act synergistically to increase the risk, and (3) therapy with multiple biologic agents act synergistically to increase the risk.

Yielded Benefits:

As is often the case with research impact, there is a time delay between research publication and clinical impact. Therefore, much of the impact on clinical practice between 2011 and 2018 was from research published in the preceding years. However, the evidence-impact gap for the BSRBR-RA remains relatively small. The BSRBR-RA has had a significant influence on clinical practice in the UK and more widely. Evidence emerging from the BSRBR-RA has fed directly into National Institute for Health and Care Excellence (NICE) technology appraisals (TA), UK and other clinical practice guidelines, and patient information sheets. These influences have resulted in more consistent prescribing across the country. The data held in the BSRBR-RA have also been used to study the impact of changes in clinical guidelines and safety warnings. Evidence from the BSRBR-RA has contributed to several NICE TAs. For example, two UoM analyses from 2006 (1;2) demonstrated the benefits of combining TNFi treatments with continued background MTX (unless contraindicated); the study contributed directly to NICE TA195 (published 2010; updated with TA375, published 2016). Initial guidelines did not specify that MTX treatment should be continued (TA36). Subsequent analysis using the register demonstrated that, year on year from 2001-8, the proportion of patients continuing MTX with TNFi increased in the UK, with corresponding improvements in treatment responses (3). Output from the BSRBR-RA has also contributed to BSR guidelines outlining eligibility criteria for TNFi. The analyses indicated that biologic drugs should no longer be reserved for patients with high disease activity, but will also benefit patients with moderate disease activity despite csDMARDs (4). Unfortunately the same eligibility criteria were not adopted by NICE in 2016, meaning it remains difficult to prescribe biologics in this setting. Study research into the risk of intracellular infections such as listeria and salmonella (5) has led to new information being incorporated into Arthritis Research UK Drug Information Leaflets (provided to every patient in the UK considering TNFi therapies). It also prompted the FDA to update product labelling. Specifically, the information now warns of the risk of consuming undercooked or unpasteurised foods, similar to the advice provided to pregnant women. Analysis of the BSRBR-RA dataset in 2013 showed that since updating the Arthritis Research UK Drug Information Leaflets in 2006 there has been a 73% decrease in the rate of new cases of intracellular infection in RA patients exposed to TNFi in the UK from 2007-12 (6). Study publications on outcomes among women exposed to anti-TNF therapy during pregnancy (7;8) have contributed to a significant change in national pregnancy guidelines. It is now stated that women can continue TNFi therapies into pregnancy if clearly needed (9), whereas previous labelling stated that treatment should be discontinued in the months leading up to conception (which often resulted in a disease flare). This is a major advance within rheumatology, as other non-biologic DMARDs, many with the risk of teratogenicity, are contra-indicated in pregnancy. This research contributes to the weight of evidence for safer options for disease control in the months leading up to conception and in early pregnancy. One of the most common questions which both healthcare professionals and patients have is around whether biologic therapy increases the risk of cancer. The initial size of the original Enbrel, Remicade and Humira cohorts were chosen to ensure enough power to detect a doubling in the risk of lymphoma compared to csDMARD therapy alone. Reassuringly, all of the findings to date have shown that biologics do not appear to increase the risk of cancer in patients with no prior history of malignancy (10;11;12). These findings have been incorporated into national guidelines to help provide reassurance to both patients and their healthcare professionals. A major challenge for all research studies is the dissemination and implementation of results. The close collaboration with BSR representatives has ensured that study data are disseminated as widely as possible, including to policy makers such as NICE. The study team also work with rheumatologists to generate ideas for new analyses based on clinically relevant questions (examples include moderate disease activity), refractory disease and interstitial lung disease). In recent years the study team have also used a lunchtime session at the annual BSR conference to promote the register including its scientific output. The study team have also convened a scientific session at the last 2 annual conferences to provide a more comprehensive review of the scientific output from the study. The study have also established an ongoing and mutually beneficial collaboration with the National Rheumatoid Arthritis Society (NRAS) (a patient-led organisation) as one route of dissemination to patients. On average, 1-2 lay articles per year for the NRAS Magazine are written. The UoM BSRBR-RA team also receive regular queries (average 3 per month) from doctors, nurses, pharmacists as well as via the NRAS helpline asking about evidence to help support treatment decisions. Often these queries are in areas for which little evidence exists, such as malignancy or pregnancy, but the collective UK experience can be helpful and reassuring to support these decisions.

Expected Benefits:

For any research project to achieve maximum benefit for all stakeholders it is essential to understand the needs of each stakeholder, all the while asking the “so what?” question. Capturing data without a specific purpose will lead to disinterest and disinvestment from the people the study team want to benefit the most. The BSRBR-RA has many stakeholders, not only clinicians and patients, and each of these is considered in turn.

Clinicians:
The study will continue a programme of outputs to address questions related to key safety concerns of biologic therapies based on email queries, discussions at conferences (locally, nationally and internationally) and other communications as well as the clinical practice of the Chief Investigator herself. The fact that these safety queries can be discussed with the team will be advertised more widely through newsletters and websites (BSR and BSRBR-RA). The pharmacoepidemiological research programme within the BSRBR-RA will be further driven by knowledge gaps identified in systematic reviews, such as during guideline development.

Patients:
The study have developed a strong relationship with NRAS which also provides insight into the questions patients have about these therapies. To explore this further, in 2019/20 the study team will host a series of patient events, coordinated in collaboration with NRAS, to run research prioritisation exercises directly with patients.

Pharmaceutical companies:
The study is inherently linked with the pharmaceutical companies through the products that are monitored in the BSRBR-RA and the study forms part of the Risk Management Plans for these products. The study will continue to have annual meetings at UoM with the pharmacovigilance teams from each company to ensure that processes remain in line with their regulatory needs and the current regulatory environment. Similarly the study will continue to attend and support the annual BSR Pharmaceutical Company Sponsors meeting in London. Although the study have always valued the level of independence that the University has from the supporting pharmaceutical companies in terms of academic output, the study team will continue to respond to regulatory requests that companies receive and continue open discussions with representatives of these companies about what research questions the BSRBR-RA data can answer. Only BSRBR-RA study data will be shared with the pharmaceutical companies; no NHS Digital data will be shared.

Regulators:
The study do not report safety events directly to the EMA and/or the UK Medicines and Healthcare products Regulatory Agency (MHRA) as all safety reporting is via the pharmaceutical companies. However, increasingly, the study’s relationship with the regulators has become more direct and the regulators have recognised the value of embedding pharmacovigilance within patient registers (as opposed to independent pharmaceutical company sponsored observational research). In 2014, as part of a UoM REF2014 Impact Case Study submission, The Director of Vigilance and Risk Management of Medicines at the MHRA stated: `The greatest regulatory impact of the BSRBR has been in helping to define the clinical safety profile of biological agents in the treatment of rheumatoid arthritis, particularly over the long term. The unique scale of the register provides the opportunity to study the risks of rare serious safety concerns with unusual precision.' The study will continue to provide a vehicle for such risk management as new products come to market.

British Society for Rheumatology:
The study’s most important stakeholder is the British Society for Rheumatology. The BSR and UoM have worked closely together on the BSRBR-RA since the outset and the study team share the pride of the BSR in its ongoing success. The team at UoM continually strives to maintain the highest standards of this flagship study in part to contribute to the global profile and reputation of the BSR. The study is committed to this ongoing partnership and will maintain an open dialogue including attendance at 3 BSR Registers Committee meetings per year, bi-annual face to face meetings with the BSR Project staff in Manchester, as well monthly teleconferences with the BSR Project team and the BSR Registers Committee Chair/Vice-Chair. The study will continue to present both study and scientific updates at the annual Rheumatology spring conference and work with the BSR to explore even greater ways to disseminate findings, support recruitment and data collection, and to promote the study.

Outputs:

The outputs for the BSRBR-RA study will include reports, submissions to peer reviewed journals and presentations and posters at relevant conferences. BSRBR-RA Study data will be combined with NHS Digital data to maximise data available and used in the outputs. Only aggregated data will be included in any study outputs and data will be safeguarded by ensuring that results which include small numbers which could identify study participants will be excluded.

Dissemination and communication of results to stakeholders includes regular review by BSRBR project boards, including the BSRBR Steering Committee and BSRBR Data Monitoring and Ethics Committees. The study also has active social media channels (Twitter/Facebook) and a comprehensive study website (www.bsrbr.org) which has areas aimed at Hospitals/Sites participating, study participants and researchers. In terms of exploitation of the results/outputs, the British Society for Rheumatology also has links back to the BSRBR study site, in addition to details and the process of applying to access study data for research purposes (https://www.rheumatology.org.uk/practice-quality/registers).

To date, over 60 original papers have been published on the BSRBR-RA data since 2001 and the research outputs from the BSRBR-RA study have refined the way anti-TNF therapies are prescribed in RA with a goal of maximising patient benefit and minimising patient risk. As an example, the research into the influence of anti-TNF therapy on incidence/risk of solid cancer is cited in the 2019 BSR DMARD safety guidelines in inflammatory arthritis in terms of reassuring patients that their risk of cancer is not increased by taking the medication. The guideline states that, “patients should be advised that there is no conclusive evidence for an increased risk of solid tumours or lymphoproliferative disease linked with biologic therapy, but that ongoing vigilance is required (grade 1A, SOA 99%)… Most observational studies using national registry data have also been reassuring; recent studies from BSRBR-RA … registries have all failed to show a significant association between anti-TNF use in RA and overall malignancy, with up to 52 549 patient-years exposure.”

The BSRBR-RA study releases occasional newsletters which can be accessed via the study website which describes the progress of the study and latest study findings in language targeted at study participants or individuals from a hospital site.

Planned future analysis/output will focus on two related and equally important research questions: (1) What is the long term risk of exposure to established biologic therapies? (2) What is the absolute and relative effectiveness and risk of newer biologic therapies?

(1) What is the long term risk of exposure to biologic therapies?
The first patient was enrolled into the BSRBR-RA in 2001. Therefore, over the next 5 years the study team will start to observe patients who have been receiving TNFi therapies for >20 years. This is an unexplored area due to the very nature of when these drugs were introduced. Hence, the BSRBR-RA can and will offer a unique insight into the very long term use of TNFi drugs, including treatment persistence and long-term safety, such as the late occurrence of malignancies. The presence of equally long follow-up in an untreated comparison cohort will add to these analyses.

Most of the University of Manchester’s long-term analysis has focussed on TNFi but the study now have over 10 years of follow-up data on patients receiving rituximab (MabThera). Within the dataset, RTX is the most common second line biologic drug in RA treatment, with over 5,800 exposures. This drug is different from other biologic therapies not only because of its mechanism of action but also because of its long half-life, making it challenging to study. Therefore, its use in daily practice is less well understood than other RA therapies. Outcomes of interest include long-term effectiveness and safety, including the safety of repeated dosing of MabThera. The University of Manchester plan to continue to study the risk of serious infection, and will also move to consider other adverse events such as malignancy and cardiovascular disease in patients treated with MabThera.

The University of Manchester will also analyse the data held within the register on work to provide further insight into the effect of response to biologics and related therapies on productivity at work (presenteeism) and the ability for patients to remain in work.


(2) What is the effectiveness and risk of newer biologic therapies?

IL6 inhibitors are one of the newer classes of biologic drugs on the market and therefore, their safety is less well understood. In the UK, it is primarily used as a second (or higher) line biologic therapy following TNFi. The BSRBR-RA has now accrued data from over 2,700 tocilizumab (TCZ) treated patients in the register. The risk of infection over the short term is already being studied as part of collaboration with King’s College London, but specifically a comparative study of infection risk between different choices of second line treatments, including a second TNFi and RTX, will be undertaken. There are concerns over the risk of cardiovascular disease due to aberrations in lipid levels with TCZ and therefore, the risk of MI and stroke following IL6 inhibition will also be investigated.

The most challenging analysis will be to study the risks of biosimilar therapies. The switch from originator Remicade and Enbrel to biosimilar products has been swift and extensive in the UK, despite a lack of supporting evidence about the safety of switching. The study team’s analyses in this area will include a comparison of first line biosimilar TNFi use with originator, using recent historical originator data as a comparison, as well as the safety of a switch programme. An analysis of outcomes after switching needs careful consideration due to inherent selection bias (patients who switch between originator and biosimilar have usually experienced a response to the originator and by definition, have not experienced a treatment limiting adverse event). This selection bias will have to be considered when changes in disease activity and occurrence of adverse events following a switch are analysed and the rich historical data within the BSRBR-RA should allow for this. The University of Manchester and BSR have already amended the study’s data collection forms to ensure it captures as much disease activity data at the point of switching as possible.

JAK inhibitors form a new oral therapeutic option in the treatment of RA. These drugs are now available within the NHS and over the next 5 years, the study team’s efforts will focus on recruitment of patients starting these therapies. Early analyses will include a descriptive study of how JAK inhibitors are being used in the UK and their early effectiveness in routine clinical practice. With the introduction of an oral targeted therapy for RA, the study team will use the opportunity to introduce simple patient questionnaires to gather further data about treatment adherence across modes of therapy.

Processing:

The study team provides the following identifying details for BSRBR-RA study participants to NHS Digital to allow the flagging for mortality and cancer notifications to take place:
- BSRBR-RA Study Number
- Date of Birth
- Gender
- NHS number

Participants' patient entries are traced and flagged by the Medical Research Information Service at NHS Digital. The data the study team will receive back from NHS Digital will include participants' BSRBR Study Number and cancer and death details.

Data obtained from NHS Digital data will be downloaded into the University of Manchester’s Data Safe Haven for two purposes:

Element 1:
A flag (BSRBR-RA Study Number, occurrence of cancer/death – Yes/No) will be transferred from the Data Safe Haven to the BSRBR-RA portal to show that the patient has had a cancer/death. No other patient level NHS Digital data will be transferred to the BSRBR-RA portal, just the fact that either of these events has been reported by NHS Digital. This will allow the study team to contact the hospital to obtain further details surrounding the event. Once the study team have this information confirmed by the hospital team, this is no longer classified as NHS Digital data and is considered to be BSRBR-RA study data which can be used to enhance the safety data captured in the study.

Element 2:
The full NHS Digital dataset will be stored in the University Data Safe Haven. When the BSRBR-RA study reaches a sufficient size, usually at pre-determined points based on the number of patients recruited to the study and the length of exposures to their treatments, a pre-specified analysis will be undertaken against the primary objectives of the study. Prior to starting this analysis, a more detailed analysis plan will be prepared by the statistician.

A copy of the BSRBR-RA dataset will be transferred into the Data Safe Haven and, when required according to the analysis plan, the full NHS Digital data on deaths and cancers will be linked in order to perform statistical analyses of the larger combined dataset to allow the University of Manchester to undertake the specified analysis. Only aggregated data in the form of summary data tables will be exported out of the Data Safe Haven to allow the University of Manchester to publish the results of the study which will help inform clinicians, patients, regulators and policy makers on the long-term safety of these drugs. An updated research plan, devised by the University of Manchester, is shared annually with the BSR.

Only the University of Manchester will process the NHS Digital data under this Data Sharing Agreement. The participating pharmaceutical companies will not be allowed access to NHS Digital data. Should they require this data, they will be advised they need to set up a separate Data Sharing Agreement with NHS Digital. The Data processing is only carried out by substantive employees of the University of Manchester who, as part of their employment, carry out regular mandatory training in data protection. Processing will only occur within the University of Manchester’s Data Safe Haven. The study data will not be linked with any other sources. There will be no requirement/attempt to re-identify individuals.

MR1135 - Manchester self-harm project - Mortality and suicide after self-harm- a cohort study — DARS-NIC-147916-DPQ3Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, N, Anonymised - ICO Code Compliant, Identifiable, 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), 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(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(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), 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(5)(d); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (Academic)

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

When:DSA runs 2019-08-01 — 2021-07-31 2017.09 — 2024.06.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

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. Civil Registration - Deaths
  6. Demographics
  7. MRIS - Scottish NHS / Registration
  8. MRIS - Bespoke
  9. Civil Registrations of Death

Objectives:

Self-harm is a major public health problem, with 220,000 presentations per year at
UK Emergency Depts. Reduction in the numbers of suicide following self-harm is an important
objective of the National Suicide Prevention Strategy for England, with research showing that
suicide prevention is key to achieving this. As well as suicide, self-harm is associated with
increased risk of other causes of mortality. Therefore, all-cause mortality is an important outcome
to monitor following self-harm.

Yielded Benefits:

Data contributed to the Multicentre Study of Self-harm has resulted in multiple annual reports to the Department of Health, multiple empirical studies published in scientific journals and presented at international conferences. Such published findings inform Department of Health policies e.g. the Suicide Prevention Strategy for England (2002, 2012, 2017) and NICE guidelines for the management of self-harm (2004, 2011) and suicide prevention (2018).

Expected Benefits:

Data will be stored in locked filing cabinets, or secure standalone server, in secure
offices in a secure department accessed by swipe card only. Only research staff from MaSH, with
honorary contracts with Manchester Mental Health trust who have been authorised by Dr J
Cooper will have access to identifiable data. The data will be stored in accordance with strict
security protocols that conform to BS7799 standards and DPA 1998. Electronic files of patient
identifiable will be password protected and accessed only by named investigators. Data will be
anonymised and transferred to separate database where patients are identified by numerical
coding. Named information will be destroyed once analyses are complete.

Outputs:

To investigate mortality as an outcome following self-harm to inform and assess national
strategies on self-harm and suicide prevention. Some studies within the mortality investigation will
be carried out in Manchester on Manchester data alone, with other studies carried out as part of a
multicentre collaborative project. Aims of the multicentre mortality studies are to : determine
current risk of suicide following self-harm over time, in gender/age subgroups; identify risk factors
for suicide to inform assessment procedures; provide data on risk of death from non-suicidal
causes; and provide information on mortality following self-harm in important subgroups
highlighted in the National Suicide Prevention Strategy.

Processing:

Patient data collected on approx 20,000 persons by the Manchester Self-
Harm Project from 1997 to 2007 will be submitted to MRIS, to flag for long-term mortality follow-
up. Local Manchester mortality data will be linked to the existing Manchester Self-Harm database
and pseudoanonymised. For the multicentre project, pseudonymised data from each centre will
be integrated into the main database at the coordinating centre (Oxford). The mortality data will
be used to calculate suicide rates and standardized mortality ratios. Potential risk factors for
suicide and other cause of mortality will be investigated using survival analyses. The data will
also be analysed by gender and age.

Quality, safety and clinical governance in NHS and independent hospitals — DARS-NIC-695075-J7Y2H

Type of data: information not disclosed for TRE projects

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(2)(a)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2024-03-15 — 2026-03-14 2024.04 — 2024.04.

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Manchester and the University of York require access to NHS England Data for the purpose of the following research project: “Quality, safety and clinical governance in NHS and independent hospitals”

The following is a summary of the aims of the research project provided by the University of Manchester and the University of York:

“The overall aim of this research is to provide evidence on the quality and safety of patient care in NHS and independent hospitals and the effectiveness and impact of shared arrangements for clinical governance.

The research aims to answer the following questions:

1. What are the characteristics of the patient population and the care provision in NHS and independent hospitals in England, and what differences are observed by funding type (NHS or private), care setting (NHS or independent), specialty, procedure, geography and over time?

2. Can we map and measure the overall scope of practice of doctors providing care in both NHS and independent hospitals, and explore how well those organisations understand and oversee that scope of practice through the separate and shared arrangements for clinical governance that they have in place?

3. How does the quality and safety of care provided in NHS and independent hospitals differ, and what hospital, consultant, or other characteristics are associated with such variations?

4. How have the practice and working arrangements between NHS and independent hospitals changed during and after the COVID19 pandemic, and what effects have those changes had on clinical governance and the quality and safety of care?”

The following NHS England Data will be accessed:

• Hospital Episode Statistics (HES) Admitted Patient Care (APC) – necessary to:

1) characterise the patient population and care provision in NHS hospitals over time, for comparison to equivalent data for independent hospitals provided by the Private Healthcare Information Network (PHIN)

2) assess differences in coding of hospital inpatient admission data between providers and sectors

3) map the scale and scope of practice of doctors who provide care across the NHS and the independent sector

4) analyse whether and how the quality and safety of care differs according to the factors stated in the research questions above

5) to inform case study selection of NHS hospitals for a qualitative exploration of clinical governance processes

6) to generate ‘scope of practice’ profiles for selected consultants and hospitals and explore how these could be used in clinical governance

The level of the Data will be pseudonymised.

The Data will be minimised as follows:
• Limited to individuals who have had inpatient hospital admissions within England
• Limited to Data between April 2017 and March 2024

The University of Manchester as the research sponsor, and the University of York as the main collaborator are joint controllers as the organisations 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 aims to evaluate the quality, safety and clinical governance in NHS and independent hospitals, and thereby ensure that patients receive consistently high-quality care in all care settings.

The funding is provided by the National Institute for Health Research. The funding is specifically for the project described. Funding is in place until 30/06/2025.

The funder(s) will have no ability to suppress or otherwise limit the publication of findings.

The Project Advisory Group comprises individuals from the General Medical Council, NHS England, NHS Resolution, the Department of Health and Social Care, the Care Quality Commission, the Nuffield Trust, The Health Foundation, University of Oxford, PHIN, BUPA Healthcare, the Independent Healthcare Providers Network. The Project Advisory Group provides guidance to the research team, gives feedback on emerging findings from the research, and advises on their dissemination.

The study has patient and public involvement (PPI) embedded throughout. The whole scope of the project has been informed by discussions with the PPI group and PPI co-applicant. Discussions with the study’s PPI group and PPI co-applicant continue to shape the study design and planning.

Expected Benefits:

The findings of this research study are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform policy around shared arrangements for clinical governance and patient choice about which hospital setting is likely to provide the best quality care for their needs.

The use of the Data could:
• help the system to better understand the health and care needs of populations.
• lead to the identification or improvement of health and care system design to improve health and care outcomes or experience.
• advance understanding of regional and national trends in healthcare 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 which need to be addressed.
• support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).

Patients are expected to be better informed about the types of care that are provided and financed by the NHS as compared to the independent sector. This may help patients make more informed choices about the care they might receive, and how this differs by clinical speciality. The research is expected to identify which types of procedures are conducted more routinely within which sector. This is a factor which might help a patient to decide where they would prefer to receive their care.

It is also expected that if the research findings support that particular procedures are more suited to certain settings or patient groups, this could facilitate decisions about which setting is most appropriate for NHS-funded care.

It is hoped that through publication of findings in appropriate media and direct engagement of key stakeholder organisations, the findings of this research will add to the body of evidence that is considered by the organisations and individual care practitioners charged with making policy decisions for or within the NHS and across the independent sector. Providing information in the form of consultant profiles on scale and scope of clinical activity is expected to have benefits to the clinical governance process. For example, the process for medical revalidation may be improved with access to more information on scale and scope of clinical activity.

The University of Manchester and University of York intend to share emerging research findings with the Project Advisory Group and Patient and Public Involvement (PPI) group to facilitate dissemination to other key stakeholders nationally, as well as share key findings via social and traditional media coverage, in order to optimise the potential public benefits.

Outputs:

The expected outputs of the processing will be:

• Submissions to peer reviewed journals such as Health Economics, Health Services Journal, Journal of Health Services Research & Policy, and BMJ Quality & Safety in autumn 2024 and spring 2025
• Presentations at national and international conferences to audiences of academics and health professionals, such as the UK Health Economists’ Study Group and Health Services Research UK
• A report of findings to the National Institute for Health and Care Research (NIHR) on the final outcome of the research project (anticipated mid-late 2025)
• Bespoke hospital and consultant level reports defining scale and scope of clinical activity

The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.

The outputs will be communicated to relevant recipients through the following dissemination channels:
• Journals
• Summary reports/ video presentations shared via social media, aimed at practitioners and at the public/ patients
• Press/ media engagement
• Seminars for NHS and independent hospital staff involved in clinical governance
• Direct bilateral engagement with existing forums such as the NHS England Responsible Officer networks

The Data requested from NHS England is one component of a larger project which will also involve analysis of data from the private healthcare sector, and qualitative research across the NHS and private sector. Outputs will largely relate to the project as a whole, therefore dissemination dates may be impacted by delays to other work packages.

Processing:

No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).

NHS England will provide the relevant records from the HES Admitted Patient Care dataset to the University of Manchester. The Data will contain no direct identifying data items. The Data will be pseudonymised and patients cannot be reidentified through linkage with other data in the possession of the recipient.

The Data will not be transferred to any other location.

The Data will be stored on servers at the University of Manchester.

The Data will be accessed onsite at the premises of the University of Manchester, or by authorised personnel via remote access.

The Controllers must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.

For remote access:

- Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;
- Access controls granting users the minimum level of access required are in place;
- Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;
- Multifactor authentication (MFA) is required for remote access;
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
- All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.

The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).

Remote processing will be from secure locations within England. The Data will not leave England at any time.

Access is restricted to employees of the University of Manchester who have authorisation from the Principal Investigator or nominated co-Investigator.

The University of York is not permitted to access the Data.

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

The aggregated information derived from the Data will be combined with aggregated equivalent private hospital inpatient admission data from the Private Healthcare Information Network (PHIN). This will enable comparison of, for example, how many procedures are undertaken in each sector overall and by consultant. All analysis of HES APC Data and PHIN data will be conducted in separate secure environments.

The Data will be linked to data which is publicly available in the General Medical Council (GMC) register (https://www.gmc-uk.org/registration-and-licensing/the-medical-register). The consultant code available in the HES Data will be used to retrieve the gender, qualification, year of qualification, place of qualification country, full registration date, registration status, specialty, sub-specialty, and revalidation status of consultants. The Data will not be linked with any other data.

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

Researchers from the University of Manchester will analyse the Data for the purposes described in Objectives for Processing.

MR1102 - British Association of Dermatologists' Biologic and Immunomodulators Register (BADBIR) — DARS-NIC-147941-XX4JP

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Yes - patient objections upheld, Identifiable, Yes (Mixed, Mixture of confidential data flow(s) with consent and flow(s) with support under 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(7), Health and Social Care Act 2012 – s261(7); Other-Informed Patient Consent (for those recruited since 11/09/2017); Other-Section 251 NHS Act 2006 (for those recruited prior to 11/09/2017), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

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

When:DSA runs 2019-12-01 — 2020-05-31 2017.06 — 2024.04.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, BRITISH ASSOCIATION OF DERMATOLOGISTS, THE UNIVERSITY OF MANCHESTER

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. Hospital Episode Statistics Admitted Patient Care
  6. MRIS - Scottish NHS / Registration
  7. Cancer Registration Data
  8. Civil Registration - Deaths
  9. Demographics
  10. Hospital Episode Statistics Admitted Patient Care (HES APC)
  11. Civil Registrations of Death

Objectives:

The data supplied by the NHS IC to BADBIR (University of Manchester Medical School) will be used only for the approved Medical Research Project - British Association of Dermatologists' Biologic Interventions Register (BADBIR)

Yielded Benefits:

Published data will help clinicians and patients make more informed decisions about psoriasis treatment options. There have been three publications in high impact journals on drug effectiveness. One paper concerned drug survival of biologic treatments for psoriasis received as first-line therapy. Biologics in clinical practice have a good overall survival rate in psoriasis patients but decrease over time; ustekinumab had the highest first-course drug survival in biologic-naive patients, followed by adalimumab. A paper followed on second-line drug survival which wound that 77% of patients who were switched to a second biologic continued on the new treatment for at least 12 months. This shows clearly that patients experiencing treatment failure with one biologic therapy can benefit from switching to another. The results of this study should support clinical decision making when choosing second-line biologic therapy for psoriasis patients. In 2017 a publication was made on the risk of serious infections for patients with psoriasis being treated with biologics. The University of Manchester did not find a statistically significant higher relative risk of serious infections for etanercept, adalimumab and ustekinumab as compared to non-biologic therapies for patients with psoriasis. There was no difference in the risk of serious infections between etanercept, adalimumab and ustekinumab. The risk of serious infection, therefore, should not be a primary concern for patients and clinicians when deciding between non-biologic systemic therapies or these three biologic therapies for psoriasis. There are no direct benefits for participants in BADBIR but the publications that arise from the project will contribute to the knowledge on the long-term safety of these new treatments thus benefiting patients in the future.

Expected Benefits:

Real world evidence on drug safety and effectiveness is a pivotal part of evidence-based medicine. The results can be used to inform clinical practice and also clinical guidance e.g. NICE

The methodology employed will also allow for an evaluation of the potential for the sole use of data collected via routine healthcare to be used for further research of this type. This would potentially result in very substantial efficiency savings for future studies such as:

1. Collection of events (which requires training and employment of skilled staff) will be greatly streamlined
2. The need for trial participants to attend study clinics (which can be onerous and expensive if travel costs are not reimbursed) may be reduced

This, in turn will enable such future research to be conducted on a greatly reduced budget, which is vital given the limited funding that national and charity funding bodies can typically offer.

This may be particularly important for long term safety studies or trials of generic drugs in common conditions (e.g. aspirin in cancer prevention) which do not currently attract industry funding.

Such methodological research is becoming increasingly important to the efficiency, design and data collection strategies of future trials and studies, and hence will be of benefit to public health at home and abroad.
In summary, expected benefits include;

Researchers/health and social care:
1. Reduced costs
2. Greater efficiency leading to increased throughput of research and associated enhancement of evidence based medicine, with impact upon national clinical guidelines
3. Increased awareness of the potential for routinely collected data to augment existing understanding and knowledge of a therapeutic area

Patients:
1. Increased knowledge which will be used to inform healthcare decisions leading to improved quality of patient care
2. Reduction in the demands upon study participants in terms of time and inconvenience in attending study visits

Outputs:

Publication of the results of analyses is ongoing. Results of the analyses are presented at relevant national and international scientific meetings, such as the British Association of Dermatologists, American Academy of Dermatology, International Conference on Pharmacoepidemiology (ICPE) and in peer reviewed journals e.g. British Journal of Dermatology (BJD), Journal of Investigative Dermatology (JID), JAMA Dermatology.

These will be targeted to ensure that the results are disseminated widely among the dermatology and the research community, including and the UK Dermatology Clinical Trials Network.


The following is a list of publications using BADBIR data in peer-reviewed journals to date (13/09/18):

1. The British Association of Dermatologists Biologic Interventions Register (BADBIR): Design, Methodology and Objectives, A.D. Burden, R.B. Warren, C.E. Kleyn, K. McElhone, C.H. Smith, N.J. Reynolds, A.D. Ormerod, C.E.M. Griffiths, BADBIR Study Group., British Journal of Dermatology, March 2012.

2. Biological therapies for the treatment of severe psoriasis in patients with previous exposure to biological therapy: a cost-effectiveness analysis, L.M. Sawyer, D. Wonderling, K. Jackson, R. Murphy, E.J. Samarasekera, C.H. Smith, PharmacoEconomics, February 2015.

3. Baseline characteristics of patients with psoriasis enrolled in the British Association of Dermatologists' Biologic Interventions Register, I.Y.K Iskandar, Z.N. Yiu, R.B. Warren, K. McElhone, M. Lunt, A.D. Ormerod, N.J. Reynolds, C.H. Smith, C.E.M. Griffiths, D.M. Ashcroft, British Journal of Dermatology, July 2015.


4. Differential Drug Survival of Biologic Therapies for the Treatment of Psoriasis: A Prospective Observational Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), R.B. Warren, C.H. Smith, Z.Z.N. Yiu, D.M. Ashcroft, J.N.W.N. Barker, A.D. Burden, M. Lunt, K. McElhone, A.D. Ormerod, C.M.Owen, N.J. Reynolds, C.E.M. Griffiths, Journal of Investigative Dermatology, June 2015.

5. Identification of factors that may influence the selection of first-line biologic therapy for people with psoriasis: a prospective, multi-centre cohort study, N.J. Davison, R.B. Warren, K.J. Mason, K. McElhone, B. Kirby, A.D. Burden, C.H. Smith, K. Payne, C.E.M. Griffiths, British Journal of Dermatology, April 2017.

6. Patterns of biologic therapy use in the management of psoriasis: cohort study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), I.Y.K. Iskandar, D.M. Ashcroft, R.B. Warren, I. Evans, K. McElhone, C.M. Owen, A.D. Burden, C.H. Smith, N.J. Reynolds, C.E.M. Griffiths., British Journal of Dermatology, March 2017.

7. Comparative effectiveness of biologic therapies on improvements in quality of life in patients with psoriasis., I.Y.K. Iskandar, D.M. Ashcroft, R.B. Warren, M. Lunt, K. McElhone, C.H. Smith, N.J. Reynolds, C.E.M. Griffiths, British Journal of Dermatology, March 2017.

8. Differential Drug Survival of Second-Line Biologic Therapies in Patients with Psoriasis: Observational Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), I.Y.K. Iskandar, R.B. Warren, M. Lunt, K.J. Mason, I. Evans, K. McElhone, C.H. Smith, N.J. Reynolds, D.M. Ashcroft, C.E.M. Griffiths, Journal of Investigative Dermatology, December 2017

9. Risk of Serious Infection in Patients with Psoriasis Receiving Biologic Therapies: A Prospective Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), Z.N. Yiu, C.H. Smith, D.M. Ashcroft, M. Lunt, S. Walton, R. Murphy, N.J. Reynolds, A.D. Ormerod, C.E.M. Griffiths, R.B. Warren, Journal of Investigative Dermatology, October 2017

10. Intentional and Unintentional Medication Non-Adherence in Psoriasis: The Role of Patients' Medication Beliefs and Habit Strength, R.J. Thorneloe, C.E.M. Griffiths, R. Emsley, D.M. Ashcroft, L. Cordingley, Journal of Investigative Dermatology, November 2017

11. Generating EQ-5D-3L Utility Scores from the Dermatology Life Quality Index: A Mapping Study in Patients with Psoriasis, N.J. Davison, A.J. Thompson, A.J. Turner, L. Longworth, K. McElhone, C.E.M. Griffiths, K. Payne. Value in Health, December 2017.

12. Comparison of Drug Discontinuation, Effectiveness, and Safety Between Clinical Trial Eligible and Ineligible Patients in BADBIR, K.J. Mason, J.N.W.N. Barker, C.H. Smith, P.J. Hampton, M. Lunt, K. McElhone, R.B. Warren, Z.Z.N. Yiu, C.E.M. Griffiths, A.D. Burden, JAMA Dermatology, March 2018.

13. Cumulative exposure to biologics and risk of cancer in psoriasis patients: A meta‐analysis of Psonet studies from Israel, Italy, Spain, United Kingdom and Republic of Ireland, I. Garcia‐Doval, M.A. Descalzo, K.J. Mason, A.D. Cohen, A.D. Ormerod, F.J. Gómez‐García, S. Cazzaniga, I. Feldhamer, H. Ali, E. Herrera‐Acosta, C.E.M. Griffiths, R. Stern, L. Naldi, British Journal of Dermatology, May 2018.

14. Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study, R.B. Warren , A. Marsden, B. Tomenson K.J. Mason, M.M. Soliman, A.D. Burden, N.J. Reynolds, D. Stocken, R. Emsley, C.E.M. Griffiths, C. Smith, British Journal of Dermatology, August 2018.

Most of these papers have also been presented at local, regional and international medical, nursing, scientific and patient conferences thus informing evidenced based clinical care. In addition, this “real world” experience of safety and effectiveness of these drugs will contribute to national guidelines of management of psoriasis e.g. NICE

The up to date list of all publications arising from BADBIR data is posted on the BADBIR website (http://www.badbir.org)

It is expected that other papers will be published in the 2018/2019 including: incidence of suicide in the BABDIR population, risk of keratinocyte cancers in patients treated with biologic therapy and risk of MACE in patients treated with biologic therapy. An up to date publication plan is available on the BADBIR website http://www.badbir.org

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

Six-monthly summary reports are provided to the BADBIR Data Monitoring Committee to include crude unadjusted rates of specific events of interests. Should a safety signal be identified, then further analysis can be requested and in the event of a significant concern the wider dermatology community will be informed. In addition, this “real world” experience of safety and effectiveness of these drugs will contribute to national guidelines of management of psoriasis e.g. NICE and thereby to the clinical management of patients with psoriasis.

A yearly Participant (patient) Newsletter which includes a summary of any published paper is also provided via the BADBIR website and the recruiting dermatology centres.

YouTube and Twitter have also been used to disseminate information on BADBIR including two presentations at the Psoriasis Association annual meetings (https://www.youtube.com/watch?v=OtmX-3uDtfY&t=618s and https://www.youtube.com/watch?v=LDYfOIuIGug), a short summary of the keratinocyte carcinoma presentation to the BDNG (https://www.youtube.com/watch?v=AcR4GRLK7l0), a summary of a Psoriasis Association-funded PhD student’s upcoming project (https://www.youtube.com/watch?v=4aoawSeODZM) and two videos by an NIHR doctoral fellow who completed his PhD using BADBIR (https://vimeo.com/251302895 and https://youtu.be/aQ64ePHVRKE).

It is expected that other papers will be published in 2019 including: incidence of suicide in the BABDIR population, risk of keratinocyte cancers in patients treated with biologic therapy and risk of MACE in patients treated with biologic therapy. A research grant has been obtained from the Psoriasis Association to explore the risk of solid tumour cancer in patients treated with biologic therapy. It is anticipated that this analysis will be undertaken in 2019/2020. An up to date publication plan is available on the BADBIR website http://www.badbir.org


Data dissemination is managed by a data writing group who are responsible to the BADBIR Steering Committee. This group comprises of dermatologists, research scientists, dermatology nurses and patients with psoriasis and guide the potential for output for disseminated data to provide a measurable benefit.

The BADBIR Steering Committee is comprised of representatives of the data controller and will therefore contribute to this responsibility.

Processing:

The data will be accessed only by substantive employees of University of Manchester and only for the purposes described in this agreement. All data will be stored securely on servers at the University of Manchester.

NHS Digital already hold the cohort data and will link this with mortality, cancer and HES data.

The identifiers that were sent to NHS Digital: Study ID, NHS Number, Postcode, Sex, DoB. CAG approval is in place all of these identifiers in addition to Name however Name is not required for the matching and so has not been shared with NHS Digital. The study previously received NHS Number, Supplied Identifiers and Latest Identifiers but has removed these for data minimisation purposes.

Data supplied by NHS Digital will be identifiable mortality, cancer data and HES data all supplied with a study ID.

The researchers at University of Manchester will link and compare the NHS Digital data with the existing BADBIR database which includes:
• Unique study Patient ID
• Clinical data on clinical events
• Laboratory results
• Study treatment

In summary, each record from the NHS Digital data will be reviewed against existing study data to avoid duplicate entries. Any new͛ adverse events will then join the main study data for analysis.

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).

There will be no data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the agreement.

Data will only be accessed and processed by substantive employees of The University of Manchester and will not be accessed or processed by any other third parties not mentioned in this agreement.

Understanding the role of adult community health services in avoiding hospital admissions — DARS-NIC-482271-S6S1V

Type of data: information not disclosed for TRE projects

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(5)(d)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2022-10-10 — 2025-10-09 2023.04 — 2023.04.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. Community Services Data Set
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Outpatients
  6. Civil Registrations of Death - Secondary Care Cut
  7. Community Services Data Set (CSDS)
  8. Hospital Episode Statistics Accident and Emergency (HES A and E)
  9. Hospital Episode Statistics Admitted Patient Care (HES APC)
  10. Hospital Episode Statistics Outpatients (HES OP)

Outputs:

A description of the planned outputs is provided below. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

WP2: Workforce and supply
Final report of work package 2 findings will be produced in summer of 2023, reporting estimates of how supply is meeting population demands in each local authority in England and identification of areas experiencing potential issues in patient access. This will highlight areas with potential issues in patient access, unwarranted variation in care provision, and workforce pressures. These findings will be shared with NHS England and Improvement to inform their service planning.

A journal article based on the analysis will be written up for publication, and submitted to a peer-reviewed journal in autumn of 2023. This article will focus on the degree of variation in supply and demand misalignment across the country.

WP4: Impact on hospital use
Final report of work package findings will be produced in May 2024, reporting estimates of the magnitude and mechanisms by which community health service provision affects hospital utilisation. Together these two sets of analyses conducted in this work package will provide a detailed understanding of whether community health service provision does impact individuals’ hospital use, if so how, and whether these impacts are seen at a system level. These findings will be shared with NHS England and Improvement to inform their service planning.

Two journal articles based on this analysis will be written up for publication, and submitted to a peer-reviewed journal in July 2024. The first journal article will report the results of the aggregate-level analysis, demonstrating the impact of community services provision on hospital utilisation at the level of the local system. The second journal article will report the results of the person-level analysis, demonstrating the impact of community services on the hospital use of patients who have contact with those community services.

WP5: Economic case for investment
Report of work package findings will be produced in February 2025, demonstrating the costs and benefits of adult community health services. This will contain evidence on community health service activity configurations to meet current and future population demands. These findings will be shared with NHS England and Improvement to inform their service planning.

A journal article based on the analysis will be written up for publication, and submitted to a peer-reviewed journal in March 2025.

Overall project report
A report summarising the entire research project and its findings will be submitted to the NIHR in May 2025, and published in the NIHR journals library following external peer review.

Wider dissemination
In addition to the above written outputs, the University of Manchester project team will enact their dissemination plan to ensure that the research findings reach the relevant stakeholders outside of academia. The University of Manchester project team will engage provider and commissioner audiences through Integrated Care Systems (ICS) leadership. The University of Manchester project team will also disseminate through additional existing networks such as the NHS R&D Forum, NIHR Applied Research Collaborations (ARCs), Academic Health Science Networks (AHSNs), and the Community Network hosted by the NHS Confederation and NHS Providers which acts as the national voice for community providers. At policy level, the University of Manchester project team we will use their networks to seek engagement of key NHS England and Improvement. The University of Manchester project team are already in regular contact with the community transformation team at NHS England and Improvement, who have expressed great interest in the research. The University of Manchester project team will also disseminate through their established regular contacts with Department for Health and Social Care (DHSC).

The University of Manchester will use press releases to publicise key findings and engage with communications teams to localise the implications for communities and staff groups. Interactive stakeholder engagement events such as webinars will be hosted at regular points throughout the project to avoid overreliance on written outputs. For example, the University of Manchester project team will host a national webinar on key findings with a facilitated expert panel for Q&A and break-out rooms to consider potential system responses to the findings.

The University of Manchester project team will apply to host an organised session at the Health Services Research UK conference to raise awareness in the research community and gain feedback. Interim findings will be presented at conferences throughout the lifetime of the project in order to gain timely engagement and feedback.

The University of Manchester project team will engage with our patient and public involvement (PPI) panel to develop ways of disseminating our research results that are accessible to patients and members of the public. These will include infographics and short lay summaries of the findings of each work package and the overall project findings. These outputs will be published at the same time as the journal articles are submitted for publication (WP2 summary September 2023, WP4 July 2024, WP5 March 2025, overall project findings May 2025).

Processing:

No data will flow into NHS Digital.

Pseudonymised (CSDS/HES/Civil Registrations deaths) data will flow from NHS Digital to the University of Manchester. The data will be stored in a Common Internet File System (CIFS) share accessible only from a specified workstation, via a mapped network drive. This specified workstation will be a University of Manchester managed device, complying with the University’s Technical Security Standard. Access control is managed via Active Directory groups, and drives are mapped via the network login script for individual user accounts with relevant group membership. Only members of the research team working on the research project in question will be added to the group Active Directory group (NIHR134436 – Understanding the role of adult community health services in avoiding hospital admissions). Access will therefore be restricted to individuals working on the research project in question, all of whom will be substantive employees of the University of Manchester. Network drives are automatically disconnected when users log off. Together, this means that the data flowing from NHS Digital is stored securely on a share that can only be accessed by pre-authorised members of the project team.

The CIFS share infrastructure is hosted within an ISO27001 certified Equinix data centre located 4 miles from the University of Manchester campus. The data centre is managed by Equinix. However, access to University of Manchester infrastructure within the data centre is physically secured and only accessible by University of Manchester data centre staff. Equinix have no access to the data or infrastructure. All servers, storage and network infrastructure used by the University of Manchester within the data centre is owned by the University of Manchester, operated and maintained by the University of Manchester IT staff and securely caged off. The University of Manchester lease physically secure hosting space from Equinix. No shared services are consumed. Therefore, Equinix is responsible for the physical hosting environment and utilities (standard co-location service, power and cooling).

Data derived from the pseudonymised (CSDS/HES/Civil Registrations deaths) data from NHS Digital is made and stored in a store visible to The University of Manchester’s high performance computing processing platform, where this store is specific to the research project in question and only accessible by the research team that has permission to use the data. For example, derived versions may be aggregated to the hospital level, or contain minimised information on only certain subgroups relevant for a specific part of the analysis. This derived data is stored in the store visible to the high performance computing platform only for the period during which the member of the research project permitted to access the data is logged on to the high performance computing platform to conduct the analysis for which they are responsible. This transfer is secure and encrypted. The high performance computing platform is designed specifically for interactive computationally-intensive work, such as use of applications such as Stata (the statistical analysis software package which will be used by the project research team to analyse the requested data). Access to the high performance computing processing platform is protected by strict project level access controls and multi-factor authentication. Security is by a Unix group Access Control List, restricting access. Only members of the research team working on the research project in question will be added to the group Access Control List (NIHR134436 – Understanding the role of adult community health services in avoiding hospital admissions). Access will therefore be restricted to individuals working on the research project in question, all of whom will be substantive employees of the University of Manchester. Access is via a secure encrypted connection. Account credentials are unique to each member of staff and only the account owner knows the password.

Once processed, the same secure channel will be used to transfer data back to the CIFS where it will be stored. All transfers are encrypted.

This means that extracts from the original pseudonymised NHS Digital data transfer will be temporarily visible to approved members of the research team on a secure platform designed specifically to run computationally intensive statistical analyses. These extracts will be minimised versions of the original data which contain only the information needed to undertake the specific part of the analysis being conducted by the researcher at that time. The approved member of the research team will access the data extract on the secure platform, and undertake the necessary statistical analysis within this secure platform. When they have finished conducting this analysis, the data extract they were temporarily viewing and any further data or information which they have generated will be securely transferred back to the secure share where the original data from NHS Digital is stored. All transfers are secure and fully encrypted. Strict access controls are in place, so only pre-approved members of the research team working on the research project in question will be able to access the data. Members of the research project will have to go through multiple levels of authentication each time they access the data, including individual-specific usernames and passwords, and further authentication through another trusted device (fingerprint or security code entered into an app on the individual’s pre-registered mobile device).

Back-up disaster recovery storage is hosted at Reynolds House within an ISO27001 certified data centre located less than 1 mile from the University of Manchester campus. Reynolds house conforms to the same security standards as the primary storage location (Joule House). The Reynolds House data centre is managed by Ascendas Reit. However, access to University of Manchester infrastructure within the data centre is physically secured and only accessible by University of Manchester data centre staff. Ascendas Reit have no access to the data or infrastructure. All servers, storage and network infrastructure used by the University of Manchester within the data centre is owned by the University of Manchester, operated and maintained by the University of Manchester IT staff and securely caged off. The University of Manchester lease physically secure hosting space from Ascendas Reit. No shared services are consumed. Therefore, Ascendas Reit is responsible for the physical hosting environment and utilities (standard co-location service, power and cooling).

University of Manchester staff are not based on site, however, if required, will be in attendance at Reynolds House (Ascendas Reit). Physical access to the data centre is strictly limited to University of Manchester data centre staff and a limited number of authorised University of Manchester IT services staff. The data centre is protected by physical and electronic access security systems, swipe card access in and out of the data centres and CCTV coverage. The data centre is locked down out of hours and access is discouraged but can be arranged by prior agreement with the data centre manager. The data centre manager is a substantive employee of the University of Manchester.

HES, CSDS and Civil Registrations (deaths) will be linked using common pseudo-IDs applied by NHS Digital, as described above. These pseudo-IDs will be specific to the project, and only NHS Digital will hold the personal information upon which these pseudo-IDs are based (NHS number). The University of Manchester will not have access to the original IDs. Linkage with publicly available area-level information (Local Authority District) will be made in order to control for area-level characteristics in the multivariable regression analyses (for example the average age and sex composition of the population in the Local Authority District within which an individual resides). No individual-level linkage will be undertaken, beyond the HES-CSDS-mortality linkage described above. There will be no attempt to re-identify individuals.

Data processing will only be carried out by substantive employees of The University of Manchester, who is both the sole data processor and the data controller. All individuals processing the data will be appropriately and regularly trained in data protection and confidentiality, and no access to the data will be granted until this training has been confirmed.

No data will be shared with third parties.

University of Manchester researchers granted access to the high performance computing platform share will be able to conduct the analyses proposed above. This will involve loading the data into Stata (a statistical analysis software package) and conducting multivariable regression analyses and sample summary measures. No individual-level output will be made available in any dissemination of the results. Results and findings will be of aggregated volumes and percentage shares across multiple strata.

ISARIC4C Coronavirus Clinical Information Network (COCIN) GPES record linkage — DARS-NIC-402963-P0Y5D

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - Statutory exemption to flow confidential data without consent, No - data flow is not identifiable, Identifiable, Anonymised - ICO Code Compliant, No (Statutory exemption to flow confidential data without consent)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; Other-(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002), Other-(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002), Other-Other(CV19: Regulation 3 (1) 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(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002, Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002

Purposes: No (Academic)

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

When:DSA runs 2020-09-28 — 2023-09-28 2020.11 — 2023.02.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF OXFORD

Sublicensing allowed: No

Datasets:

  1. Mental Health Services Data Set
  2. COVID-19 Second Generation Surveillance System
  3. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  4. Secondary Uses Service Payment By Results Accident & Emergency
  5. COVID-19 Hospitalization in England Surveillance System
  6. Secondary Uses Service Payment By Results Outpatients
  7. Secondary Uses Service Payment By Results Spells
  8. Secondary Uses Service Payment By Results Episodes
  9. NHS 111 Online Dataset
  10. Emergency Care Data Set (ECDS)
  11. Civil Registration - Deaths
  12. GPES Data for Pandemic Planning and Research (COVID-19)
  13. Hospital Episode Statistics Accident and Emergency
  14. Improving Access to Psychological Therapies Data Set
  15. National Diabetes Audit
  16. Secondary Uses Service Payment By Results Accident & Emergency
  17. Improving Access to Psychological Therapies Data Set_v1.5
  18. COVID-19 Vaccination Adverse Reactions
  19. COVID-19 Vaccination Status
  20. HES-ID to MPS-ID HES Accident and Emergency
  21. Hospital Episode Statistics Admitted Patient Care
  22. Civil Registrations of Death
  23. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
  24. COVID-19 Second Generation Surveillance System (SGSS)
  25. COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
  26. Hospital Episode Statistics Accident and Emergency (HES A and E)
  27. Improving Access to Psychological Therapies (IAPT) v1.5
  28. Mental Health Services Data Set (MHSDS)
  29. Hospital Episode Statistics Admitted Patient Care (HES APC)
  30. COVID-19 SGSS First Positives (Second Generation Surveillance System)

Objectives:

The Coronavirus Clinical Information Network (CO-CIN) has collected data for the International Severe Acute Respiratory Infection Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium through a commission from the Chief Medical Officer to conduct Urgent Public Health Research to provide evidence that informs public health policy in response to the COVID-19 emergency.

ISARIC’s purpose is to prevent illness and deaths from infectious disease outbreaks. ISARIC is a global federation of clinical research networks, providing a proficient, coordinated and agile research response to outbreak-prone infectious diseases.

The ISARIC Coronavirus Clinical Characterisation Consortium is a UK-wide consortium of leading experts in outbreak medicine with a proficient, coordinated, and agile research response to COVID-19.

In 2019 a new virus, SARS coronavirus-2 (SARS-Cov-2) emerged. It seems highly likely that SARS-CoV-2 and its associated disease COVID-19 will cause mortality unprecedented in modern times. This is a new disease. There is a high chance that clinical trials will fail to detect therapeutic effects, by enrolling at the wrong time, or missing key subgroups or endpoints. Concurrent biological phenotyping can mitigate these risks, providing rapid, efficient clinical evidence.

CO-CIN response has been planned and tested over the past 8 years within the International Severe Acute Respiratory Infection Consortium (ISARIC).

CO-CIN informs the Department of Health and Social Care (DHSC) on a weekly basis about the clinical evolution of disease in the United Kingdom. To achieve this, clinical research nurses and administrators gather anonymised data from clinical notes and enter it into a simple online database. This allows the characterisation of the patients’ clinical features as well as risk factors associated with severity, risk of hospitalisation and death. The information gathered is essential to help health service planning and provision, and to rapidly evaluate the impact of interventions such as new therapeutics or vaccines.

The legal basis for the processing and storage of personal data for COCIN is that it is a task in the public interest Article 6(1)(e) it is in the public interest to conduct public health research to provide evidence to inform public health policy in response to the COVID-19 emergency and to understand and report on the risk factors associated COVID-19 and that sensitive personal data is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes Article 9(2)(j).

The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS Digital for linkage.

Patients are recruited into one of three Tiers. Tier 0 sites are recruited for data collection only without consent, while Tiers 1 and 2 provide consent for sample collection in addition to data collection. The distinction of the study into three Tiers was made to allow for a resource appropriate implementation of the protocol, as it was understood that data and/or sample collection may be limited in some settings.

For Tier 0 patients clinical data is collected but no additional biological samples are obtained for research purposes. The minimum clinical data set summarises the illness episode and outcome, with the option to collect additional detailed clinical data at frequent intervals, according to local resources/needs.

Given the scale of the current COVID-19 pandemic, and because initially data collection for Tier 0 participants was clinical data only from which the participant could not be identified, consent was not sought. The data is collected by a health care professional who has access to the patient's information by virtue of their clinical role. The addition of collection of NHS number, Date of Birth (DOB) and postcode for Tier 0 participants means they are now able to be identified from the dataset in order to support linkage to other NHS data sources and is currently being done under Control of Patient Information Regulations (COPI). The identifiable data is not made available to researchers. Tier 0 is being retrospectively and prospectively completed with identifying data relying on COPI.

The datasets are required primarily to enable CO-CIN to report early and accurate findings to the Scientific Advisory Group for Emergencies (SAGE). Since the early growth phase of COVID-19 in the UK, CO-CIN has presented near real-time epidemiological descriptions and analyses of hospitalised patients with COVID-19. CO-CIN have presented analyses of patient factors including ethnicity, age, comorbidity, and their association with in-hospital mortality, enabling SAGE to make decisions based on near real-time evidence. SAGE will have no access to the NHS Digital data shared under this Agreement.

Each of the datasets, including the GDPPR data (which will provide data on shielded patients), is essential to support the analysis for the use cases as these questions cannot be answered using just the available CO-CIN data. Specific questions about shielding, pre-existing patient co-morbidities, and the outcomes for patients are important to be able to understanding the full impact of the disease and interventions. CO-CIN was set up as a pandemic Case Report Form (CRF), and comorbidity categories are broad. There is a need to understand the duration and severity of comorbidities in more granularity. CO-CIN need to be able to report and respond to the impact that multi-morbidity and frailty have on COVID. CO-CIN have detailed data regarding in-hospital sequelae of COVID-19, but require post-discharge follow-up data in. Follow up of contacts with primary and secondary care, and in particular cardio-respiratory and psychiatric sequelae will be imperative to understanding the long-term impact of severe COVID-19.

CO-CIN need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. CO-CIN have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission. CO-CIN need to be able to report the longer-term all-cause and excess mortality for patients hospitalised with covid-19. The data will help to understand the longer-term mortality for patients admitted to hospital with COVID-19 (long-COVID).

CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality

CO-CIN have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). CO-CIN have developed a secure, password protected dashboard where SAGE members are able to access aggregated data with small numbers suppressed. This data is accurate to the same day.

The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.

CO-CIN have supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV.
In a subgroup of 2,500 patients, CO-CIN have linked with detailed biological and follow-up data.

Use cases for supporting SAGE reporting include the following research questions:
- What are the outcomes for patients on the shielding list?
- How do patient comorbidities/multi-morbidity contribute to in-hospital mortality in patients admitted to hospital with covid-19?
- What is the impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
- Does access to hospital affect outcomes?
- What are the longer term sequelae for hospitalised survivors of covid-19?
- What is the longer term mortality in patients admitted to hospital with covid-19?
- What is the association between diabetes and in-hospital mortality?

The following organisations are involved in the study:
University of Oxford are the lead organisation for the study and host the data collection, they are the data controller.
University of Edinburgh are hosting the research databases and have the data science team that will be analysing the linkage, they are a data processor.
University of Liverpool support the hospital recruitment sites.
Imperial University are solely analysing the collected sample data and no NHS Digital data.
Only the University of Edinburgh will store or have access to the NHS data from England, within its data safe haven.

Expected Benefits:

Expected benefits within the project timescales to April 2021 will include:
Reduce deaths associated with COVID-19
Assist commissioners in making decisions to better support patients
Identifying COVID-19 trends and risks to public health
Enables Department of Health and NHS England to provide guidance and develop policies to respond to the outbreak
Controlling and helping to prevent the spread of the virus
The Department of Health and NHS England can share a common understanding of activity levels across the system in regard to COVID-19. Better activity data will also enable a more robust national planning process and improve the allocation of resources across the system.

This will support the response to the pandemic but also the recovery of services.

Outputs:

The primary outputs will include regular and ad-hoc reports to SAGE and the UK Government with only aggregated data and small number suppression.
These will be done on a regular basis for the duration of the project (October 2020 to April 2021).

The project will also produce submissions to peer reviewed journals, again with only aggregated data and small number suppression. It is expected that at least one submission will be made prior to the end of the project.

Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS Digital requirements).

Within 1 week of CO-CIN receiving the data from NHS Digital, it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:

- Support the NHS and government response to COVID-19
- Analyse the spread of patients hospitalised with covid-19 geographically and demographically, to identify any trends. Appointment activity will also be analysed to better understand use of non-face to face consultation trends and potential differences across geographical areas.
- Analyse potential hospital-acquired covid-19 geographically and demographically
- Diagnosing and monitoring the effects of COVID-19 at a national level.
- Ensuring the Department of Health and Social Care and NHS England has adequate data to inform that interventions and measures put in place to reduce the transmission of COVID-19 are being effective and impactful.
- Analyse factors that result in increased service utilisation for COVID-19 patients.
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of care. Learning from and predicting likely patient pathways in order to influence early interventions and other alternatives for patients and develop new predictive modelling and tools for use by care professionals and commissioners.

The ISARIC website has recently been updated, and is under continuous review with regards to ensuring that it is up to date and relevant to inform the public of the programme. The ISARIC programme is also working with HDRUK to ensure that the use of data is made available to the general public as part of the overall national strategy and work with them to ensure that feedback from the public is heard. Senior researchers have also been active in promoting the study on social media and mainstream news outlets.

Processing:

Data will only be used for the purposes within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.

NHS Digital will be provided with a mapping file containing a list of NHS numbers of patients in England within the CO-CIN cohort, as well as the patient date of birth, postcode and the matched CO-CIN subject ID.

NHS digital will then create an extract , removing any identifiers from the datasets except for the subject ID. The linked pseudonymised data will then be sent securely to University of Edinburgh where it will be stored in the National Data Safe Haven (DSH) managed by the Edinburgh Parallel Computing Centre (EPCC) within an ISO27001 accredited data centre and processes. The EPCC manages its own data centre and services for the DSH.

There is a flow of identifiable data into NHS Digital and the flow out of NHS Digital will be pseudonymised, with the identifying data removed and replaced with a subject study id.

University of Oxford hold the CO-CIN database (the clinical data that is collected from the hospitals) and Oxford will flow the England data to NHS Digital, NHS Digital then send the pseudonymised extract to University of Edinburgh. University of Edinburgh hold all the pseudonymised CO-CIN data and will also hold the linked NHS D and CO-CIN data (England only) separately.

Identifiable data from CO-CIN and NHS Digital will never be stored within the same location and the linkage will be managed solely by NHS Digital.

PHS are named as a data processor due to their role supporting CO-CIN data flows and as the managers of the Serv-U gateway. They provide services to extract CO-CIN data but do not have any access to the DSH or any ability to access NHS Digital data.

Access to the data within the DSH is strictly controlled, and the linked, de-identified data will only be available to named data scientists within the University of Edinburgh and, where necessary, system administrators, all of whom are substantive employees of University of Edinburgh. All access will be logged. Access to the de-identified dataset will be controlled by the CO-CIN data manager and, where data is made available for research principles of anonymisation and minimisation will be applied in line with the HES analysis guide and ICO best practice.

Data cannot be extracted from the DSH without going through a managed process, including statistical disclosure control checks, to ensure that only anonymised data will be extracted for the purposes of reporting to SAGE and the Department of Health and Social Care (DHSC), or covid-19 research. No direct identifiers will be used in these analyses.

The data will be stored in a pseudonymised form. Only anonymised data with small numbers suppressed will be used for reporting. The data will not be onwardly shared and accessed by other external partners without approval from NHS Digital and appropriate data sharing agreements being established.

AUDIT:
All processing and use of data provided is auditable by NHS Digital in accordance with the Data Sharing Framework Contract and NHS Digital terms.
Under the Local Audit and Accountability Act 2014, section 35, the Secretary of State has power to audit all data that has flowed, including under COPI.
The DSH and EPCC processes ensure that all data flows and activity will be recorded and auditable in line with ISO27001.

Data Minimisation:

CO-CIN are conscious that, in accordance with GDPR, only data required to answer the research questions should be requested, and as such, CO-CIN have only selected the variables that are relevant. CO-CIN have not requested identifiable data including patient address, forename and surname. CO-CIN have not requested GPES data regarding declines, contraindications and other exceptions; or review and monitoring codes. The main priority of the work is to answer as yet unasked questions directed by SAGE, and as such it is difficult to be absolutely sure which variables may or may not be required to answer these questions.

Evaluating the NHS Diabetes Prevention Programme (NHS DPP): the DIPLOMA research programme (Diabetes Prevention – Long term Multimethod Assessment) — DARS-NIC-196221-K4K3Y

Type of data: information not disclosed for TRE projects

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 - '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)(b)(ii)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2020-06-22 — 2023-06-21 2020.10 — 2022.03.

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. National Diabetes Audit

Objectives:

This Data Sharing Agreement permits access to two datasets: National Diabetes Audit (NDA) and Non-Diabetic Hyperglycaemia (NDH). These datasets are collected from the same sources at the same time. NHS Digital treats them as separate datasets because one (NDA) is for collected for the purpose of auditing diabetes and the other (NDH) is by definition a dataset of people without diabetes.

The University of Manchester requires extracts of NDA and NDH data for the purpose of the DIPLOMA (Diabetes Prevention - Long term Multimethod Assessment) research programme, the aim of which is to provide a comprehensive assessment of the implementation, delivery and outcomes of the NHS Diabetes Prevention Programme (NHS DPP) to inform commissioning.

Type 2 diabetes is a common health condition that can cause serious health problems and reduce people’s quality of life, as well as costing a lot of money to treat. Some people are at higher risk of diabetes and can be identified with a blood test. If people at risk make changes to their lifestyle (more exercise, losing weight), they can substantially reduce their chances of getting type 2 diabetes. The NHS and Diabetes UK have introduced a new scheme called Healthier You: the NHS Diabetes Prevention Programme (NHS DPP). People in England who are at risk of type 2 diabetes, referred to as having Non-Diabetic Hyperglycaemia (NDH), will be offered a practical course which will help them change their lifestyle. The aim is that this course will improve peoples’ health and reduce their diabetes risk.

The people managing the NHS DPP need to know if it really works to prevent diabetes and whether it is a good use of NHS resources. The DIPLOMA programme was designed to help answer these questions.

Personal data can be lawfully processed for this purpose because it is a task carried out in the public interest (General Data Protection Regulation Article 6 (1) (e)). The task concerns the enactment of the NHS Diabetes Prevention Programme and the performance concerns the use of this programme in the population and sub-groups of the population of England.

Processing of the data is necessary for scientific research purposes and will be used to evaluate the effectiveness of the NHS Diabetes Prevention Programme (General Data Protection Regulation Article 9 (2) (j)). The DIPLOMA (Diabetes Prevention - Long term Multimethod Assessment) project has been funded by the National Institute for Health Research to evaluate the NHS Diabetes Prevention Programme and is comprised of eight work packages.

The data required are from the National Diabetes Audit (NDA) dataset which contains details of individuals with all types of diabetes and the Non-Diabetic Hyperglycaemia (NDH) which contains data on individuals at risk of Type 2 diabetes with.

The datasets will be used to identify which individuals with NDH go on to develop type 2 diabetes.

As a contracted data processor for NHS England, NHS Digital processes a separate dataset called the Minimum DataSet (MDS). With NHS England’s permission, NHS Digital will link the NDA and NDH data with the MDS and will disseminate linked MDS data alongside the NDA/NDH data to the University of Manchester. All data will be pseudonymised and linked using common pseudo-IDs applied by NHS Digital. The linkage of these datasets will enable the University of Manchester to track which NDH patients were offered a place in the NHS DPP; which patients accepted the offer, and which patients were not offered a place.

The MDS contains participant information that providers of the DPP are contractually obliged to collect in order to receive payment. It contains the date that participants were referred to the programme, as well as information about how many sessions they attended, as well as certain outcome measures, as recorded by the providers of the programme. Importantly however, this dataset contains no information on the wider eligible population who may benefit from the programme, only those that have been referred. To accurately evaluate the efficacy of the NHS DPP, it is important to know the characteristics of the eligible population.

The NDA dataset also contains information about the characteristics of the people with NDH or diabetes and, in combination with the MDS, this can be used to identify trends such as inequalities in access to the NHS DPP and variations in outcomes of the NHS DPP linked to certain characteristics. The data contains demographic and socio-economic characteristics of patients which can be used to assess the characteristics of those with the condition and whether any activity delivered is more or less prominent based on these characteristics. This data will also provide national level data to facilitate the calculation of statistics on the prevalence of NHS DPP participants, the characteristics of those who go on to develop Type 2 diabetes, as well as inform parameters to be used in a decision-analytic model, which will be used to assess the long term cost-effectiveness of the programme.

The data subjects are all individuals in the NDA dataset aged over 15 years from 2017/18 to 2018/19 with type 2 diabetes and all individuals in the NDH dataset aged over 15 years from 2017/18 to 2018/19 with non-diabetic hyperglycaemia.

The data will be used in three of eight work packages in the overall DIPLOMA research project. These are:

• Work package 1: Access and Equity – the aim is to assess the accessibility of the NHS DPP and identify inequalities in access.
• Work package 5: Comparative Effectiveness – the aim is to examine whether the NHS DPP leads to a reduction in the prevalence of Type 2 Diabetes and other outcomes related to Type 2 Diabetes compared to those without access to the NHS DPP.
• Work package 7: Economic Evaluation – the aim is to explore the cost-effectiveness of the NHS DPP, from the perspective of the NHS and Personal Social Services.
Further details on these work packages are provided below.

Quality will be assessed in terms of equitable use of the NHS Diabetes Prevention Programme (for Work Package 1); the effectiveness of reducing the prevalence of Type 2 diabetes (Work Package 5), and whether the programme is considered to be cost-effective (Work Package 7).

DIPLOMA is funded by the National Institute of Health Research (NIHR) and will be delivered by researchers at the University of Manchester over four years (2017-2021). Its aim is to produce a comprehensive evaluation of the NHS DPP, comprised of eight distinct but related work packages, using various research techniques and different sources of data. The DIPLOMA research programme is designed to provide:

(a) feedback regularly to NHS DPP stakeholders on the delivery and outcomes of the programme to support ongoing development and quality improvement,

(b) a rigorous longer-term assessment of the success of the NHS DPP in meeting the aim of reducing diabetes prevalence in a way that is cost-effective and sustainable for the NHS.

The University of Manchester is the sole data controller. There are two joint Principal Investigators leading the DIPLOMA work programme. Both are substantive employees of the University of Manchester. The Contract with the Department of Health and Social Care for undertaking the evaluation of the NHS DPP was signed with Salford Royal NHS Foundation Trust (SRFT) as the Contractor. The SRFT are subcontracting all of the work to the University of Manchester. It is agreed between SRFT and University of Manchester that all the research and decisions on how this is conducted, including the data management, are the University of Manchester’s responsibility, whilst SRFT’s role is essentially to administer the contract. The University of Manchester is reporting directly to the NIHR, not through SRFT.

The University of Manchester is the sole organisation processing the data. No other organisations process the data for this purpose.

The Department of Health and Social Care and Public Health England are in liaison with the DIPLOMA evaluation. These advise on approaches taken and provide updates on the NHS DPP. The findings of the DIPLOMA study will be distributed to these wider organisations but no data will be made available to them.

A Professor employed by the University of Oxford is providing consultancy for work package 7, as he is an expert in developing economic decision models for diabetes. He will advise the University of Manchester DIPLOMA researchers involved in work package 7, on the methods used, but will not have any access to the data. His role is strictly advisory.

A Professor from the University of Sheffield is also funded as a consultant on the DIPLOMA project, because of his clinical expertise. He will not have access to the data under this Agreement and has purely an advisory role.

The data requested will not be used or accessible to other work packages within the project beyond those mentioned in this data request (Work packages 1, 5 and 7). The anonymous findings (aggregated with small number suppression) may be used to inform the work of other work packages. The data is restricted to only those with type 2 diabetes and those identified as at risk of type 2 diabetes (Non-diabetic hyperglycaemia). Data has been further minimised by only selecting the fields required for analysis.


The following provides additional detail on the three work packages for which the data will be used:

Work Package 1 – Access and Equity:

This work package is interested in the accessibility of the DPP. The work package will use the NDA and NDH data as well as national survey data (accessed via the UK data archive) to explore the characteristics of patients who enter the NHS DPP, and those who are eligible and do not enter. Inequalities in health have been shown across England. There are many reasons why inequalities may exist, but one of the reasons might be because there is inequality in a patient's access to health care. Access may be related to multiple factors: availability of the NHS DPP in the area; awareness of NHS DPP among patients and professionals, acceptability of NHS DPP to professionals and patients; and the perceived ‘costs’ of NHS DPP (such as taking time off work).

To understand any issues in access to the NHS DPP and the impacts of the programme on diabetes incidence, assessments will be made to understand whether inequalities in protected characteristics exist for:
1. the identification of patients eligible for the programme (‘NHS DPP eligible’ patients)
2. the referral of patients to the programme (‘NHS DPP attenders’ and ‘non-attenders’)
3. programme delivery and completion
4. the effectiveness of the programme

The evaluation concerns the population that are non-diabetic hyperglycaemic (NDH), this patient group will start to be collected from 2017-2018 as part of the NHS Diabetes Prevention Programme fields (DPPOFF_COD; DPPATT_COD and DPPCOMP_COD).

The University of Manchester intends to compare the patients identified as non-diabetic hyperglycaemic (NDH) to populations identified in national surveys to identify whether identification of patients is representative of the general population. The national surveys are the UK Household Longitudinal Survey (UKHLS); the Health Survey for England (HSE), and the English Longitudinal Study for Ageing (ELSA). These population surveys are accessed via the UK Data Archive and are not controlled by NHS Digital.

Comparisons of uptake and completion of the NHS Diabetes Prevention Programme, by patient characteristics will inform whether uptake and completion of the programme differs by certain types of patients.

The data will be analysed in order to measure differences in the prevalence of prediabetes (NDH) by age-groups, gender, ethnicity and area deprivation, as well as differences in the treatment availability and uptake by age-groups, gender, ethnicity and area deprivation. In order for this to occur, pseudonymised patient-level data is required.

There will be two main strands of analyses:
1. Identifying the population with NDH
The data will be used to describe the characteristics of patients with NDH. This will involve presenting the share of patients in the data by the following patient characteristics:
[AGE]: Year of age
[DERIVED_CLEAN_SEX]: Gender
[IMD_QUINTILE]: IMD quintile
[DERIVED_CLEAN_ETHNICITY]: Ethnicity
[LD]: Learning disability flag*

*The learning disability flag is required as this is an NHS protected characteristic. As such, and in line with the other protected characteristics requested such as ethnicity, age etc., the analysis seeks to understand whether access to the DPP is associated with this characteristic.

The unit of analyses will be individual. The distribution of people with NDH by their age, gender, IMD deprivation quintile, ethnicity and disability status will be examined. Comparisons of these distributions with data from three population survey datasets (UKHLS, HSE and ELSA) and to those patients under the NHS diabetes prevention programme (the ‘Minimum DataSet’/MDS). The datasets will be pooled and statistical tests will be conducted to ascertain whether distributions of people by age, gender, IMD deprivation quintile, ethnicity and disability differ between the datasets. The analytic approach will not involve linking individuals across datasets.

Additional analyses will stratify patients with NDH on the basis of various clinical measures concerning blood pressure, body mass index, and cholesterol to further see whether survey data is a good approximation of the population with NDH.

These additional analyses will inform whether certain types of patients with NDH are over- or under-represented in the NDA/NDH data in comparison to survey data.

2. Identifying whether inequalities in programme participation exist by patient characteristics.
Additional analyses will test for associations between NHS Diabetes Prevention Programme participation and explanatory variables.

The distribution of NHS Diabetes Prevention Programme outcome variables by patient characteristics (age, gender, ethnicity, IMD deprivation and disability status) and additional explanatory variables would be processed in order to see whether there are particular groups of patients ceasing to attend and complete the programme.

The University of Manchester request years 2017/18 and 2018/19 of NDA and NDH data to carry out this work with the linked Minimum Dataset. The linked datasets will be pseudonymised as none of the datasets contain patient identifying information.

To ensure comparability with survey data the full geographic spread of patients in the data are requested. No alternative data would enable the proposed evaluation to be performed. This is because the data requested covers all general practice. In addition, the analysis requires individual-level data to enable comparisons of patients by age, gender, ethnicity, IMD deprivation, and disability status.


Work Package 5 - Comparative Effectiveness:
Work Package 5 seeks to examine whether the NHS DPP achieves its primary objective of reducing the prevalence of Type 2 Diabetes and other outcomes related to Type 2 Diabetes such as hospitalisation, primary care visits, HbA1c and BMI/weight levels, and death, compared to those without access to the NHS DPP. To do this, the University of Manchester will use NDA/NDH data for the years 2017/18 and 2018/19 (from when the NHS DPP programme has been implemented) of NDA/NDH data, with the linked Minimum Dataset. The DPP is implemented nationally and therefore data is required at national level, with regions where the NHS DPP was not implemented or not part of Wave 1 being used as comparators.

The conversion rate from non-diabetic hyperglycaemia (NDH) to Type 2 Diabetes will be compared between people referred to the NHS DPP, those not referred and those declining referral. Matched cohort designs will be used to compare the rates of conversion to diabetes within a year of NDH diagnosis, across these three groups. We intend to match both within practices and also across practices (accounting for their time of participation in the DPP) to better account for unmeasured confounding. Preliminary work will examine overall conversion rates and the prevalence of NDH nationally and regionally.

Specific Aims:
1. To estimate the prevalence of Non-diabetic hyperglycaemia (NDH), i.e. people at risk of diabetes.
2. To identify the characteristics associated with NDH (e.g. deprivation, age, sex).
3. To calculate the conversion rate from NDH to Type 2 Diabetes, nationally and regionally.
4. To assess whether the NHS DPP is more effective than usual care in reducing conversion of NDH to diabetes, thus eventually reducing diabetes prevalence in England.

CPRD data will also be used for this comparison. However, CPDR data does not provide national coverage and the numbers are relatively small. The data will not be linked to anything at the patient level. At the practice level data will be linked to practice characteristics that are freely available through NHS Digital and the ONS: practice list size, rural/urban, practice location deprivation (IMD 2015). There is no additional identification risk as linkages are not at the patient level.


Work Package 7 – Economic Evaluation:
Work Package 7 aims to explore the cost-effectiveness of the NHS DPP, from the perspective of the NHS and Personal Social Services. It is important to evaluate if the NHS DPP represents good value for money, as the resources to provide healthcare are limited, and so it is important that funds are allocated to the most effective intervention or prevention strategies.

To evaluate the cost-effectiveness of the NHS DPP, the analysis will take on two main strands:
1) An economic evaluation using observed data from the MDS and
2) A long-term decision analytic model, evaluating the theoretical impact of the NHS DPP over the lifetime of participants, compared to those who do not enrol in the programme.

The NDA/NDH data is required to inform parameters in the long-term decision model. Decision-analytic models are frequently used within economic evaluations, as often the benefits of a programme are not entirely captured within the relatively short time horizon that data is captured. Therefore, decision-analytic models are used to extrapolate beyond observed data, to evaluate final outcomes which are of economic interest. This often involves modelling a hypothetical cohort over a lifetime time horizon. Inputs into decision-analytic models include parameters such as the probabilities of different events occurring (referred to as transition probabilities), the costs associated with events or resource use, and utility scores, which can be used to estimate quality adjusted life years.

Whilst several models evaluating prevention strategies for Type 2 diabetes have been developed, there is currently no model that evaluates the NHS DPP, and as such data from the NDA/NDH is required to inform parameters, such as the number and characteristics of patients being offered the NHS DPP and the uptake rate. Information on the time taken to develop Type 2 diabetes is also needed to produce survival/time to event analysis curves, and use these rates to calculate transition probabilities, to be used within the model. As is standard, (according to the NICE Reference Case), the benefits of the NHS DPP will be evaluated in terms of the quality adjusted life years generated by the programme, in comparison to not attending. As such, the data within the NDA/NDH will be used to produce quality of life estimates using a validated mapping tool and regression analyses to predict EQ-5D index values from the data.

The NDA is the only dataset with national coverage of those diagnosed with NDH. CPRD is no longer reflective of the general population, and not all GP practices that have participated in the NHS DPP are captured within it. The required information is also not available within the MDS, as this dataset only has information on patients who have taken up the invitation to enrol on the programme, and therefore does not inform the number, or the characteristics of those eligible (patients with non-diabetic hyperglycaemia) nor the number of people who were offered but refused to attend (available in the NDA by looking at the differences between numbers offered and accepted). The publicly available extract of the NDA is insufficient in the data it provides, as it does not provide the characteristics of those offered the NHS DPP, only the summary characteristics of those diagnosed with NDH, across limited variables, such as BMI and age groups.

Therefore, pseudonymised patient level data for the years 2017 to 2019 are required, with the linked Minimum Dataset. This linkage will facilitate the analysis of the programme, showing the characteristics of the eligible population (from the NDA/NDH), the subset within this who have participated in the NHS DPP, along with information on their adherence to the programme (from the MDS). As the NHS DPP has been rolled out across England, NDA data from across the whole of England is required.

Yielded Benefits:

Researchers at the University of Manchester were able to access the data on the 3rd November. As such, the University has only been able to work on formatting and validating the dataset, preparing for preliminary analyses. At this stage the University has not been able to produce any of the outputs listed within this agreement and as such has been unable to realise any of the expected benefits from this processing. However, the University still expects to meet the expected benefits described in this agreement.

Expected Benefits:

Access to the data requested will enable the University of Manchester to carry out the DIPLOMA research project. This project will evaluate the efficacy of the nationally implemented NHS Diabetes Prevention Programme. It is important to know whether the national programme is effective, to ensure that NHS funds are being used effectively, and that patients have access to the best diabetes prevention care.

There are considerable health implications of type 2 diabetes; in terms of both symptoms, cardiovascular health complications, and costs.

Evidence suggests that making changes to lifestyle behaviours which reduce weight, such as increasing physical activity, can decrease the risk of non-diabetic hyperglycaemia developing into type 2 diabetes by 50%.

However, the asymptomatic nature of non-diabetic hyperglycaemia means that people often go undiagnosed and untreated, therefore remaining at a higher risk of developing type 2 diabetes.

Providing accessible, non-pharmacological support using proven behaviour change techniques to improve lifestyles and prevent future disease is fully aligned with the priorities of the NHS Five Year Forward View. Better lifestyle (especially weight loss) could have a profound effect on diabetes outcomes, and the benefits could well generalise to other conditions.

Outputs:

The purpose of the data processing activities performed on these data is to produce statistical outputs to be used in peer-reviewed journal articles, presentations and reports for the DIPLOMA research programme. These statistical outputs will be of three types: 1) descriptive tables summarising the data, 2) graphs and figures summarising the data, and 3) regression results tables. Outputs will contain only data which is aggregated, with any small numbers suppressed in line with the following disclosure control policy (applicable if one of something is necessarily one person e.g. length of stay – one day as an inpatient can only relate to one person in many data sets):
• 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.

The overall research project includes a number of work packages. The number of outputs, in terms of articles/presentations/reports, will be determined by the findings of the analysis and by the academic peer-review process. There is a PPI (Patient and Public Involvement) forum which will be engaged in an advisory capacity in producing these reports and their dissemination, they have also provided guidance on the fair processing statement, which will need to be published following approval of the NDA/NDH data request. The work packages each have aims to output the results of their analyses:

• Work Package 1 aims to produce a series of reports and journal articles investigating social inequalities in NDH and how they are affected by the NHS DPP. Journals that will be targeted for these publications will be: Diabetic Medicine and British Journal of General Practice. Journal article 1 submission date will be around October 2020.

• Work Package 5 will disseminate the projects outputs through a variety of media, including conference presentations and conventional academic publications, seminars and short accessible reports for stakeholders, and plain English summaries and podcasts for patients and the public. The research team are experienced at writing for a wide variety of audiences. We will work closely with our Stakeholder Advisory Group and the Research Advisory Group to maximise the utility of our dissemination. We also plan to disseminate the research findings by publications in peer-reviewed journals and presentations in relevant conferences organised by the Society for Academic Primary Care (SAPC), the Royal College of General Practitioners (RCGP), the Royal College of Physicians (RCP) and the Royal Statistical Society (RSS). When reporting, the principles outlined in the STROBE and RECORD statements will be followed. Data will not reported in the outputs only summary statistics (descriptive statistics) and model estimates. We plan to target our outputs from this work package by the end of 12 months from data availability.

• Work Package 7 intends to prepare a report of the findings and recommendations of the cost-effectiveness of the NHS DPP. All reports are submitted to the Director for the NHS Diabetes Programme at NHS England, with the final report expected in March 2021. Results of the analyses will also be presented to the Expert Reference Group, which has representation from the three DPP funders: NHS England, Public Health England and Diabetes UK. A report will also be prepared which outlines the findings of the long-term economic model, which subject to acceptance, will be published in a high impact peer review journal such as Value in Health or Diabetes, Obesity and Metabolism. Presentations will be given at national and international conferences to audiences of academics and health professionals. These conferences will include the Mt Hood Diabetes Challenge Network, Chicago conference 2020, where the long term economic model will be presented, and the Health Economists’ Study Group (HESG) in January 2021 conference to present initial findings of the cost-effectiveness of the NHS DPP (both subject to abstract acceptance).

Reports will be provided for the DIPLOMA research programme on the progress and outcome of the research. Target report publication dates are as follows:
a) The DIPLOMA research programme interim report:
Twice annually (in April and October), subject to data transfer timing, UoM anticipate the October 2020 interim report will contain findings from the evaluation of the requested data.
b) The DIPLOMA research programme final report: March 2021.

Reports and journal articles will be published online and made available to all. Submitted versions of all journal articles will be freely available on the University of Manchester library website.

Processing:

Pseudonymised (NDA/NDH) health data will flow from NHS Digital to the University of Manchester. The data will be stored within the University of Manchester Data Safe Haven (DSH) Service which has been purpose built for the secure management and processing of personal, special category and confidential information. The DSH infrastructure is hosted within an ISO27001 certified Equinix data centre located 4 miles from the University of Manchester (UoM) campus.

The data centre is managed by the owners. However, access to University of Manchester infrastructure within the data centre is physically secured and only accessible by University of Manchester data centre staff. Equinix have no access to the data or infrastructure. All servers, storage and network infrastructure used by University of Manchester within the data centre is owned by University of Manchester, operated and maintained by University of Manchester IT staff and is securely caged off. University of Manchester lease physically secure hosting space from Equinix. No shared services are consumed. Therefore, Equinix is responsible for the physical hosting environment and utilities (standard co-location service, power and cooling).

University of Manchester staff are not based on site, however, if required, will be in attendance at Joule House (Equinix). Physical access to the data centre is strictly limited to University of Manchester data centre staff and a limited number of authorised University of Manchester IT Services staff. The data centre is protected by physical and electronic access security systems, swipe card access in and out of the data centres and CCTV coverage. The data centre is locked down out of hours and access is discouraged but can be arranged by prior agreement with the data centre manager. The data centre manager is substantively employed by the University of Manchester.

The data will be stored and processed within the University’s Data Safe Haven (DSH) environment which provides a walled garden security model via virtual desktop infrastructure and virtual applications. Access to DSH is protected by strict project level access controls and multi-factor authentication.

The workstations used for accessing the University’s Data Safe Haven (DSH) environment do not directly access the data. Access is via Virtual Desktop Infrastructure (VDI) technology to ensure the data is only processed within, and never leaves the DSH virtual environment. Due to this ‘painted screen’ approach, no remnants of the data are ever stored on the client device through mechanisms such as temp files or browser caches. Access is restricted to on-site computers Only. No mobile devices will be used to access the DSH environment.

The principle of least privilege is applied to all projects hosted within the DSH environment. Only a Principal Investigator (PI) or Study IG Lead can move data in or out of the environment and the PI also has to approve researcher access to data within the project.

Role based access control is managed on a per project basis via Active Directory groups. A Standard Operating Procedure (SOP) is in place to govern data transfers by the PI and Study IG Lead: UM/17/SOP/NHSIGTK004 The Data Safe Haven. All other authorised project team members can only process the data within the secure DSH environment. File transfers, copy/paste and print functionality has been disabled within the DSH.

The data will be made available to specified and authorised members of research staff in the Data Safe Haven as described below. Data processing will only be carried out by substantive employees at the University of Manchester who have undertaken the necessary training in data protection and confidentiality.

UoM Researchers granted access to the Data Safe Haven will be able to conduct the analyses proposed above. This will involve loading the data into STATA and conducting multivariable regression analyses and sample summary measures/descriptives of the data. The data will be linked to the Minimum Dataset (as described above). Other than linkage described above, the data will not be linked with any other datasets. There will be no attempt nor requirement to reidentify individuals. No individual-level output will be made available in any dissemination of results either internally within the DIPLOMA team or externally. Results and findings will be of aggregated volumes and percentage shares across multiple strata.

University of Manchester IT Services staff will implement, configure and maintain the security aspects of the DSH environment.

Naevoid melanoma: Comparing prognosis of two subtypes — DARS-NIC-294590-B6V3F

Type of data: information not disclosed for TRE projects

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

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

When:DSA runs 2021-10-07 — 2022-10-06 2021.11 — 2021.11.

Access method: One-Off

Data-controller type: ROYAL SURREY NHS FOUNDATION TRUST, THE UNIVERSITY OF MANCHESTER, ROYAL SURREY COUNTY HOSPITAL NHS FOUNDATION TRUST, THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

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

Objectives:

This agreement relates to a collaborative project with the University of Manchester (Cancer Research UK Manchester Institute) and Royal Surrey County Hospital NHS Foundation Trust which asks for follow-up data for the Naevoid Melanoma Study, a longitudinal observational study that has shown that two different subtypes of naevoid melanomas have distinct clinical, histopathological and immunochemical profiles that may be prognostically significant (Cook et al. 2017). These comprise of papillomatous and maturing naevoid melanomas. Nevoid melanoma is a type of skin cancer. It begins when the melanocytes in the skin grow out of control and form tumours. Melanocytes are the cells responsible for making melanin, the pigment that determines the colour of the skin.

Preliminary clinical follow-up data suggests different outcomes for these two naevoid melanoma.

A cohort of 151 patients that were identified by a Consultant Histopathologist at Royal Surrey County Hospital NHS Foundation Trust as melanomas that have been classified as naevoid melanomas, comprising of two subtypes; papillomatous and maturing, identified retrospectively from the time period 01/04/2004 and 30/06/2017 at Royal Surrey County Hospital NHS Foundation Trust. These 151 patients are the only cohort of patients that will are looking at.

This research is very important and is hoped to directly benefit patients and their families. One of these subtypes of naevoid melanoma requires very minimal follow up whereas the other subtype can require extensive follow up and treatments involved over a patients lifespan. This study will increase the precision of diagnosis and positively influence the different managements of these subtypes of melanoma.

It is in the public interest to obtain the full follow up data of these 151 patients that have been observed, leading to improved understanding of disease and opportunities for improved treatment resulting in lives saved and improved health.

NHS Digital Civil Registration (Deaths) data is required to inform the current status (alive/dead) and dates and causes of death. This data specifically informs the prime question of the study, helping to identify prognostic indicators (within causes of death) and the time from diagnosis until death (all-cause mortality), an important measure of health status and outcome). This data needs to be provided for the specific 151 individuals (by linkage through NHS number with verification by month/year of birth) to allow appropriate data for personal level risk model development and evaluation. As a longitudinal study, it is important to follow as many subjects as possible until death, the plan will be to ask for data on all patients that were diagnosed as naevoid melanoma by a Consultant Pathologist at Royal Surrey County Hospital NHS Foundation Trust between 2004- 2017. Therefore data will only include patients that have been diagnosed at Royal Surrey County Hospital NHS Foundation Trust within this time period. Alternative methods of assessing mortality status (e.g. re-contacting individuals) would be either more intrusive (likely to cause harm/distress), less informative or incomplete.

Patient identifiable information has been collected and used for linkage directly from subjects (NHS Number, Date of Birth, First Name and Surname) and a unique Study ID is added . NHS Digital use these identifiers for linkage to NHS Digital data sets to extract the required data, and then remove the identifiers, leaving only the Study ID and Data returned is subsequently linked only via the unique Study ID.

It is proposed to access Civil Registration (Deaths) data to ascertain who among the naevoid melanoma cohort has died. Since this is a retrospective cohort, consent cannot be gained from these deceased participants, but the same global reasons pertain to them as for those still alive for not obtaining consent as explained above and to the canvassed melanoma patients, CEO of Melanoma UK and CAG. The Melanoma UK charity supports the use of melanoma patient information in this study and believes the use of the data is appropriate for the achievement of the aims of the study.

A proportion of the study patients with naevoid melanoma will have died since diagnosis, either from their melanoma or unrelated causes. Since the death of a patient is not known since diagnosis, this needs to be ascertained through the mortality data.

It is essential that it is ascertained that they have died and their cause of death at the outset for the following reasons:

1) It is hugely labour-saving. Immediately tell which of the study patients has died and their cause of death, saving many months of work in tracing and contacting multiple doctors per patient and seeking responses about vital status and details of death (if applicable).

2) It advances the time of project completion by directly ascertaining one of the main study outcomes: melanoma-specific death in the study cohort.

3) There is no undue extra work requested by project staff and potentially many past doctors about their patients in the study if date of death of those patients is known. This would also eliminate the distress caused to the participants family, if by contacting a study candidate without knowing if they have died

Royal Surrey County Hospital NHS Foundation Trust will be joint Data Controller with the University of Manchester (Cancer Research UK Manchester Institute). Royal Surrey County Hospital NHS Foundation Trust and the University of Manchester (Cancer Research UK Manchester Institute) will also process the data.

The work undertaken by Royal Surrey County Hospital NHS Foundation Trust is funded by Cancer Research UK (CRUK). Their role is to provide funding for the collaborative research between Royal Surrey County Hospital NHS Foundation Trust and CRUK Manchester Institute. They also support and encourage adherence to the highest standards of governance, public dissemination of results and scientific rigour. However, their role does not include a part in the decision making regarding the study methodology, purpose or what data to collect and thus they are not considered a Data Controller.

The data will be minimized by a linkage to identifiers of the 151 patients from Royal Surrey County Hospital NHS Foundation Trust linked to limited fields in the Civil Registration (Deaths) data extract in a single drop of data.

Both the University of Manchester and Royal Surrey County Hospital NHS Foundation Trust rely on the GDPR Article 6(1)(e) for the legal basis for processing data (processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller). Additionally, as health data is a special category of Personal Data, both Data Controllers rely on 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).

Safeguards are in place that protect the interests of the data subject, these have been judged as proportional to the substantial public interest, including approval by Confidentiality Advisory Board (CAG) under section 251 of the NHS Healthcare Act 2006 (which together with consent also safeguards the common law duty of confidentiality). Safeguards include physical and operational barriers protecting privacy and allowing processing of personal identifying and sensitive data only by staff contractually obliged to fulfil the requirements set out by Royal Surrey County Hospital NHS Foundation Trust Data Protection Policy, compliant with all laws and approvals. There is no alternative way of achieving this purpose of data collection for the study.

Expected Benefits:

Looking at the differences between the prognosis of these subtypes aims to allow clinicians to better treat and manage these patients clinically.
Obtaining the outcome data of 151 patients with known naevoid subtypes hopes to better inform future treatment and knowledge of these lesions in relation to patient care.

This study has the potential to change the way patients with these two subtypes of naevoid melanoma are managed in the patient care pathway. The study hopes to benefits patients and also minimise costings to the NHS through better management of patients associated with their prognostic indicators of their melanoma. One of these subtypes of naevoid melanoma requires very minimal follow up whereas the other subtype can require extensive follow up and treatments involved over a patients lifespan. This study hopes to increase the precision of diagnosis and positively influence the different managements of these subtypes of melanoma.

This outcome data aims to support the main data collection, which has already been collected. The aim is for the data to be analysed and disseminated by the end of 2021.

The impact of the results of this study is envisaged to be measured retrospectively by reviewing naevoid melanoma cases to see if treatments relate back to the outcomes for patients for the two different subtypes.

Depending on the findings of this study, this can be translated into the clinical setting with the aim to make changes to patient management corresponding to their subtype. This would be actioned within a suitable timeframe when the minimum 2 year follow up is achieved.

Outputs:

It is intended to report and disseminate the results of this study through submission to peer reviewed high impact factor dermatological and histological journals (Cancer, Histopathology, Journal of Dermatology etc) and presentations at health-related conferences. The study hopes to present its findings at conferences attended by the Senior Research Scientists and the Project Officer.

When publishing results, only aggregate patient data with small number suppression as per the HES analysis guidance will be presented. No patient identifiable data will be published or presented.

Patient and Public Involvement (PPI)
The study team - during the study - aim to inform patients and the public of this research through the Royal Surrey Research and Development website and the CRUK Manchester Institute website and associated other media platforms. It will be made clear on the study website that any registered objections will be upheld.
The results will also be made available in the relevant GP surgeries and hospital. Patient Information sheets on the study will also be displayed in Royal Surrey Couty Hospital and GP surgeries.

Outputs aim to include;
1. Reports and Publications - It is anticipated that the analysis from this study will be included in internationally renowned oncology, epidemiology and public health journals. A publication will be prepared for late 2021 and will be available on the study website and PubMed. PubMed is a free resource supporting the search and retrieval of free full-text archive of biomedical and life sciences journal literature and is used for literature reviews related to research. The PubMed database contains more than 32 million citations and abstracts of biomedical literature. The PubMed database can be found here: https://pubmed.ncbi.nlm.nih.gov/.

Journals for consideration include:
Cancer
Histopathology
Journal of Dermatology
Scientific Reports
Oncology Letters

More information on past publications can be found on the study website: https://www.cruk.manchester.ac.uk

2. Presentations - The study team aim to give presentations at major cancer conferences for this study. Conferences are expected to include:

American Association for Cancer Research Annual Meeting
National Cancer Research Institute Annual Meeting
European Society of Medical Oncology
European Organisation for Research and Treatment

Scientists, health professionals, policy makers, e.g: NICE, Royal College of Physicians, British Association of Dermatologists are hoped to attend the presentations and read the study papers which will promote the study findings.

The primary aim of this study is to investigate whether there is a difference between two subtypes (papillomatous and maturing) of naevoid melanoma. If it is found that there is a prognostic difference between these 2 subtypes then treatment management plans and pathways can be reviewed that - it is hoped - will benefit the patient and healthcare system.

Processing:

Processing activity and storage of personal and sensitive (special category) data is limited according to Royal Surrey County Hospital’s best practice (both in terms of scope and timing), but recognising that longitudinal studies require long-term data linkage and that research studies require anonymised and pseudonymised data to be available for a significant amount of time.

The flow of data into NHS Digital is limited to personal identifying information required for linkage (NHS number) and for verification of that linkage: Unique Study ID, Surname, Forename, Date Of Birth, and NHS Number.

1. Royal Surrey County Hospital NHS Foundation Trust provide a cohort of 151 study participants, to include identifiers: Study ID, NHS Number, Date of Birth, First Name, and Surname via Secure Electronic File Transfer (SEFT) service.

2. NHS Digital applies National Data Opt out, and then extract the Date of Death and Cause of Death fields for 151 participants, remove identifiers, and provide pseudonymised data extract back to Royal Surrey County Hospital NHS Foundation Trust via Secure Electronic File Transfer (SEFT) service.

Royal Surrey County Hospital NHS Foundation Trust have obtained support under section 251 of the NHS Act 2006 from CAG to permit the processing of confidential data without consent.

Data Minimisation
Royal Surrey County Hospital NHS Foundation Trust aim to follow up the outcomes of patients who have been diagnosed with one of two distinct subtypes of naevoid melanoma, in order to assess possible important differences in prognosis between them. This knowledge will help guide treatment of such melanomas in future patients with these subtypes.

For this study Royal Surrey County Hospital NHS Foundation Trust have been granted access to patient identifiable data (patient’s name and hospital number) without patient consent (under section 251), because results of this project will have no bearing on these patients’ treatments or outcomes. In many instances the melanoma was diagnosed long in the past and indeed many of the patients may have already passed away. Hence the need to ascertain who in the naevoid melanoma cohort has died so that Royal Surrey County Hospital NHS Foundation Trust know not to send letters to their doctors asking about their health and if their melanoma is still active (has recurred) (second step for those not known to have died).

In order to obtain the first step of these novel clinical outcome data, exact month and year of death is needed for precise analysis of survival time since age at death is too imprecise (age at death is compatible with a 2-year range of dates of death which would introduce substantial error).

Causes of death are also critical for the study team as the study must be able to distinguish melanoma-related deaths from deaths due to other causes. This will allow the study team to confirm the relative aggressiveness of the two different subtypes of naevoid melanoma: they expect the suspected aggressive naevoid subtype will cause melanoma deaths, the maturing subtype will not.

The NHS Digital pseudonymised data extracts are downloaded directly from the secure electronic file transfer (SEFT) system to an encrypted drive on a dedicated Royal Surrey County Hospital NHS Foundation Trust NHS N3 server computer. This computer is only accessible to a limited number of qualified staff and is password protected within a managed environment.

Data supplied by NHS Digital is processed for inclusion into the naevoid melanoma database. The data is linked at patient level with data in the naevoid melanoma database using the subject specific unique Study ID only. When the data is received from NHS Digital, the date of death and cause of death of these patients will be obtained. This will then be correlated with their subtype of naevoid melanoma to determine whether this outcome data is prognostically significant.

Royal Surrey County Hospital NHS Foundation Trust will then send the pseudonymised record-level data on to the University of Manchester (Cancer Research UK Manchester Institute) via secure encrypted NHS Mail as per the NHS Digital Secure Email specifications. The pseudonymised data extracts are downloaded directly to the Data Safe Haven (DSH) environment at the University of Manchester and further processing is conducted only on this server.

The University of Manchester’s Data Safe Haven (DSH) environment provides a walled garden security model via virtual desktop infrastructure and virtual applications. The DSH has been purpose built for the secure management and processing of personal, special category and confidential information.

Access to DSH is protected by strict project level access controls and multi-factor authentication.

The principle of least privilege is applied to all projects hosted within the DSH environment. Only the PI and Study IG Lead can move data in or out of the environment and the PI also has to approve researcher access to data within the project.

Role based access control is managed on a per project basis via Active Directory groups. A Standard Operating Procedure (SOP) is in place to govern data transfers by the PI and Study IG Lead: UM/17/SOP/NHSIGTK004 The Data Safe Haven. All other authorised project team members can only process the data within the secure DSH environment. File transfers, copy/paste and print functionality has been disabled within the DSH.

Statistical data analysis will be carried out via University of Manchester owned remote device connected to the Data Safe Haven 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.

There will be no attempt to re-identify individuals nor any linkage to other data sets not already described in this agreement.

The ultimate flow of data is into publications made publicly available for the benefit of the wider research community. Special care is taken to ensure confidentiality is maintained and re-identification is not possible. All outputs will be aggregated with small number suppressed as per the HES analysis guide.

After processing is complete, the pseudonymised data on the Royal Surrey and Data Safe Haven at the University of Manchester will be destroyed and one copy of the data stored for archiving purposes on a secure data network at the CRUK Manchester Institute Molecular Oncology for a period of 12 months before being destroyed. This network is only accessible to authorised University of Manchester study staff and is password protected. Data will be located in folders that have secure access permissions for named study staff only.

All hardware is in secure environments: servers within the Institute and computers within research buildings with swipe-access control. Procedural control ensures that computers are never left accessible. All data is stored on servers (rather than individual computers/held on the M drive) and is backed-up routinely with the same level of protection (i.e. the encrypted server has an encrypted back-up).

The DSH infrastructure is hosted and processed within an ISO27001 certified Equinix data centre located geographically separate from the University of Manchester (UoM) campus. All server, storage and network infrastructure used by UoM within the data centre is owned by the University of Manchester, and operated and maintained by University of Manchester IT staff and is securely caged off from other Equinix customer’s equipment. The University of Manchester lease physically secure hosting space from Equinix. Therefore, any access to the data held under this agreement by Equinix would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Additionally, encrypted data back-ups are hosted and securely stored in a Digital Reality data centre located geographically separate from the University of Manchester (UoM) campus. All server, storage and network infrastructure used by UoM within the data centre is owned by the University of Manchester, and operated and maintained by University of Manchester IT staff and is securely caged off from other Digital Reality customer’s equipment. The University of Manchester lease physically secure hosting space from Digital Reality. Therefore, any access to the data held under this agreement by Equinix would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

No patients will be contacted and consented about the study as this would cause more harm and distress to patients. Therefore again no patients or relatives will be contacted that have deceased for the same reasons. The Confidential Advisory Group have been approached with this application and has have been given favourable approval to complete this study without approaching patients for their consent.

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:
• 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.

Trauma Audit and Research Network HES and Civil Registrations Mortality application — DARS-NIC-338773-H5J5S

Type of data: information not disclosed for TRE projects

Opt outs honoured: Identifiable, Anonymised - ICO Code Compliant, No, Yes (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 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(7), 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 2020-12-22 — 2023-12-21 2021.06 — 2021.10.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

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

Objectives:

The Trauma Audit and Research Network (TARN) is a non-commercial organisation affiliated with the University of Manchester and funded by membership fees from participant Trusts that submit data. TARN is the mandated organisation for the audit of trauma care in England, as set out in and endorsed by the Information Standards Board (ISB) 1606. Data submission is mandated by NHS England for Hospitals to be considered as trauma units / major trauma centres. TARN holds Europe's largest database of traumatic injury, with Hospitals within participating Trusts entering details of relevant cases into TARNs online system. Any Trusts receiving trauma patients are eligible to submit data.

The aim of TARN is to support service improvement by providing analytical feedback to Trusts, in reports such as the major trauma dashboards. TARN supports the funding mechanism Best Practice Tariff. The TARN site hosts the ‘Best Practice Tariff’ report, which major trauma centres use to report to commissioners and receive payment. To ensure that analyses are as accurate as possible, TARN needs to ensure that its dataset is as complete as possible.

TARN requires pseudonymised and identifiable HES data to measure completeness of submission to the TARN database for individual Trusts and Hospitals. Trusts are fed back results for their individual Trust, via the secure NHS email account. Identifiable HES data will help Trusts (via TARN) ensure all cases that are eligible for payment are submitted and can be reported on.

3 years of HES pseudonymised and identifiable HES data would greatly assist in the execution of the outlier policy and will ensure greater accuracy in the outcomes. When monitoring outliers two complete years of TARN data are examined to monitor performance over time. Any positive or negative outlier Hospitals are contacted, and a data quality review is performed. Underpinning this process is a review of the number of cases a Trust has submitted vs. the expected number based on HES data. Whether a Hospital has submitted all deaths based on HES for those years is also reviewed. Having the data for 3 years will increase the accuracy of the outcomes and allow this process to be executed effectively and efficiently as currently data is obtained from the Hospital IT departments and analysed for TARN eligible cases.

Case ascertainment is also used in published TARN reports, the Severe Injury in Children’s report and the Major Trauma in older persons report. Both available to view https://www.tarn.ac.uk/Content.aspx?ca=4&c=3572.

Healthcare Quality Improvement Partnership (HQIP) National Clinical Audit Benchmarking (NCAB) is an online publication and is a key example of how case ascertainment is used to inform performance (https://ncab.hqip.org.uk/reports/card/audits/TARN/), an example of case ascertainment based on information from TARN has been provided to NHS Digital. Case ascertainment is also used to inform the national Major Trauma Peer Review process to ensure this information is as accurate as possible TARN needs to ensure its dataset is complete as possible.

Recent projects completed by TARN have also used Case ascertainment. There are 4 recent projects (2 published, 1 accepted and 1 ongoing) where case ascertainment has been used 1 - Time to definitive care within major trauma networks in England
N. R. Haslam , O. Bouamra, T. Lawrence, C. G. Moran and D. J. Lockey . BJS open 2020. DOI: 10.1002/bjs5.50316 2 - Changing the System - Major Trauma Patients and Their Outcomes in the NHS (England) 2008–17. Christopher G. Moran , Fiona Lecky , Omar Bouamra , Tom Lawrence , Antoinette Edwards , Maralyn Woodford , Keith Willett , Timothy J. Coats. EClinicalMedicine 2–3 (2018) 13–21. https://doi.org/10.1016/j.eclinm.2018.07.001 3 - National Temporal Variation in Major Trauma. Will Kieffer, Daniel Michalik, Jason Bernard, Omar Bouamra, Benedict Rogers. Trauma (Sage publication). In press. 4 – Ongoing Older person trauma. Will Eardley. To ensure all analyses are as accurate as possible, TARN needs to ensure that its dataset is complete as possible.

TARN requires identifiable Civil Registrations Mortality data to supplement data already collected (concerning the acute phase of care for patients that have suffered a traumatic injury). TARN need to determine the outcome of patients post discharge. Civil Registrations Mortality data will be used as part of a statistical model to determine rates of survival at 30 days which is then fed back to Hospitals. TARN can then compare the results of this model to another which considers outcome at discharge and possibly determine the efficacy of post discharge support. Furthermore, measuring mortality in this fashion is in line with other mortality assessments such as the Summary Hospital-level Mortality Indicator (SHMI). The information may be fed back to the treating Hospital at patient level to provide individualised feedback in the context of their injuries and treatment, any feedback will be done using the secure NHS email address.

TARN require identifiable HES and Civil Registration Mortality data. Identifiable HES and Civil Registration Mortality data may possibly be shared, at patient level, with the Hospital who originally treated the patient to allow individualised feedback in the context of the patient’s injuries and treatment. Any feedback at patient level will be done using the secure NHS email address.

TARN has developed an outcome prediction model using a 30-day cut-off for mortality/survival of patients. Patients discharged before the 30-day cut-off are considered Alive even if they died after their discharge. To avoid an overestimation of survival TARN seeks a “true” outcome within 30 days, this requires a data linkage using the NHS number of TARN patients, with Civil Registration Mortality data to obtain the outcome. Primarily, NHS Digital data will be used as part of a statistical model to determine rates of survival at 30 days which is then fed back to Hospitals.

The combined (identifiable and pseudonymised) HES and identifiable Civil Registration Mortality data will provide all information needed to complete this project.

The University of Manchester is the Data Controller who will also process data. There are no commissioners involved, data will only be accessed and processed by substantive employees of The University of Manchester.

This project relies on Article 6 (1) (e) and Article 9 (2) (j) as the GDPR legal basis for processing purposes. ’Processing of personal data “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”. 9 (j) for research purpose is used as 9(i) cannot be used as TARN is not a health professional. Special category 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. TARN is the mandated organisation for the audit of trauma care in England, as set out in and endorsed by Standard ISB 1606.

TARN requires HES data to measure completeness of submission to the TARN database for individual Trusts and Hospitals. The HES data will be stored and processed entirely in the Data Safe Haven. The Extract for the HES V TARN comparison will be password protected and emailed out to the individual hospitals via the secure NHS email account. In this way the identifiable HES extract will not be available on the TARN website. HES data helps TARN to help Trusts ensure that all cases that are eligible for payment are submitted and can be reported on.

Yielded Benefits:

Taken from NIC-326033: Following the provision of patient lists linked to the TARN dataset there have been significant improvements in case ascertainment where hospitals have engaged with the information, ranging from 5 to 50% with an average of 20%. Significant improvements in case ascertainment continue to be seen at trusts that have engaged with the TARN/HES data. Those that have done so having an average case ascertainment rate of over 90%, compared to 70% for those that have not. One example is Birmingham Children’s hospital, a children’s major trauma centre – they used the TARN/HES data to identify an extra 42 cases, improving their data completeness from 81 to 94%. As they’re a major trauma centre these additional cases will also have attracted best practice tariff payment. Another example is Sunderland Royal Infirmary, prior to using the TARN/HES data they felt that they were identifying all of their eligible cases, however their case ascertainment rate was consistently around 80%, making use the data they found a tranche of patients admitted to a medical ward that they were inadvertently missing . After submitting these cases their data completeness rose considerably. The combined TARN/HES data has also proven to be a useful tool for responding to outlier surveillance. It can be used to determine if a site is failing to submit a particular cohort of patients (e.g. over 70s, died etc.) and case ascertainment figures are used to inform the reliability of a given sites outlier status.

Expected Benefits:

The aim of TARN is to improve patient care. TARN carries out several planned and ad hoc analyses for hospitals and trusts to highlight good performance and areas where performance could be improved. A key element of TARNs work is analysing rates of survival to identify hospitals with excess deaths, based on injury and demographic profiling. This work is dependent on the quality/accuracy of information emanating from the trusts so the use of HES data to monitor and improve completion rates contributes towards this overall aim. Using HES data to calculate data completeness assists in determining whether apparent poor performance is related to poor data collection, or whether other issues exist that need to be examined further.

Using HES data allows TARN to derive a denominator of expected cases, which in turn allows TARN to identify which sites may be missing patients. TARN can then work with those sites on case identification. An additional benefit of improving case identification is Best Practice Tariff, where Major Trauma Centres receive payment for meeting national standards on a patient by patient basis. The notification to trusts of potentially eligible patients that were not reported by the provision of patient lists and linked to the TARN dataset using NHS number has resulted in significant improvements in rates of case ascertainment. Improvements ranged from 5 to 50% with an average improvement of 20%.

Information recorded on TARN is regularly fed back to trusts in the form of reports and analyses, these may then be used to identify areas of concern and/or best practice in the process of care. The reliability of the reports and analysis is directly related to the proportion of the relevant population they represent. In instances where a low proportion of the population is represented within analysis; bias may be introduced or important results missed.

The Case ascertainment and Survivor/Death ratio derived from the HES data therefore enable TARN to validate the information TARN receive from Hospitals, by effectively putting a Data Quality stamp on each Hospital’s reports.

3 years of HES Data would greatly assist TARN in the execution of TARNs outlier policy. When monitoring outliers TARN look at 2 complete years of TARN data and monitor performance over time. TARN will then contact any positive or negative outlier hospitals and perform a data quality review. Underpinning this process is a review of the number of cases a Trust has submitted vs the expected number based on the HES data. TARN will also review whether a hospital has submitted all deaths based on HES for those years. Having the data for 3 years will allow this process to be executed more effectively and efficiently as currently TARN must get the data from the hospital IT departments and then analyse it for TARN eligible patients.

Outputs:

TARNs outputs relevant to this application are:
Case ascertainment measures at site level which are published in the Major Trauma Dashboard. Circulated securely and confidentially to Major Trauma Centres in February, May, August, and November. The Dashboard measures were drawn up and agreed by clinical experts on the Clinical Reference Group (CRG) to allow effective benchmarking in relation to specific measures between Major Trauma Centres.

Case ascertainment measures at site and Trust level which are published in the TARN clinical reports. Circulated securely and confidentially to TARN member Hospitals in March, July, and November. Case ascertainment measures contain no identifiable or record level personal data. All published data is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Case ascertainment at site level which are published in the TARN clinical reports. Shows comparison to the target of 80% case ascertainment and compares them to last year. These are circulated securely and confidentially to TARN member Hospital in March, August, and November

The Civil Registration data will be used as part of the TARN clinical reports to provide a true 30-day outcome of patients. Circulated securely and confidentially to TARN member Hospitals in March, July, and November. The Civil Registration data will also be used as part of the TARN Older Persons and Children's reports.

Outcome (alive or dead) at 30 days from injury has historically been used in the calculation for Ws. However, many patients are discharged before this 30-day point. To include these patients, TARN need to know whether patients died at or before the 30-day point after leaving Hospital. To do this, TARN need to receive information about post discharge deaths from the Civil Registry and use this information in one of the calculations of Ws for Hospitals.

Processing:

TARN requires HES data from NHS Digital for patients with at least one ICD-10 code relating to traumatic injury. Any of these patients are potentially eligible for the TARN audit, which is concerned only with traumatically injured patients.

TARN does not audit all traumatically injured patients: TARN further processes the received data to create a reduced cohort that correlates with TARN inclusion criteria. Records would be linked to Hospitals and Trusts using Organisation Data Service (ODS) codes. The purpose of this is to derive a number of TARN-eligible cases for each participating Hospital and Trust, which is then used to assist Hospitals with case identification and to measure completeness of data.

TARN requires 2 different copies of HES. Pseudonymised HES is required as this extract will not have patient objections applied and as such will be a complete record of all the cases which will enable to audit to calculate true case ascertainment figures for each of the eligible trusts.
Identifiable HES is required as the extract will be used to notify Trusts of potentially individual eligible cases that were not reported to the Audit, via the secure NHS email.

PSEUDONYMISED EXTRACT
• Containing no identifiable information from which no data will be removed because of national patient opt-out. The two pseudonymised and identifiable extracts will not contain common identifiers that could be used to link them.
• The dataset will be filtered by ICD-10 codes to identify cases that appear to be eligible for the TARN audit
• The pseudonymised HES dataset containing all potentially eligible episodes will be used to determine the total number of eligible cases used in data completeness (case ascertainment) calculations

IDENTIFIABLE EXTRACT
• Containing NHS Number for all eligible patients and excluding data for all patients who have opted-out of having their data shared. Data filters applied to limit data to specific primary or secondary diagnoses relating to traumatic injury 2 extracts requested. Datasets will be filtered by ICD-10 codes to identify cases that appear to be eligible for the TARN audit.
• The two pseudonymised and identifiable extracts will not contain common identifiers that could be used to link them
• The HES data required is the case (submission) ID, TARNs own data will be filtered to this. TARN will use the identifiable NHS number for matching, but the NHS number will then be deleted immediately.
• The identifiable HES dataset containing NHS number will be used to notify Trusts of potentially eligible cases that were not reported by linking to the TARN database using NHS number. This is achieved through the provision of lists of patients (containing NHS number, arrival date, age, length of stay in Hospital, discharge destination and the first 5 ICD-10 codes) sent securely and confidentially to the Hospital that provided the treatment. Only data relevant to the specific Hospital is sent to each Hospital via secure NHS email address and no provider will receive data about individuals for whom they did not provide treatment. Data will be only sent via NHS email address to confirmed users of the TARN EDCR, with access to that hospital, to another NHS email address. In this way no NHS Trust will receive data that did not originate from them.
The information may be fed back to the treating Hospital at patient level to provide individualised feedback in the context of their injuries and treatment.

TARN also requires Civil Registration deaths secondary care cut data with NHS number as the sole identifier. TARN has developed an outcome prediction model using a 30-day cut-off for mortality/survival of patients. Patients discharged before the 30-day cut-off are considered Alive even if they died after their discharge. To avoid an overestimation of survival TARN seeks a “true” outcome within 30 days, this requires a data linkage using the NHS number of TARN patients, with Civil Registration Mortality data to obtain the outcome. Primarily, NHS Digital data will be used as part of a statistical model to determine rates of survival at 30 days which is then fed back to Hospitals.

TARN require a flag at 30 days of patient outcome, whether the patient is dead or alive. TARN would like to ascertain the outcome of patients 30 days post discharge. Outcome (alive or dead) at 30 days from injury has historically been used in the calculation for comparative survival rate (Ws). This is calculated using observed and expected survivors and the total number of patients in the Hospital’s rate of survival dataset.

Ws Excess deaths or survivors (W) standardised according to Hospital case mix using the TARN fraction. A Hospital with the same case mix as the overall TARN population will have identical W and Ws values. A Hospital whose case mix differs from the overall TARN population will have different W and Ws values. However, many patients are discharged before this 30-day point. To include these patients, TARN need to know whether patients died at or before the 30-day point after leaving Hospital. To do this, TARN needs information about post discharge deaths from NHS Digital and use this information in the calculations of Ws for hospitals submitting to TARN.

Data will only be processed as described in this agreement and access will be restricted to substantive employees of The University of Manchester and Hospitals that provided the treatment with which data may be shared. No data will be sent to other organisations other than to Hospitals about the patients they have treated.

TARN requests one data drop data for 2016/17, 2017/18, 2018/19 and then an annual drop for the following year 2019/2020. Each year TARN will request the latest available year of data and delete the oldest year of data that TARN hold, if older than 3 years TARN will delete agreed data within 3 months of receipt of new data on a rolling basis. TARN will provide a data destruction certificate.

BILAG Biologics Prospective Cohort: The Use of Novel Biological Therapies in the Treatment of Systemic Lupus Erythematosus (SLE) — DARS-NIC-148247-CH0Z6

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable, Yes, 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(7), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No, Yes (Academic)

Sensitive: Sensitive

When:DSA runs 2011-12-19 — 2026-12-18 2017.03 — 2020.03.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report
  4. MRIS - Scottish NHS / Registration
  5. MRIS - Members and Postings Report
  6. MRIS - Personal Demographics Service
  7. Cancer Registration Data
  8. Civil Registration - Deaths
  9. Demographics
  10. Civil Registrations of Death

Objectives:

This study will document use of biologic drugs in patients with Systemic Lupus Erythematosus (SLE) in order to assess their efficacy and safety during routine clinical use. 2 cohorts will be recruited: a cohort treated with biologic therapy and a biologically naïve group.

The primary aim of establishing the BILAG Biologics Prospective Cohort is to ascertain whether using biologics in the routine treatment of SLE is associated with an increased risk of being hospitalised for infection, compared to SLE patients with similar disease activity receiving conventional therapies. The secondary purpose of the BILAG Biologics Prospective Cohort is to determine the long-term efficacy of biological therapies in the treatment of SLE.

A further aim of the study is to collect biological samples from patients receiving biologic therapy in the UK to allow us to test whether there are variations in genes which can predict who will respond and who may get serious side effects.

Expected Benefits:

There have been very few major advances in the treatment of Systemic Lupus Erythematosus (SLE) over the past 35 years. In the past 5 years however, there has been an explosion of interest in developing new molecules for the treatment of SLE. A number of approaches have been proposed and are currently in various stages of development including B-cell depleting therapies, IL6 and IL10 blockade as well as inhibition of co-stimulatory molecules, TNF-blockade and lymphodepletion. As these drugs become available for diseases such as RA, off-licence use in SLE is already underway and it is likely that several of these products will gain licences for use in SLE over the next 5 years. However, clinical trials are limited by patient numbers and study duration and therefore are under powered to study potentially important adverse events. In addition, clinical trials tend to exclude patients who have been exposed to other biological therapies in the past and therefore the potential medium-term interactions between various therapeutic approaches cannot be adequately studied. It was therefore decided to establish the BILAG Biologics Prospective Cohort to monitor the long-term safety and efficacy of these new biologic treatments in patients with SLE

Processing:

This is an observational prospective cohort study to compare the risk of development initially over 3 years, of the endpoint in two cohorts: (i) a group of patients with SLE newly exposed to a biologic therapy and (ii) A comparison cohort of patients with SLE newly exposed to conventional, non-biologic therapy, with an equivalent disease severity. We intend to flag both the biologic cohort and the comparison cohort for notification of mortality and cancer registration. A copy of the death certificate will be required for those who die and a copy of the histology for those who develop a malignancy. This will enable us to monitor any adverse events in both cohorts

MR1210 - The Long-term Safety and Efficacy of Biologic Therapies in Children with Rheumatic Diseases — DARS-NIC-147774-MZT95

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable, Yes, No

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, 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 (Academic)

Sensitive: Sensitive

When:DSA runs 2011-01-21 — 2021-01-20 2016.09 — 2020.03.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

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. MRIS - Personal Demographics Service
  6. MRIS - Scottish NHS / Registration
  7. Cancer Registration Data
  8. Civil Registration - Deaths
  9. Demographics
  10. Civil Registrations of Death

Objectives:

Juvenile idiopathic arthritis (JIA) is a chronic disease characterised by the onset of inflammatory arthritis before the 16th birthday. Historically, the treatment of this condition has been limited to non-steroidal anti-inflammatory drugs (eg. ibuprofen), anti-rheumatic drugs, particularly methotrexate (MTX) and corticosteroids. The advent of biologic drugs has revolutionized the treatment of this and other rheumatic diseases. Unlike MTX and other traditional therapies for JIA, which offer general immune suppression in an attempt to control the disease, these new biologic therapies are directed specifically at one specific protein or cell that is felt to be important in driving the arthritis, with a hope of turning off the disease. It was decided to establish the Biologics for Children with Rheumatic Diseases study to monitor the long term safety and efficacy of these new biologic treatments in children with Juvenile Idiopathic Arthritis

Expected Benefits:

This study will document use of biologic drugs in children with Juvenile Idiopathic Arthritis (JIA) and other rheumatic diseases in order to assess their efficacy and safety during routine clinical use. A further aim of the study is to collect samples of DNA (via a blood or saliva sample) of children receiving biologic therapy in the UK to allow us to test whether there are variations in genes which can predict who will respond and who may get serious side effects.

Processing:

This is an observational prospective cohort study to compare the risk of development initially over 5 years, of the endpoint in two cohorts: (i) a group of patients with Juvenile Idiopathic Arthritis newly exposed to a biologic therapy and (ii) A comparison cohort of patients with JIA newly exposed to DMARD therapies (e.g, Methotrexate). We intend to flag both the biologic cohort and the comparison cohort for notification of mortality and cancer registration. A copy of the death certificate will be required for those who die and a copy of the histology for those who develop a malignancy. This will enable us to monitor any adverse events in both cohorts

Investigation of the association between different forms of healthcare support for care home residents and both hospital admissions and place of death — DARS-NIC-186860-T7H5K

Type of data: information not disclosed for TRE projects

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: No (Academic)

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

When:DSA runs 2018-11-01 — 2020-09-30 2019.03 — 2019.08.

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

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

Objectives:

This agreement from the University of Manchester (UoM) relates to a National Institute for Health Research School for Social Care Research (NIHR SSCR) funded study entitled ‘Effective Healthcare Support to Care Homes’. By this the University of Manchester mean the support that visiting healthcare professionals such as GPs, district nurses, geriatricians, pharmacists, palliative care staff and specialist care home support teams provide to meet the physical healthcare needs of older, long-term care home residents. The research is being undertaken by staff from the Personal Social Services Research Unit (PSSRU). The Personal Social Services Research Unit (PSSRU) was originally established at the University of Kent in 1974. The branch at Manchester was established in 1996 and there are now three branches – Manchester, LSE and Kent. For the purpose of this agreement data will only be accessed by substantive employees of University of Manchester and record level data will only be stored and processed at the university of Manchester.

In England, over 375,000 older people live in care homes. These individuals have multiple and complex needs, many of which stem from progressive, chronic conditions including neuro-degenerative, musculoskeletal and cardio-respiratory disease. Visual and hearing deficits are common; about 40% of residents are depressed; and the majority of residents have dementia. Median life expectancy in care homes is just 15 months.

Surveys suggest that the healthcare support that care homes receive varies across the country and that the majority of care homes do not have access to all the services and support that they require. It can thus be difficult for staff to obtain timely and appropriate diagnosis and treatment for their residents. In consequence, care home residents have high rates of potentially avoidable admissions to hospital, with significant implications for both NHS costs and residents’ well-being. At present, around a third of residents with unplanned admissions to hospital die during their stay. A report by the National Audit Office suggests that many of these people could be supported at their care home if appropriate services were available.

International evidence indicates that specialist assessment and management of care home residents (including enhanced primary care services) can improve resident outcomes and save money. Nevertheless, although a range of new models of care home support are emerging, little is known about the most effective ways to structure and deliver this input. This includes evidence on the precise organisational forms, staff mix and clinical processes associated with reducing avoidable hospital admissions and enabling residents to remain in the care home at the end of life. Against this background, this study will investigate the association between different forms of healthcare support provided for long-term care home residents and both unplanned (emergency) hospital admissions and place of death.

A 2017/18 survey of care homes in Greater Manchester undertaken by the PSSRU has already collected information on the healthcare support that these facilities receive and subsequent analysis has identified seven different ways (Care Home Support Models 1 to 7) of classifying this. This has enabled the research team to categorise the care homes into subgroups of homes receiving similar forms of support. For example, one model groups the homes according to the mix of professionals that provide support, another according to the frequency of the input they receive and a third according to the nature of the support that they receive and whether this includes care home staff training as well as advice on individual care home residents. This strand of the research did not involve any NHS Digital data.

The information now sought from NHS Digital will be used to explore the extent to which these different models of support are associated with the unplanned hospital admission and place of death of care home residents. This data will enable the researchers to move beyond a simple description of the patterns of service delivery to answer the following research question: ‘What are the outcomes of different arrangements for healthcare support to care homes?’ There is no other way the PSSRU can access this data.

Expected Benefits:

The study is designed to increase understanding of the extent to which different models of care home support are associated with better resident outcomes. In particular, it will explore differences in the unplanned admission to hospital and place of death of residents of care homes receiving different forms of support, controlling for care home size. As such it will add to the limited existing knowledge base about models of support for care homes, and will yield information with direct implications for:

i/ Health and social care planners and commissioners seeking to promote the delivery of timely and appropriate healthcare services for care home residents and the more efficient use of acute hospital beds.

ii/ Managers and senior staff in provider organisations, enabling them to identify gaps in access to specialist care and negotiate improvements.

iii/ Front line care home staff, care home managers and members of healthcare support services by identifying those care home residents at greatest risk of inappropriate hospitalisation and facilitating the planning of anticipatory care.

iv/ Care home residents, who may achieve increased well-being through the provision of more appropriate health care.

In addition, to shape the influence of this study upon the way care services are delivered, further subsequent research funding will be sought to enable the research team to explore changes in service arrangements and care home resident outcomes over the next five years. These findings will also identify locations where care outcomes for residents are particularly good, highlighting where more in-depth case studies can be undertaken to further identify key service components associated with better quality and safety. These are the elements needed for commissioners to arrange better care for care home residents.

The anticipated dates of the publication of the analysis and plans for its wider dissemination are given in the previous section. The fact that Manchester is likely to have a more diverse range of models than some other places is designed as a strength of the study. Furthermore, the findings are likely to be of additional interest locally in the context of aims to reduce the number of hospital admissions by 60,000 per year. The research findings will contribute to that debate.

Outputs:

The production of outputs utilising the requested data will begin soon after data receipt and will continue for up to five years. In all cases, all outputs will be at aggregated level and small numbers will be suppressed in line with NHS Digital guidelines. Specific outputs are described below.

A final report on the wider study (including brief details of the methodology, findings and conclusions of this analysis) will be submitted to the study’s funders (the NIHR SSCR) in the spring of 2019. Following peer review, a publicly available version of this report will be posted on the websites of the funder (https://www.sscr.nihr.ac.uk/) and the University (http://research.bmh.manchester.ac.uk/pssru/).

Given the nature of the final report (a short summary document) further analysis of the data will be reported in two academic papers detailing the association between the different models of care home support and the hospital admission and place of death of care home residents respectively. The main audience for these papers will comprise commissioners, managers and providers (locally, nationally and internationally) as well as other academics. These papers will thus be submitted to peer-reviewed journals such as Age and Ageing, Primary Healthcare Research and Development and the Journal of the American Medical Directors Association (JAMDA) and will be available via open access where possible. Target publication date: Summer 2020.

The findings of the analysis will also be presented at a national conference such as the SSCR’s Annual Conference, and at local care home owner, manager and staff forums (regular meetings between commissioners and providers) within Greater Manchester. Target presentation date: Spring 2019.

The applicants will also produce a short, evidence-based briefing document on the study’s findings, available free of charge as a web-based document. This will form part of the PSSRU at Manchester Expert Briefing series which is designed to inform service development, both nationally and locally. As such it is expected to be of interest to personnel who commission and deliver healthcare support to care homes, as well as care home providers. Target publication date: Autumn 2019.

In addition, a PSSRU Research and Policy Update summary document detailing the key research findings will be distributed to clinical commissioning groups and local authorities in England, CQC, major care home providers and umbrella organisations for care homes through our national mailing lists. Target publication date: Winter 2019/20.

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

Processing:

Only substantive employees of the University of Manchester (UoM) will have access to the data, and only for the purposes described. The data will not be shared with third parties or linked to any other datasets.

Below is a summary of the dataflow and processes;

1. The University of Manchester will send NHS Digital a spreadsheet containing the names and postcodes of the 418 care homes in Greater Manchester along with their first line of their addresses. NHS Digital will run that information through Personal Demographic Service (PDS) and extract the NHS numbers and use these to identify people at each of these addresses. They will then add the seven variables classifying the external healthcare support received by their care home (Care Home Support Models 1 to 7) and eight further variables relating to the care home type (Care Home Type 1 to 8) and append these to the data. NHS Digital will not disseminate PDS data, they will use it purely to identify any individual living at the care home addresses over the age of 75. No individual care home resident identifying information will be provided.

2. NHS Digital will link the requested HES data and Mortality data to the provided postcodes, Care Home Support Models 1 to 7 and Care Home Type variables 1 to 8.

3. NHS Digital will remove the postcodes and first line of address from the linked data before delivering this to the UoM. No individual patient identifying information will be provided. The data will be pseudonymised at record level.

The linked HES and Mortality data will be entered into two statistical software suites [Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS)] and a range of descriptive, bivariate and multivariate statistics as well as generalised linear models will be employed. The analyses will profile the full sample of admissions from care homes in Greater Manchester and will explore differences in unplanned hospital admissions and place of death between residents of care homes receiving different models of support, controlling for care home size.

All outputs will only contain aggregate level data and small numbers will be suppressed in line with Hospital Episode Statistics (HES) analysis guidance.

Data will be stored in the University of Manchester Data Safe Haven (Joule House), which has been built following ISO27001 standards and NHS Digital security requirements. The Data Safe Haven is located on a separate premise (Joule House) to that where the data will be accessed and analysed (Crawford House).

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 impact of hospital regulation on performance — DARS-NIC-23410-W8N9L

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, N, Y, 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, 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(2)(c), 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), Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (Academic)

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

When:DSA runs 2017-11-28 — 2020-11-27 2019.04 — 2019.04.

Access method: One-Off, Ongoing

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Patient Reported Outcome Measures (Linkable to HES)
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Outpatients
  5. HES:Civil Registration (Deaths) bridge
  6. Civil Registration - Deaths
  7. Office for National Statistics Mortality Data (linkable to HES)
  8. Office for National Statistics Mortality Data
  9. Civil Registration (Deaths) - Secondary Care Cut
  10. Emergency Care Data Set (ECDS)
  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 Outpatients (HES OP)

Objectives:

The University of Manchester has been awarded research funding from the Department of Health Policy Research Programme to analyse the impact of the Care Quality Commission's new inspection and rating system on provider performance.

The data will be used to uncover the relative impact of healthcare regulation, compared to other influences, on hospital performance. It would not be possible to measure hospital performance, and therefore evaluate the CQC, without the datasets requested.

The research sets out to examine how providers, the public and other stakeholders respond to provider ratings, and what impacts inspection and ratings have on the quality of care and on improving performance. Understanding how ratings work, as part of the wider inspection process, is essential if the Department of Health are to increase the potential benefits for the quality of healthcare, and reduce the costs and any adverse or unintended consequences.

There are four main questions in the research:
- How is the system of ratings meant to work, and bring about improvements in the quality of care?
- How does it work in practice, and what do providers, the public and others do in response to ratings?
- What impact does the system of ratings have on measures of the quality of care and provider performance?
- How could the system of ratings be improved?

Hospital performance will be measured in the following way:
- Mortality [within hospital/out of hospital/within 30 days]
- Readmissions within 30 days
- Length of stay
- Waiting times
- Occurrence of adverse events
- Cancellations of surgery
- PROMs

The data provided by NHS Digital will only be used by the members of the research team detailed in this application. The analysis conducted on these data will feed into the evaluation of the CQC which also includes qualitative researchers based at the University of Manchester and The Kings Fund. Only employees of the University of Manchester will process the data. Only aggregated outputs will be shared with researchers outside of the University and small numbers will be suppressed in line with the HES Analysis Guide

Yielded Benefits:

The final report has been submitted to the Department of Health and shared with the CQC. In addition the findings have been presented to and discussed with the CQC board. The research is being discussed by the DH and CQC and will be used to inform future policy and practice for health care regulation. Additional benefits will be understood once feedback is received from the DH.

Expected Benefits:

The Care Quality Commission developed its system of provider ratings as part of its new regulatory model and inspection regime in 2013, piloted it in the NHS acute care sector in 2013-14 and is now developing it for use in other sectors. The new approach is radically different from the model it replaced. It uses larger and more expert inspection teams, a wider range of data and fieldwork, and produces provider ratings in five domains (safe, effective, caring, responsive and well-led) using a four-point rating scale (inadequate, requires improvement, good or outstanding) as part of a detailed narrative inspection report.

The key purpose of these provider ratings is to drive improvements in the quality of care and in provider performance, but the ways in which this might happen are complex, and there is potential for unintended consequences. This research will help CQC, the Department of Health and other stakeholders to understand how the system of provider ratings works, and to find ways to increase its impact on the quality of care, minimise costs and prevent or reduce any adverse or unintended effects. This will allow the Department of Health to impact on the future direction of policy, and also contribute to a wider body of knowledge on regulation/inspection and performance ratings.

The final report to the Department of Health will be submitted in December 2017. The dissemination of the research findings will include journal articles published after the final report. The University will continue to work on the data following the final report due to the lengthy peer review/revisions stage for journal articles.

The CQC and the Department of Health will both receive regular updates on the research methods and outputs. Several meetings have already been held with the CQC to establish links by which the research can be disseminated within the CQC. These updates are in addition to the final report and journal articles.

Outputs:

The purpose of the data processing activities performed on these data is to produce statistical outputs to be used in peer-reviewed journal articles, presentations and reports for the Department of Health Policy Research Programme. These statistical outputs will be of three types: 1) descriptive tables summarising the data, 2) graphs and figures summarising the data, and 3) regression results tables. Outputs will contain only data which is aggregated, with any small numbers suppressed in line with the HES Analysis Guide.

The overall research project includes a number of work packages. At the completion of these, seminars will be held for invited participants from the research case studies and interested organisations, for feedback on early and emerging findings and member checking as well as to promote knowledge mobilisation. For case studies, a short report will be produced and the findings may be presented locally to stakeholders in the “system of care”.

The number of outputs, in terms of articles/presentations/reports, will be determined by the findings of the analysis and by the academic peer-review process. There is a PPI (Patient and Public Involvement) forum who will be engaged in producing these reports and their dissemination.

The aim is to produce a series of journal articles investigating hospital performance and how it is affected by regulation and inspection. Journals that will be targeted for these publications will be: 'Health Economics', 'The Journal of Health Economics', 'Health Services Journal', 'Journal of Health Services Research & Policy'.

Target journal publication dates are as follows:
c. Journal Article 1 submission target: May 2017
d. Journal Article 2 submission target: June 2017
e. Journal Article 3 submission target: July 2017

Presentations will be given at national and international conferences to audiences of academics and health professionals. Typical conferences are the UK Health Economists' Study Group.

Reports will be provided for the Department of Health Policy Research Programme on the progress and outcome of the research.

Target report publication dates are as follows:
a. Department of Health interim report 2: April 2017
b. Department of Health final report: August 2017


Reports and journal articles will be published online and made available to all. Submitted versions of all journal articles will be freely available on the University of Manchester library website.

Processing:

- Data will only flow from the HSCIC to the University of Manchester.
- No data will be shared with third parties.
- The data will be analysed in order to measure hospital performance. This will not involve linking data to other datasets (apart from the linkage to PROMs and ONS provided by NHS Digital). Trends in hospital performance over the period of time requested will be analysed.
- Requested are the years prior to the introduction of the CQC inspection in order to accurately account for historic trends in performance. This is a standard approach and required in order to measure changes in these trends, which may be due to the CQC inspection.

The data will be analysed using the statistical packages Stata and R. Regression methods will be used which will output regression results tables. No record level data will be produced as an output at any stage, only aggregated date (with small numbers suppressed in line with the HES Analysis Guide). Descriptive statistics tables of the data will be produced which will aggregate the data by year. Graphs will be produced to describe the data and these graphs will also aggregate the data by year. The outputs produced cannot be used to identify patients or sensitive information.

The Norfolk Arthritis Register (NOAR) a longitudinal observational study — DARS-NIC-333021-B6W2C

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Anonymised - ICO Code Compliant, Yes

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2017-02-01 — 2020-02-01 2018.03 — 2018.12.

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Admitted Patient Care (HES APC)
  4. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

Rheumatoid arthritis (RA) and its subset inflammatory arthritis (IP) are chronic auto-immune diseases with a prevalence of about 1.5% in the adult population. Patients with IP are at increased risk to develop cardiovascular diseases, lung disorders and certain types of cancer. However, limited data are available on risk factors for developing these outcomes. It is therefore important to follow patients since disease onset is over a long period of time. The Norfolk Arthritis Register (NOAR) includes patients with early IP and follows these patients over time.

The purpose of the Norfolk Arthritis Register is to identify genetic and nongenetic factors which may be related to the onset of inflammatory arthritis, response to treatment and its long-term outcomes. The principal research objective of this substudy is to establish the frequency and risk factors for certain medical conditions known to be linked to inflammatory polyarthritis in particular coronary heart disease, stroke, certain types of cancer and infection. Data obtained from HSCIC will enable the University of Manchester to identify which patients have experienced the outcomes of interest: - co-morbidities associated with rheumatoid arthritis; cardiovascular disease, lung disorders, cancer and potentially other co-morbidities the University of Manchester plan to investigate. Based on the information the University of Manchester have already collected as part of the NOAR study they will establish whether cumulative disease activity, degree of physical activity, treatment or other disease related factors are risk factors or protective factors for the different outcomes of interest.

The prevalence of RA in the UK is about 1.5% and patients with RA are at increased risk of cardiovascular comorbidities: eg unrecognized myocardial infarction (HR 2.13, 95%CI 1.13-4.03), and heart failure (HR 1.87, 95%CI 1.47-2.39). In addition, it has been estimated that over 50% of premature deaths in RA are due to cardiovascular disease.

The output from this study will inform health professionals working in the field of rheumatology about the occurrence of the most common co-morbidities in patients with IP. Identification of possible (modifiable) risk factors will also enable early intervention to prevent the development of these comorbidities in the future.

Yielded Benefits:

Expected Benefits:

A better understanding of risk factors of comorbidities including cardiovascular diseases, lung disorders and cancer will directly impact of better intervention resulting in less morbidities and reduced likelihood of premature death, better quality of life for the patient and less direct and indirect costs for the NHS and society.

The research the University of Manchester intend to carry out, utilising HES data, has the potential to influence the National Institute for Health and Care Excellence (NICE) guidelines and other guidelines regarding clinical practice in rheumatology based on any outputs. The overall aim of the research is to improve the health care of patients with IP/RA resulting in better quality of life of these patients. For most of the studies investigating the occurrence and development of co-morbidities, utilising HES data, the University of Manchester will develop prediction models identifying possible demographic (e.g. age, gender, smoking status) and clinical predictors of these co-morbidities. Identification of these predictors will inform health care professionals on how to manage IP/RA with an aim to reduce the risk of the development of co-morbidities. In addition, if certain lifestyle factors are identified, the study will seek further collaborations with patient organisations to provide better information for patients about these risk factors. Although the University of Manchester overall aim is to improve the health care of patients with IP/RA, it may take a number of years before the impact of any changes in management can be measured.

Benefits achieved to date

To date the University of Manchester already have shown that there is still an increased risk of mortality due to rheumatoid arthritis associated co-morbidities such as cardiovascular diseases and the prevalence of obstructive lung disease is higher in the rheumatoid arthritis compared to the general population based on spirometry tests. The University of Manchester have also shown that early treatment and achieving early remission is associated with less functional disability and improved survival in patients with inflammatory polyarthritis.

Future expected benefits

The University of Manchester envisage outcomes from the proposed research will also feed into NICE guidelines and published guidelines by the British Society of Rheumatology on management of arthritis and prevention of co-morbidities.

Outputs:

Outputs achieved to date

A NOAR research forum took place at Norfolk and Norwich University Hospital September 2013 - presentations by NOAR consultant rheumatologist and senior research fellow; alongside other consultants and researchers.

An abstract including HES data was presented at the international EULAR conference in 2014 (respiratory morbidity and mortality in early rheumatoid arthritis, S Ramanujam, DPM Symmons, T Marshall, J Chipping, IN Bruce, SMM Verstappen). The University of Manchester are awaiting HES follow-up data to finalise the manuscript.

To date one abstract looking at the development of lung disorders in patients with inflammatory polyarthritis (IP)/rheumatoid arthritis (RA) was presented at EULAR in 2014. However, the number of patients with IP developing lung disorders was too small and the University of Manchester are currently awaiting follow-up HES data to increase the number of cases and write the manuscript on this topic.

Future outputs

Abstracts of outputs will be submitted for presentation at national (e.g. British Society for Rheumatology (BSR)) and international conferences (e.g. European League Against Rheumatism (EULAR) and American College of Rheumatology (ACR)). In general, the University of Manchester endeavour to write a manuscript of all abstracts presented at national and international conferences.

The University of Manchester plan to target a number of publications looking at the occurrence and development of co-morbidities in patients with IP based on the information provided by HES and linked with NOAR data. Manuscripts will be submitted to high impact peer-reviewed journals in the field of rheumatology (e.g. Annals of Rheumatic Diseases, Arthritis & Rheumatology) or to general medical journals (e.g. The Lancet, Annals of Internal Medicine).

Outputs will contain only aggregate level data with small numbers suppressed. Within the Arthritis Research UK Centre for Epidemiology all manuscripts containing data from the Norfolk Arthritis Register (NOAR) will be open access publications and thus freely available for everyone to access.

Once manuscripts are accepted a lay summary will be written and uploaded on the Centre for Musculoskeletal Research (CfMR) website. Funders of the University of Manchester research, Arthritis Research UK and the NIHR, will be informed about the publication and all publications will be referenced in the annual reports of these funding bodies.

After publication, clinically relevant publications may be cited on websites of charity organisations such as Arthritis Research UK and the National Rheumatoid Arthritis Society or the British Society for Rheumatology website. If the University of Manchester feel that the output is of high significance they will inform the press officer of the University of Manchester as well. It is very likely that one of these manuscripts will be submitted for REF (Research Excellence Framework - a system for assessing the quality of research in UK higher education institutions).

The University of Manchester work closely with patient organisations (Arthritis Research UK and National Rheumatoid Arthritis Society) to implement their research findings into patient management information leaflet or website blogs.

Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide.

Processing:

In the Norfolk Arthritis Register (NOAR) all patients have a unique ID number and after linkage with hospitalisation data all identifying data will be removed.

Data will be linked with information obtained as part of the NOAR study including patients with IP.

For linkage of HES data with clinical data obtained by NOAR the following procedures will be followed:

1) The University of Manchester will submit NHS number, patient ID, postcode, gender and DOB and their date of study entry to HES.

2) NHS Digital will return to the University of Manchester an episode level report of all admissions for these patients since 2000 or the date of NOAR study entry, depending on inclusion date. HES data will be stored in a separate password protected database to which only key personnel to the study have access.

3) As part of the NOAR study the University of Manchester collect data on dob, gender and postcode to determine socioeconomic status. This information is stored in the NOAR database. However, using the NOAR ID number the University of Manchester will link the NOAR demographic and clinical data with the HES data. This linked information will be stored on a standalone encrypted and password protected computer with limited access to researchers involved in data analysis of HES data. Before linking data between NOAR and HES, the University of Manchester will generate age and deprivation scores and remove dob and postcode from the NOAR database to be linked with HES data.

4) No individual identifiable patient data will be published. For publications the University of Manchester will aggregate data and events with small numbers (N<5 events) will not be published. All analyses are performed on groups of patients.

Only eligible researchers and the NOAR data management team working within the Centre for Musculoskeletal Research who are substantive employees of the University of Manchester, will have access to the HES data and the University of Manchester will not share HES data with other organisations.

Researchers analysing HES data linked with NOAR data are researchers working within the Centre for Musculoskeletal Research, University of Manchester.

All data are saved on secure servers and researchers are not allowed to copy HES data on external computers/laptops.

The University of Manchester will not share HES data with 3rd parties.

Project 21 — DARS-NIC-365623-T3W4S

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y

Legal basis: Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2017.09 — 2017.11.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care

Objectives:

The primary aim of the study is to establish the number and rate of sudden unexplained deaths (SUD) among psychiatric in-patients. National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH) also conduct a detailed examination of circumstances leading up to a SUD in order to inform the process of improving quality of care.

There has been growing concern about the incidence of sudden and unexplained death among psychiatric in-patients and little is known about any associations between clinical factors and these deaths. The aims of the Sudden Unexplained Death (SUD) study are to firstly determine the number and rate of sudden unexplained death among psychiatric in-patients in England and Wales, and secondly to conduct a detailed examination of the social and clinical circumstances leading up to the SUD. The data requested from HES are all psychiatric in-patient discharges with the discharge code of 4 (i.e. deceased), the local patient ID or NHS number, date of birth, the provider code, and the consultant code.

This data enables the University of Manchester to identify the NHS trust where the death took place and enquire about whether the patient death falls into the study criteria of SUD. If it does, the University of Manchester then send a questionnaire to the consultant who was responsible for the patient’s care, in order to collect information on the antecedents of the death. The SUD study is part of the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH) and study findings are included in the NCISH annual report published each July.

The team who carry out the SUDs are based at the Centre for Suicide Prevention and also work on other projects for the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH). Only personnel listed as working on the SUDs study have access to the SUDs data, which is kept on a separate computer system and which is not backed up by the NCISH server.


The Inquiry was initially funded directly by the Department of Health in 1999 to collect and report sudden unexplained death of psychiatric in-patients with regard to identifying whether there was an association with poly-pharmacy (prescription of more than one anti-psychotic drug) and because of heightened public concern at the time about sudden death following restraint - particularly in respect to restraint of Black and Minority Ethnic (BME) patients. The Secretary of State for Health at that time stated that these numbers should be collected and reported annually which the Inquiry was commissioned to do.

Currently, the Inquiry is commissioned by the Healthcare Quality Improvement Partnership, on behalf of NHS England and the Department of Health, to continue its collection of a national case series of suicides, homicides and the sudden unexplained deaths of in-patients cared for under mental health services. The current contract extends to March 2018 and the extract of in-patient deaths we are requesting is crucial for this year’s annual report. To date the findings of the study have contributed to major developments in mental health policy – recently the increased emphasis on the importance of in-patient services providing for physical health care needs as well as mental health needs and in the guidance from the DoH and Royal College of Nursing regarding safer restraint practices.

Expected Benefits:

NCISH are able to monitor the occurrence of SUD in England and Wales and establish any trends (as the study is on-going since it began in 1999). NCISH aim to increase the knowledge of the clinical antecedents of SUD, including the role of restraint, anti-psychotic drugs taken prior to death and any ECG abnormalities, to ultimately inform prevention efforts.

In the annual reports NCISH provide clinical implications from the findings which are viewed as output for clinicians. NCISH’s findings from the SUD study are presented in annual reports and NCISH give recommendations and key messages for services that may help in prevention of these deaths. NCISH have also published a paper from the SUDs study which was published in the Journal of Psychopharmacology. This journal has a wide readership, including psychiatrists, pharmacists, nurses, and other clinicians who would gain knowledge into our findings on sudden unexplained deaths.

Data collected since 1999 has enabled examination of trends of sudden unexplained death in England over time. The University of Manchester are also able to describe the characteristics of SUD cases and determine if any particular groups are at high risk. This data is presented in the annual reports which are available on the NCISH website. The University of Manchester also have a “report launch” event which, like a conference, the report findings are discussed by senior members of the team. This enables dissemination of the results to clinicians, policy makers, commissioners, and service users. The University of Manchester have also written an article on SUD which was published in a peer-reviewed journal. NCISH also actively seek dissemination of the findings and encourage discussion through daily tweeting on the NCISH twitter account.

Input from service users and families and carers has been a feature of NCISH since it began. The original study questionnaires were developed following consultation with service user and family/carer organisations and many of the current items reflect their concerns. NCISH continue to invite suggestions for areas of further study from individual service users and representative organisations. NCISH issue a general call to service users and specifically invite national charities to contribute to their selection of topics to investigate in detail. NCISH also have a secure portal on their website where service users and family/carers can tell them about their experiences so that these can be investigated in their studies.

In terms of dissemination, the NCISH website has a new page for service users, entitled “What our findings mean for your care”, based on the 2015 annual report. Their use of social media has helped them reach service users far more widely – people who are not attached to any organisation. Much of social media response to the annual reports, including the 2014 Q&A and the 2015 panel questions, has come from service users. This has helped them to develop a style on social media that is suited to a non-professional audience.

In terms of other benefits, NCISH are in a unique position in being able to determine the number of SUD cases at a national level. Findings from the most recent annual report found that a number of SUD in younger psychiatric in-patients continue to occur. NCISH intend to study these deaths more closely for possible antecedents and background risk. Recommendations already made by NCISH are firstly that these deaths should always be subject to investigation and reporting by the mental health trust and to coroner referral. Secondly that wards should take precautionary measures including physical health assessment as soon as practicable after admission. Thirdly, avoidance where possible of high drug dosage and polypharmacy. These recommendations were disseminated at the NCISH report launch which was attended by commissioners, policy-makers, service users, clinicians, and other relevant groups. The recommendations have also been regularly cited on their twitter account.

NCISH partnership group includes the anti-stigma campaign Time to Change and its constituent mental health charities Mind and Rethink. These organisations are now in a position to comment on the future priorities of NCISH more directly and to prepare public comment on their publications before they appear in print.

NCISH are currently working with their funders and Independent Advisory Group on a Strategy for Engagement summarising recent work, current plans and future developments. This strategy includes a section on service user engagement.

Outputs:

The data is requested to establish the number and rate of SUD in psychiatric in-patients in England and to get a better understanding of the circumstances leading up to a sudden unexplained death. The study is on-going.

As covered, Identifiers will only be shared with the relevant consultant responsible for the individual’s care in order to obtain the further information specified. Once received NHS Digital data is deleted.

All outputs from analysis will be in aggregate form with small numbers suppressed in line with the HES Analysis guide and therefore all sudden unexplained death (SUD) cases are always anonymised and there is no risk of re-identification.

Information on the number, rate and trends of SUD as well as key social and clinical characteristics are provided in NCISH annual reports publically available on the NCISH website. NCISH have also written a publically available paper published in the Journal of Psychopharmacology, available at: http://jop.sagepub.com/content/25/11/1533.short

Processing:

Receiving admission and discharge data of patients under mental health services enables the University of Manchester to identify those who have died as a psychiatric in-patient. Through the NHS hospital number or Local Patient Identifier, these patient details are then followed up with NHS Trusts (using the Provider Code) in order to establish whether the death would fulfil the criteria for the SUD study. For those deaths that do meet the SUD criteria, the University of Manchester then ask the clinician responsible for their care (using Consultant Code) to complete a detailed questionnaire on the clinical circumstances and antecedents of the death. The University of Manchester also collect information on whether restraint was involved, the anti-psychotic drugs taken prior to death, and any electrocardiogram abnormalities.

As part of NCISH, the SUDs study based at University of Manchester has National Information Governance Board for Health and Social Care (NIGB) Section 251 approval. All sensitive data collected from HES is protected by the following strict processes: the data is stored on a password protected standalone network accessible only to staff engaged on the project; the network computers are in rooms which are locked when unoccupied and are on a corridor accessible by swipe card only; the rooms are on the 2nd floor of a building with 24 hour security; electronic copies of data are stored in a locked room in a locked filing cabinet. Only individuals who are substantively employed by the University of Manchester and are directly involved in NCISH will have access to the data.


The HES data which is processed is minimised at NHS Digital using the filters:
The HES data requested is filtered to only those episodes of care which are finished consultant episodes where the patient has been discharged from hospital with a discharge method of ‘4’ – died.

Furthermore, the data is filtered by ICD10 code to only include episodes where the primary diagnosis begins with ‘F’ – Mental and behavioural disorders or where the primary diagnosis contains a code in the ‘Z’ chapter of ICD10 which is “Factors influencing health status and contact with health services”, specifically those “persons with potential health hazards related to socioeconomic and psychosocial circumstances” and where the Main Specialty/Treatment Specialty of the consultant is in the field of learning disability, mental illness, psychiatry or psychotherapy.

Neighbourhoods and Dementia — DARS-NIC-33318-X4Q1B

Type of data: information not disclosed for TRE projects

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

Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-04-01 — 2021-03-31 2017.06 — 2017.11.

Access method: One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, LANCASTER UNIVERSITY, THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Outpatients
  4. Hospital Episode Statistics Accident and Emergency (HES A and E)
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)
  6. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The Economic and Social Research Council (ESRC) have funded the Universities of Manchester and Lancaster to investigate the impact of hospital staff training in best care for patients with dementia, on key hospital outcomes for patients with dementia. This objective is part of a larger 5-year programme of ESRC-funded research aimed at improving the lived experience of people with dementia across many areas of their lives (http://www.neighbourhoodsanddementia.org/).

There is a mix of training packages in dementia care being delivered in NHS general hospital settings as shown from the findings from the 2010 and 2012 NAD national surveys. Given the rapid development in this area much more understanding is needed about the impact of training on hospital outcomes for patients with dementia compared to those without dementia, the cost-benefits of training, and what types of training hospital staff find most useful. To the best of the University of Manchester and Lancaster's knowledge this is the first investigation of the effect of hospital training on hospital outcomes for patients with dementia using a large national sample of hospital admissions data.

The study will use HES data to construct measures of key hospital outcomes (see below) for patients with dementia, and also for matched patients without dementia, and compare these to hospital-level information on staff training from the National Audit on Dementia (NAD) administered in 2010, 2012 and 2016, plus the University of Manchester and Lancaster's own national training survey which captures information about availability of training at an individual hospital level. The survey was administered in January 2017. The University of Manchester and Lancaster will also undertake an in-depth survey of random samples of individual staff at 20-30 hospitals and will be comparing the hospital-level HES-based outcomes data to that data for these specific hospitals, to examine relationships to staff knowledge and confidence in caring for patients with dementia.

The focus of the statistical and Health Economics (HE) analyses of admissions data will be on the financial years 2010/11 and 2012/13 in the first instance. The analysis will be repeated including the final 2016/17 data when it becomes available.

The HE analyses will in addition use A&E and OP data for these financial years.

HES APC from 2005-6 (and the following 4 years) is requested as part of the process for identifying patients with dementia in 2010. Dementia is not routinely coded when a person enters hospital, therefore the usual process for determining dementia is to look for an ICD10 code for dementia in one of the diagnosis fields for any spell in the previous 5 years: this method has been previously used by the Care Quality Commission and the Centre for Health Economics at York. For this reason University of Manchester have requested APC data starting from 2005 to correspond to the first analysis year 2010/11. This includes spells in mental health hospitals as well as acute hospitals. However, the only data they are asking for over this period is the diagnosis code data and fields to identify spell dates.

The statistical and health economics analyses of HES APC data will only be on the financial years 2010/11, 2012/13 and 2016/17 to correspond with the hospital-level information on staff training from the National Audit on Dementia surveys and also the University's own national hospital-level survey of dementia administered in January 2017. The statistical analysis will be based on comparing outcomes for patients with and without dementia in acute hospitals during these three years. For the statistical methods a matched cohort study of APC patients in acute hospitals with dementia will be matched to controls using several variables such as age and sex. The other variables requested will be used to construct outcomes such as length of stay or used as covariates in the statistical models where it is important to control for confounding.

Only the three analysis years (i.e., 2010/11, 2012/13 and 2016/17) are required for the HES OP and HES A&E data where only health economics analyses will be undertaken.

All the data handling and analysis will be conducted in the UK at the University of Manchester. None of the HES data provided by NHS Digital will be made available to third parties including Lancaster University.

Yielded Benefits:

The project is in process of conducting our finalised analyses of the data. A preliminary finding is that the increased length of stay for people with dementia appears to be mainly a result of different demographics, e.g., gender and age, pre-existing health, and the reasons for hospital admission. Thus concerns that longer stays may be due to differences in care whilst in hospital may be unfounded. However, a tangential discovery is that people with dementia appear less likely have elective admissions for a range of interventions they might benefit from, such as hip replacements and cardiac surgery. The project is yet to add their measures of staff dementia training into the analysis models.

Expected Benefits:

According to a NAD dementia survey carried out by the Royal College of Psychiatrists a quarter of acute hospital beds are occupied by people with dementia (2013). However, a high percentage of dementia is not recognised at admission and most hospital staff lack the knowledge of how best to care for patients with dementia. In 2013 Health Education England (HEE) was mandated to ensure training is made available so that all NHS staff looking after patients with dementia have foundation level dementia training, and a number of other local and national training initiatives have been launched. However, there is no available evidence, apart from anecdotal, on the impact such training is having on hospital outcomes for patients with dementia, on the cost-effectiveness of the training being delivered, or on what aspects of dementia care training hospital staff find most valuable.

This study aims to produce that evidence, and in addition to identify those components of hospital staff training in dementia care that are particularly influential in affecting outcomes. However, it may be that it is found the impact of the training being delivered to be negligible relative to all other internal and external factors impinging on patient outcomes. In either case, it is envisaged that the published findings will inform policy at both the national and Hospital Trust level.

Research findings will help Trusts to ensure that the dementia training they provide is cost-effective and best meets the needs of both staff and patients. The results will also identify Trusts where care outcomes for patients with dementia are particularly poor or particularly good, providing a sampling frame for developing more in-depth case studies to further increase knowledge of the role of training, other dementia initiatives, and other factors, in influencing quality of care. Consideration will be taken on how to sensitively feed the findings back to specific hospitals, in a way that can best help those struggling to provide good quality of care to patients with dementia. At the national policy level, it is envisaged the research will feed into the development of guidance on dementia awareness training and the care of dementia patients in hospital, and into the development of recommendations around future research.

Outputs:

Outputs will include a final report to the research programme funder ESRC, papers in peer-reviewed medical journals, and presentations at appropriate health research conferences. The final report will be submitted towards the end of the funded programme, in Spring 2019. There will be three main peer-reviewed journal publications: the first concerning the analysis of the 2010/11 and 2012/13 linked datasets and submitted in late 2017; the second extending the analysis to include the 2016/17 time-point, to be submitted in late 2018; and the third on the findings of the hospital staff survey. These papers will be published in open-access journals where they will be publically and freely available. Target journals will be PlosOne and BMC Public Health and target conferences will include the Health Services Research UK Meeting, the British Society of Gerontology conference, the joint Royal College of Nursing/British Geriatrics Society conference, the UK Dementia Congress conference and Kings Fund conferences.

The University will set up a ‘Neighbourhoods and Dementia’ project website hosted at the University of Manchester and provide regular postings and invites for project engagement and interaction. This will include a blog and will allow for comments to be posted and the principal investigator will take responsibility for coordinating inputs onto the site. The research programme will take advantage of other social media outlets, such as twitter. The University will monitor access and record comments for evidence of impact.

The University will disseminate the project through a variety of publication resources from high impact peer reviewed journals through to practice and professional outputs, such as briefing updates distributed through INVOLVE, DeNDRoN, Age UK, the Alzheimer’s Society and Alzheimer Scotland. The University will present the work at international, national and local conferences, sharing the conference stage with people with dementia and carers at every opportunity.

The University will engage with television and media outlets and will look to develop a series of features on ‘neighbourhoods’ work on dementia in national newspapers, e.g. the Guardian’s Society page. In Sweden, similar media outlets and impacts will be sought. The University will host an international conference on ‘Dementia-Friendly Neighbourhoods’ at the end of the research programme to bring each work programme together; people with dementia and carers will be planners, coordinators and speakers at this event.

In addition to the above, the research programme also includes a Dementia Use Involvement stream, which has provided a co-researcher education programme to a number of people living with dementia to enable them to participate as co-researchers in the study and to facilitate their further participation as co-developers of user-engagement outputs. An Impact on Policy conference is also planned for the end of the research programme with the Rt Hon Hazel Blears and Prof Alistair Burns.

Outputs will report only results aggregated across all patients in any particular analysis, for example in the form of means, variances and regression coefficients. Graphs such as scatterplots may display derived values for individual hospitals, but without any hospital identifying information. Small numbers will be suppressed in line with HES analysis guidelines.

Only the statisticians and health economists from the University of Manchester will have access to the HES data. Their University of Lancaster collaborative colleagues will have access to the aggregated outputs for the purpose of journal and conference abstract submissions. These outputs will not be given to a third party.

Processing:

The data will only be accessed by substantive employees of the University of Manchester and only for the purposes described in this document.

HES admissions data will be used to derive the following patient-level outcomes: length of stay (LoS); emergency re-admission within 30 days after discharge and measures of care whilst in hospital, such as in-hospital falls and potentially avoidable conditions (eg UTIs and bed sores). In addition, NHS Digital provide an outcome variable (completed for the last episode) indicating whether a patient died in hospital or within 30 days after discharge. The use of hospital services data (LoS, A&E and OP visits) will be costed using the currency and service codes and the NHS Reference costs for the cost analysis models.

A number of essential covariates will also be defined, including age, gender, additional diagnoses, and place of residence (e.g., care home). These variables will then be linked at the hospital level to the NAD training data and the University of Manchester and Lancaster's own national training survey in order that the analyses can be undertaken.

A matched cohort study will be used to compare outcomes and costs for patients with a known diagnosis of dementia to matched patients without a known diagnosis, with a focus on how hospital training scores relate to differences in outcomes between these two groups. For financial years 2010/11 and 2012/13 and 2016/17, any patient with a dementia diagnosis (based on the 20 ICD10 codes for each episode) or a dementia report in any acute hospital or NHS Mental Health Hospital in England during the previous 5 years prior to their admission will be considered as having dementia. Control patients will be those with no recorded dementia diagnosis in the last five years. A subset of control patients matched on age, sex and other covariates to the group of dementia patients will be selected for the main analysis. Diagnosis classification will be carried forward to subsequent A&E, admissions and OP visits. Data for the same patient across time-points and products will be linked by using a common encrypted HESID.

Descriptive statistics (mean, SD, median and interquartile range) will be produced to summarize each outcome and cost for each financial year 2010/11 and 2012/13 and 2016/17 for those with dementia versus matched control patients. Summary results will also be split by key variables including level of dementia care training, elective versus emergency admission, geographical region and discharge destination (e.g., care home). An additional descriptive analysis will examine the trend in identification of patients with dementia at admission across the period 2010/11 to 2016/17.

Multi-level models will be used for the main statistical analyses, which will investigate the impact of training variables on outcomes and costs for dementia patients compared to those without dementia, controlling for confounding covariates. The LoS outcome will be analysed using a multi-level model for continuous outcome data or a survival approach, as appropriate. The mortality and re-admissions outcomes will be analysed by multi-level models for binary outcomes. The cost models will include the costs of the admission and associated use of A&E and OP visits. The costs will be analysed using the best fit distribution for skewed data (eg gamma or poisson) For all statistical and cost models the regression parameters (coefficients or odds ratios), confidence intervals and associated P-values will be reported. Only data aggregated at the hospital level will be reported; no patient-record level data will be produced as an output at any stage.

For the analysis of the smaller survey of staff at 20-30 hospitals, the HES-based measures will be aggregated to the hospital-level, and reported descriptively. Individual hospitals will not be identified in any publication.

Other than those already specified in the agreement The University of Manchester will not link this data to any other patient level dataset or attempt to re-identify patients or attempt to calculate dates of death using the data supplied by NHS Digital.

Project 23 — DARS-NIC-317873-H3L1R

Type of data: information not disclosed for TRE projects

Opt outs honoured: N

Legal basis: Health and Social Care Act 2012

Purposes: ()

Sensitive: Non Sensitive

When:2017.03 — 2017.05.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Outpatients

Objectives:

The purpose of this application is to obtain data to be used in specific academic research projects evaluating the impact of financial and organisational reforms of the English National Health Service.

Over the past 10 years, multiple reforms have aimed at creating incentives for performance improvements amongst providers, and higher quality of care for patients. The aim of this research is to assess the extent to which the structure and design of these reforms has led to the intended improvements in productivity and outcomes. The data will be used in regression analysis using advanced econometric modelling (including linear and non-linear regressions models, multilevel models and difference-in-differences designs) to generate causal inferences on the impact on health policy and service structure and design on health outcomes. The data will only be used for the following projects:


Project 1: The effects of non-payment for performance

Since 2011 the English National Health Service (NHS) has aimed at reducing readmission rates by not paying for individual readmissions occurring within 30 days of discharge from hospital (Department of Health, 2011). In a similar spirit, the “never events” policy framework withholds payment for hospitalisations leading to undesired patient outcomes e.g. pressure ulcers during hospitalisation and wrong side surgery. The objective of this project is to conduct academic research on the effect of the non-payment policies on patients’ health outcomes directly and indirectly affected by those policies.


Project 2: The effects of the ‘payment by results for drug recovery’

The aim of this project is to evaluate the effects of the effect of the Payment by Results for Drug Recovery (PbRDR) Pilot Programme on patient health and hospital outcomes. Providers of services for substance misusers have their payments linked to the ‘national outcomes framework’. PbRDR was introduced to ‘facilitate better care leading to better outcomes for service users’ (DH 2010: 7) including patients’ health and wellbeing. The project will examine whether the introduction of PbRDR pilots led to changes in emergency hospital admissions and associated costs for conditions that indicate complications of drugs misuse.


Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme

Pay-for-performance (P4P) programmes link financial payments by purchasers to the quality of care supplied by health care providers, and have grown in popularity over recent years. Advancing Quality (AQ) was the first hospital-based P4P scheme to be introduced in England in October 2008. The objective of processing this data is to conduct academic research to investigate the effects of AQ on health outcomes of importance to patients: mortality, length of stay, and readmissions rates. This will be compared to the costs of the programme to the NHS to evaluate whether the AQ P4P programme represents a cost-effective use of NHS resources. No data will be made available to third parties.


Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes

There are long-standing concerns that patients admitted to hospital at night and at weekends, when staffing levels are lower and some services are not available, suffer higher complication and mortality rates that patients admitted at times when the hospital is fully operational. Salford Royal Foundation Trust (SRFT) has been gradually extending fully-operational service provision since 2007. The objective of processing this data is to conduct academic research to investigate outcomes for patients admitted to SRFT at the weekend and compare with those admitted to other hospitals in England that have not yet extended service provision. Outcomes of interest are length of stay, mortality, complications and readmissions. Analysis will estimate the effect of fully-operational hours on the quality of care provided to patients, and any improvements in outcomes as a result of increased quality. Data will not be made available to any third parties.

Expected Benefits:

Please see the individual project descriptions for details. The results from the research projects described above are expected to be published by 2018 and will shed important new light on how the framing and design of organisational performance incentives affect the outcome of such reforms. In addition to its academic value, the research will thus inform policy makers on efficient design of health care service organisation and performance incentives and how to best allocate scarce health care resources in the English NHS for the benefit of patients.


Project 1: The effects of non-payment for performance

The non-payment policies analysed in this project represent a genuine novelty in the approach to incentivising higher quality care in the English NHS. Where previous initiatives to improve health care quality has relied on paying a bonus for improved care quality – so called Pay for performance (for example Best Practice Tariffs) - the reimbursement policies analysed in this project relies on financial penalties for “poor performance” – the occurrence of ‘never events’ and high readmission rate – to improve quality.
England first introduced a non-payment policy for so-called ‘Never Events’ in 2009. A Never Event has been defined by the National Patient Safety Agency (NPSA) as “[a] serious, largely preventable patient safety incident that should not occur if the available preventative measures have been implemented by healthcare providers.” (NPSA, 2009). The list of Never Events is updated regularly, and for 2012/13 contained 25 events including wrong site surgery, severe scalding of patients, and unintended retention of a foreign object in a patient after surgical intervention (Department of Health, 2012a). If a Never Event occurs, providers must initiate an investigation into the causes of the event. In addition, the provider is not reimbursed for both the episode of care that involved the event, and for the costs of consequential treatment (Department of Health, 2012b).
In April 2011, England introduced a policy (Department of Health, 2011) according to which hospitals would no longer be reimbursed for emergency readmissions occurring within 30 days of discharge from an elective admission. Around 40% of all readmissions, including those for children under four years of age, maternity, childbirth and cancer patients, and those who self-discharge against clinical advice, were however excluded from these non-payment rules.
The policy was expanded after its first year of operation, and now mandatorily applies to both emergency and elective first admissions (Department of Health, 2012c). Any savings made by commissioners due to non-payment for readmissions must be reinvested in post-discharge reablement services which support rehabilitation, reablement, and the prevention of future readmissions.
Nonpayment policies are potentially more cost effective than pay for performance because there is no cost in terms of bonus payments.
However, although these policies that apply to all hospital in-patients have been a part of the Department of Health’s hospital reimbursement policies for years, the intended and unintended effects of the policies on patients’ health outcomes and on hospital performance is hitherto unknown.
The research outcome of this project will thus shed important light on the effect of non-payment policies on the intended and unintended effects of NP4P on patients’ health outcomes, and assess whether non-payment are effective in improving hospital performance on the targeted areas.
In addition to publishing the findings in academic research journals that are also read by policymakers, the findings from the project will be disseminated at conferences such as the UK Health Economic Study Group and the International Health Economics Association. These conferences are regularly attended by representatives from the Department of Health and NHS England, and the relevant actors in NHS policy making can learn about the results through these channels. In addition, when publishing research findings that are thought to be of importance to the public and policy makers, the University of Manchester generally issues press releases to ensure a wider dissemination of research findings.
If the findings are positive, policy makers in NHS England and the Department of Health involved in the design of payment policies for health care may consider applying non-payment policies to other areas of the health service or expand to other outcomes than readmission rates and never events. If the results indicate unintended consequences for patients’ health outcomes, or hospital gaming of the policies, policymakers may consider changing the design of the non-payment policies to avoid adverse consequences and gaming. The results have thus potentially important impact on both the patients using the health care service and the future design of health care reimbursement policies.
References:
Department of Health, 2011. Payment by Results Guidance for 2011-12. Leeds.
Department of Health, 2012a. The “Never Events” list 2012/13. London.
Department of Health, 2012b. The Never Events Policy Framework: an update to the never events
policy. London.
Department of Health, 2012c. Payment by Results Guidance for 2012-13. Leeds.
NPSA, 2009. Never Events Framework 2009/10. National Reporting and Learning Service -
National Patient Safety Agency.


Project 2: The effects of the ‘payment by results for drug recovery’

It is an important requirement of projects funded by the NIHR and the DH that applicants make a convincing case for the expected benefits of the research. This is assessed by expert reviewers and the funding panels at the award stage. Projects that do not offer benefits to health and social care are not funded by NIHR and the DH.


Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme

It is an important requirement of projects funded by the NIHR and the DH that applicants make a convincing case for the expected benefits of the research. This is assessed by expert reviewers and the funding panels at the award stage. Projects that do not offer benefits to health and social care are not funded by NIHR and the DH.


Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes

The research will provide evidence on how best to allocate scare NHS resources in order to obtain the maximum benefits in terms of patient health. This will inform policy makers on the costs and benefits of extending fully-operational hours for hospital services in England, and aid the efficient organization of NHS hospital services.

Outputs:

In summary, the research output from all projects will be used for academic research papers that will be submitted to peer reviewed academic journals in health policy, economics, health economics and health services organisation and delivery. In addition, the research will be included in PhD projects (project 2,3 and 4), and for reports made as part of work undertaken for the Department of Health (project 2) work funded by the NIHR HS & DR programme (project 4). Please see specific details in each project.

All outputs will consist of aggregate data only with small numbers suppressed in line with HES analysis guide.


Project 1: The effects of non-payment for performance

The project outcome is expected to be 2 publications in peer reviewed academic journals in the fields of health economics and health policy.
• A first version of the paper on the readmission policy will be presented at the Royal Economic Society conference in March 2016. The target submission date is 1st August 2016, target journal American Economic Journal: Economic Policy or similar,
• Paper on Never Events, target submission date 1st December 2016, target journal Health Economics or similar


Project 2: The effects of the ‘payment by results for drug recovery’

• A report was submitted to the Department of Health on the evaluation of the policy; initially submitted Autumn 2015 - currently awaiting peer review; further changes and use of the data may be required after peer review.
• A chapter for the PhD thesis of a PhD student which will be submitted to The University of Manchester, with a target submission date of January 2017.



Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme

The specific outputs expected are two publications in peer-reviewed academic journals in the field of health economics and health policy, and a PhD thesis, which will contain one of these papers. This will follow three previous peer-reviewed publications which have already been produced as a result of the project, making significant contributions to the literature on the effects of P4P on patient health outcomes:
• Sutton et al. (2012). Reduced mortality with hospital pay for performance in England. New England Journal of Medicine, 367, 1821-1828.
• Meacock et al. (2014). Paying for improvements in quality: a recent experience in the NHS in England. Health Economics, 23, 1-13.
• Kristensen et al. (2014). Long-term effect of hospital pay for performance on mortality in England. New England Journal of Medicine, 371, 540-548.
One paper, which assesses the impact of AQ on mortality for patients affected by the policy, has already been submitted to Medical Decision Making and is currently being revised for resubmission to the journal.


Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes

The specific outputs expected are four publications in peer-reviewed academic journals in the fields of health economics and health policy, and an overall project report to be submitted to the National Institute for Health Research (NIHR). These will follow one previous peer-reviewed publication which has already been produced as a result of the project, making significant contributions to the literature on the costs and benefits of extending weekend hospital services:
• Meacock et al. (2015). What are the costs and benefits of providing comprehensive seven-day services for emergency hospital admissions? Health Economics, 24, 907-912.
Papers will investigate the following questions:
1. What is the impact of changes to fully operational hours on access to services for different population groups?
2. How do service re-configurations affect quality of care and patient outcomes, and their

Processing:

The data will be stored within an access restricted data share on the University’s network storage infrastructure which is the recommended location for storing sensitive or critical University data. The storage infrastructure is hosted across two data centres (Kilburn Building and Reynolds House (approx. 2KM apart)) for resilience and disaster recovery purposes.

The research group are based in offices on the 4th floor of the Jean McFarlane Building and the data will be hosted on a strictly controlled data share within the University’s network storage infrastructure to which only six designated members of research group staff will have access permissions.

The data will only be used for the purpose listed in the four projects above and not for any additional projects without further permission. All five projects are addressing the same theme but there will be no cross-project sharing of data. Only the University of Manchester will have access to the data provided and no data will be shared with a third party or used for commercial purposes.


Project 1: The effects of non-payment for performance

The effect of the policies on patients’ health outcomes and resource use will be assessed using difference in differences analysis and duration analysis using advanced econometric methods appropriate for linear and nonlinear multilevel data. The outcomes analysed will be readmission rates within 30 and 90 days, time to readmission, resource use in the inpatient, outpatient and A&E setting, and the probability of experiencing a never event. All analysis will include controls for patient and provider characteristics.


Project 2: The effects of the ‘payment by results for drug recovery’

The effect of the policy will be assessed using difference-in-differences regression analysis, comparing risk-adjusted emergency admission rates between pilot and non-pilot areas over time.


Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme

The effect of the policy on the three outcomes listed above will be assessed using difference-in-differences regression analysis. Survival analysis will also be performed on the mortality data.


Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes

The effect of the policy on the three outcomes listed in the objective for processing will be assessed using difference-in-differences regression analysis.

Project 24 — DARS-NIC-326033-G1P7Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Non Sensitive

When:2016.09 — 2017.02.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care

Objectives:

The Trauma Audit and Research Network (TARN) is a non-commercial organisation affiliated with the University of Manchester and funded by membership fees from participant trusts. TARN is the mandated organisation for the audit of trauma care in England, as set out in and endorsed by Standard ISB 1606.

TARN holds Europe's largest database of traumatic injury, with hospitals within participating trusts entering details of relevant cases into TARN’s online system. Any trusts receiving trauma patients are participants.

The aim of TARN is to support service improvement by providing analytical feedback to trusts, in reports such as the major trauma dashboards. TARN is the means by which funding mechanisms such as Best Practice Tariff operate. The TARN site hosts the Best Practice Tariff report, which major trauma centres use to report to commissioners and receive payment. To ensure that analyses are as accurate as possible, TARN needs to ensure that its dataset is as complete as possible.

TARN requires HES data in order to measure completeness of submission to the TARN database for individual trusts and hospitals. Trusts are fed back results for their individual trust and information on completeness for all trusts is also displayed on TARN’s website. HES data helps TARN to help trusts ensure that all cases that are eligible for payment are submitted and can be reported on.

Expected Benefits:

The ultimate aim of TARN is to improve patient care. TARN carries out a number of planned and ad hoc analyses for hospitals and trusts to highlight good performance and areas where performance could be improved. A key element of TARN’s work is analysing rates of survival to identify hospitals with excess deaths, based on injury and demographic profiling.

This work is dependent on the quality/accuracy of information emanating from the trusts so the use of HES data to monitor and improve completion rates contributes towards this overall aim. Using HES data to calculate data completeness assists in determining whether apparent poor performance is related to poor data collection, or whether other issues exist that need to be examined further.

Using the HES data allows TARN to derive a denominator of expected cases, which in turn allows TARN to identify which sites may be missing patients. TARN can then work with those sites on case identification.

An additional benefit of improving case identification is Best Practice Tariff, where Major Trauma Centres receive payment for meeting national standards on a patient by patient basis.

The notification to trusts of potentially eligible patients that were not reported by the provision of patient lists linked to the TARN dataset using NHS number has resulted in significant improvements in data completeness. Improvements ranged from 5 to 50% with an average improvement of roughly 20%.

Outputs:

TARN’s outputs relevant to this application are:

• Data completeness measures at site and trust level which are published on the TARN website and are accessible to the public. The number of TARN cases for a given time period is expressed as a percentage of the denominator derived from the processed HES data. The site is updated three times a year, in March, July and November. See the Performance Comparison section at https://www.tarn.ac.uk/Content.aspx?ca=15 for details.
• Data completeness measures at site level which are published in the Major Trauma Dashboard. Circulated securely and confidentially to Major Trauma Centres in February, May, August and November.
• Data completeness measures at site level which are published in the Trauma Unit Major Trauma Dashboard. Circulated securely and confidentially to Trauma Units in February, May, September and November.
• Data completeness measures at site and trust level which are published in the TARN clinical reports. Circulated securely and confidentially to TARN member hospitals in March, July and November.

Data completeness measures contain no identifiable or record level personal data. All published data is aggregated with small numbers suppressed in line with the HES Analysis Guide.

• Patient lists sent securely and confidentially to sites for assistance with case identification. TARN sends hospitals lists of patients identified as TARN eligible from HES data for comparison with lists produced by local systems and procedures. These contain identifiable data as detailed above.

Processing:

TARN requires HES data from NHS Digital for patients with at least one ICD-10 code relating to traumatic injury. Any of these patients are potentially eligible for the TARN audit, which is concerned only with traumatically injured patients. TARN does not audit all traumatically injured patients: TARN further processes the received data to create a reduced cohort that correlates with TARN inclusion criteria. Records would be linked to hospitals and trusts using Organisation Data Service (ODS) codes. The purpose of this is to derive a number of TARN-eligible cases for each participating hospital and trust. This is then used to assist hospitals with case identification and to measure completeness of data.

TARN requires 2 different copies of the HES data:
1. A pseudonymised version containing no identifiable information from which no data will be removed because of Type 2 patient objections.
2. An identifiable version containing NHS Number for all eligible patients and excluding data for all patients who registered Type 2 patient objections;

Both datasets will be filtered by ICD-10 codes to identify cases that appear to be eligible for the TARN audit. The pseudonymised dataset containing all potentially eligible episodes will be used to determine the total number of eligible cases used in data completeness (case ascertainment) calculations. The identifiable dataset containing NHS number will be used to notify trusts of potentially eligible cases that were not reported by linking to the TARN database using NHS number. This is achieved through the provision of lists of patients (containing NHS number, arrival date, age, length of stay in hospital, discharge destination and the first 5 ICD-10 codes) sent securely and confidentially to the hospital that provided the treatment. Only data relevant to the specific hospital is sent to each hospital and no provider will receive data about individuals for whom they did not provide treatment. The data sent is derived from the HES data supplied by NHS Digital but is data already held by the trusts to which they are sent as the HES data originated from the trusts in the first place.

Data will only be processed as described above and access will be restricted to substantive employees of the University of Manchester and the hospitals that provided the treatment with which data may be shared. No data will be sent to other organisations other than to hospitals about the patients they have treated.


The HES data that TARN currently holds will be destroyed as it potentially contains identifiable information about patients who have opted out. Each year TARN will request the latest available year of final HES data and destroy the oldest year of data that TARN have if older than 3 years to ensure that TARN never holds more than 3 years at any one time.

MR806: BSPAR Enbrel Cohort Study (BSPAR EN) (Formerly: BSPAR, BNDR (Biologics and New Drugs Registry) for Juvenile Idiopathic Arthritis ( JIA ) patients) — DARS-NIC-179285-7RS6G

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Identifiable, Anonymised - ICO Code Compliant (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)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-11-01 — 2020-05-31 2016.04 — 2017.02.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, BRITISH SOCIETY FOR RHEUMATOLOGY, THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Scottish NHS / Registration
  3. MRIS - Cause of Death Report
  4. MRIS - Flagging Current Status Report
  5. MRIS - Members and Postings Report
  6. Cancer Registration Data
  7. Civil Registration - Deaths
  8. Demographics
  9. Civil Registrations of Death

Objectives:

The BSPAR Etanercept Cohort Study
Aims;
1) Safety monitoring of biological therapies and new drugs in compliance with drug
licences and NICE guidelines, and methotrexate (current standard therapy) in
juvenile idiopathic arthritis patients, with adverse event monitoring.
2) Collection of information on long term course of the condition, long term outcomes
and use of therapeutics in a cohort of children treated with biologic agents,
methotrexate or other new drugs for rheumatic disease.
Objectives;
1) To establish a register patients with severe juvenile arthritis.
2) To collect and record data on children and young people prescribed biological
therapies or new drugs for Juvenile ldiopathic Arthritis (JlG) including
demographic data, disease type and activity, outcomes and safety data.
3) To collect data on appropriate methotrexate treated controls.
4) To be able to extend data collection to incorporate new treatments
5) To provide for the further collection of data on longer term outcomes through
flagging with the NHS Central Register (now the HSCIC)

Yielded Benefits:

Information from the BSPAR ETN has had significant influence on the clinical practice in the UK and more widely. Evidence emerging from the BSPAR ETN has influenced National Institute for Health and Care Excellence (NICE) technology appraisals (TA), UK and other clinical practice guidelines, and patient information sheets. Particularly regarding the NHS England Clinical Commissioning Policy Statement “Biologic Therapies for the treatment of Juvenile Idiopathic Arthritis (JIA)” published in 2015. These influences have resulted in more consistent prescribing across the country. In addition, the NHS England Statement has included a paragraph encouraging registration into the register: “All children who commence treatment with a Biologic should be offered the option of enrolling in the appropriate long-term national Registries. These Registries are designed to provide long-term safety data for all these drugs and enrolment of data to the Registries is strongly recommended.” A major challenge for all research studies is the dissemination and implementation of results. The close collaboration with BSR representatives has ensured that study data are disseminated as widely as possible, including to policy makers such as NICE. The study team also work with rheumatologists to generate ideas for new analyses based on clinically relevant questions. The majority of the research is presented at national and international conferences. The study has also established an ongoing and mutually beneficial collaboration with “JIA at NRAS” (National Rheumatoid Arthritis Society; a patient-led organisation) as one route of dissemination. Lay summaries are created to make the research more accessible to the patients and their families.

Expected Benefits:

Planned future analysis/output will focus on two related and equally important research questions:

(1) What is the long term risk of exposure to established biologic therapies?
(2) What is the absolute and relative effectiveness and risk of new biologic therapies?

(1) What is the long term risk of exposure to established biologic therapies?
The first patient was enrolled into BSPAR ETN in 2004. Therefore, over the next few years the study team will start to observe who has been receiving etanercept therapy for over 10 years. This is an unexplored area due to the very nature of when this drug was introduced. Hence, the BSPAR ETN can and will give unique insight into the very long-term use of etanercept, including treatment persistence and long-term safety, such as late occurrence of malignancies. The presence of equally long follow-up in an untreated comparison cohort will add to these analyses.

(2) What is the absolute and relative effectiveness and risk of new biologic therapies?
The most challenging analysis will be to study the risks of biosimilar therapies. There has been a noticeable switch in some patients from etanercept originator Enbrel to etanercept biosimilar products in the UK, despite a lack of supporting evidence about the safety of switching. The study team’s analyses in this area will include a comparison of first-line etanercept biosimilar use with etanercept originator, using recent historical originator data as a comparison, as well as the safety of a switch programme. An analysis of outcomes after switching needs careful consideration due to inherent selection bias (patients who switch between originator and biosimilar have usually experienced a response to the originator and by definition, have not experienced a treatment limiting adverse event). This selection bias will have to be considered when changes in disease activity and occurrence of adverse events following a switch are analysed and the rich historical data within the BSPAR ETN should allow for this. Additional study data collection forms are in place to ensure as much data as possible is captured regarding disease activity data at the point of switching.


Ensuring output achieves maximum benefit for both clinicians and patients:
For any research project to achieve maximum benefit for all stakeholders it is essential to understand the needs of each stakeholder, all the while asking the “so what?” question. Capturing data without a specific purpose will lead to disinterest and disinvestment from the people the study team want to benefit the most. The BSPAR ETN has many stakeholders, not only clinicians and patients, and each of these is considered in turn.

Clinicians:
The study will continue a programme of outputs to address questions related to key safety concerns of biologic therapies based on email queries, discussions at conferences (locally, nationally and internationally) and other communications as well as the clinical practice of the Chief Investigator herself. The fact that these safety queries can be discussed with the team will be advertised more widely through newsletters and websites (BSR and BSPAR ETN). The pharmacoepidemiological research programme within the BSPAR ETN will be further driven by knowledge gaps identified in systematic reviews, such as during guideline development.

Patients:
The study team have developed a strong relationship with JIA-at-NRAS which also provides insight into the questions patients have about these therapies. The study has a website www.sites.manchester.ac.uk/bcrdbspar with a dedicated section for study participants. This is regularly updated with information about the study, with lay summaries of all work being of particular importance.

Regulators:
The study team do not report safety events directly to the EMA and/or the UK Medicines and Healthcare products Regulatory Agency (MHRA). However, increasingly, the study’s relationship with the regulators has become more direct and the regulators have recognised the value of embedding pharmacovigilance within patient registers (as opposed to independent pharmaceutical company sponsored observational research). The study will continue to provide a vehicle for risk management as new products come to market.

British Society for Rheumatology:
The study’s most important stakeholder is the British Society for Rheumatology. The BSR (formerly BSPAR) and UoM have worked closely together on the BSPAR ETN since the transition of the study to UoM in 2012 and the study team share the pride of the BSR in its ongoing success. The team at UoM continually strives to maintain the highest standards of this study in part to contribute to the global profile and reputation of the BSR. The study is committed to this ongoing partnership and there is representative from BSR at the bi-annual face to face Steering Committee Meetings. The study will continue to present both study and scientific updates at the annual Paediatric and Adolescent Rheumatology autumn conference and work with the BSR to explore even greater ways to disseminate findings, support recruitment and data collection, and to promote the study.

Outputs:

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

Subject to a future application, Tthe outputs for the BSPAR ETN study will include reports, submissions to peer reviewed journals and presentations and posters at relevant conferences. BSPAR ETN study data will be combined with NHS Digital data to maximise data available and used in outputs. Only aggregated data will be included in any study outputs and data will be safeguarded by ensuring that results which include small numbers which could identify study participants will be excluded.

Dissemination and communication of results to stakeholders includes regular review by BSPAR ETN project boards, including the BSPAR ETN Steering Committee and BSPAR ETN Data Monitoring and Ethics Committees. The study also has a comprehensive study website (https://sites.manchester.ac.uk/bcrdbspar/) which has areas aimed at Hospitals/Sites participating, study participants and researchers. In terms of exploitation of the results/outputs, the British Society for Rheumatology also has links back to the BSPAR ETN study site, in addition to details and the process of applying to access study data for research purposes (https://www.rheumatology.org.uk/practice-quality/registers).

To date, 6 original papers have been published on the BSPAR ETN data since the study moved to the University of Manchester in 2012. The research outputs from the BSPAR ETN study have contributed to a recent request from the Data Monitoring and Ethics Committee for the Steering committee to review the biologics and cancer risk statement, used by paediatric rheumatologists and specialist nurses when counselling families, to potentially provide further reassurance. The Committee decided not to amend it currently, but to re-confirm the position, with further cancer data provided (on the additional patients in the BSPAR ETN) the University of Manchester could be a position to fully review this statement should DMEC raise an updated request.

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 Manchester. Under this interim extension all devices containing data will be securely locked away in a locked cabinet at the University of Manchester 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 Manchester 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 April 2016.

Project 26 — DARS-NIC-147993-QY3ZL

Type of data: information not disclosed for TRE projects

Opt outs honoured: N

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2017.02.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration

Objectives:

Rheumatoid arthritis (RA) affects about 0.8% of the adult population in the UK. RA is also associated with increased and premature cardiovascular (CV) mortality. Almost half of all deaths in RA (and about 35 - 40% of the excess deaths) are due to cardiovascular disease (CVD). High levels of inflammation in RA sufferers can cause damage to blood vessels. Statins are drugs that can help to prevent diseases of the heart and blood vessels by reducing cholesterol and possibly inflammation.

This trial will investigate whether the statin called Atorvastatin (Lipitor) can reduce the occurrence of conditions such as heart attack and stroke in patients with RA. The trial also aims to investigate whether Atorvastatin can reduce the level of inflammation in the joints in people who have significant RA disease activity at the time that they are included into the trial.

MR559 - The Norfolk Arthritis Register (NOAR) — DARS-NIC-147811-YTH88

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Identifiable (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)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Sensitive

When:DSA runs 2019-06-01 — 2021-02-28 2016.04 — 2017.02.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Cause of Death Report
  3. MRIS - Scottish NHS / Registration
  4. MRIS - Flagging Current Status Report
  5. MRIS - Members and Postings Report

Objectives:

NOAR started in 1989 and is a long-term study of inflammatory arthritis in the community. The purpose of the Register is to study the natural history of arthritis and to identify genetic and non-genetic factors which may be related to the onset of arthritis, response to treatment, and to long-term outcome. We are also interested in learning more about the effects which arthritis may have on other medical conditions.

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 Manchester. Under this interim extension all portable devices containing data will be securely locked away in a locked cabinet at one of the storage addresses 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 Manchesher of participants’ deaths (date and cause) when they occurred. The ‘flagging for long-term follow up’ service transferred from ONS to the HSCIC in 2008. Data was last supplied in February 2017.

MR1002 - Correlation of Genotype & Phenotype in Myositis — DARS-NIC-147776-69CX7

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable (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 2022-01-06 — 2023-01-05 2017.06 — 2017.02.

Access method: Ongoing, One-Off

Data-controller type: THE UNIVERSITY OF MANCHESTER, UNIVERSITY OF MANCHESTER

Sublicensing allowed: No

Datasets:

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

Objectives:

The data supplied by the NHSIC to Salford Royal NHS Foundation Trust will be used only for the approved Medical Research Project identified above

Yielded Benefits:

Details of yielded benefits will be provided in a subsequent Agreement.

Expected Benefits:

Findings will be disseminated to clinicians through journal publication and presentation at specialist meetings (e.g. annual UKMYONET meeting). Clinician implementation of findings can lead to improved clinical outcomes for patients.

Implementation of previously identified clinical association with myositis specific autoantibodies (e.g. Jo-1 association with interstitial ling disease) has led to improved patient outcomes.

Improved knowledge of cancer/mortality associations with myositis specific auto antibodies can improve early cancer diagnosis rates and allow implementation of preventative mortality measures in patients with myositis.

All patients with myositis (estimated 10,000 people in the UK) will benefit from potential early cancer diagnosis and implementation of measures to prevent early mortality.

Outputs:

A paper has been accepted for publication (in-print) to Rheumatology. This paper utilised cancer data generated from NHS Digital.
- Oldroyd, A., Sergeant, J., New, P., McHugh, N. J., Betteridge, Z. E., Lamb, J., Ollier, W., Cooper, R. & Chinoy, H. The temporal relationship between cancer and adult onset anti-transcriptional intermediary factor 1 antibody positive dermatomyositis. Rheumatology. 16 Sep 2018

Several conference abstracts, utilising data from NHS Digital, have been accepted, published and presented at international conferences:
- American College of Rheumatology annual scientific meeting 2017 – “Anti-TIF-1 Antibody Positivity Is Associated with a Five-Fold Increase in Cancer Risk in the Idiopathic Inflammatory Myopathies”
- British Society of Rheumatology annual conference 2016 – “The Risk of Premature Death of both Cancer Associated and Non-Cancer Associated Myositis in UK Adult-Onset Myositis Patients is Significantly Raised Compared to the General Population”
- Annual European Congress of Rheumatology 2015 – “The Standardised Mortality Rate in UK Adult-Onset Myositis Patients is Seven Times Higher than the UK General Population”


Further planned analysis will take place to delineate the risk of death premature death and cancer associated with myositis specific autoantibodies. Mortality data from NHS Digital will be utilised. Findings will be submitted for publication in high-impact open-access journals.

All outputs, past and present, contain aggregated data in line with NHS Digital guidelines.

Processing:

Under previous iterations of this Data Sharing Agreement, Salford Royal NHS Foundation Trust (SRFT) provided files of identifiers (Patient’s forenames, surnames, date of birth, NHS Number) to the Health and Social Care Information Centre (now known as NHS Digital) for flagging. Prior to this Agreement, a total of 746 individuals had been flagged.

Since 2008 NHS Digital has provided linked mortality and cancer data along with the associated participant's forename, surname, date of birth and NHS number to SRFT.

SRFT stored the data on a server in the Clinical Science Building on encrypted, password protected Trust computers which can be only accessed at SRFT.

This data is accessed only by the study coordinator. A pseudonymised version of this dataset (i.e. containing no patient identifiers other than study ID numbers) is transferred to the University of Manchester where it can be accessed by researchers working on study analysis.

Under this Agreement, an additional 550 patients will be sent to NHS Digital and will contain NHS Number, full name, date of birth and pseudonymised study ID. These participants will be flagged and added to the MR1002 cohort that NHS Digital currently hold. Reports containing mortality, cancers, and exits will be sent to SRFT assigned to pseudonymised study ID only and applied to the database at SRFT.

The data received from NHS Digital can only be accessed by authorised individuals within SRFT and the University of Manchester for the purposes described, all of whom are substantive employees of one of those organisations.

Blood samples from recruiting centres are posted to University of Manchester (UoM) where the samples are processed and stored. UoM carries out genetic analysis on the extracted DNA and then batch transfers the extracted plasma to University of Bath for the purpose of performing antibody analysis. No data from NHS Digital is transferred to the University of Bath.

The data has been and will continue to be used to calculate mortality rate in myositis population and compared to norms. Cancer rates were studied in myositis population and compared to norms as well as comparison in between myositis sub groups in order to answer questions such as: ‘Do more people with dermatomyositis develop cancer as a result of cancer associated myositis (CAM) when compared with patients with polymyositis?’ and ’'Is cancer more common in patients with the Tiff 1 antibody in blood sample when compared to absence of tiff 1 antibody?’ Therefore, cancer data from NHS Digital has been linked with antibody data from research blood analysis within the University of Manchester.

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).

Project 29 — DARS-NIC-148412-BC33Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2017.09 — 2017.02.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report

Objectives:

The data supplied will be used only for the approved medical research project - MR739: Rates of Cognitive Changes Preceding Death in Later Life

Project 30 — DARS-NIC-179438-WCHHZ

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y

Legal basis: Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Non Sensitive, and Sensitive

When:2016.04 — 2016.11.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - List Cleaning Report

Objectives:

Tarn is a national audit for trauma care across England and Wales and has been commissioned by the Department of Health to look at the long terms outcomes of injured patients.
This request is to access mortality data in order to assess the long term outcomes of patients and obtain a better picture of the overall impact of traumatic injury ("the purpose").

Project 31 — DARS-NIC-148264-XXC29

Type of data: information not disclosed for TRE projects

Opt outs honoured: N

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Purposes: ()

Sensitive: Sensitive

When:2016.04 — 2016.11.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report

Objectives:

European Male Ageing study: Prevalence, Incidence and Geographical Distribution of Symptoms of Ageing in Men and their Endocrine, Genetic and Psychosocial Correlates.

Title appearing in all publications: the European Male Ageing study (EMAS).

Identify and quantify any disparities in the prevalence, incidence, nature and severity of symptoms and disabilities of ageing in the general male population from different regions of the EU.
Explain regional differences in the health status of ageing men on the basis of the decline in endocrine (hormonal) functions.
Elucidate the relationships between clinical features associated with ageing and the attendant hormonal changes.
Characterise threshold hormone levels for symptomatic and asymptomatic deficiency states in elderly men (i.e. identify the low hormone levels at which older men may become unwell or impaired).
Identify potentially modifiable region-specific and/or race/ethnicity-specific risk factors for the evolution and progression of symptoms, disabilities, changes in body composition and hormonal decline associated with ageing in the EU.

Inform European policy-makers and industry and formulate recommendations for screening, prevention, diagnosis and treatment of individual functional disabilities or an ageing-related clinical syndrome in men if it is found to exist.