NHS Digital Data Release Register - reformatted

University Of Sheffield projects

1046 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

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

A comparison of the effectiveness of different treatment regimens for pancreatic cancer using English cancer registry data — DARS-NIC-656862-L4M7T

Type of data: information not disclosed for TRE projects

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

Legal basis: Health and Social Care Act 2012 - s261(5)(d), Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2023-02-27 — 2024-03-23

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS Linked HES AE
  3. NDRS Linked HES APC
  4. NDRS Linked HES Outpatient
  5. NDRS National Radiotherapy Dataset (RTDS)
  6. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objectives:

This project aims to investigate whether or not English cancer registry data is sufficient for reliably comparing the effectiveness of different cancer treatments given in the NHS.

National Cancer Registration and Analysis Service (NCRAS) data will be used to replicate clinical trials that have already been done in patients with pancreatic cancer. The results from the registry-based analyses will then be compared to the results from the trial-based analyses. If the results are similar, this suggests that
registry data may be sufficient for comparing the effectiveness of different cancer treatments.

This is an important first step in showing whether registry data can be relied upon to compare the effectiveness of different cancer treatments. If it can, we can use
registry data to compare the effectiveness of different treatments in real world populations – going beyond the highly selected patient groups usually included in
clinical trials.

If it is not possible to successfully replicate clinical trial results using the registry data this suggests that the registry data is not good enough, or that effectiveness is different in the real world compared to in trials. We will investigate this and if there are problems with the data we will identify areas where data collection needs to be improved in order for registry data to be most useful.

Yielded Benefits:

The work I am leading, of which this project is part, has helped drive progress in the use of real world data in health technology assessment. Several related projects have now been initiated, and publication of the NICE Real World Evidence Framework is significant. Through seminars and presentations nationally and internationally I have disseminated information on planned research and also on methodological developments that will inform subsequent analyses.

Expected Benefits:

(a). Cutting-edge research resulting in a deeper understanding of the adequacy of English cancer registry data for estimating the comparative effectiveness of cancer treatments given in the NHS.
(b). World-leading applied and methodological research to inform best practice for evaluating effectiveness of cancer treatments using real world data.
(c). Better informed national and regional healthcare decision making, to the benefit of patients and the public.

Research outcomes (a) and (b) will inform the treatment of pancreatic cancer, potentially improving outcomes for patients in England, and involving innovative research of importance nationally and internationally. Through close collaborations with NICE and NICE's Decision Support Unit, the research will contribute knowledge that will inform methods used in national healthcare decision making through the NICE process, enabling better decision making and treatment for patients (research outcome (c)).

Outputs:

I have co-authored the following papers in this area since applying for these data:

- Gomes M, Latimer N, Soares M, Dias S, Baio G, Freemantle N, Dawoud D, Wailoo A, Grieve R. Target trial emulation for transparent and robust estimation of treatment effects for health technology assessment using real-world data: opportunities and challenges. PharmacoEconomics. Published online 25 Mar 2022.
- Gray J, Sullivan T, Latimer NR, Salter A, Sorich MJ, Ward RL, Karnon J. Extrapolation of survival curves using standard parametric models and flexible parametric spline models: Comparisons in Large Registry Cohorts with Advanced Cancer. Medical Decision Making 2021 Feb;41(2):179-193.

I have also taken on two PhD students to investigate methods for estimating the comparative effectiveness of cancer treatments using real world data sources, who have each made their own applications for data from NHS England. I have also been given the role of theme lead for the University of Sheffield's cancer research strategy, focusing on epidemiology, screening and early diagnosis, utilising big data sources. I have led a proposal for a 5-year research programme (currently under review) further investigating the use of cancer registry datasets to inform the health technology assessment process and the development of targeted interventions. I have presented a plan of the research that my PhD students and I seek to deliver in numerous seminars nationally and internationally. I also contributed to the development of the National Institute for Health and Care Excellence (NICE) Real World Evidence Framework, published earlier this year.

The analysis of the pancreatic cancer registry data that is the subject of this data access extension request remains a key part of the research programme, but conducting the analyses has been delayed due to the time commitments associated with the work described above. Hence my request for an extension. The work described above helps demonstrate the growing importance of comparative effectiveness analysis using real world data sources, and hence my planned analyses will make an important and valuable contribution to understanding in this area, which will directly influence the use of these data sources by health care decision makers.

Processing:

The University of Sheffield have identified pancreatic cancer as a suitable disease area for undertaking Target Trial analyses using NCRAS data. In the following section we justify this choice, and provide background information on pancreatic cancer and treatment options in England. We then specify 4 Target Trial analyses that we will undertake. Finally we specify the NCRAS data required to facilitate these analyses. Pancreatic Cancer In 2016, approximately 10,000 people were diagnosed with pancreatic cancer in the United Kingdom, and often pancreatic cancer is diagnosed at an advanced stage.

The prognosis is poor even for people diagnosed at an early stage of pancreatic cancer, where surgical resection is possible, with 5-year survival rates estimated at between 7% and 25%. Survival rates are extremely poor for people with metastatic disease, with median survival of between 2 and 6 months if untreated. A NICE Guideline on the diagnosis and management of pancreatic cancer, published in 2018,recommends that gemcitabine plus capecitabine should be offered as adjuvant treatment for people who have had sufficient time to recover after pancreatic cancer resection. Gemcitabine monotherapy should be considered for people who are not well enough to tolerate combination chemotherapy. FOLFIRINOX, a combination regimen consisting of oxaliplatin, in rinotecan, leucovorin and fluorouracil, is not mentioned in the NICE guideline, but is beginning to be offered as adjuvant treatment in the NHS, due to trial results published in December 2018.

For metastatic pancreatic cancer the NICE guideline recommends that FOLFIRINOX should be offered to people with an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. Gemcitabine combination therapy should be considered for people not well enough to tolerate FOLFIRINOX, with the first combination option being gemcitabine plus capecitabine. For people for whom FOLFIRINOX and gemcitabine plus capecitabine are unsuitable gemcitabine plus nab-paclitaxel is an option.[9]Gemcitabine monotherapy should be offered to people not well enough to tolerate combination chemotherapy. These guidelines seem to present a clear hierarchy of treatments for adjuvant and metastatic pancreatic cancer, and seem to suggest that there might be little overlap in prognostic characteristics of patients receiving different treatments. However, the NICE technology appraisal of gemcitabine plus nab-paclitaxel notes that some patients for whom FOLFIRINOX is otherwise suitable choose not to have this treatment because of its considerable toxicity. Further, it is noted that the current treatment options have a number of limitations, including serious adverse effects –in particular, the most effective treatment option(FOLFIRINOX) is associated with the most significant adverse events, whereas the least effective (gemcitabine monotherapy) is associated with the least significant adverse events. In addition, it is unfortunately the case that prognosis remains poor even with the most effective treatment. Therefore, it is likely that due to patient choice, there will be overlap in prognostic characteristics between patients who receive FOLFIRINOX and patients who receive gemcitabine for metastatic pancreatic cancer. Similarly, because gemcitabine combination therapies have lower effectiveness and toxicity than FOLFIRINOX, and higher effectiveness and toxicity than gemcitabine monotherapy, it is likely that there is some overlap in prognostic characteristics between patients who receive FOLFIRINOX, gemcitabine combination therapies, and gemcitabine monotherapy. The NICE technology appraisal guidance for gemcitabine plus nab-paclitaxel states that there is evidence of use of gemcitabine doublet chemotherapy for pancreatic cancer in the NHS.

Similar is likely to be true for adjuvant treatment for pancreatic cancer, where gemcitabine plus capecitabine is more effective than gemcitabine monotherapy, but where toxicity is lower for the monotherapy option and prognosis is relatively poor with both treatment options. Hence, it is likely that there is variation in treatments received for adjuvant and metastatic pancreatic cancer in the NHS, with an overlap in characteristics of patients receiving different treatments. This echoes clinical expert opinion from Professor Jonathan Wadsley, who states that for both adjuvant and metastatic pancreatic cancer there is substantial overlap between patients receiving different treatments. For adjuvant treatment, Professor Wadsley believes that due to the additional side effects and limited increase in effectiveness associated with combination treatment, some patients choose gemcitabine monotherapy instead of gemcitabine plus capecitabine, and in fact some patients choose no treatment at all. For metastatic disease, Professor Wadsley believes that treatment with gemcitabine monotherapy remains common, with people choosing it instead of the highly toxic FOLFIRINOX regimen, whilst some patients receive gemcitabine combination therapy. To be able to compare the effectiveness of different treatment options in registry data there needs to be some overlap in prognostic characteristics between patients receiving the different treatments. Based on statements made by clinical and patient experts in NICE technology appraisal documents and information from a practicing clinician who treats people with pancreatic cancer, we are confident that such overlap exists for the treatment of both adjuvant and metastatic pancreatic cancer in the NHS. Target Trial Analyses We have identified four pancreatic cancer trials that we will try to replicate using NCRAS data, using Hernan and Robins’ Target Trial framework. The details of these analyses, under the headings used by Hernan and Robins, are presented for each Target Trial in the following four tables. For each Target Trial, two sets of analyses will be completed. One set of analyses will be undertaken whereby the population analysed will match that included in the RCT being emulated as closely as possible, based on the eligibility criteria of the RCT. These analyses will be compared to the RCT results, allowing us to determine whether or not it has been possible to successfully emulate the RCT. A second set of analyses will be undertaken for a broader real world population, without applying the same eligibility criteria used in the associated RCT. For example, in Target Trial 1, the ESPAC-4 RCT included strict eligibility criteria (shown in the Table below). In our first set of analyses we will attempt to replicate the trial population using these eligibility criteria. In our second set of analyses we will not use these eligibility criteria, instead analysing the effectiveness of gemcitabine monotherapy compared to gemcitabine plus capecitabine in any patient aged 18 or older who had adjuvant pancreatic cancer and received either of these regimens. The second set of analyses will allow us to estimate the effectiveness of treatment in a more general real world population. Note that for all main analyses the minimum follow-up time used in the Target Trials will match that used in the trials being emulated. However, to make full use of available data, supplementary analyses will be conducted that do not include a minimum follow-up time.

The data will be stored securely on centrally provisioned University of Sheffield virtual servers and research data storage infrastructure as Stata datasets for a period of two years. Access control is by authorised University computer account username and password. Off-site access is facilitated by secure VPN connection authenticated by University username and remote password. By default, two copies of data are kept across two physical plant rooms, with a 28 day snapshot made of data and backed up securely offsite at least daily. This service is maintained by the University’s Corporate Information and Computing Services. We will comply with the Data Protection Act and the University's own Information Security and Data Protection Policies as well as the School of Health and Related Research (ScHARR) Information Governance Policy. Because the data will be de-personalised rather than completely anonymous data will not be placed in a repository or made publicly available. On or before the effective date of termination or End Date of the data sharing agreement (expected to be 2 years after data receipt), the data provided will be securely and permanently destroyed or erased such that it cannot be recovered or reconstructed, together with all hard or soft copies of the manipulated or derived data generated from the data.

In order to allow the analyses conducted during this study to be re-produced detailed information regarding the exact data extract received and the programming code used to analyse it will be recorded and made publicly available. This would allow an interested party to request the same extract of data from ODR, and to re-produce the analyses. The data will be analysed in Stata by Dr Nicholas Latimer to estimate the comparative effectiveness of treatments for pancreatic cancer, as described above. All analyses will be documented in Stata .do files. Dr Nicholas Latimer will be responsible for implementing the data management plan, and ensuring it is reviewed and revised if required. ODR operate a cost recovery framework, and charge for the time taken to provide the data extract.

All data will be processed by substantive employees and a registered PhD student of the University Of Sheffield, all individuals processing the data will receive appropriate training in data protection and confidentiality. The research team and student will comply with the Data Protection Act and University Of Sheffield’s own Research Degree Students Code of Practice, University Of Sheffield’s Misconduct Regulations, University Of Sheffield Information Security Policy and Data protection policy. The listed data controller accepts liability for any action of the PhD student in relation to NHSD data.

Pre-Hospital Early Warning Scores for Sepsis — DARS-NIC-324608-Q0G8L

Type of data: information not disclosed for TRE projects

Opt outs honoured: Identifiable, 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(7)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-08-01 — 2022-06-30

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Civil Registration (Deaths) - Secondary Care Cut
  2. HES:Civil Registration (Deaths) bridge
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Critical Care
  6. Civil Registrations of Death - Secondary Care Cut
  7. Hospital Episode Statistics Accident and Emergency (HES A and E)
  8. Hospital Episode Statistics Admitted Patient Care (HES APC)
  9. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

The School of Health and Related Research (ScHARR) at the University of Sheffield are running a research study to address the National Institute for Health and Care Excellence (NICE) research recommendation “Can early warning scores be used to improve the detection of sepsis and facilitate prompt and appropriate clinical response in prehospital settings and in emergency departments?” (NICE 2016). The specific research question to be addressed is “What are the accuracy, impact and cost-effectiveness of prehospital early warning scores for adults with suspected sepsis?” This study is not part of a wider project or collaboration.

Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection. Early recognition and treatment of sepsis are essential to reducing mortality. The Surviving Sepsis Campaign Bundle 2018 update recommends delivery of intravenous fluids and broad-spectrum antibiotics within one hour of presentation. This can only be achieved if sepsis is recognised and prioritised in the emergency care system.

Sepsis can be recognised by identifying clinical features such as altered mental state, low blood pressure or rapid respiratory rate. Early warning scores use simple measurements to calculate a score, with a higher score indicating a higher risk of serious illness and adverse outcomes. They can be used by prehospital providers, such as ambulance paramedics, to identify people with suspected sepsis who need to be prioritised for treatment.

An effective early warning score needs to ensure that people with the potential to benefit from urgent treatment are not missed. This can lead to delayed treatment and avoidable mortality and morbidity; whilst also ensuring that people without sepsis are not inappropriately prioritised

Prehospital early warning scores can be used to prioritise treatment for people at high risk of sepsis but the accuracy of existing scores and the impact of their use is uncertain.

ScHARR at the University of Sheffield have the specific study objectives to:

1. Estimate the accuracy of early warning scores for predicting potential to benefit from time-critical treatment for sepsis

2. Estimate the operational consequences and cost-effectiveness of using early warning scores to guide prehospital decisions for adults with suspected sepsis

The data provided by NHS digital will allow the study team to identify a manageable number of patients for focused instigation by hospitals, rather than looking at tens of thousands of patient records. The NHS digital data will allow the study team to describe the study cohort, the treatments they have received, and their diagnoses. The NHS digital data will also allow the study team to estimate the health and financial benefit of implementing any early warning scores. These actions would not be possible in any reasonable or accurate way within the constraints of the study budget or time without the support of data from NHS digital.

The study is a retrospective diagnostic cohort study using routine data sources across four acute hospitals with associated ambulance services and decision-analytic modelling. The target population is adults transported to hospital by emergency ambulance with possible sepsis. Patient report form data will be used to identify eligible cases and collect pre-hospital measures used to calculate early warning scores. Cases will be linked to routine hospital data sources. The index tests will be prehospital early warning scores identified through a systematic literature search and selected by an expert group, who will also consider if there is a need to create any additional scores.

The passing of NHS Digital data to ScHARR University of Sheffield has three purposes 1) to allow ScHARR University of Sheffield to identify those patients who may have had sepsis and warrant investigation for inclusion to the study reference standard. The reference standard is how we define if someone has sepsis and therefore warrant further investigation. 2) To securely send NHS numbers to participating hospitals to allow them to identify patients and the relevant episode for inclusion to the reference standard. 3) To allow the University of Sheffield to effectively describe the patient cohort, the treatments they received, and their outcome, e.g. mortality.

The data subjects in this research study are patients conveyed from two participating ambulance trusts to the four participating hospital trusts. The study will include all adult patients transported to hospital by emergency ambulance unless they would clearly not be managed as suspected sepsis. A group of approximately 200 patients with confirmed sepsis will be identified within this cohort. This sub-group will act as the reference standard positive for early warning score testing. This number of patients will allow ScHARR University of Sheffield to estimate how accurate existing scores are at detecting sepsis.

ScHARR University of Sheffield will create a decision-analytic model to estimate the impact of using early warning scores to: (i) Alert the receiving hospital so that the patient is seen immediately on arrival; (ii) Provide prehospital treatment for sepsis. The model will simulate the prehospital management of a hypothetical cohort of patients with suspected sepsis and then model their flow through an emergency department (ED), alongside all other patients attending the ED. The model will estimate the operational consequences of using different early warning scores, in terms of the number of attendances appropriately and inappropriately prioritised and/or given prehospital treatment. They will adopt a health service perspective to estimate costs and will value outcomes as Quality Adjusted Life Years (QALYs) to estimate the incremental cost per QALY of using different strategies and will undertake a full incremental analysis.

ScHARR University of Sheffield are requesting NHS Digital data to help estimate the number of patients transported to hospital that may require a pre alert of their condition to an ED, allowing the ED to provide more timely care.

The data being requested is individual-level health data, identifiable by NHS number, and includes:
Civil Registration Deaths (Secondary Care Cut) - This will be used to identify patient outcomes including mortality and recorded cause of death.

HES Admitted Patient Care (APC) and HES A&E – These will be used to describe the patient cohort (including characterising patients who would be identified by an early warning score but who do not have sepsis – false positives; and characterising patients who have a low early warning score but meet the study’s reference standard of sepsis – false negatives); identify cases with the reference standard of sepsis in the cohort for assessment against early warning scores; and contribute to the care impact and economic modelling of early warning scores.

HES Critical Care (CC) – This will assist with identification of sepsis in the cohort, and contribute to the care impact and economic modelling of early warning scores.

Identifying false positives and false negatives, alongside the outcomes (e.g. treatments and diagnoses) of patients in the reference standard will help estimate the operational consequences and economic modelling of early warning scores. The NHS Digital data will also allow the study to identify secondary benefits to early warning scores, e.g. identification of other conditions.

Data fields being requested provide information on investigation activity conducted for patients, treatments provided to patients, patient diagnosis, comorbidities, discharge information, and basic demographics.

Individual-level data is required to be able to achieve effectively the study aim by linking individual predictor data to NHS Digital data and the reference standard. Individual-level patient data is required for us as a study team to link together records from Ambulance services, NHS digital, and hospitals. Without individual-level data the study team could not assess early warning scores for which is most effective at detecting sepsis and sepsis related adverse in a pre-hospital setting. No less intrusive ways have been identified to achieve the study purposes to the same level of academic rigour. The level of data requested for ScHARR University of Sheffield is considered the minimum required to complete the study effectively.

The period of time requested has been chosen as the most recent year where complete data is likely to be available without interference from COVID-19, this being 2018/19 & 2019/20. The aim is to access data from the whole of 2019. One whole year of data is being requested as there is a need to fully capture different seasonal episodes of sepsis.

ScHARR University of Sheffield are only requesting Ambulance conveyance to EDs between Yorkshire Ambulance Service or West Midlands Ambulance Service and the four participating hospitals. This geographical spread, i.e. the number of Ambulance trusts and hospitals, has been selected to allow for the required number of patients with Sepsis to be identified for inclusion in the reference to effectively test early warning scores.

Combined, the Ambulance services of West Midlands and Yorkshire expect to identify an estimated 92,000 cases across one year transported to the four participating hospitals. The Pre-Hospital Early Warning Score (PHEWS) research team anticipate that around 200 will be reference standard positive. The estimate of the incidence of reference standard positive cases is based on data from the Intensive Care National Audit and Research Centre (ICNARC, 2012) and National Confidential Enquiry into Patient Outcome and Death (NCEPOD, 2015) investigations of critical care cases with sepsis.

Data minimisation
. The following steps have been taken to minimise the data being requested in the application:
• Only data relevant to the cohort of patients provided to NHS Digital by the two participating Ambulance Trusts is being requested.
• Only requesting 12 months worth of data.
• Ambulance services are removing patients who meet the study exclusion criteria, i.e. under 18s, patients with injury, mental health problems, cardiac arrest, and direct transfers to specialist services e.g. maternity, cardiac or stroke services.
• Only the data fields that will assist in achieving the stated study aim and objectives are being requested.

We will not be excluding patients with symptoms such as delirium. The diagnostic classifications used by the two ambulance services code presentations such as delirium in ways that we have classified as non-specific presentations, such as confused/distressed/upset, and have therefore included in our cohort. We only exclude those coded with clear mental health classifications, i.e. Schizophrenia, Depression, Bipolar Disorder, Anxiety Disorder, OCD, Eating Disorder, Personality Disorder, Dementia, Brain Injury, Alzheimer's, Schizoaffective Disorder.

We specifically stated in our funding application that we would exclude children on the basis that “The presentation and management of sepsis differs markedly between adults and children so the use of early warning scores in these patients therefore needs to be studied separately”. We are therefore not funded to undertake an evaluation involving children. The National Institute for Health Research, which funded our study, has funded other research into an assessment tool for unwell children, including those with suspected sepsis. Specifically, this project - https://www.fundingawards.nihr.ac.uk/award/PB-PG-0815-20034.

We do not specifically exclude pregnant women, but will exclude women presenting with obstetric diagnostic classifications (i.e. Eclampsia, Ectopic, Hyperemesis, Labour, Miscarriage, Pregnancy, PV Bleed, Child Birth, Obstetric (other)) that would result in their being taken to a specialist maternity service rather than a general emergency department. Paramedics would not use an early warning score for sepsis to prioritise women who were being taken to a specialist maternity service with an obstetric complaint.

The processing of NHS Digital data under this data sharing agreement is considered to be in the public interest in the area of public health due to the potential to improve the standard of care provided to patients with sepsis with early identification of their condition in a pre-hospital setting. The General Data Protection Regulation legal bases are Article 6(1)(e) - Processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller and Article 9(2)(j) - Processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. This research will be working towards ensuring high standards of quality and safety of health care provided to the public by prehospital and emergency department services in the early detection and treatment of Sepsis.

The University of Sheffield is the data controller and processor in this application. The four participating hospital trusts are also acting as data processors of data provided by NHS digital to the University of Sheffield. The four hospitals are University Hospitals Coventry & Warwickshire, Doncaster and Bassetlaw Teaching Hospitals NHS Foundation Trust, The Rotherham NHS Foundation Trust, and Sheffield Teaching Hospitals NHS Foundation Trust. Additionally, Yorkshire Ambulance Service NHS Foundation Trust and West Midlands Ambulance Service University NHS Foundation Trust will provide NHS Digital with data.

This is an NIHR funded study that commenced on July 1st 2019 and is scheduled to finish on the 30th June 2022.

Expected Benefits:

The dissemination of results from this study will hopefully contribute to the debate on the improvement in “the provision of health care” within the health care system. This will aim to be achieved by providing evidence on what ways pre-hospital early warning scores for sepsis can detect sepsis and if they can be cost effective.

The ultimate aim of this project is to identify an optimal prehospital early warning score for the NHS, in terms of maximising prioritisation of treatment for people with sepsis without over-burdening the emergency care system. If this is achieved, then the early warning score would be recommended by the study team in relevant national guidance and leading to potential implementation across the NHS. This may lead to better treatment for people with sepsis and/or more targeted prioritisation of treatment across the emergency care system. In turn this could contribute to a reduction in mortality and morbidity.

The study outputs should help to inform policy makers, professionals, and the public on the accuracy of pre-hospital early warning scores for predicting potential to benefit from time-critical treatment for sepsis. Additionally, outputs should further inform policy makers, professionals, and the public on the operational consequences and cost-effectiveness of using early warning scores to guide prehospital decisions for adults with suspected sepsis.

We believe that the publications on the accuracy, impact and cost effectiveness of pre hospital early warning scores for sepsis is in the public interest. No individual would be identifiable from the information that we propose to be published from this study. However, details about patient groups, e.g. those who have had sepsis, would be published. We are consulting with their patient representatives group on their views of the study being in the public interest, including those from an interest perspective of emergency care, Ambulance, and sepsis.

The benefits from this study, processing of data, and subsequent publication outputs is to reduce mortality and morbidity, and can contribute to practice and/or debate on the pre-hospitalisation early detection of sepsis, appropriate targeting of resources for sepsis, and the cost effectiveness of using early warning scores for sepsis. Depending upon the results the study may lead to a change in the pre-hospital early warning score approach used in the NHS to improve patient care and/or cost benefit.

Outputs:

This project is being undertaken to address a NICE research recommendation: “Can early warning scores be used to improve the detection of sepsis and facilitate prompt and appropriate clinical response in prehospital settings and in emergency departments?” (NICE 2016). It is expected to communicate their findings to the NICE Guideline Development Group in the form of a study report due July 2022. The University of Sheffield is expected to also send their findings to other key organisations responsible for producing guidelines for the management of sepsis, including the UK Sepsis Trust and the Joint Royal Colleges Ambulance Liaison Committee.

The study's dissemination is expected to be built around informing health care professionals, policy makers and the wider public on the effectiveness of sepsis early warning scores in pre-hospital/ambulance settings. With this in mind it is hoped that dissemination channels will be selected to maximise the impact and reach of results, depending on their applicability to that audience. Dissemination channels is expected to include but not be limited to: open access peer reviewed publications, online report, website, social media, presentations and posters. All publications are hoped to be open access, funds have been secured within the project grant for this purpose. The study outputs will be drafted in conjunction with the studies patient and public involvement (PPI) group. Additionally, two members of the PPI group are study co-applicants, meaning they are involved in all project management meetings and will be co-authors on the study’s final report.

It is expected that The University of Sheffield will prepare plain language and professional summaries of their findings, downloadable presentations and materials that can be used to calculate early warning scores and present their operational consequences in a tailored format (for example, the consequences for an ambulance service or for an Emergency Department (ED)). These plain language summaries will be created and approved in partnership with members of the study’s PPI group.

The University hope to publish their findings in high-impact, open access, peer-reviewed journals and aim to present at relevant professional meetings, such as the 999 Emergency Medical Services Research Forum and the Royal College of Emergency Medicine Annual Scientific Meeting. They will send a summary of their findings to each NHS ambulance service, with links to dissemination materials.

To reach the public and interest groups in the sepsis community organisations such as the sepsis trusts will be contact to pass on if appropriate disseminate results from the study. This aspect of the study dissemination plan will be developed alongside the study's patient representative group, which has members from, Sheffield Emergency care forum, Yorkshire Ambulance Trust, the UK Sepsis Trust.

The data in publications will be aggregated data with small numbers suppressed in line with the HES analysis guide.

Expectations from the funder will be a minimum of an end of study report. The study has already secured funding from the funder for two potential publications and a conference.

Processing:

Yorkshire and West Midlands Ambulance Services will identify patients transferred to any of the four participating hospital trusts between 1st January and 31st December 2019 who meet study inclusion criteria. Patients to whom any of the study exclusion criteria apply, i.e. under 18s, patients with injury, mental health problems, cardiac arrest, and direct transfers to specialist services e.g. maternity, cardiac or stroke services will not be included the cohort.

The Ambulance Services will send NHS Digital the direct patient identifiers (NHS number, date of birth, and sex) along with unique study identifiers for each member of the cohort via NHS Digital’s Secure Electronic File Transfer system. They will also send ambulance patient data affiliated with the unique study identifier to ScHARR University of Sheffield, but no direct identifiers. Data from the Ambulance Services will be transferred to an authenticated account on ScHARR University of Sheffield servers, provided by the University for this purpose, using an encrypted connection. Two separate passwords (1. to access the server account and 2. To unencrypt the encrypted datasets) will only be provided by telephone.

NHS Digital will retrieve the patient data that ScHARR University of Sheffield has been approved to receive. NHS Digital will screen the patient list for patients who have opted out of research and remove them. NHS Digital will then provide the HES and mortality data with the affiliated unique study identifiers and patient NHS numbers and securely send it to ScHARR University of Sheffield. ScHARR University of Sheffield will screen the patient data received from NHS Digital to identify patients who may have ultimately received the reference standard diagnosis of sepsis after being transported to one of the participating hospitals.

All data received by the University of Sheffield will be stored on a virtual machine with access controlled by the Information Asset Owner. Access to the virtual machine will be restricted to a minimum number of people. All users granted access to the study data will be compliant with the ScHARR Information Governance policy and processes.

ScHARR University of Sheffield will provide participating hospitals with data on patients who attended their hospital only. The four participating hospitals will have read-only remote access to NHS numbers, unique study identifiers, and date of linked attendance/ admission only for relevant episodes that the ScHARR study team believe warrant investigation for inclusion to the study reference standard. The four participating hospitals will only have this data provided on patients that meet the study inclusion criteria, including arrival in the relevant hospital’s emergency department in 2019. This data will be held on a RedCap database hosted on University of Sheffield servers. Only members of the study team at the hospital will have access to these identifiers; in order to identify the patient and the relevant episode of care. The Information Asset Owner at ScHARR University of Sheffield will control access. The four hospitals will use the data from NHS Digital along with patient notes to determine who did or did not have sepsis.

Participating hospitals will inform ScHARR University of Sheffield on whether a patient had sepsis and some observations made on the patient in the hospital’s emergency department. This includes: admittance to critical care, if no critical care admission why not, details of sepsis 3 criteria (internationally recommended criteria for diagnosing sepsis), was treatment withdrawn on the basis of a lack of response, the potential value of early treatment, patient’s clinical frailty score (a measure to assess a patient’s frailty), was there a Do Not Attempt Resuscitation order or upper level of care decision (commonly known as a ceiling of care) present for the patient, patient's Charlson Co-morbidity Index (the number of pre-existing illnesses that the patient has), and patient survival details. Further details found about the reference standard are in the study protocol. Data from participating hospitals is being returned to ScHARR University of Sheffield through a Red Cap database hosted on University of Sheffield servers. The data provided by participating hospitals is vital to assess the severity of sepsis and the treatment provided. This is data that cannot be obtained from other sources and compliments data provided by NHS digital.

ScHARR University of Sheffield will link patient data provided by Ambulance Services, NHS Digital, and reference standard details provided by participating hospitals. These linkages will be achieved by the unique study identifier being present at each stage of data collection/identification and sent to ScHARR University of Sheffield. This study will not be seeking to re-identify participants for research purposes outlined in this application.

Data processing is being conducted by participating hospitals and ScHARR University of Sheffield. ScHARR University of Sheffield will process NHS provided data to 1) link to data provided by Ambulance services, 2) to identify patients that warrant further investigation for the study reference standard 3) describe the cohort of patients, 4) provide direction to participating hospitals with NHS numbers, study identifiers and date of attendance/admission for relevant episodes for patients to be investigated for inclusion in the reference standard. The description of the cohort is integral for the study to assess the impact of early warning scores, e.g. their economic impact on services or secondary benefits. Additionally, identifying patients to be investigated for inclusion in the reference standard is integral to the study/hospitals being able to identify a full range of patients with sepsis and therefore allowing for a more accurate assessment for early warning scores.

A statistical method called receiving-operator characteristic (ROC) curves will be used to graphically represent and compare how accurately different approaches or early warning scores identify sepsis. The ROC curve allows us to unite data from patients who had a correctly identified case of sepsis (true positive rate) against those who had sepsis but were not treated correctly (false positive rate). A range of ROC curves will be created to test the scores’ different ranges. True positive rate, true negative rate, and the area under the ROC curve (a measure of diagnostic accuracy) will be reported.
This study will also explore whether a clinically credible new score can be derived to improve upon existing scores. This would entail the best predictor variables being selected from a set of potential predictors, with the aim of reducing complexity and enhancing prediction accuracy.
The proposed approach to data processing in this study has been carefully considered by members of the studies patient and public involvement (PPI) group. This group includes members with personal experience of sepsis and care in emergency care settings. Two of these members are co-applicants to the study and have been involved in the design and ongoing management of the study as members of the study’s project management group.

Trauma and comorbidity of psychosis with other psychiatric disorders — DARS-NIC-242486-R1G4D

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), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2019-03-11 — 2022-03-10

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Adult Psychiatric Morbidity Survey
  2. Adult Psychiatric Morbidity Survey (APMS)

Objectives:

The University of Sheffield requires the APMS dataset for use in a rolling programme of research which aims to address two scientific questions:

Research question 1: What is the comorbidity between psychosis and other psychiatric conditions, especially bipolar disorder, post-traumatic stress disorder and autism.

Research question 2: What is the relationship between early traumatic experiences, other social and demographic characteristics and the symptoms of psychosis (schizophrenia and bipolar disorder)?

Research question 1 arises from the observation that many psychiatric disorders are comorbid (that is, that diagnoses overlap to an extent that conditions that are usually thought as separate may in fact be different manifestations of the same underlying conditions). There is already evidence that this is the case for ‘schizophrenia’ and ‘bipolar disorder’ (Tamminga et al., 2014) and that ‘autism’ may share some common genetic (Owen, 2012) and psychological mechanisms with psychosis (Craig, Hatton, & Bentall, 2004).

Research question 2 arises as a consequence of previous work showing that trauma in childhood is a strong predictor of severe mental illness (Varese et al., 2012). There is some evidence that specific psychotic symptoms are related to specific childhood adversities e.g (Bentall, Wickham, Shevlin, & Varese, 2012) but this has been disputed (van Nierop et al., 2014).

The data from the Adult Psychiatric Morbidity Survey 2014 (APMS2014) includes variables of childhood trauma, bipolar symptoms, autism, and psychotic symptoms collected from a large representative UK sample, so it will allow these issues to be investigated in the UK population. The data subjects are adults aged 16 or older who contributed to APMS201.

The analyses of the data will be carried out in accordance with Article 6(1)(e) and 9(2)(j) of the GDPR - the processing is necessary to perform a task in the public interest. In order to inform the treatment of psychiatric disorders, and to develop a principled approach to public mental health, it is necessary to identify how different social determinants are associated with psychiatric disorders. This problem is made difficult by disputes about how psychiatric disorders should be optimally classified.

Ethics approval is not required for secondary analysis of pseudonymised epidemiological datasets.

The applicant on behalf of the University of Sheffield has been conducting research with the aim of answering the two questions above since 1985. Different analyses, utilising new research techniques and methodologies as they evolve, will be undertaken to answer the above questions.

The University of Sheffield conducts independent research but collaborates with a network of other Universities (specifically the Universities of Ulster, Manchester and Liverpool) to share findings and discuss approaches, priorities, etc. Outputs from the processing of the APMS data may be shared with these collaborating organisations but such outputs will contain only aggregated data with appropriate small number suppression.

The University of Sheffield is the sole data controller with sole autonomy for determining the purposes for processing the APMS data and the manner of processing. The data will only be processed by the applicant and his PhD students – all of whom are personnel of the University of Sheffield. No other organisation will process the data.

Expected Benefits:

The research will contribute to improving understanding of the structure of psychiatric disorders (e.g. whether psychosis and autism are separate conditions) and of social determinants of severe mental illness. This will inform the treatment of psychiatric disorders and assist in developing a principled approach to public mental health. There will be implications for clinical practice (psycho-diagnostics) and social policy (public mental health) but no set target date for achieving these benefits.

Current National Institute for Health and Care Excellence (NICE) recommendations for cognitive behaviour therapy as a primary psychological treatment for severe mental illness are based on the results of clinical trials and other research in this field and including work which has been undertaken by the applicant. Further studies of the relationship between attachment difficulties and complex trauma are amongst NICE’s current recommendations for research.

Outputs:

All outputs will only contain data that is aggregated in line with NHS Digital guidelines.

Together with colleagues and postgraduate students, the researcher will write research papers describing the findings. The first paper on co-morbidity between psychotic symptoms and autism, is expected to be ready for journal submission in Spring 2019. Analyses using more advanced approaches such as network analysis, to be conducted with postgraduate students, will take considerably longer. This is an ongoing programme of work which will yield periodic outputs. These will include keynote presentations at relevant national and international conferences and impact journals.

As an example, the applicant will be delivering a key note presentation at the 9th World Congress of Behavioural and Cognitive Therapies in July 2019.

CUREd+ Research Database — DARS-NIC-589868-W0K1B

Type of data: information not disclosed for TRE projects

Opt outs honoured: Identifiable, Anonymised - ICO Code Compliant, 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: Sensitive, and Non-Sensitive

When:DSA runs 2023-07-17 — 2026-07-16 2023.11 — 2024.09.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Civil Registrations of Death
  2. Demographics
  3. Emergency Care Data Set (ECDS)
  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)
  7. Medicines dispensed in Primary Care (NHSBSA data)
  8. Mental Health Services Data Set (MHSDS)
  9. PersonID Bridge File

Outputs:

Outputs are expected to inform, amongst other areas, the pathway that patients take prior to, and through the Urgent and Emergency Care (UEC) system, and factors that influence when, how, and why individuals within specific cohorts of patients join the UEC system in the first place.

The expected outputs of the processing will be:
> Reports aimed at lay audiences, available annually through the University of Sheffield website, indicating the projects completed during the year.
> Submissions of manuscripts to peer-reviewed scientific and medical journals such as the British Medical Journal Quality & Safety and Annals of Emergency Medicine – after the first year, expected to be approximately two peer-reviewed publications per year.
> Presentations at appropriate national and international scientific and medical professional conferences (e.g., Health Services Research UK, European Society for Emergency Medicine Congress)
> Annual report for NHS England which will detail the outputs from all active and finished projects, which have been delivered during the year, and the planned outputs from new projects. The report will reference the associated strategic priorities and programme(s).
> This Agreement is not in direct support for a PhD or postgraduate research study but extracts from the CUREd+ research database may be used in PhD or postgraduate research studies by PhD candidates or members of the University of Sheffield.

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.

Projects that use a CUREd+ extract will typically have a dissemination plan for the results of the project that is regularly updated and discussed at project meetings.

The outputs will be communicated to relevant recipients through the following dissemination channels:
> Journals
> Social media
> Press/media engagement
> Reports aimed at lay audiences, available through the University of Sheffield
> Briefings for NHS commissioners and trusts and to parliamentary select committees.

The University of Sheffield is also dedicated to public engagement and supports academics to undertake public engagement with research and enhance the cultural vibrancy of the city. The University of Sheffield delivers a series of festivals and events which provide a platform for academics to engage the public with their research. These consist of university-organised cross-disciplinary events (such as Festival of the Mind and the Mobile University), and national or international festivals (such as Pint of Science, ESRC Festival of Social Science, Being Human, Medical Research Council Festival and International Clinical Trials Day). The University of Sheffield also seeks to help University of Sheffield researchers to achieve impact through public engagement.

It is expected that the first projects to utilise the CUREd+ research database would begin soon after the data has been received, and initial results and outputs coming towards the end of these projects, estimated to be 1/1.5 years. Outputs will continue to be produced throughout the lifespan of the CUREd+ research database, which has a current end date of December 2026.

Processing:

University of Sheffield (cohort A1) and the Yorkshire Ambulance Service (YAS) (cohort A2) will transfer data to NHS England, which combined, will form cohort A. The data will consist of identifying details (specifically NHS Number, Date of Birth, Postcode, Forename, Surname, unique person ID) for the cohort to be linked with NHS England data.

NHS England data will provide the relevant records from the HES, ECDS, Mental Health, Death, Demographics and NHSBSA datasets to the University of Sheffield.
> Demographic data for cohort A will contain directly identifying data items including latest address and postcode, which are required to access information about the type of residence.
> All other data will contain no other direct identifying data items but will contain a unique person ID which can be used to link the data with other record level data already held by the recipient. Cohort A (YAS cohort) will also have a study ID unique to each individual. Cohort B (Whole of England cohort) will only have unique person ID. A mapping table will be disseminated to the University of Sheffield to enable mapping of individuals in cohort A - this does not risk identifiability.

For cohort A, NHS England will supply data from:
i. Medicines Dispensed in Primary Care data (NHSBSA),
ii. Demographic (including address data), and
iii. Civil Registration – death data

For cohort B, NHS England will supply data from:
i. Hospital Episode Statistics (HES);
1. Emergency Care Data Set (ECDS)
2. Accident & Emergency (A&E)
3. Outpatient (OP)
4. Admitted Patient Care (APC)
ii. Mental Health Services Data Set (MHSDS)
iii. Demographic, and
iv. Civil Registration – death data

Cohort A is largely a subset of Cohort B. The datasets provided for Cohort B are therefore expected to include data for patients identified in Cohort A who will have used the services in Cohort B. Demographics and Deaths data is also required separately for cohort A to capture demographic and death data on YAS patients who have used no other services besides NHS111 or the ambulance service, and would therefore not appear in cohort B.

Experienced data management personnel prepare approved projects’ data extracts following a strict protocol for effective de-identification and minimisations, as stipulated in the project DARF. These extracts are transferred onto a separate secure environment (on a per-project basis) to which the project researchers will be granted access to carry out their analysis without accessing the main health database.

The data will not be transferred to any other location.

The University of Sheffield stores data on the Cloud provided by AWS.

The data will be accessed by authorised personnel via remote access using the University of Sheffield VPN only. The data will remain within the University of Sheffield secure computing environment at all times.

Personnel are prohibited from downloading or copying data to local devices.

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

Access is restricted to employees, agents, and Visiting Academic staff members of the University of Sheffield, and postgraduate students supervised by University of Sheffield personnel, who have authorisation from the DRC. Non-substantive employees of the University of Sheffield accessing the data are subject to the same information governance framework as UoS employees and would be required to meet the level required to access the secure computing environment.

Access to confidential patient identifiable data is restricted to select employees or agents of the University of Sheffield. Select University of Sheffield data management staff will produce subsets of the data that will be accessed by individuals who have had their project approved by the DRC. Access to the confidential patient identifiable data will be restricted to substantive employees or agents of the University of Sheffield.

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

The data will be linked at person record level with data obtained from the original CUREd and Yorkshire Ambulance Service (YAS) data using study ID. The original CUREd database consists of routine clinical ambulance data and includes CAD and NHS111 data from April 2011 - March 2017 (inclusive). The YAS data consists of routine clinical data and includes electronic patient records (ePR), computer aided dispatch (CAD), and NHS111 data from April 2017 to present.

Mental health data contained within the original CUREd database, originally obtained from Sheffield Health and Social Care NHS Foundation Trust, will not be linked to the CUREd+ database which contains NHS England data.

The data will be linked/processed with reference datasets such as Ordnance Survey geographical data to create pseudonymised fields, including travel distance/time to care activity; geographic based deprivation information; output area classification; and (for Cohort A only) place of residence type, for use within individual projects. These examples represent just some of the reference datasets that may be used in conjunction with the CUREd+ data: there will be other reference datasets in addition to these. End users of project-specific, de-identified extracts from the processed CUREd+ database may, where appropriate, link or process the extract with publicly-available (e.g., geographical or organisational) data, subject to an assessment of the risk of re-identification carried out during the Data Release Committee's review of each application for a CUREd+ database extract.

The data will not be linked with any other data than is included in this Agreement.

The identifying details, including address details and date of death, will be stored in a separate database to the linked dataset used for analysis and will never be accessed by the CUREd+ study team.

The address data provided for cohort A will allow the generation of pseudonymised Unique Property Reference Numbers (UPRNs), which will enable the grouping of patients who share a household and can be used to access information about the type of residence, without the need to retain the patient address data itself. Once a pseudonymised Unique Property Reference Number (UPRN) has been generated by UoS for all non-institute addresses, this will be linked to the other data (ECDS, HES A&E, HES OP, HES APC, MHSDS, Demographics, Deaths and NHSBSA), and the original, non-institutional, historical address will be destroyed. Historic institutional addresses will continue to be kept on the separate secure computing environment to datasets (ECDS, HES A&E, HES OP, HES APC, MHSDS, Demographics, Deaths and NHSBSA). All analyses will use the pseudonymised dataset.

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

Analysts from or associated with the University of Sheffield will analyse the relevant subset of data for the purposes described above.

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

Investigation of the geographic and socioeconomic variation in alcohol and tobacco related hospital admissions to inform decision support models for policy in England and Wales — DARS-NIC-366216-Z9H9Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Anonymised - ICO Code Compliant, No

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', Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2018-03-02 — 2021-02-28 2017.12 — 2024.09.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Admitted Patient Care (HES APC)
  3. HES-ID to MPS-ID HES Admitted Patient Care

Objectives:

Summary of programme of work
===
The data provided are used for an on-going programme of work at The University of Sheffield (“The University”) to develop public health economic models for policymaker decision support in the fields of alcohol and tobacco control. The work is entirely for public benefit and not for private commercial gain. That programme of work includes several research projects, some of which have their own associated funding. Only University of Sheffield employees process or otherwise access the data.

An ethics approval for the programme of work was given by School of Health and Related Research at The University (ref 004422). The University obtain further ethics approvals for funded research projects that fall within their general programme of work as required.

The University of Sheffield’s programme of work began with the development of the Sheffield Alcohol Policy Model (SAPM), which has been developed over 9 years by a large multidisciplinary team and funded to the tune of several million pounds. It has informed strategic thinking across policy options that target alcohol related harms at national and local authority levels, understanding socio-economic differentials in consumption, in the burden of ill-health and associated costs.

The University is in the process of using the HES data to develop modelling work that focuses on smoking only, and on alcohol and smoking jointly.



Objectives of the programme of work
===
The University of Sheffield’s programme of work includes 1) understanding past trends, and 2) forecasting to estimate the potential effects of new policy or interventions. This work is being conducted at a range of geographic scales, including national and local authority level. Note that the work tends to consider a range of outcomes of which hospital admissions and costs are a part.

Objective One
---
To investigate past trends in alcohol and/or smoking related hospital admissions and costs. The data will also be used to investigate past policy effects on hospital admissions and associated costs.

Objective Two
---
To investigate the potential future effects on hospitalisations and costs of proposed changes to alcohol and/or tobacco policy. The University typically use the most recent years of data to inform the baseline rates of hospitalisations. They then update these rates according to estimate policy effects and project the outcomes over around 30 years.



Programme-level funding
===
The UK Centre for Tobacco and Alcohol Studies (UKCTAS) (website: http://ukctas.net/ ) is a network of thirteen universities (12 in the UK, 1 in New Zealand) funded by the UK Clinical Research Collaboration. The UKCTAS aims to deliver an international research and policy development portfolio, and build capacity in tobacco and alcohol research. It is not linked to the tobacco or alcohol industry; research is conducted without industry funding or influence. Neither is it a lobbying group, but UKCTAS does have close links with advocacy organisations and will assist them where appropriate. The funding from UKCTAS is used to provide a full-time research associate at the University of Sheffield. All projects using HES data must have a UK focus. No data (other than aggregated, with small numbers suppressed in line with the HES Analysis Guide) will be shared with any 3rd party, including funding organisations.



Current projects under the programme of work
===
No funder influences the outputs in any way. The funders do not receive anything except regular reporting on project progress and association with published articles/reports. For removal of doubt, these outputs will never contain patient data and at the bare minimum will only consist of aggregated data with small numbers suppression (in line with the HES Analysis Guide).

1. UK Centre for Tobacco and Alcohol Studies (UKCTAS). Funding is provided to contribute health economic evaluations to projects within the centre. These projects might not have their own dedicated funding. In addition, this funding has been used to support the extension of The University of Sheffield’s alcohol modelling to tobacco.

2. Royal College of Physicians report on improving delivery of smoking interventions in the NHS. This project does not have its own source of funding – it is supported by the above UKCTAS funding. The HES data will be used as part of The University’s estimation of the healthcare costs of common diseases caused or exacerbated by smoking. The University will also use HES data to estimate the savings released by funding comprehensive smoking cessation services, with time frames.

3. NIHR Public Health Research Project 15/129/19 (www.journalslibrary.nihr.ac.uk/programmes/phr/1512919/#/) – Appraising the effect of implementing local Minimum Unit Pricing under the Sustainable Communities Act on alcohol consumption and health in the North West of England. Under MUP, the price at which alcohol can be sold is linked to the amount of pure alcohol (e.g. under a MUP of 50p per alcohol unit, a bottle of wine containing 10 units could not be sold for less than £5). The University will use HES data in the appraisal of the potential long-term health economic effects of this policy. The work will provide evidence about the likely scale of impact of MUP in the NW region. The Local Authorities, with whom The University have engaged in developing this bid, plan to use this to make a submission under the Sustainable Communities Act.

4. Department of Health Policy Research Programme PR-R14-1215-24001 - A comprehensive evaluation of the impact of nine years of English tobacco control policy using secondary data. The aim of the proposed research is to provide policy makers with evidence on the short, medium and long-term impact of recent tobacco control policies implemented in England, and to develop an innovative method to aid stakeholders in monitoring and assessing changes in the tobacco control landscape. The University will use HES data in estimating the long-term impact of the statistically identified effects of interventions on health and healthcare costs.

5. NIHR Health Technology Assessment Programme 13/155/05 - Helping people cope with temptations to smoke to reduce relapse: A factorial randomised controlled trial. The project will test whether providing an additional nicotine product and/or a structured planning intervention to people who have quit for 4 weeks reduces relapse at a year by comparing the rate of relapse between 1 and 12 months in those who received each RPI with those who did not. The University will use HES data in conducting the long-term health economic evaluation of to establish whether RPI provide acceptable returns for their costs in terms of long-term cost savings to the NHS and health benefits to the population.

6. NIHR Public Health Research Programme 16/105/26 - Integrated evidence synthesis for joint appraisal of tobacco and alcohol tax interventions for harm reduction in the UK. The project will investigate how tobacco and alcohol taxation can be changed to improve health for all. We will talk with consumers, policymakers and experts and use survey and sales data to look at how tobacco and alcohol tax can work together: to change consumer behaviour and health; to benefit disadvantaged communities; and reduce NHS costs at a time of limited budgets. The University will use HES data in estimating the long-term impact of a range of alternative tax options for tobacco and alcohol on health and healthcare costs.

7. NIHR Public Health Research Programme 15/129/11 – Exploring the impact of alcohol licensing in England and Scotland. The aim of the project is to map the extent to which Public Health teams in a sample of Local Authorities in England and Scotland are engaging with the process of alcohol licensing and the nature of that engagement and to estimate the impact of that engagement on health and crime outcomes. The University will use HES data in estimating the scale and distribution of effects of this engagement across the population.

Yielded Benefits:

The University of Sheffield modelling has been used to produce several national reports and academic publications that provide decision-support, all of which have made use of HES data. The structure of SAPM has proved particularly successful in assessing policy options in terms (the model outcomes) that are useful to policy makers. University of Sheffield have, for example, provided an assessment of policies directed at alcohol price for the UK’s devolved governments, and for the Irish government. See http://www.shef.ac.uk/scharr/sections/ph/research/alpol/publications for more details of publications/reports.

Expected Benefits:

General benefits of the research programme
===
One in five adults in the UK smokes and one in five adults drinks alcohol in hazardous or harmful ways. These ‘lifestyle factors’ are leading causes of preventable illness and death, including from heart disease and cancers. Over 80,000 people a year die earlier than they should from diseases caused by drinking or smoking. For those who smoke and drink alcohol, the risk of developing these preventable diseases is even greater. This preventable human loss is compounded by an annual cost to the NHS of over £6 billion.

The broad aim of the research programme is to identify and evaluate approaches to reducing the harm from tobacco and alcohol, with the aim of improving commissioning in a public health policy context i.e. providing knowledge to support benefits achieved by policymakers. The findings will provide evidence to inform choices between policy options that aim to reduce the harms that arise from tobacco and alcohol consumption. Such strategies may include, for example, interventions on price, advertising and availability licensing. There is a particular need for more evidence in this area because patterns of alcohol and tobacco consumption are changing rapidly, demanding new policy approaches at national and local authority levels.

As this is an ongoing programme of research, the future benefits achieved by the programme would be lost if the data sharing ceases.

Specific benefits for projects currently in progress (as at September 2017)
===
1. UK Centre for Tobacco and Alcohol Studies (UKCTAS) - extension of The University of Sheffield’s alcohol modelling to tobacco. The new tobacco and alcohol model will produce detailed appraisals of the potential effects of tobacco control policies. As common methods are used for both tobacco and alcohol, The University will therefore be able to compare the relative benefits of policies focused on tobacco vs. alcohol.

2. Royal College of Physicians report on improving delivery of smoking interventions in the NHS. The report will show the potential gains from improving the delivery of smoking interventions in NHS settings.

3. Appraising the effect of implementing local Minimum Unit Pricing under the Sustainable Communities Act on alcohol consumption and health in the North West of England. The project will produce evidence that will be submitted as part of the legal case for minimum unit pricing in the North West of England.

4. A comprehensive evaluation of the impact of nine years of English tobacco control policy using secondary data. By showing the past impact of tobacco control policy the project will provide evidence to support decisions on future policy.

5. Helping people cope with temptations to smoke to reduce relapse. The results of this trial will provide evidence to inform policy decisions on the best way to help people who have recently quit smoking to avoid relapse.

6. Joint appraisal of tobacco and alcohol tax interventions. There is evidence that people buy less cigarettes and alcoholic drinks when the price increases. Health advocates are therefore calling for higher taxes and changes to tax structures on alcohol and tobacco products to encourage people to quit smoking and reduce their drinking. The results of this project will provide a range of stakeholders with estimates of the potential effects of changing the UK tax regime across tobacco alcohol.

7. Exploring the impact of Public Health engagement in licensing. The results of this project will provide direct evidence to Public Health teams in Local Authorities across England and Scotland about their potential to improve population health through engagement with the alcohol licensing process. It will also inform national policy makers about the impacts of differences between current licensing regulations and processes between England and Scotland.

Outputs:

General outputs of the research programme
===
The outputs of the research programme span multiple projects; target dates are given for each project underway (as at September 2017).

In general terms the outputs of the projects generate knowledge for policymakers to support decision-making. These outputs generally comprise reports to funders, publications in peer-reviewed journals and a variety of formal and informal modes of communication of our findings to policymakers.

It is anticipated that publications will follow the research team’s track record of publishing in medical journals (BMJ, Lancet), specialist alcohol/tobacco journals (Addiction), and health economics journals (Journal of Health Economics).

All outputs will only contain results in highly aggregated format and as statistical summaries and measures of association. Small numbers will be suppressed in line with the HES Analysis Guide. Record level information will not be released to any third party.

In general outputs are made public through The University’s website and through free open-access peer-reviewed publications that are deposited in the White Rose online repository http://eprints.whiterose.ac.uk/. The University also engage with policymakers and other stakeholders before and after publication through regular meetings and responding to specific requests for evidence.


Specific outputs for projects currently in progress (as at September 2017)
===
1. UK Centre for Tobacco and Alcohol Studies (UKCTAS) - extension of The University of Sheffield’s alcohol modelling to tobacco. The output is a new computer programme that is the Sheffield Tobacco and Alcohol Policy Model. Specific outputs come through the use of this model in the projects below.

2. Royal College of Physicians report on improving delivery of smoking interventions in the NHS. The report is expected in 2018 and The University will also publish findings in peer-reviewed journals.

3. Appraising the effect of implementing local Minimum Unit Pricing under the Sustainable Communities Act on alcohol consumption and health in the North West of England. In addition to peer-reviewed articles etc., statements of evidence suitable for submission under the Sustainable Communities Act will be developed by the end of 2018.

4. A comprehensive evaluation of the impact of nine years of English tobacco control policy using secondary data. The modelling work will take place in 2019 and outputs will be made public online, reports to the Department of Health and peer-reviewed journals.

5. Helping people cope with temptations to smoke to reduce relapse. Modelling work is due to be conducted in 2019 after completion of the trial.

6. Joint appraisal of tobacco and alcohol tax interventions. The project is due to start in March 2018 and the main modelling work will be conducted in the 3rd year of this 3 year project. Outputs will be made public online, reports to the NIHR and peer-reviewed journals.

7. Exploring the impact of Public Health engagement with licensing in England and Wales. The modelling work will take place between April 2018 and March 2020 and outputs will be made public online, through reports to the NIHR and peer-reviewed journals.

Processing:

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

Processing activities common across the programme of research
===

Justification for the data required
---
The data will be used primarily to examine conditions that have been classed as wholly or partially attributable to the consumption of tobacco or alcohol. The list of conditions is therefore large (approximately 60 separate ICD-10 categories), as it includes several cancers, cardiovascular diseases and respiratory diseases.

The diagnosis and procedure codes may feature in the first or subsequent episodes in an admission and may also feature in any of the diagnosis and procedure code fields. The data will be analysed on admissions as well as on patients. This is because a small number of patients can have multiple admissions for the focal conditions. The pseudonymised HES-ID will be used to understand patients’ history of hospital episodes.

As the research programme involves the analysis of on-going time trends, data is needed from 2002/3 to the latest available. Currently the University has a project funded by the Department of Health that requires data from 2003 to 2015 (A comprehensive evaluation of the impact of recent English tobacco control policy using secondary data). The project proposal states: "In this study, we will consider theories of how laws and policies implemented between 2003 and 2015 are likely to work, whom they are most likely to affect, and what their influence will be on smoking and related ill-health."
In general, the University anticipates that the programme of work will involve the analysis of trends in tobacco- and alcohol-related hospital admissions. For example, the University has previously described time trends in alcohol consumption and availability. Future analyses are likely to extend this to investigate the relationship to trends in hospital admissions, as the University are doing currently for smoking.
Angus C, Holmes J, Maheswaran R, Green MA, Meier P, Brennan A (2017) 'Mapping patterns and trends in the spatial availability of alcohol using low-level geographic data: A case study in England 2003-2013', International Journal of Environmental Research in Public Health, 14(4), 404
Purshouse RC, Brennan A, Moyo D, Nicholls J, Norman P. (2017) 'Typology and dynamics of heavier drinking style in Great Britain: 1978-2010', Alcohol and Alcoholism, DOI:10.1093/alcalc/agw105

There are five reasons to request the HES data (note no data is required relating to Augmented Care Periods, Maternity, or Psychiatric episodes):

1. The list of diseases related to tobacco and alcohol is also expanding as more epidemiological information on the relationships between certain diseases and tobacco/alcohol becomes available. The University monitor the published evidence on disease attribution and periodically update the list of diseases of interest.

2. Due to the health economic focus of the programme of research, it is important to know about other (co-morbid) non-alcohol or tobacco related conditions that individuals have been diagnosed with as this can inflate the healthcare costs generated by that individual. The University are therefore requesting the data so that they can assess how co-morbid conditions (including conditions that are not associated with tobacco or alcohol) might modify the healthcare costs that are estimated to be associated with tobacco or alcohol related conditions.

3. Variation among local authorities in the rates of admission from non-alcohol or tobacco related conditions can modify the proportion of that region’s healthcare costs accounted for by alcohol and tobacco related conditions. The University are therefore requesting the HES data (excluding sensitive items) so that they can assess the costs of admissions related to alcohol and tobacco relative to the cost of non-alcohol or tobacco related admissions by local authority.

4. Completeness of coding and the type of codes used (e.g. “bucket” codes) may vary by Trust across the country and across time and have the potential to bias small area level studies. The University are therefore requesting the HES data so that they can assess completeness and coding and make adjustments for these factors if required.

5. Data is required for all patients as the effects of tobacco/alcohol are not limited to a particular demographic group. Currently, the University requires data on inpatient admissions for young children for analysis for the Royal College of Physicians upcoming report. One component of the work for this report is to estimate the costs to the NHS that arise through the hospital admissions of mothers and children for conditions (such as for children birth defects or asthma). In general, the University anticipates that the programme of work will involve analysis of effects on the health of children e.g. the investigation of the potential effects of exposure to second-hand smoke.



Data preparation
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The University apply basic data processing functions e.g. removal of duplicate records etc. To prepare for processing activities that are specific to particular projects, the pseudonymised data is first aggregated in rates of hospital admissions for specific ICD-10 code categories for specific population subgroups.


Project-specific processing activities
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The University’s analyses are split between investigations of retrospective trends and prospective estimation of potential policy effects: How different new policies/interventions might affect several outcomes, including hospital admissions and associated costs, over and above the continuation of past trends.

The University has 1 current project (as at September 2017) that is investigating past trends: Comprehensive evaluation of the impact of nine years of English tobacco control policy. Using external data on trends in tobacco consumption in relation to policy, and published risk functions, The University will estimate the extent to which past trends can be attributed to variation in tobacco consumption.

The University has 6 current projects (as at September 2017) looking at prospective effects of tobacco/alcohol policy:
1. Development of joint alcohol and tobacco policy modelling.
2. Royal College of Physicians report on improving delivery of smoking interventions in the NHS.
3. Appraising the effect of implementing local Minimum Unit Pricing
4. Helping people cope with temptations to smoke to reduce relapse
5. Joint appraisal of tobacco and alcohol tax interventions
6. Exploring the impact of Public Health engagement in alcohol licensing
The data on hospital admissions and associated costs will form 10-20% of the evidence that University of Sheffield use to generate policy-relevant outcomes from these new models that aim to answer the question of what might be the effects of proposed changes to the alcohol and/or tobacco policy strategy? Using the data requested, the University will select the set of ICD-10 codes attributable to alcohol and tobacco, aggregate these into rates by population subgroup, and use these rates as the baseline in the University of Sheffield model. The model will translate the estimated effects of policy on tobacco and/or alcohol consumption to outcomes in terms of the hospital admissions and associated costs that might be prevented.

Investigating the Application of Causal Inference Methods for Modelling the Impact of Treatment Sequences in Health Economic Evaluations: Utilising Real-world Evidence from the English Cancer Registry — DARS-NIC-661854-W9V1H

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: Non-Sensitive

When:DSA runs 2023-02-27 — 2025-02-26 2023.06 — 2023.09.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS Linked Cancer Waiting Times (Treatments only)
  3. NDRS Linked HES AE
  4. NDRS Linked HES APC
  5. NDRS Linked HES Outpatient
  6. NDRS National Radiotherapy Dataset (RTDS)
  7. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objectives:

On 1 February 2023, NHS Digital merged with NHS England. NHS England has assumed responsibility for all activities previously undertaken by NHS Digital. The merger was completed by a statute change. Any reference made to NHS Digital within this Data Sharing Agreement is in reference to the merged organisation known as NHS England.

The University of Sheffield aims to investigate whether the English Cancer registry data is sufficient for reliably comparing the effectiveness of different sequential treatments in treating cancer patients in the NHS.

Patients can sometimes receive a series of treatments in a sequence instead of a single line of therapy. Alternating the order of treatments may result in different overall effectiveness and costs of medical treatments. Thus, it is essential to consider the sequence of treatments when making health resource allocation decisions, particularly for cancer treatments as they usually impact a patient’s survival.

Treatment effects are usually compared in clinical trials. However, a major limitation of some clinical trials is that they often do not provide details about patients’ treatment histories or treatment sequences. In this case, analysing routine healthcare data may help provide a better understanding of the effect of sequential treatments.

Analysing routine healthcare data without proper adjustments may lead to incorrect results in clinical trials. Therefore, several large international initiatives have been investigating methods to obtain reliable treatment effects using routine health care data. This generally involves replicating clinical trial results using routine healthcare data and further extrapolating the results to a wider population. None of the studies has examined the scenario of comparing the effectiveness of sequential treatments. Additionally, most of these studies were conducted in a non-UK setting. Thus, the project aims to fill this knowledge gap with two case studies comparing the effectiveness of different sequential treatments in treating prostate cancer and kidney cancer patients in the NHS. Specifically, the study plans to replicate the results of two clinical trials using the UK cancer registry data. For the purpose of clarity, the studies referenced above are not in any way allied to this project, and will not have access to data disseminated under this Agreement.

To support this work the University of Sheffield requests pseudonymised sub-sets of the following datasets:
• NDRS Cancer Registry
• NDRS Radiotherapy Dataset (RTDS)
• NDRS Linked Cancer Waiting Times (Treatments Only)
• NDRS Linked Hospital Episode Statistics (HES)- inclusive of the Admitted Patient Care, Accident and Emergency and Outpatient subsets
• NDRS Systemic Anti-Cancer Therapy (SACT) Dataset

All the above datasets will aid in determining what treatments a patient received, and the sequence in which a patient received the treatments. The cancer registry contains basic patient information at the time of diagnosis (e.g. age, gender), important prognostic factors, such as tumour stages and sizes, and patient performance status. Moreover, the Systemic Anti-Cancer Therapy (SACT) data set collects information on cancer therapies, including those available through the Cancer Drugs Fund (CDF). These NCRAS datasets can also be linked with NHS hospital records (e.g. Hospital Episode Statistics (HES)) to provide a complete picture of patients’ treatment trajectory.

To address the UK General Data Protection Regulation (GDPR) Principal of Data Minimisation the data requested is limited to individuals aged over 18 with a diagnosis of prostate cancer (C61) or renal cell carcinoma (C64), which is the minimum amount of data necessary to achieve the purposes outlined within this DSA. Patients below 18 will be excluded from the analysis due to the following reasons: (1) The study team aim to apply the same exclusion criteria from trials in the same diseases as this study is comparing results with those from existing trials. (2) Treatment patterns among younger patients may be significantly different from adults, and thus less comparable for the scope of this study. (3) The incidence of prostate cancer and renal cell carcinoma has been extremely low among adolescents and children in England. Excluding cases below 18 may help prevent small cell numbers being produced in our results. The study team have undertaken extensive work with the NHS Digital data production team to ensure compliance with the principle of data minimisation.

The data period requested to identify the primary patient cohort for both case studies is from January 1st 2011 to the latest NCRAS data available upon extraction. A subset of minimised historical data of the identified patient cohort dated back to 6 years prior to patient’s cancer diagnosis (i.e. since January 1st 2005 or the earliest available date of a dataset (if later than 2005)). The necessity for requesting over 10 years of data can be justified by the following feature of the study:
(1) This study will investigate the treatment effects on cancer survival and, therefore, long-term data are essential.
(2) This study involves emulating existing RCTs that have a minimum of 5 years of follow-up, and therefore long-term data is needed to make adequate comparisons between our analyses and those included in the existing RCTs.
(3) This study assesses the effectiveness of treatment sequences, which have changed over time. Therefore, it is important to have data over a prolonged time period.

There are no alternative, less intrusive ways of achieving the purposes outlined within this Data Sharing Agreement (DSA). The study has taken the appropriate steps to obtain ethical approval from an NHS Research Ethics Committee (REC).

The lawful basis for processing falls under the UK General Data Protection (GDPR) Article 6(1)(e), the processing is necessary for you to perform a task in the public interest and falls under the official functions outlined within the Universities Royal Charter. Processing of special category data falls under UK GDPR Article 9(2)(j), this processing is necessary for archiving purposes in the public interest, for scientific and statistical purposes in accordance with Article 89(1), based on Union 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 research has been deemed to be in the public interest as evidence of the effectiveness of different treatment sequences in treating cancer patients is scarce. Clinical trials are not usually designed to assess treatment sequences. However, in reality, most people with cancer receive a sequence of treatments and therefore evidence on the relative effectiveness of different treatment sequences would be valuable for patients, clinicians, and healthcare decision-makers. Information on patients who receive different treatment sequences is available in the cancer registry and linked datasets, and these data could be used to assess the relative effectiveness of different treatment sequences. However, analyses of observational data sources are prone to biases and our study aims to investigate this, to ascertain whether it is possible to estimate the relative effectiveness of different treatment sequences using an English cancer registry and linked datasets.

For the purposes of investigation, this study proposed using trial-mimicking procedures to identify the study cohort and to conduct statistical analyses. By mimicking the procedures of a randomised control trial as closely as possible, the study team may minimise biases associated with analysing observational data (e.g. confounding bias) so as to enhance the validity of our results. The study team aim to test the performance of different causal inference statistical methods for mimicking the randomisation procedures such as propensity score weighting and g-methods.

The study is in the public interest because it will provide evidence on whether it is possible to use these datasets to inform healthcare decision-making on treatment sequences. It is of scientific and statistical importance due to our proposed investigation of advanced statistical methods on routinely collected national datasets.

The University of Sheffield is the sole data controller who processes the data for the purposes outlined within this Agreement. The Wellcome Trust fund this work, other than funding the work the Wellcome Trust play no role in this project.

Yielded Benefits:

The data requested under this Agreement has not yet been disseminated and therefore there are no yielded benefits.

Expected Benefits:

This dissemination has the potential to benefit the provision of health and social care in England, it may allow the study team to determine whether NCRAS data can be reliably used as a source of data when comparing the effectiveness of different treatments and sequences of treatments.

This dissemination may benefit decision-makers in health regulatory bodies:
1. The project aims to provide evidence on the value of using the NCRAS data as an alternative source of evidence when evaluating treatment sequences in health economic evaluations and clinical decision-making. That is, the study may be able to provide guidance on a systematic approach for providing reliable comparative effectiveness estimates of sequential treatments using UK routine health care data. Such guidance could be used to investigate many other questions that involve the comparison of treatment sequences. This can be important for decision problems where clinical trial data are lacking, but also for refining best practice guidance for research using national registry data.
2. If analyses are unsuccessful, the study will make suggestions as to whether the national health data collection may be improved to enable better analyses in the future.

This dissemination may produce several social benefits:
1. This project looks to examine whether data collected from NHS patients can be reliably used to support future decisions making for the patients in the NHS (i.e. using NHS data for NHS patients). Typically, health resource allocation decisions are heavily based on evidence from clinical trials. However, patients treated in the NHS are not always like those who are eligible to attend a clinical trial. Therefore, evidence from trials is often not generalisable, while evidence from the real world may offer additional insights. Ensuring the generalisability of trial results is essential to ensure that decisions are based on valid and reliable results.

The study team aim to liaise with the National Institute for Health and Care Excellence (NICE) decision support unit, as the study findings may be of relevance to the NICE Real-World Evidence Framework. The NICE Real-World Evidence Framework acknowledges the growing use of real-world data in the health technology assessment process but describes concerns around potential biases that can occur in analyses of non-randomised data. Issues around data quality are also discussed. The study will provide further information on the use of the English cancer registry and linked data to assess the effectiveness of cancer treatments used in clinical practice, which will help decision-makers such as NICE understand whether and how these datasets can be used to inform healthcare decision-making.

In summary, our study may demonstrate whether NCRAS data is of sufficient quality to allow reliable comparison of treatment sequences in treating prostate cancer and kidney cancer patients in healthcare care settings. As such, this study may extend the user experience of NCRAS data and provide public health benefits.

There is no specific target date or measure for the health and social benefits of this project. These benefits are likely to follow the study outputs described in the previous section.

Outputs:

Primarily, the results of this project will form part of a PhD thesis submission. The submission will be provided to the University of Sheffield and the study’s funder, the Wellcome Trust. The current submission date for this project is May 2023. The Wellcome Trust have an open source publishing policy (with funding) to increase the visibility of the research when its ready to be submitted to a peer-review journal.

The University of Sheffield also plans to submit the findings to peer-reviewed journals to ensure wider dissemination of the study findings.

The study team currently aims to present the study findings at international conferences, such as the International Society for Pharmacoeconomics and Outcomes Research Conference (May 2023, Boston; November 2023, Copenhagen) and the International Health Economics Association Congress (July 2023, Cape Town). Presenting at such conferences will allow the findings to reach scientists, innovative technology-focused organisations and policy-making communities.

The University aims to publish the study findings in a lay-friendly format on its web pages to reach that the results reach interested groups and civil society. Such publications may also be shared on social media.

Any data contained in the outputs references above will be aggregated with small numbers suppressed.

Processing:

There is no flow of identifiable data into NHS Digital to support this request.

NHS Digital will flow pseudonymised sub-sets of the following datasets to the University of Sheffield via a secure file transfer system:
• NDRS Cancer Registry
• NDRS Radiotherapy Dataset (RTDS)
• NDRS Linked Cancer Waiting Times (Treatments Only)
• NDRS Linked Hospital Episode Statistics (HES)- inclusive of the Admitted Patient Care, Accident and Emergency and Outpatient subsets
• NDRS Systemic Anti-Cancer Therapy (SACT) Dataset

There will be no subsequent flows of data.

The University of Sheffield aims to determine if it can replicate results from two existing clinical trials using the National Cancer Registration and Analysis Service (NCRAS) data.

Treatment effectiveness will be compared between patients receiving different treatment sequences. The results of the study’s analyses will be compared with those from the clinical trials. If results are similar, the study may conclude that English cancer registry data is sufficient to obtain reliable comparative effectiveness of different sequential treatments.

The study will proceed to the second stage if the English cancer registry data is demonstrated to be sufficient. That is, the University of Sheffield will extrapolate the analyses to a broader population (i.e. removing the trial-matching exclusion criteria when selecting patients). This will allow the University of Sheffield to determine the effectiveness of sequential treatments being used in“a "non-trial” population.

The data being disseminated under this Agreement will not be linked to datasets not already referenced within this Agreement. The University of Sheffield is not permitted to reidentify individuals from the pseudonymised data.

All those processing the data disseminated under this Agreement will be either substantive employees of the University of Sheffield or University of Sheffield students with honorary status. Those accessing data will receive appropriate data protection and confidentiality training. Data will be processed by project team members only, and only for the purposes outlined within this DSA.

The data will be accessed via a secure virtual environment running on the University of Sheffield-owned and managed infrastructure in England. Analysts will conduct all data processing including the statistical analyses to fulfil the research objectives in this secure virtual environment. Off-site access is facilitated by a secure VPN (virtual private network) connection authenticated by a University username and remote password. This agreement prohibits the transfer of data from the virtual environment at the university to other machines. This service is maintained by the University’s Corporate Information and Computing Services. The University of Sheffield will comply with the Data Protection Act and the University's own Information Security and Data Protection Policies as well as the School of Health and Related Research (ScHARR) Information Governance Policy. In line with ScHARR’s Information Governance Policy. Only aggregated data/outputs, with small numbers suppressed, will leave the secure virtual environment.

Safety INdEx of Prehospital On Scene Triage (SINEPOST): The derivation and validation of a risk prediction model to support ambulance clinical transport decisions on scene. — DARS-NIC-284866-L7K4D

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(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(7), 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); Health and Social Care Act 2012 – s261(7)

Purposes: Yes (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-02-15 — 2023-02-14 2021.06 — 2021.06.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Emergency Care Data Set (ECDS)
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Accident and Emergency (HES A and E)

Objectives:

The University of Sheffield (UoS) are proposing to link an existing regional cohort of patients from the Yorkshire Ambulance Service (YAS) electronic Patient Care Records with Emergency Care data (using both the Emergency Care Data Set (ECDS) and the Hospital Episode Statistics Accident & Emergency (HES AE) datasets). NHS Digital hold the Emergency Care datasets and the data linkage will be completed by NHS Digital.

This request is for the purpose of medical research which aims to determine whether ambulance service clinical data can predict an avoidable attendance at the Emergency Department (ED) in adults using newly developed risk prediction models. These models could subsequently be used to develop a tool for paramedics on scene which can help them to determine the likelihood of treatment at an ED being of benefit to the patient.

In 2014 in Yorkshire, up to 16.9% of patients could have avoided being taken by ambulance to the ED. When the ED is busy, ambulances have to wait a long time to hand over the care of their patients. This delay stops ambulances being free to respond to the next emergency. The research conducted in the past using ambulance service episodes only used epidemiological descriptions as their output. Furthermore, they only collected data that is created in the ambulance call centre, whereas this project uses the clinical information on scene created by clinicians. Linking up individual patient care pathways from their YAS attendance data through to their respective ED admission, (the details of which are given in the ECDS and HES AE datasets) will allow the UoS to formulate the new risk prediction models and subsequent decision-making tools for paramedics on scene.

Within the ECDS and HES AE datasets there are variables which outline the experience of each patient. This includes what investigations and treatments they had, how they left the department and whether they reattended. These variables can be transformed into a single outcome variable which would show whether each individual patient had a clinically necessary attendance or whether their experience would have been better in a different clinical setting. By creating this single variable from the ECDS and HES AE datasets, the risk prediction model can then be developed. All the prehospital (ambulance service) data variants would be used to inform the model as to which of these variants are more predictive of a clinically necessary attendance or an avoidable one.

All subjects are adults defined as >18 years of age at the time of attendance. They all have contacted the ambulance service and received a face-to-face assessment with a paramedic. They all were transported to an ED in Yorkshire. The cohort for this study from the YAS will contain 328,763 attendances to the ED between July 1st 2019 and February 29th 2020. The dates proposed are the minimal amount needed to overcome seasonal biases that can be inherent in emergency care.

The cohort provided by YAS to NHS Digital will contain identifiable patient information (NHS number, date of birth, sex and postcode (unit level)) to allow for linkage to the Emergency Care data. A more limited number of fields are not possible to specify or collect as the nature of YAS data is such that for different cases, different personal identifiers will be missing.

The data provided by NHS Digital to the UoS will be record-level pseudonymised ECDS and HES AE data. Only variables that will provide the potential to predict the ED attendance outcome will be requested. There are no alternatives or less intrusive ways that would allow the purposes of the research to be fulfilled due to the required sample size making it impossible to consent every participant within the study.

This project will use data from the whole of Yorkshire. This region covers a wide range of different geographical settings which will overcome certain biases (such as urban vs rural and social deprivation). This is the smallest geographical footprint which will allow our model to account for these differences when applied on the national scale. Accessing emergency care data for Yorkshire from NHS Digital will cover ED attendances which were conveyed to: Airedale General Hospital, Barnsley Hospital, Bradford Royal Infirmary, Calderdale Royal Hospital, Dewsbury and district Hospital, Doncaster Royal Infirmary, Harrogate district Hospital, Huddersfield Royal Infirmary, Hull Royal Infirmary, Leeds General Infirmary, Northern General Hospital, Pinderfields Hospital, Rotherham Hospital, Scarborough Hospital, St. James’s University Hospital and York Hospital.

Consultations with the YAS, the UoS and the Research Design Service have all helped to minimise the amount of data requested to fulfil the required study purposes whilst satisfying the required sample size for the study.


The justification for the processing of this request is Article 6(1)(e) of the GDPR as this research project has been designed and funded with the premise of being in the public interest. The application also falls under Article 9 (2) (j), as scientific research. It is anticipated that the research output of the risk prediction model and subsequent tools for paramedics on scene will be of benefit to all patients that call the ambulance service; leading to more patients in the UK getting the right care; first time.


The UoS are the sole data controller who also process data. They will make all final decisions for the study design. The lead applicant is a student with the UoS to complete an National Institute of Health Research (NIHR)/Health Education England (HEE) Integrated Clinical Academic Doctoral Research Fellowship.

The YAS are the sponsor of the research study and will contribute the data to identify the cohort to NHS Digital. The lead applicant is a substantive employee of the YAS but is completing this project in the capacity of student with UoS. The YAS will not have access to NHS Digital data.

Access to the pseudonymised data will be limited to named individuals within the UoS who have successfully completed Information Governance training specified by the School of Health and Related Research (ScHARR) and agreed to abide by the School’s Information Governance Policy. All such individuals are substantive employees or students of the UoS.

Any breaches by substantive employees or students will result in disciplinary action with the UoS. This includes student expulsion if required. Any students undertaking a fellowship with the NIHR would need to report this immediately which would breach the fellowship contract and further disciplinary actions would be taken by the sponsor (YAS) and the NIHR.

This application is part of an NIHR/ HEE funded research project which commenced on the 1st April 2019 and is due to finish in March 2022. It is a stand-alone project which does not form part of a wider project or collaboration. The NIHR will not have access to NHS Digital data.

Processing:

The data flows are summarised as follows:
1) The Business Intelligence team of the Yorkshire Ambulance Service (YAS-BI) will run a single data query of the electronic Patient Care Records (ePCRs) stored in their data warehouse. This will retrieve all information for every patient aged 18 years or older that was treated by a paramedic, then transported and booked in as a patient to an ED between July 2019 and February 2020. Unique IDs will be generated, assigning a random number to each individual ePCR. The retrieved ePCRs will be run through the national ‘opt out’ database with any qualifying records being removed. Records in this form will not be seen by the University of Sheffield (UoS).

2) The YAS-BI will proceed to split the whole dataset into two extracts. The first is the identifiable information and the second is the clinical information. Both extracts will contain the unique ID variable, but no identifiers will be contained within the clinical information extract. The YAS-BI will send the clinical information securely to the ScHARR at the UoS, and send the identifiable information (NHS number, date of birth, sex and postcode (unit level)) to NHS Digital. Once YAS-BI have sent the data extracts to their appropriate destinations, their data processing ends.

3) NHS Digital will use the submitted identifiable information to link the cohort to their respective Emergency Care Data Set (ECDS) and Hospital Episode Statistics (HES) Accident & Emergency (AE) records (an anticipated 328,763 records). The methodology used is transparent and well documented. Following data linkage, NHS Digital will remove all identifiable information and securely deliver the linked pseudonymised records containing special category (health) data to the School of Health and Related Research (ScHARR) where they will be stored in accordance with the UoS Information Governance policies. NHS Digital will also return the ‘unlinked’ unique ID only for any individuals where no ECDS or HES AE data could be retrieved.

4) The ScHARR will merge the linked and unlinked records from NHS Digital with the YAS clinical information already held using the previously generated unique ID. Any ambulance service data with no associated ED record would undergo bias screening before being destroyed. This means ensuring there are no significant differences between the patients who are going to be deleted and those who remain in the dataset for analysis. The remaining data will form one coherent pseudonymised record level dataset which maps the journey of each patient from ambulance service to ED. Data will be analysed in the ScHARR but stored on a remote virtual machine (VM) hosted on UoS infrastructure and maintained by the UoS Corporate Information and Computing Services (CiCS). CiCS administrators have policies in place which address network security (especially threats from outside the campus network) and software maintenance. Access to the VM (only possible from specified IP addresses) is granted only to a limited number of user accounts, all of which require authentication by username and password. Remote working is completed on UoS computers in users’ homes. These computers have encrypted hard drives, with encryption keys stored solely by UoS Information Technology staff. Other UoS storage locations listed provide increased security.

5) There will be no onward flows of record level data under this data sharing agreement. The UoS will be the only data processor from the point of receiving the linked ECDS and HES AE data from NHS Digital. The data processing will only be carried out by substantive employees and students of the UoS who have been appropriately trained in data protection and confidentiality. There will be no requirement or attempts to re-identify individuals. To mitigate the risk of any individual becoming identified once the data has been transferred to the UoS, each variable will be checked for any small parameters of data. These will have complete case deletion and therefore will not be re-identified during the data processing. Any publications that arise from this project will only include aggregated data. This further mitigates re-identification at the output stage.

In order to generate the risk prediction models which can be used by paramedics to help prevent avoidable ED attendance in future, the ScHARR will process the data as follows:

1) There will be an initial exploration to examine the quantity of candidate variables within the YAS clinical dataset and the parameters within each one. The candidate variables can be broadly divided into three categories. The first are the physical factors such as blood pressure, temperature etc. The second are the social factors for example patient location, mobility, or whether they have dementia or not. The purpose of including social factors is so the tool accounts for each patient’s social support requirements. The third are the interventional factors, which describe what the paramedics did with the patient on scene. Examples include what drugs they gave, or any interventions they needed. An exploration will also identify missing data and highlight any variables that may need cleaning or preparing in anticipation of model development.

2) The single outcome variable will be created using the variables derived from the ECDS and HES AE (collectively – ED) data, including investigations and treatments performed, how patients left the department, and whether they reattended. Once created, the ED variables will be removed from the dataset. This completes the data preparation stage.

3) Any missing data from the full linked dataset will be assessed for randomness using statistical methods. Any missing data within the ambulance service clinical variables will be assessed for its ability to bias the models and removed or retained as appropriate. Following on from this, the data will be separated into linked and unlinked cases. These will then be compared to assess for screening bias. Once this is complete, the unlinked data will be destroyed.

4) Predictive models will be developed and applied to the linked data independently. The reason for building different models is they can be more or less accurate in making predictions depending on the dataset. As this has not been done before, the plan is to build three types of models and then select the highest performing one. The three methods being used are: logistic regression, random forest modelling and a neural network. The methods vary in how they make predictions. For example, logistic regression looks at how each candidate variable is associated with the outcome. A random forest model builds multiple decision trees and then aggregates the outcome of each one. Neural networks are a combination of both of these techniques. Due to their differences in design, by building all three on the data it will give the best chance of creating an accurate prediction model that can be successful on future live data. Also, building three models with different methodology allows for alternative model selection at the implementation stage. For example, neural networks struggle to handle any missing data. Even if it was the most accurate model built, if there is naturally missing data in the dataset then it will not work in practice. Alternatively, logistic regression can handle missing data when making a prediction.

5) There will be no predetermined candidate variables selected. Instead, all clinical variables collected during the paramedic patient assessment and/or treatment will be included in the initial development. It is anticipated there will be thirty-nine variables across three categories described above (physical, social and interventional). Within each method being used (logistic regression, random forest modelling and neural networks), there will be a statistical way of eliminating variables that do not have a strong association with the outcome. This is to ensure the final model is parsimonious. A parsimonious model is one which is as simple (fewer variables) as possible without losing accuracy. Very complicated models with lots of input variables can often fail and are harder to implement in practice.

6) In order to produce an accurate model, it is important to be able to evaluate its performance. For each model, a contingency table will be devised so summary accuracy statistics can be calculated which explain how accurate each model is at making predictions. In addition, a calibration plot will show the agreement between observed and expected results. Calibration is assessing what the model predicted vs what actually happened. In addition to this, it is important that discrimination is accounted for and analysed. This is important as the models being built in this study are classifying patients. Discrimination is assessing how many times the model correctly identifies a person with the outcome and one without and is a measure of accuracy. By using all the statistics mentioned, the models can be directly compared. This helps with model selection, but also allows for other people to assess how accurate the model is.

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

MR1466 - Life and Bladder Cancer : The Yorkshire Cancer Research Bladder Cancer Patient Reported Outcomes Survey ( Longitudinal Study) — DARS-NIC-374924-C8S5Y

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2020-05-21 — 2021-02-17 2020.06 — 2021.02.

Access method: Ongoing

Data-controller type: UNIVERSITY OF LEEDS, UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Demographics

Objectives:

Bladder cancer is one of the most common human cancers. Its treatment can affect the physical, psychological and sexual function of a patient, which reduces their overall quality of life. It is important to collect information about the experiences of patients as they reflect outcomes, identify areas of care that need improvement, and how to improve this care. These patient reported outcome measures (PROMs) are important measures of healthcare delivery and identify concerns that matter most to patients. The study team will develop a questionnaire that records these measures in patients with bladder cancer during and after treatment. The study team will survey all new and existing patients within Yorkshire, North Derbyshire, South Tees and the Humber and will compare outcomes across the region, across the spectrum of disease states and treatments, and over the first 12 months since diagnosis. The study team will use this information to understand outcomes within the population, to identify gaps in care and barriers to care improvement, and to shape clinical care delivery.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall health related quality of life of individuals. One way of finding out about the impact of bladder cancer and its treatment on patient health related quality of life is by asking patients directly using Patient Reported Outcome Measures (PROMs). Although there have been some studies evaluating the health related quality of life of people with bladder cancer, many of these have been small scale or restricted to subsets of patients. In the main there is a dearth of large scale research examining PROMs in people living with and beyond bladder cancer. The importance of PROMs as healthcare measure is recognised, and with this is mind, the study will survey PROMs for patients with bladder cancer across Yorkshire, Humber, South Tees and North Derbyshire.

The data requested under this agreement will be used in relation to a longitudinal cohort, however the LABC project has two complementary sub studies that use different patient cohorts in different designs;

1. Longitudinal survey of PROMs within the first year of diagnosis requires informed consent to be taken from participants. The study requires access to the NHS Digital list cleaning service to provide fact of death.

2. Cross sectional survey of PROMs within patients living with and beyond bladder cancer (for information only- this part of the study is covered by a separate Data Sharing Agreement).

All patients alive within 10 years of a current or previous diagnosis of bladder cancer having been treated by one of the NHS hospitals in Yorkshire, Humber, South Tees and North Derbyshire will be invited to complete a single survey unless they have registered a type 2 objection. Patients with all types and stages of bladder cancer will be included. The study requires access to check the most current address and to provide fact of death.

The NHS Digital list cleaning service will be used to carry out mortality checks and retrieve current patient addresses for those people in the Life and Bladder Cancer (LABC) survey cohort for the purpose of administering a Patient Reported Outcome Measures (PROMs) survey of people diagnosed with bladder cancer in Yorkshire, Humber, North Derbyshire and South Tees.

Expected Benefits:

The list cleaning with NHS Digital data has 2 key benefits:
1) Latest addresses are obtained so that follow-up has greater coverage, and
2) As far as possible, surveys are not sent out to addresses of patients who are deceased, which could cause distress.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall health related quality of life of individuals.

The primary aims of the Life and Bladder Cancer (LABC) study are to describe the health related quality of life of patients living with bladder cancer diagnosed in Yorkshire, Humber, North Derbyshire and South Tees, to gain a deeper understanding of the variation in outcomes and to identify areas of unmet need.

Outputs:

The objective of the longitudinal arm of the study will be to evaluate changes in PROMs in patients with bladder cancer over time. All new incident cases within a 12-month period (est. n=1900) at 3,6,9 and 12 months post- diagnosis will be studied. Participants will give informed consent.

The study requires use of the NHS Digital list-cleaning service to:
1) Provide fact of death. The list clean will remove people who have died from the cohort list. This will minimise the risk of surveys being sent to people who have died potentially causing upset to their relatives. Death checks will be carried out immediately prior to survey mail out (initial and two reminders).
2) To check the most current address for mailing the survey. By providing up-to-date addresses NHS Digital will help the study work towards achieving the highest possible response rate and therefore make the results more representative of the population.

The following outputs from the study are envisaged:
1) Empirical knowledge of key clinical, socio-demographic and psychosocial factors that predict patients generic and cancer- specific Health Related Quality of Life (HRQL). Findings will be disseminated through a series of reports, academic papers (open-access) and conference presentations, and all findings will be available on the dedicated study website.
2) The electronic report and toolkit will be available to key stakeholders to provide detailed anonymised information. The toolkit will enable each NHS Trust, Clinical Commissioning Group and Strategic Clinical Network to visualise the results for their organisation and to compare them against the national 'average'
3) A validated survey tool for the collection of health outcomes of bladder cancer survivors. This would be made available for use by other organisations and researchers (dependent upon appropriate conditions of use).

None of the above outputs from the study will contain data from NHS Digital.

Processing:

There are a number of organisations involved in the LABC study, and their involvement is detailed below. However, data provided by NHS Digital will be received and processed only by Quality Health Ltd.

Quality Health Ltd
Quality Health is a Care Quality Commission (CQC) approved national contractor and works for 360 NHS Trusts throughout England on the National Patient and Staff Surveys. Quality Health are the LABC study data processor. They will send and receive the questionnaires, storing the survey mailing and response data on their systems. Quality Health will destroy identifying data needed for mailing when the surveys have closed and the questionnaire information that is retained will only be identified by a unique identification number. The returned completed questionnaires will be stored in paper and electronic formats within the secure systems used routinely by Quality Health Ltd. The electronic version of the survey data will be encrypted and sent securely to the National Cancer Registration and Analysis Service.

National Cancer Registration and Analysis Service (NCRAS), Public Health England (PHE):
NCRAS is run by Public Health England and is responsible for cancer registration. PHE will store patient details received from the recruiting hospitals and send the collated patient details to Quality Health Ltd.

University of Sheffield:
Joint study research location and sponsor of the LABC study. The CI and the Project Coordinator are based at the University of Sheffield. No patient identifiable data will be held at the University of Sheffield.

University of Leeds and Leeds Institute for Data Analytics (LIDA):
Joint study research location. The CI and the PROMs design and statistical team members are based at the University of Leeds. The Leeds research team are also part of the Leeds Institute for Data Analytics (LIDA) The linked survey response data will be analysed by the study team at Leeds University (in pseudonymised format only). The cleaned and pseudonymised data will be sent to the University of Leeds using a secure transfer mechanism (Leeds Institute for Data Analytics (LIDA) web drop system) and stored securely on the LIDA integrated research campus (IRC) platform.

Study Methodology Summary:
Participants who have given informed consent to take part in the study may provide information by completing the survey either via post, telephone or online. The survey will not ask for personal details such as names and addresses and each survey will include a unique study ID number. Participants will complete their survey online, by telephone or return it in the post to an NHS approved survey company, Quality Health Ltd, (the data processor). Quality Health Ltd will remove any personal information that may directly identify participants. This data will then be sent to the University of Leeds for analysis.

Quality Health require NHS Digital to perform a list-cleaning service and to provide the latest demographic details including fact of death and, for living participants who elected to receive surveys by post, confirmation of address details.

Access to the data is limited to Quality Health Ltd and will only be used for the purpose of this Agreement.

Quality Health will submit a data file to NHS Digital containing the following limited patient identifying data fields for patients in the LABC PROMs cohort:
- Name
- Surname
- Address
- Postcode
- NHS number
- Date of birth
- Gender

For each individual who elected to complete the survey by telephone or by email, the data file will include an indicator so that latest address details will not be supplied for these individuals.

NHS Digital will supply the output from the list-cleaning service to Quality Health.

The administration of the questionnaire (following the list cleaning) will be carried out by Quality Health who will act as a central data collection centre. NCRAS at Public Health England will transfer minimal patient identifiable information including names and addresses, post code, date of birth, NHS number, gender and a unique LABC identifier to Quality Health. Quality Health will pass these details on to NHS Digital for list cleaning as described above.

Following receipt of the completed questionnaires, Quality Health will clean and pseudonymise the data to remove any identifying information. The cleaned data is sent back to the study team in Leeds for analysis. The data will be stored within the secure environment at the Leeds Institute for Data Analytics (LIDA).

Data from NHS Digital is only provided to Quality Health Ltd, and only for the purpose of administering the surveys.

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)

MR1466 - Life and Bladder Cancer : The Yorkshire Cancer Research Bladder Cancer Patient Reported Outcomes Survey ( Longitudinal Study) — DARS-NIC-194387-K3H5K

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-02-18 — 2021-02-17 2019.05 — 2021.02.

Access method: Ongoing

Data-controller type: UNIVERSITY OF LEEDS, UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. MRIS - List Cleaning Report
  2. Demographics

Objectives:

Bladder cancer is one of the most common human cancers. Its treatment can affect the physical, psychological and sexual function of a patient, which reduces their overall quality of life. It is important to collect information about the experiences of patients as they reflect outcomes, identify areas of care that need improvement, and how to improve this care. These patient reported outcome measures (PROMs) are important measures of healthcare delivery and identify concerns that matter most to patients. The study team will develop a questionnaire that records these measures in patients with bladder cancer during and after treatment. The study team will survey all new and existing patients within Yorkshire, North Derbyshire, South Tees and the Humber and will compare outcomes across the region, across the spectrum of disease states and treatments, and over the first 12 months since diagnosis. The study team will use this information to understand outcomes within the population, to identify gaps in care and barriers to care improvement, and to shape clinical care delivery.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall health related quality of life of individuals. One way of finding out about the impact of bladder cancer and its treatment on patient health related quality of life is by asking patients directly using Patient Reported Outcome Measures (PROMs). Although there have been some studies evaluating the health related quality of life of people with bladder cancer, many of these have been small scale or restricted to subsets of patients. In the main there is a dearth of large scale research examining PROMs in people living with and beyond bladder cancer. The importance of PROMs as healthcare measure is recognised, and with this is mind, the study will survey PROMs for patients with bladder cancer across Yorkshire, Humber, South Tees and North Derbyshire.

The data requested under this agreement will be used in relation to a longitudinal cohort, however the LABC project has two complementary sub studies that use different patient cohorts in different designs;

1. Longitudinal survey of PROMs within the first year of diagnosis requires informed consent to be taken from participants. The study requires access to the NHS Digital list cleaning service to provide fact of death.

2. Cross sectional survey of PROMs within patients living with and beyond bladder cancer (for information only- this part of the study is covered by a separate Data Sharing Agreement).

All patients alive within 10 years of a current or previous diagnosis of bladder cancer having been treated by one of the NHS hospitals in Yorkshire, Humber, South Tees and North Derbyshire will be invited to complete a single survey unless they have registered a type 2 objection. Patients with all types and stages of bladder cancer will be included. The study requires access to check the most current address and to provide fact of death.

The NHS Digital list cleaning service will be used to carry out mortality checks and retrieve current patient addresses for those people in the Life and Bladder Cancer (LABC) survey cohort for the purpose of administering a Patient Reported Outcome Measures (PROMs) survey of people diagnosed with bladder cancer in Yorkshire, Humber, North Derbyshire and South Tees.

Expected Benefits:

The list cleaning with NHS Digital data has 2 key benefits:
1) Latest addresses are obtained so that follow-up has greater coverage, and
2) As far as possible, surveys are not sent out to addresses of patients who are deceased, which could cause distress.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall health related quality of life of individuals.

The primary aims of the Life and Bladder Cancer (LABC) study are to describe the health related quality of life of patients living with bladder cancer diagnosed in Yorkshire, Humber, North Derbyshire and South Tees, to gain a deeper understanding of the variation in outcomes and to identify areas of unmet need.

Outputs:

The objective of the longitudinal arm of the study will be to evaluate changes in PROMs in patients with bladder cancer over time. All new incident cases within a 12-month period (est. n=1900) at 3,6,9 and 12 months post- diagnosis will be studied. Participants will give informed consent.

The study requires use of the NHS Digital list-cleaning service to:
1) Provide fact of death. The list clean will remove people who have died from the cohort list. This will minimise the risk of surveys being sent to people who have died potentially causing upset to their relatives. Death checks will be carried out immediately prior to survey mail out (initial and two reminders).
2) To check the most current address for mailing the survey. By providing up-to-date addresses NHS Digital will help the study work towards achieving the highest possible response rate and therefore make the results more representative of the population.

The following outputs from the study are envisaged:
1) Empirical knowledge of key clinical, socio-demographic and psychosocial factors that predict patients generic and cancer- specific Health Related Quality of Life (HRQL). Findings will be disseminated through a series of reports, academic papers (open-access) and conference presentations, and all findings will be available on the dedicated study website.
2) The electronic report and toolkit will be available to key stakeholders to provide detailed anonymised information. The toolkit will enable each NHS Trust, Clinical Commissioning Group and Strategic Clinical Network to visualise the results for their organisation and to compare them against the national 'average'
3) A validated survey tool for the collection of health outcomes of bladder cancer survivors. This would be made available for use by other organisations and researchers (dependent upon appropriate conditions of use).

None of the above outputs from the study will contain data from NHS Digital.

Processing:

There are a number of organisations involved in the LABC study, and their involvement is detailed below. However, data provided by NHS Digital will be received and processed only by Quality Health Ltd.

Quality Health Ltd
Quality Health is a Care Quality Commission (CQC) approved national contractor and works for 360 NHS Trusts throughout England on the National Patient and Staff Surveys. Quality Health are the LABC study data processor. They will send and receive the questionnaires, storing the survey mailing and response data on their systems. Quality Health will destroy identifying data needed for mailing when the surveys have closed and the questionnaire information that is retained will only be identified by a unique identification number. The returned completed questionnaires will be stored in paper and electronic formats within the secure systems used routinely by Quality Health Ltd. The electronic version of the survey data will be encrypted and sent securely to the National Cancer Registration and Analysis Service.

National Cancer Registration and Analysis Service (NCRAS), Public Health England (PHE):
NCRAS is run by Public Health England and is responsible for cancer registration. PHE will store patient details received from the recruiting hospitals and send the collated patient details to Quality Health Ltd.

University of Sheffield:
Joint study research location and sponsor of the LABC study. The CI and the Project Coordinator are based at the University of Sheffield. No patient identifiable data will be held at the University of Sheffield.

University of Leeds and Leeds Institute for Data Analytics (LIDA):
Joint study research location. The CI and the PROMs design and statistical team members are based at the University of Leeds. The Leeds research team are also part of the Leeds Institute for Data Analytics (LIDA) The linked survey response data will be analysed by the study team at Leeds University (in pseudonymised format only). The cleaned and pseudonymised data will be sent to the University of Leeds using a secure transfer mechanism (Leeds Institute for Data Analytics (LIDA) web drop system) and stored securely on the LIDA integrated research campus (IRC) platform.

Study Methodology Summary:
Participants who have given informed consent to take part in the study may provide information by completing the survey either via post, telephone or online. The survey will not ask for personal details such as names and addresses and each survey will include a unique study ID number. Participants will complete their survey online, by telephone or return it in the post to an NHS approved survey company, Quality Health Ltd, (the data processor). Quality Health Ltd will remove any personal information that may directly identify participants. This data will then be sent to the University of Leeds for analysis.

Quality Health require NHS Digital to perform a list-cleaning service and to provide the latest demographic details including fact of death and, for living participants who elected to receive surveys by post, confirmation of address details.

Access to the data is limited to Quality Health Ltd and will only be used for the purpose of this Agreement.

Quality Health will submit a data file to NHS Digital containing the following limited patient identifying data fields for patients in the LABC PROMs cohort:
- Name
- Surname
- Address
- Postcode
- NHS number
- Date of birth
- Gender

For each individual who elected to complete the survey by telephone or by email, the data file will include an indicator so that latest address details will not be supplied for these individuals.

NHS Digital will supply the output from the list-cleaning service to Quality Health.

The administration of the questionnaire (following the list cleaning) will be carried out by Quality Health who will act as a central data collection centre. NCRAS at Public Health England will transfer minimal patient identifiable information including names and addresses, post code, date of birth, NHS number, gender and a unique LABC identifier to Quality Health. Quality Health will pass these details on to NHS Digital for list cleaning as described above.

Following receipt of the completed questionnaires, Quality Health will clean and pseudonymise the data to remove any identifying information. The cleaned data is sent back to the study team in Leeds for analysis. The data will be stored within the secure environment at the Leeds Institute for Data Analytics (LIDA).

Data from NHS Digital is only provided to Quality Health Ltd, and only for the purpose of administering the surveys.

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).

Pandemic Respiratory Infection Emergency System Triage (PRIEST) Study — DARS-NIC-377644-X9J4P

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251 NHS Act 2006)

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

Purposes: No (Academic)

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

When:DSA runs 2020-09-08 — 2023-09-07 2020.10 — 2021.01.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. Demographics
  3. Hospital Episode Statistics Critical Care
  4. Emergency Care Data Set (ECDS)
  5. Hospital Episode Statistics Admitted Patient Care
  6. GPES Data for Pandemic Planning and Research (COVID-19)
  7. Civil Registrations of Death
  8. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
  9. Hospital Episode Statistics Admitted Patient Care (HES APC)
  10. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

The Pandemic Respiratory Infection Emergency System Triage (PRIEST) study is a National Institute for Health Research (NIHR) funded project aimed at evaluating and optimising the triage of people using the emergency care system (111 and 999 calls, ambulance conveyance, or hospital emergency department) with suspected respiratory infections during the COVID-19 pandemic.

By the 17th of June 2020 231,889 people in the UK had been confirmed to have been infected with the COVID-19 virus and over 40,000 people had died because of infection. At the peak of the pandemic NHS 111 received nearly twice the normal number of calls and over 3000 patients were admitted to hospital daily in the UK due to COVID-19 infection.

It is currently unknown how safely and effectively the emergency care system (NHS 111, the ambulance service and hospital Emergency Departments) assessed patients with suspected COVID-19 infection during the pandemic and determined whether patients needed to attend hospital or required hospital admission. There are currently no validated evidence-based risk stratification tools that can be used by clinicians in the pre-hospital and Emergency Department environments to identify patients at higher risk for deterioration with suspected COVID-19 infection who require further assessment.

In order to accurately assess whether patients with suspected COVID-19 infection who accessed the emergency care system during the pandemic had serious adverse outcomes it is necessary to link prehospital and Emergency Department cohorts with the proposed Emergency Care data Sets, Hospital Episode Statistics and Mortality data. Linking pre-hospital cohorts to GPES Data for Pandemic Planning and Research is also necessary to accurately identify patient factors associated with serious adverse outcomes.

Given the possibility of a second peak of COVID-19 infections in the UK as “lock-down” measures are relaxed the proposed research is urgently needed. Early analysis of the pre-hospital and Emergency Department cohorts will identify the characteristics of any patients advised to self-care at home who subsequently deteriorated and patients who are at high risk for serious adverse outcomes.

Study Governance

The data controller is the University of Sheffield. The study sponsor is Sheffield Teaching Hospitals NHS Foundation Trust. The study funder is the National Institute for Health Research. The study has been recognised as a nationally prioritised study in response to the COVID19 pandemic.

Background

The term triage is often used to describe a brief initial assessment in the emergency department to determine patient order of priority in the queue to be seen. In this project, the research team will use the term triage more broadly to include the full process of emergency department and pre-hospital assessment (by 111 and the ambulance service) used in decision-making regarding whether patients should attend hospital, require hospital admission or be referred for high dependency or intensive care.

On 26th March 2020, the PRIEST study began recruitment of patients with suspected COVID-19 attending Emergency Departments at participating NHS Trusts in England, Scotland, Wales and Northern Ireland. Data has been collected on over 20,000 patients. This work package (WP) is informally known as “core-PRIEST” and the data collected under this WP is known as the “core-PRIEST data”.

Between 18 March 2020 and 9 April 2020, the NHS online service completed 1,911,161 COVID-19 online assessments across England, resulting in 348,125 triages to NHS 111 or 999. Data from NHS England report an average of 95,600 calls per day to NHS 111 in March 2020, compared to an average of 46,700 a day in March 2019. Ambulance Services in England received a record number of calls per day to 999 in March 2020, possibly influenced by the COVID-19 pandemic. NHS 111 call handlers use structured questions and clinical advice to determine whether a 999 response is required. If no response is required, the patient is advised to self-care or contact their GP. If a 999 response is required, the attending ambulance personnel can use their clinical judgement to determine whether transport to hospital is needed. There has not yet been any research into the appropriateness of prehospital triage decisions with respect to patients with suspected COVID-19 and the researchers are not aware of any validated tools applicable to this situation.

Emergency department triage methods need to accurately predict an individual patient’s risk of death or severe illness. The predicted risk can then guide decision-making. Patients with a low risk may be discharged home, those with a high risk admitted to hospital, and those with a very high risk referred for high dependency or intensive care. Current risk stratification tools used in the Emergency department to help triage hospital admissions for patients with respiratory infection are based upon research conducted on patients with bacterial pneumonia and seasonal influenza. The available research indicates that no single tool performs well enough to support its sole use to inform hospital triage decisions during a pandemic and the use of these existing tools has not yet been assessed in patients with suspected COVID 19.

Research is therefore urgently needed to determine the accuracy of pre-hospital and Emergency Department triage decisions during the current COVID-19 pandemic and explore whether they could be improved.

The specific objectives during the pandemic are:
1. To report any important emerging findings regarding the performance of the emergency care triage method (or methods) used for suspected respiratory infections during a pandemic
2. To identify clinical characteristics and routine tests associated with under-triage (false negative assessment) or over-triage (false positive assessment) during a pandemic
3. To determine the discriminant value of alternative triage methods for predicting severe illness in patients presenting with suspected respiratory infection during a pandemic
4. To inform policy makers and practitioners during a pandemic of the study’s emerging findings.

The specific objectives after the first wave and, potentially for subsequent waves, of the pandemic are, for the hospital (emergency department):
1. To determine the discriminant value of emergency department triage methods for predicting severe illness in patients presenting with suspected pandemic respiratory infection
2. To determine the accuracy of presenting clinical characteristics and routine tests for predicting severe illness
3. To determine the independent predictive value of presenting clinical characteristics and routine tests for severe illness
4. To develop new triage methods based upon presenting clinical characteristics alone or presenting clinical characteristics, electrocardiogram (ECG), chest X-ray and routine blood test results, depending upon the data available and the predictive value of variables evaluated in objective 3

The specific objectives after the first wave and, potentially for subsequent waves, of the pandemic are, for prehospital services (NHS 111 and emergency ambulance services):
1. To link NHS 111 calls, identified as potentially relating to COVID19, to participating hospital and NHS Digital data, to determine whether patients calling NHS 111 were appropriately advised or provided with an ambulance response, in terms of whether they were admitted to hospital or suffered an adverse outcome.
2. To link ambulance ePR data to hospital and NHS Digital data, to determine whether patients attended by ambulance were appropriately advised to self-care at home or transported to hospital, in terms of whether they were admitted to hospital or suffered an adverse outcome.
3. To use ambulance ePR data recording patient characteristics to determine which patient characteristics, when recorded prehospital, are useful in predicting adverse outcome and determine the discriminant value of early warning scores, such as NEWS2, for predicting adverse outcome.
4. To explore the potential for data mining to provide new insights into the prediction of adverse outcome among patients contacting NHS 111 or ambulance services with suspected COVID-19.

Data is being processed under Article 6(1)(e) and Article 9(2)(j) as a task in the public interest as developing accurate risk stratification tools which are fair, robust, reproducible and allows the rapid identification of low risk patients with suspected COVID-19 infection who can safely self-care at home would help to mitigate the risk of services becoming overwhelmed and adverse patient outcomes.

Expected Benefits:

Dissemination of such information could reduce the risk of patients with suspected COVID-19 infection being inappropriately advised to self-care and deteriorating. This would be of direct benefit to users of the emergency care system in the Health and Social care sector.

During peaks in the COVID-19 pandemic emergency care services, especially pre-hospital services, are at risk of being overwhelmed. Developing accurate risk stratification tools which are fair, robust, reproducible and allows the rapid identification of low risk patients with suspected COVID-19 infection who can safely self-care at home would help to mitigate the risk of services becoming overwhelmed and adverse patient outcomes. If successfully developed accurate risk-stratification tools for patients with suspected COVID-19 infection in the pre-hospital and Emergency Department are likely to be adopted across the UK to aid the triage of patients in any subsequent peaks of the pandemic.

Using data collected on patients attending Emergency Departments in the UK with suspected COVID-19 infection during the first peak, linked to HES and ONS mortality data, the researchers may have derived, validated and disseminated a risk-stratification tool to help triage in the Emergency Department by February 2021. If the risk stratification tool is found to improve upon current triage of suspected COVID-19 patients in the Emergency Department, it will be disseminated to the New and Emerging Respiratory Threats Advisory Group which advises the Chief Medical Officer and in publication in high impact clinical journals. A successful triage tool is likely to be incorporated by NICE guidelines for use by clinicians in the triage of patients with suspected COVID-19 and change clinical practice in the Emergency Department. This would benefit the Health and Social Care sector by reducing the risk of patients being discharged who subsequently deteriorate or unnecessarily admitting patients (using scarce health service resources during a pandemic) who are unlikely to deteriorate.

If such tools were found to improve pre-hospital triage of patients with suspected COVID-19 infection results would be disseminated to the New and Emerging Respiratory Threats Advisory Group which advises the Chief Medical Officer and in high impact clinical journals. The findings could ultimately inform national clinical guidelines, such as NICE guidelines, and change clinical practice. Pre-hospital risk stratification tools could benefit the Health and Social Care sector by reducing the risk of patients being advised inappropriately to self-care at home or unnecessarily conveying patients to hospital who are at low risk of deteriorating.

Outputs:

Findings from the weekly core-PRIEST data analyses are reviewed by the core research team. When appropriate, these emerging findings are summarised to inform policy makers and practitioners during the pandemic on the study website (https://www.sheffield.ac.uk/scharr/research/centres/cure/priest). Findings are discussed every month with the Study Steering Committee which includes a range of clinical experts and lay members to identify any important findings which require urgent dissemination.

The results of interim analysis of the cohort of patients attending the Emergency Department with suspected COVID 19 infection are being compiled for academic publications and are projected to contribute to at least two peer-reviewed scientific articles published in high impact clinical journals such as the British Medical Journal and Lancet. Submission is planned by the end of August 2020. One article will highlight factors found to be highly associated with adverse outcomes in patients attending the Emergency Department with suspected COVID 19 infection and the other article will summarise the characteristics of patients who attended the Emergency Department with suspected COVID-19 infection during the first peak of the pandemic.

A final peer-reviewed scientific article assessing Emergency Department triage of patients with suspected COVID 19 infection is planned to be submitted by December 2020. This will present a derived and validated risk stratification tool aimed at improving Emergency Department triage of patients with suspected COVID-19 infection. This is likely to be published in a high impact clinical journal such as the British Medical Journal or Lancet using the results of analysis of the complete cohort of patients attending Emergency Departments linked to Hospital Episode Statistics and Mortality data.

Retrospective cohorts of patients with suspected COVID 19 infection assessed by prehospital services (Yorkshire and Humber NHS 111 and emergency ambulance services) linked to Emergency Care Data Sets, Hospital Episodes Statistics, Office for National Statistics Mortality data and GPES Data for Pandemic Planning and Research are planned to be derived and available for analysis by the research team for the end of September 2020.

Interim analysis of the prehospital cohorts of patients with suspected COVID-19 infection will be conducted shortly after the linked data sets are available and will be assessed by the core research team and Study Steering Committee. Any important early findings will be summarised to inform policy makers and practitioners in the pre-hospital triage of patients with suspected COVID-19 to aid the clinical management of any subsequent peaks of the pandemic in 2020 and 2021. Important early findings will be published on the study website and compiled for academic publications in peer-reviewed scientific articles published in high impact clinical journals such as the British Medical Journal and Lancet by December 2020.

Two final peer-reviewed scientific articles assessing triage of patients with suspected COVID-19 infection by NHS 111 and emergency ambulance services are planned to be submitted by February 2021. These articles will present derived risk stratification tools to aid the prehospital triage of patients with suspected COVID-19 infection. These articles are likely to be published in high impact clinical journals such as the British Medical Journal or Lancet using the results of analysis of the finalised pre-hospital cohorts of patients with suspected COVID-19 infection linked to Emergency Care Data Sets, Hospital Episode Statistics, Office for National Statistics Mortality data and GPES Data for Pandemic Planning and Research.

Outputs will only contain aggregated data with small numbers suppressed in line with the HES analysis guide.





Dissemination of early findings is planned by December 2020 in peer reviewed journal articles, reports to the New and Emerging Respiratory Threats Advisory Group and to the Study Steering Committee which includes a range of clinical experts and lay members. This would potentially inform Royal College of Emergency Medicine COVID-19 related Safety Bulletins. These are sent to all college affiliated Emergency Care Practitioners based on emerging evidence by the Royal College of Emergency Medicine and would highlight high risk groups for deterioration to relevant clinicians.

Processing:

Weekly descriptive analysis of the cohort of “core-PRIEST data” (patients attending participating Trust’s EDs with suspected COVID) is being undertaken including:
1. The number and geographical distribution of new cases
2. The proportion with an adverse outcome and details of adverse outcomes
3. Potential predictor variables identified in patients who were not admitted at initial presentation but had an adverse outcome
4. Triage criteria identified in patients who were admitted to hospital and had no adverse outcome.

The “Prehospital-PRIEST” work package (within the PRIEST study) will link:
(A) the core-PRIEST data from participating NHS Trusts in England only;
(B) computer aided dispatch [CAD] and electronic patient record [ePR] data from Yorkshire Ambulance Service NHS Trust (YAS), as the emergency/urgent ambulance service provider for Yorkshire, on patients who: were identified (by attending ambulance service personnel) with confirmed or suspected COVID-19; or, were the subject of a call to the ambulance service’s Emergency Operations Centre which was managed according to the Advanced Priority Medical Despatch triage card 36 (a pandemic triage process for patients with suspected COVID);
(C) NHS111 telephone triage [NHS111] data from Yorkshire Ambulance Service NHS Trust (YAS), as the NHS111 telephone service provider for Yorkshire and Humber, on patients who received a COVID-19 related final disposition;
(D) Hospital Episode Statistics: Admitted Patient Care [APC] from NHS Digital on patients identified in (A), (B) or (C);
(E) Hospital Episode Statistics: Critical Care [CC] from NHS Digital on patients identified in (A), (B) or (C);
(F) Emergency Care Dataset [ECDS] from NHS Digital on patients identified in (A), (B) or (C);
(G) Demographics [DEMO] from NHS Digital on patients identified in (A), (B) or (C);
(H) Death Registration [DR] from NHS Digital on patients identified in (A), (B) or (C);
(I) General Practice Extraction Service (GPES) Data for Pandemic Planning and Research [GDPPR] from NHS Digital on patients identified in (A), (B) or (C);
(J) Categorisation of place of residence [RESI] data (e.g. care home) from NHS England for patients identified in (A), (B) or (C).

Historic GPES Data for Pandemic Planning and Research (GDPPR) data, HES data and demographic has been requested to obtain more complete information for patients in the cohort on their premorbid status- particularly pre-existing medical conditions and routine medication use. These are important potential risk factors for deterioration in COVID 19 and other acute respiratory illnesses which may not be comprehensively collected or recorded in an Emergency treatment setting. They may, however, be extremely important in identifying high risk patients who require treatment in hospital

The University of Sheffield will supply direct patient identifiers (from (A), (B), (C)) to NHS Digital to enable NHS Digital to :
(1) establish (trace) each individuals identity by comparing with the NHS Personal Demographics Service;
(2) remove all such individuals who have registered a NHS national data (type 2) opt-out;
(3) extract from amongst the requested NHS Digital held datasets the applicable records.

The data supplied to NHS Digital by the University of Sheffield PRIEST study team will contain no health (or other special category) data other than any individual appearing within the data must have had a COVID related contact with an urgent or emergency care service between February 2020 and September 2020 (inclusive). The specific direct patient identifiers supplied to NHS Digital may include:
~ NHS Number
~ date of birth
~ postcode of residence (or postcode of incident as a proxy for postcode of resident);
~ names
~ sex

For records with both a valid NHS Number and date of birth: only NHS Number and date of birth will be provided; otherwise, records will be supplied with all available direct identifiers.

NHS Digital will then link the data and return data with the following identifiers to the University of Sheffield:

~ NHS Number
~ Postcode of residence
~ Date of birth
~ Date of Death

Identifiers will then be stripped from the data and only pseudonymised data will be analysed by the research team.

Only retrospective data is requested as the cohort has been identified by the fact that they received care on the basis of confirmed or suspected COVID-19 between March and July 2020.

The University of Sheffield PRIEST study team will supply pseudonymised (securely hashed) NHS Numbers to NHS England in order for NHS England to supply pseudonymised categorisation of place of residence (J) data to the University of Sheffield PRIEST study team.

Data from the listed data sources ((A) - (J)) will be linked using a pseudonymised identifier. De-identified, pseudonymised datasets will be provisioned on separate secure virtual environments on which analysts will conduct the statistical analyses to fulfil the objectives of the research.

The University of Sheffield (UoS) seeks full DOB (and other patient demographic data, e.g. gender) from NHS Digital for the patient cohort to populate a "single source of truth" (SSOT) table of patient characteristics. All large data studies face issues with conflicting records, a SSOT means the researchers do not have to "choose" between conflicting records nor do they need to create and document an algorithm that does the "choosing". UoS will receive DOBs for the vast majority of the cohort (from participating NHS Trusts and Yorkshire Ambulance Service) for the purposes of identifying the cohort in data held by NHS Digital. Receiving full DOB for patients in the cohort from NHS Digital will provide the practical advantage of allowing the researcher to calculate age directly from a SSOT and avoid errors in the calculation of age.

Full postcode is required so the researcher can link to a range of local area social demographic predictors.

Only patients identified in the core-PRIEST data from participating English sites and/or NHS111 / CAD / ePRD data provided by Yorkshire Ambulance Service will form the cohort on which the researcher seeks data from NHS Digital.

There will be no requirement or attempt to re-identify individuals within the data.

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

The University of Sheffield anticipated around ~110,000 people will be in the cohort for this study - but this will only be confirmed once the matching algorithm and subsequent linkage is carried out by NHS Digital.

The PJI Study: Do Invasive Dental Procedures Cause Prosthetic Joint Infections (PJI)? — DARS-NIC-261216-Q1L2Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2020-02-01 — 2023-01-31 2020.07 — 2020.09.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. MRIS - Personal Demographics Service
  3. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Sheffield requires Hospital Episode Statistics (HES) and patient identifiable linking data for use in The PJI Study: Do Invasive Dental Procedures Cause Prosthetic Joint Infections? This is a study of the potential link between Invasive Dental Procedures (IDP) and Prosthetic Joint Infections (PJI). The results of this study will provide much-needed evidence to either support or challenge current UK dental prescribing guidelines, as well as those in use in other parts of the world.

Replacing diseased and worn-out joints with prosthetic joints is one of the great advances of modern medicine. By 2016, 2.9 million (m) joint replacements were performed annually worldwide (including 1.5m hip, 1.1m knee and 100,000 shoulder joint replacements). After loosening of the prosthesis or joint dislocation, infection is the most common complication. Most infections occur in the 3 months following joint replacement and are largely the result of wound contamination at the time of surgery – so called “early infections”. Late peri-Prosthetic Joint Infections (LPJI) – infections occurring 3 months or more after joint placement – are more often attributed to haematogenous (carried by the bloodstream) seeding of bacteria from another site such as the mouth. The cost of treating such infections is high – often 4 to 6 times higher than the cost of the original joint replacement procedure – and the associated morbidity and complications for the patient are also extremely high. This has led orthopaedic surgeons to seek ways to prevent bacterial pathogens entering the circulation (bacteraemia) that could lead to this devastating complication. Most of the focus on bacteraemia prevention in recent decades has been on dental procedures; specifically the concept of giving antibiotic prophylaxis (AP) prior to invasive dental procedures (IDP) in order to prevent bacteraemia as a result of IDP.

There is currently a divergence of guidance between the UK – where AP to patients with prosthetic joints prior to IDP is not recommended, and the USA, Canada and other countries where AP in these circumstances is recommended. Therefore this research is required to on the one hand understand the risks of the current UK guidance and on the other hand to better understand whether AP prior to IDP is warranted, or effective in the prevention of LPJI, due to decades of conflicting advice from dental and orthopaedic surgical professional bodies on the subject because of a lack of evidence in support of the practice. There is no scientific data to support the use of AP to prevent LPJI and there has never been a randomised controlled trial (RCT) of AP. Furthermore, there is little microbiologic data to support a causal link between IDP and LPJI. The American Dental Association (ADA), has therefore recommended that “In general, for patients with prosthetic joint implants, prophylactic antibiotics are not recommended prior to dental procedures to prevent joint infections.” The American Academy of Orthopaedic Surgeons (AAOS), however, has stated that “Given the potential adverse outcomes and cost of treating an infected joint replacement, the AAOS recommends that clinicians consider antibiotic prophylaxis for all total joint replacement patients prior to any invasive procedure that may cause bacteraemia”. This on-going pressure from orthopaedic surgeons for patients with prosthetic joints to receive AP when undergoing IDP means that many patients, on the advice of their orthopaedic surgeon, expect their dentist to provide AP. As a result, many, if not most, American dentists continue to give AP for fear of being considered negligent by their patients and their surgeon.

If there is no association between IDP and LPJI then this would justify the UK position that there is no benefit to the current USA practice of giving AP to the vast majority of prosthetic joint patients for virtually all dental office visits. The continuing use of AP in the USA would therefore represent a large and unnecessary financial burden on individuals and the healthcare system, as well as an unnecessary risk to patients (from adverse drug reactions) and society (from the potential development of antibiotic-resistant bacteria). On the other hand, if a clear link is found between IDP and LPJI, then the UK policy would require re-evaluation because of the significant treatment cost (often 4 to 6 times higher than the cost of the original joint replacement procedure) of LPJI, and this would provide support for the use of AP in the USA to prevent LPJI. If that clear link between IDP and LPJI is found, then it will be necessary to evaluate if there would be cost-savings in the UK from the introduction of AP to prevent LPJI against the cost of treating potentially preventable LPJI cases – policy changes as a result of this study will therefore result in a basis on which to justify cost savings in the UK.

Any association between IDP and LPJI can only be studied in the UK, where AP prior to IDP has never been advocated for patients with prosthetic joints, and where a national dataset exists; such a study in the USA is compromised by the widespread use of AP – which would tend to hide any association. Indeed, since 2000 the British National Formulary has stated that dentists or physicians should NOT give AP to patients with prosthetic joints. Consequently, there is no history of AP use prior to IDP for patients with prosthetic joints in the UK. This means that any association between invasive dental procedures and LPJI will be fully exposed in the UK data, enabling this study to provide the data to settle the confusion once and for all, and provide the evidence for, or against, any association between invasive dental procedures and LPJI. Professional organisations, guideline committees, dentists, orthopaedic surgeons and their patients can then use this evidence to make decisions about the value of prescribing AP prior to IDP for patients with prosthetic joints

Processing of personal data for this study is justified under GDPR Article 6 (1) e: processing is necessary for the performance of a task carried out in the public interest, due to the current lack of evidence on which dentists can base their prescribing decisions, the huge, potentially unnecessary, financial burden of widespread routine use of AP across the USA, and the significant, again potentially unnecessary, risk to patients (from adverse drug reactions) and global society (from the potential development of antibiotic-resistant bacteria) that must arise from widespread, routine antibiotic use. Processing of personal data concerning health is also justified under GDPR Article 9 (2) j: processing is necessary for scientific research purposes and shall be proportionate to the aim pursued; this will be by far the largest study of the purported association between IDP and LPJI ever undertaken, and will therefore have far greater statistical power to detect an association than any previous study, as well as being the first study performed in a population where AP prescribing does not confound the outcome of the study by hiding or reducing the likelihood of detecting an association between IDP and LPJI. As per GDPR Article 9 (2) j, the processing of personal health data for this study will also respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject, as detailed within the privacy notice webpages for the University of Sheffield and for this specific project.

This study will use HES data to identify all patients who had a hospital admission for a joint infection between 1st April 2010 and 31st March 2017, and also retrieve additional HES data for admissions relating to these patients which include a procedure code indicating a prosthetic joint replacement, covering the period prior to the joint infection as far back as possible (1st April 2000 to 31st March 2017), in order to retrieve details of any prior prosthetic joint replacement procedures for as many of the patients as possible. This will enable identification of the type of joint replaced for each patient, facilitating sub-analysis of the data by type of joint replacement (hip vs knee vs other type of joint). This historical HES data detailing the timing of each patient’s joint replacement procedure will also enable the exclusion of patients with an early prosthetic joint infection (within 3 months of joint replacement) from the study, as these early infections are largely the result of wound contamination at the time of surgery and this study therefore focuses on LPJI only. Using personally identifying details, the group of patients identified via the HES data will be linked to routinely-collected dental data held in the NHS Business Services Authority Dental Information Services database, to identify which patients had an IDP in the period preceding a PJI. Patient identifiable data will be processed and linked by NHS Digital and NHS Business Services Authority Dental Information Services (the Data Processor) only, with pseudonymised HES and dental data then being passed to the study team at the University of Sheffield (the Data Controller) for analysis. The University of Sheffield will not receive patient identifiable data.

The primary objective of the study is to perform a case-crossover design study to quantify the incidence of IDP in the 3 months immediately preceding an LPJI diagnosis (case periods) and compare this with the incidence of IDP in earlier 3-month periods for the same patient, i.e. 3-6, 6-9, 9-12 months before the LPJI diagnosis (matched control periods; each patient acts as their own control) - to see if there is any association between IDP and LPJI. If there is a link between IDP and LPJI, the expectation is that the frequency of IDP would be significantly higher in the 3 months immediately preceding a LPJI (case periods) than in earlier 3-month (matched control) periods. Alternatively, if there is no link between IDP and LPJI, the expectation is that there would be no significant difference in the incidence of IDP in the 3 months immediately preceding a LPJI (case periods) and earlier 3-month (matched control) periods.

In addition, as a secondary objective the study will include a case-control design study comparing the frequency of courses of dental treatment that DO (cases) and DO NOT (controls) involve an IDP in the 3 months immediately preceding a LPJI. This will enable the study to further test if there is an association between IDP and LPJI using a different analytical/statistical approach to that used in the primary objective. If there is a link between IDP and LPJI, the expectation is that there would be a significantly higher incidence of cases than controls in the three months immediately preceding a LPJI. Alternatively, if there is no link between IDP and LPJI, the expectation is that there would be no significant difference between cases and controls.

If the study finds a link between IDP and LPJI, three sub-analyses will also be performed:

1. A sub-analysis to see if the link involves all types of prosthetic joint or particularly affects specific types of prosthetic joint (hip vs knee vs other type of joint). Procedure codes from the historical HES data allow sub-analysis of the data by the type of joint replaced.

2. A sub-analysis to examine the risk of developing LPJI with different types of IDP (extractions vs dental scaling vs endodontic treatment). Because each type of IDP is recorded separately in the NHS Business Services Authority dental database, the study can look at the link between LPJI and each of these IDP individually.

3. The oral bacteria most commonly implicated in LPJI are oral viridans group Streptococci, (OVGS), and these are the organisms that are principally targeted by amoxicillin AP. If there is a link between IDP and LPJI, it should be strongest, therefore, for those cases of LPJI caused by OVGS, and weak or non-existent for cases of LPJI caused by bacteria not typically associated with the mouth, e.g. Staphyloccoci. The study will therefore use the diagnosis codes from the HES data for the PJI admission for each patient to perform a sub-analysis of the link to IDP of cases of LPJI caused by OVGS, and for comparison Staphylococci and other types of Streptococci.

Expected Benefits:

The results of this study will be of considerable interest to dentists, orthopaedic surgeons, family practitioners and patients. It will also be of interest to guideline committees around the world.

If the research confirms an association between Invasive Dental Procedures (IDP) and Late peri-Prosthetic Joint Infections (LPJI), this would be seen as further validation of the view that Antibiotic Prophylaxis (AP) might have a role in preventing the disease - although further studies would be needed to confirm this. Such a result would
provide support for the use of AP before IDP to prevent LPJI, would provide evidence to support a re-evaluation of the UK advice that AP should not be given to prosthetic joint patients undergoing IDP, and would provide support for the current US guidance. The results would also provide further justification for the need for a randomised controlled trial to assess the efficacy of AP for IDP, which would determine to what extent the UK costs for treating LPJI could be offset by an investment in the use of AP for IDP in patients with prosthetic joints.

Alternatively, if the research demonstrates no association between IDP and LPJI, it would suggest that IDP are not a clinically significant cause of LPJI and it would provide support for the UK guidance not to give AP before IDP, and that there would be little or no cost savings in LPJI in the UK by giving AP before IDP. In addition, it would provide support for the view expressed by the American Dental Association Council on Scientific Affairs that AP is unlikely to be effective in preventing LPJI. Indeed, it would provide strong evidence to support guidance that the use in the USA of AP prior to IDP should cease as a means of preventing LPJI. Implementation of this guidance would remove a huge financial burden on patients and healthcare systems (the cost of providing AP for IDP is estimated at $59,640,000 annually in the USA), as well as removing unnecessary risk to patients (from adverse drug reactions) and global society (from the potential development of antibiotic resistant bacteria), resulting from the current widespread and routine use of AP for IDP in countries such as the USA and Canada where AP prior to IDP is a common practice for patients with prosthetic joints.

In addition, there is a wider benefit to the UK health system in a body of credible research which uses HES as a powerful dataset on which to build an evidence base which can be applied globally to benefit patients, lower costs and reduce antibiotic use. However, there is currently no data available on potential cost savings in the UK were antibiotic use to be reduced as a result of this study

The study collaborators, researchers outside the University of Sheffield, and the funding organisation will not have influence on the outcomes nor suppress any of the findings of this research.

Outputs:

The precise timing and publication strategy for the results of the study will depend on when the research team receives the necessary data from NHS Digital and NHSBSA and the speed with which the team can analyse it. It will also depend on what the results show and when the results become available bearing in mind the submission deadlines for different scientific meetings.

It is likely that the results will be of interest to key dental opinion leaders in the UK and in other countries, whatever the outcome. The largest and most important dental research meeting each year is the International Association for Dental Research (IADR). This meeting is by far the most important research meeting each year for key opinion leaders within UK dentistry and their international colleagues. More key opinion leaders within UK dentistry attend this meeting each year than any other. The first IADR meeting after the team are likely to have the results is scheduled for 23rd-26th June 2021. However, the deadline for submission of abstracts for that meeting is usually in November of the year before. It is possible therefore, that the results of the study will not be available in time for that deadline. Nonetheless, the focus will be on meeting this deadline if at all possible.. If it is not possible to meet this deadline, the research team may, as an alternative, target the 2021 meetings of the British and Irish Society for Oral Medicine or the British Society for Oral and Dental Research. However, these meetings are much smaller than the IADR meeting and attended by fewer UK dental key opinion leaders. Also, the dates, location and abstract submission deadlines of these meetings are not yet available.

If the data shows that invasive dental procedures are not associated with prosthetic joint infections, this would validate the UK guidance, and prove that LPJI costs can not be reduced by giving AP. It would also be of impact to American orthopaedic surgeons, since they currently recommend that patients with prosthetic joints should have antibiotic prophylaxis before undergoing invasive dental procedures – and this result would suggest that this practice should stop. . Alternatively, if analysis showed that invasive dental procedures were strongly linked to prosthetic joint infections, then the results would be of considerable importance to a UK orthopaedic audience and may result in a change to the current UK guidance. The results obtained from the study will therefore play an important part in the decision about which orthopaedic meeting at which to present the data, particularly as there is no large international orthopaedic meeting. . However, the dates, location and abstract submission deadlines of these meetings are not yet available. Most meetings will not permit data to be presented that has already been presented at another meeting of the same type. Therefore, although it may be possible to present at one dental and one orthopaedic surgery meeting, it would not be feasible to present at more than one dental or orthopaedic surgery meeting.

With regard to publication of the results, the team can only do this in one journal. It will therefore be important to target the journal likely to give the study the biggest exposure and impact. Generally, dental journals have greater impact and wider readership than orthopaedic surgery journals. Therefore, the first choice would likely be the Journal of Dental Research. If that should fail, the next choice would likely be determined by the outcome of the research. If it showed a strong link between invasive dental procedures and prosthetic joint infections, the British Dental Journal would be the second choice, since the data would suggest that the current UK practice of not giving antibiotic prophylaxis to those with prosthetic joints undergoing invasive dental procedures could be wrong. If on the other hand, the data showed that there was no link between invasive dental procedures and prosthetic joint infection, the team would probably prioritise the Journal of the American Dental Association, as a second choice, since this would suggest that the current US practice of giving antibiotic prophylaxis to these patients is likely to be wrong.

Final decisions about where to present and publish the data will need to be made, therefore, once the results of the study are known. Successful publication would help bring it to the attention of guideline committees responsible for advising clinicians about the use of antibiotic prophylaxis prior to invasive dental procedures, to prevent late peri-prosthetic joint infections.

Plans for further dissemination to the relevant interested public in the UK will be made in the light of the results of the study, and with advice from the study team's patient and public representative colleagues.

All publications and presentations resulting from the study will contain only aggregate data with small numbers suppressed in line with the HES Analysis Guide, and will contain no record-level data.

Processing:

The PJI study will link national data on courses of dental treatment (NHS Business Services Authority Dental Information Services database) and hospital admissions for Prosthetic Joint Infections (PJI) (Hospital Episode Statistics (HES) database) to investigate if there is a link between invasive dental procedures and the development of these infections in individuals who have prosthetic joints.

NHS Digital will identify within the HES inpatient admissions data all admissions between 1st April 2010 and 31st March 2017, with any of a specified list of diagnosis codes (present in any diagnosis field) indicating a joint infection (ICD-10 codes M00.0, M00.1, M00.2, M00.8, M00.9, or T84.5). NHS Digital will then undertake an internal linkage exercise with data from the MRIS Personal Demographics Service, to produce a full set of patient identifying information for these admitted patients (NHS number, surname, forenames, date of birth, sex, full address, and postcode). From the full HES and patient identifiable data, NHS Digital will create two datasets, linked by a unique study ID (encrypted HES ID) for each patient. These are described below.

Dataset 1: patient-level, identifiable data. This dataset will contain a full set of patient identifiers for all study patients, and their encrypted HES IDs, but no HES clinical data. This dataset will be sent to NHS Business Services Authority (NHSBSA) Dental Information Services in Eastbourne, England. NHSBSA will use the supplied patient identifiers to identify study patients within their own database, and retrieve all their dental treatment records from 1st April 2009 - 31st March 2017. Included in these records will be the date of any course of dental treatment and whether the treatment included any extraction, endodontic treatment or a scale and polish. NHSBSA will create Dataset 3 (pseudonymised dental treatment records data) by removing the patient identifiers from the retrieved dental treatment records (but retaining the encrypted HES ID provided by NHS Digital). NHSBSA will send Dataset 3 to the University of Sheffield study team. After the University of Sheffield study team have confirmed to NHSBSA that they have checked Dataset 3 and it is suitable for analysis, NHSBSA will securely destroy their copy of Dataset 1, (patient identifiers received from NHS Digital), notifying NHS Digital of data destruction.

Dataset 2: pseudonymised record-level clinical and operational HES data. This dataset will contain clinical and operational HES data for all inpatient admissions between 1st January 2000 and 31st March 2017, for all of the patients within the group already identified as described above (by discharge diagnosis ICD10 code on an admission between 1st April 2010 and 31st March 2017); this historical data is for the purpose of identifying the dates of any previous joint replacement procedures for each patient, and the specific type of joint replaced in each case. All records will include the patient's encrypted HES ID, but no patient identifying information. This dataset will be sent by NHS Digital to the University of Sheffield study team in Sheffield, England. The University of Sheffield study team will link Dataset 2 (pseudonymised HES inpatient data) to Dataset 3, (pseudonymised dental treatment data provided by NHSBSA for the patients identified in Dataset 1), using the encrypted HES ID (common to both datasets) for each patient; this linked data will be used for the study analysis.

The study team at the University of Sheffield will analyse the data according to the study protocol and report the results through peer-reviewed journals and conference proceedings. Plans for further dissemination to the relevant interested public in the UK will be made in the light of the results of the study, and with advice from the study team's patient and public representative colleagues.

Identifiable patient data will only be used for linking purposes and it will only be transferred, in the form of Dataset 1, between the two NHS organisations (NHS Digital and NHSBSA). The study team at the University of Sheffield will not receive any patient identifiable data; they will only receive Datasets 2 and 3, from both of which all patient identifiable information will have been removed (and replaced with the encrypted HES ID in both datasets to enable data linking without identification of patients). There will be no attempt made by the study team to re-identify individuals.

The pseudonymised HES inpatient data will be stored and processed solely at the University of Sheffield, on a secure virtual machine maintained by the University, which is accessible only by specified individuals from specified IP locations using University-owned and maintained computers. The data will only be accessed by individuals within the University of Sheffield study team - all of whom are contracted employees of the University of Sheffield and have received University of Sheffield training in data protection, information governance and confidentiality. The team's research collaborators at The Carolinas Healthcare System (Charlotte, North Carolina USA), the Mayo Clinic College of Medicine (Rochester, Minnesota USA) and the OrthoCarolina Research Institute (Charlotte, North Carolina USA), fulfil an advisory role only, providing valuable input during the selection of ICD-10 codes to use for condition identification, advice on the analysis of causal organisms for sub-analysis of PJI, (including identification of likely oral organisms and their associated ICD-10 codes), and additional clinical perspective on the study. For clarity, research collaborators will see the end results of University of Sheffield analysis and may be involved in subsequent health policy decision making in the USA, but they
- do not have an active decision-making role
- have no control over the analysis or processing of the data
- will not have any access to the data.

University of Sheffield are therefore the sole data controller; no other organisations, including the funding organisation or any research collaborators, will have any decision-making powers over the study or will handle the data. University of Sheffield will be the sole organisation with responsibility for determining the purpose for which and the means by which data is processed, and therefore no organisation other than University of Sheffield will have data controllership over the data provided under this data sharing agreement.

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

Survival outcomes and HES-based Charlson Comorbidity Scores for women recruited to the Bridging the Age Gap in Breast Cancer study — DARS-NIC-94749-Y1R8N

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

Purposes: No (Academic)

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

When:DSA runs 2019-08-25 — 2022-08-24 2018.10 — 2020.03.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Cause of Death Report
  3. Hospital Episode Statistics Admitted Patient Care
  4. MRIS - Cohort Event Notification Report
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

Background
This work is part of an on-going study - The Bridging the Age Gap in Breast cancer study. This is a multicentre cohort study collecting direct prospective data on older women with breast cancer from multiple UK sites.

The Age Gap study, which started in January 2013, is using statistical and modelling techniques to determine the age, comorbidity, frailty and disease characteristics of women over 70 with early breast cancer to provide guidance on 2 primary questions:
1. What are the personal and cancer characteristics of women who can be safely advised that surgery is unlikely to confer any advantage for them?
2. What are the personal and cancer characteristics of women who should be advised to have adjuvant chemotherapy after surgery?

A preliminary disease and outcome statistical model has been derived using pre-existing data from the UK primary breast cancer registry held by the West Midlands Cancer Intelligence Unit (WMCIU) and the NHS Hospital Episode Statistics data (available to Northern and Yorkshire Cancer Intelligence Service, not within this agreement). The statistical model is being used to develop a web-based algorithm to support clinicians in decision making related to older women. However the cancer registry data have a number of limitations, relating to the completeness and quality of comorbidity data. In addition, staging and co-morbidity data may be less accurate in women treated non- surgically as there will be no post-operative pathology data returns.

To overcome these limitations a UK-wide data collection exercise to gather detailed data on older women, their primary disease, health status and treatment details and medium term outcomes was performed. These data are more complete and contain additional information on co-morbidities which is particularly important for informing decisions in this older age group. As part of the project the intention is to supplement the initial 2 year direct follow up (via direct data collection for the study) by longer term follow-up via cancer registry returns, for up to 10 years of follow up.

The study has full ethics approval and was awarded NIGB approval at inception. All patients have consented to take part in the study & were also asked to consent specifically to permit the study group to seek cancer registry return data from them in the longer term. Consent was approved by both the Ethics Committee and the NIGB.

The recruitment period for the 3,400 women was over 5 years, starting in 2013 and completed in Spring 2018. Data on cancer type, health & fitness, treatment & outcomes are collected for 2 years after diagnosis. The earliest tranche of recruits are therefore not under direct follow up any more.


The aims of this study are as follows:

Objective 1 : Updated survival outcomes

To update the outcomes of women with invasive breast cancer (c50) recruited to the Bridging the Age Gap cohort study at three time points.

The researchers wish to update the outcomes of these women at the following three time points:
(a) study recruitment closure (Spring 2018),
(b) median 5 year follow up (Spring 2020) and
(c) median 10 year follow up (Spring 2025)

These time points reflect the 5/10 year follow up from the median time of recruitment for the cohort - participants have been recruited from 2013 to 2018.

These longer term follow up data will be used to revise and validate the preliminary statistical model. The updated statistical model will be used within the web-based algorithm to support clinicians in decision making related to older women. The updated statistical models will also be used to develop a health economic model to estimate long-term health outcomes and costs for different intervention strategies for older women.

At all three time points, the University would like to be able to derive Overall Survival (OS) and Breast Cancer Specific Survival (BCSS) and therefore want to receive vital status, date and cause of death .

The researchers are also interested in obtaining data on recurrence, but are aware that, currently, the quality of such data is poor and will therefore not be requesting this for the first timepoint. However, at the two later time points, assuming the quality of the data has improved, researchers may also be interested in obtaining breast cancer recurrence data. This would be subject to a further application to NHS Digital.


Objective 2 : To obtain the HES-derived proxy Charlson comorbidity Index (CCI) on patients recruited to the study

As part of the Cohort Study the University have also collected detailed comorbidity data using a direct data collection of the Charlson comorbidity Index (CCI) (the Charlson Comorbidity Index predicts the ten-year mortality for a patient who may have a range of comorbid conditions).

The study team would like to obtain the HES-derived CCI on all patients in the cohort to validate the accuracy of the HES –derived CCI against the CCI calculated directly from the cohort data. This is a uniquely valuable opportunity to undertake such a validation.

Yielded Benefits:

The study itself has already, before receipt of data from NHS Digital, led to the publication of a number of papers: https://www.sheffield.ac.uk/oncology-metabolism/research/surgicaloncology/research/agegap/publications

Expected Benefits:

The broad aim of the project is to improve treatment decision making for older women with breast cancer.

Objective 1
The updated survival status will inform the statistical model used within the decision support tool.
There is a particular need for more evidence on optimum treatment for older women with breast cancer, because to date much of the evidence has focused on younger women. Within this older age group it is particularly important to predict cancer outcomes and how these interact with frailty, age and co-morbidity.

At present older women with breast cancer are treated less aggressively than younger women. Some of this variance to normal practice is fully justified. It is known that a very frail elderly lady, with a short predicted life expectancy, will struggle to come through surgery without significant side effects and that her cancer may well be controlled for the rest of her natural life by anti-oestrogen tablets. However, at present there are no guidelines for just how elderly and how unfit a woman needs to be before surgery is of no benefit. At present, rates of non-surgical therapy are highly variable across the UK with a 4 fold variation in rates of non-surgical treatment between health regions.

This means that in some areas, fitter older women are denied surgery and placed at risk of later local disease progression, whereas in other areas, where surgery is the norm, many frail older women are subjected to surgery when it will have no benefit for them and may cause them significant harm.

Similarly, rates of chemotherapy use in older women are very low relative to those in younger women despite the fact that there is evidence that fitter younger women in the over 70 age group may benefit. The breast clinicians have little or no evidence to help them to decide what the cut offs for these treatments may be.

This work will benefit the health and social care systems by providing a freely-available web based tool to support clinician and patient decision-making for older women with breast cancer. The tool will help ensure that patients receive the most appropriate treatment and aims to improve outcomes for older women - in terms of survival and/or quality of life.

Objective 2
The validation of the HES-derived CCI will provide valuable information to the health services community on the strengths and limitations of using the HES-derived CCI. This results of this validation will be published in a peer-reviewed medical journal.

Outputs:

Outputs will contain only data that is aggregated (with small numbers suppressed in line with the HES Analysis Guide).

The study has an open access website which gives updates on the status of the study on a regular basis.

The study has worked with a team of patient representatives, and with Age UK and Breakthrough Breast Cancer, during the creation of the study (in line with NIHR recommendation), and will continue to do so in order to disseminate the outputs. The outputs will be shared widely, including with the West Midlands Cancer Intelligence Unit, the Northern and Yorkshire Cancer Intelligence Service, and through members of the MRC Cognitive Function and Aging (CFAS) project.

Specific outputs will include:
(a) a web-based decision support tool (to be made available nationally, to UK breast units).

(b) a research report for NIHR

(c) publications in medical journals

It is anticipated that publications will follow the research team’s track record of publishing in medical journals (BMJ, Lancet), specialist breast cancer journals (e.g. British Journal of Cancer, European Journal of Cancer) , and health economics journals (e.g.Value in Health ). Initial publications to be submitted Summer 2018 .

The end product will be a revised statistical model incorporating retrospective and prospective collected data. Record level information will not be included within the model. The revised model will be completed Summer 2018. The model will be made available to support clinicians in decision making related to older women. This will be made freely available to clinicians in the NHS. The statistical model will be further updated when 5 and 10 year median follow- up data is made available.

In addition to the development of a statistical model that can be used to predict individual outcomes, the University are proposing to construct an economic model to inform National Guidelines on treatment choice for older women with breast cancer. The economic model is likely to take an individual level modelling approach, incorporating outputs from the updated statistical model (using both retrospective and prospective data) that allows the impact of different strategies for treatment selection to be compared. The model will be completed Summer 2018.

Processing:

The University of Sheffield will supply the details of patients in the Bridging the Age gap cohort study, who have consented to sharing their data with the study, to NHS Digital. This will include all patients who have consented to cancer registry access (around 92% of recruited women) and who have not voluntarily discontinued the study and requested their data to be deleted or withdrawn their consent (approx. 3 or 4 patients).


Data to be sent to NHS Digital:

For women who have consented to registry access the University will provide NHS Digital with information consisting of NHS number, postcode, date of birth and sex in order for NHS Digital to identify patients from the Bridging the Age Cohort.

Data requested from NHS Digital :

(1) Survival follow-up cohort

For the survival follow-up subgroup (a subgroup of the whole cohort (those diagnosed prior to June 2015) who have already completed their two year follow up within the Bridging the Age Gap study) the University would like to request the following information from NHS Digital :
(i) Vital status,
(ii) Date of death
(iii) Cause of death (all fields)


(2) Whole cohort:

The University will calculate the Charlson comorbidity Index (CCI) from the linked HES and mortality data at the time of patient baseline assessment.

Note: The Charlson Index (Charlson et al 1987) includes the following co-morbidities: myocardial infarction, congestive cardiac insufficiency, peripheral vascular disease, dementia, cerebrovascular disease, chronic pulmonary disease, diabetes, liver disease, kidney disease, and other cancers.

The CCI Score (excluding the cancer component) is calculated for each patient using the relevant diagnostic codes recorded for any inpatient or day case hospital admission in the 18 months before diagnosis of their breast cancer.
The cancer component of the Charlson Comorbidity Index is derived from the cancer registry data, in a method consistent with other similar registry data analyses (Lavelle et al 2012, Morris et al 2011).

The data will not be made available to third parties. 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).


Use of the NHS Digital-supplied data

(1) Study of long term outcomes of women in cohort study receiving current standard care for early breast cancer

As part of the project a preliminary disease and outcome statistical model has been derived using pre-existing data from the UK primary breast cancer registry held by the West Midlands Cancer Intelligence Unit (WMCIU) and the NHS Hospital Episode Statistics data.

The requested data will supplement the initial 2 year direct follow up via direct data collection for the study by providing longer term follow-up of outcomes for patients in the cohort study

The initial statistical model (using merged retrospective data from the WMCIU and HES) will be revised and refined using data from the cohort study and the updated survival outcomes. These data will be used to test and revise the initial statistical model.

Initially the study team will update the statistical model using the (up to three years’ worth of) longitudinal data from the cohort study alone as the data source. This will enable researchers to examine whether evidence is emerging of differences in model coefficients. Researchers will undertake comparison using the original statistical models to predict probabilities of early outcomes for the cohort and examine them against observed outcomes. Finally, researchers will seek to develop a new set of statistical models for time to breast cancer and non-breast cancer death linking the longer-term data from the cancer registries with the short to medium term outcomes data from the prospective cohort study.

Two further follow-up data (at median follow-up of 5 years and 10 years) will allow the models to be revisited and improved over time, based on the long term data for the cohort patients.


(2) Comparison of HES-derived proxy CCI compared with CCI derived from cohort study data
The Cohort Study has collected detailed co-morbidity data using a direct data collection of the Charlson co-morbidity Index (CCI). By requesting the HES derived CCI on all patients in the cohort researchers will be able to validate the accuracy of the registry CCI.

NIHR programme grant: The Design, Development, Commissioning and Evaluation of Patient Focused Vascular Services — DARS-NIC-16274-J8H5T

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 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 - s261(5)(d)

Purposes: No (Academic)

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

When:DSA runs 2019-02-05 — 2021-08-31 2019.01 — 2019.10.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

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

Objectives:

In providing any clinical service there is tension between maximising efficiency, cost effectiveness and other desirable features of the service. There is currently enormous pressure for the reconfiguration of vascular services due to many conflicting requirements.

An NIHR Programme Grant has been secured by The University of Sheffield and the title of the grant is “The Design, Development, Commissioning and Evaluation of Patient Focused Vascular Services”. The programme grant has four workstreams. The first workstream is Objective 1: Current Service Arrangements (https://scharr.dept.shef.ac.uk/vascular-research/our-research/workstream-1-analysis-of-routinely-collected-nhs-data/). A key element of this first workstream involves the examination of vascular service activity and outcomes using pseudonymised HES data. The data will be used to identify trends and variation in activity and aspects of case mix and outcome that can be established from routinely collected data sources.

Workstream 2 is carrying out a thorough evaluation of the various outcome measures that are already available to vascular services in order to assess the quality of the service and outcomes. Where necessary it is developing new methods of evaluating outcome that can be used to assist clinicians and patients in managing their condition and can also be used to evaluate the overall quality of services that are being provided.

Workstream 3 is focusing on evaluating the strength of patients preferences for various aspects of service that are likely to be affected by re-organisation, including both clinical aspects of the service and non-clinical aspects of the organisation, such as travelling distances and locality.

Workstream 4 will bring together the evidence from the first three workstreams in creating computer models of the ways in which patients with vascular disease are treated, This will allow a prediction of the effects of re-organising services on workload, outcomes and the use of resources.

A supporting document has been provided with further, in-depth information about each workstream.

The Programme Grant will feed into the process of reconfiguration, initially based upon data analysis and the development of organisational models for care and subsequently through the development of quality indicators that will become available and validated over the course of the research programme.

As part of the NIHR programme grant work, the initial aim is to characterise existing vascular service arrangements and establish their relationship to workload, case mix and outcomes. This aim will be achieved by analysing routinely collected sources of data, primarily Hospital Episode Statistics (HES) and Civil registration Mortality data.

Yielded Benefits:

The Researchers are still in the process of analysing the data. The original data was only received two years into the five-year programme grant duration. The analysis is therefore well behind schedule and an extension from NIHR has been granted.

Expected Benefits:

The outputs from the programme will be a set of tools of use to providers and commissioners of vascular services. There will be six specific outputs.

1. The outputs from the HES analysis will provide a report of existing service configuration, workload and referral patterns, along with a set of standardised measures for vascular activity and those outcomes that are identifiable from routine datasets. It will also identify recent and planned changes to vascular service configuration.

2. A systematic review of the relationship of outcome to identified aspects of service configuration will provide an assessment of the existing literature that links outcomes within vascular services to aspects of service configuration.

3. A library of reported utility values for outcomes relating to arterial disease will be published based upon the literature review. These values will be updated when values obtained from the outcome measure assessment in the ePAQ-VAS become available.

4. A set of validated generic and condition specific outcome measures will be developed along with a standardised and validated electronic format for data collection that will be suitable for clinical assessment and service evaluation.

5. A report will identify the attributes, other than those included in calculation of health related quality of life, that are considered important in the provision of vascular services with an estimate of QALY equivalents that can be used to guide future service planning.

6. The final report from the programme will include a set of cost utility models covering the main pathways of care in five key disease areas within vascular disease. The report will include a consideration of the cost utility of a variety of changes in practice that are identified as being potential effects of service reconfiguration, as well as allowing future assessment of the cost utility of other service changes or introductions of new technologies.

Outputs:

The results have been, and will continue to be, submitted to NIHR in the form of project reports and presented to committees overseeing national vascular service provision and strategic development. The findings will be disseminated through publication in peer reviewed journals and presentation at national and international conferences.

Project reports have been submitted to NIHR annually through the five-year duration of the project (2013-2018) and will continue to be submitted through the extension period.

The following paper has been accepted for publication:

Aber A, Tong TS; Chilcott J, Thokala P, Maheswaran R, Thomas SM, Nawaz S, Walters SJ, Michaels J. Sex differences in the rates of repair of emergency abdominal aortic aneurysm: A Nationwide Population-Based Cohort Study in England between 2002-2015. British Journal of Surgery 2018 (in press).

The following paper will be resubmitted to BMC Health Services Research following revision in line with reviewers’ comments:

Aber A, Tong TS; Chilcott J, Maheswaran R, Thomas SM, Nawaz S, Michaels J. Methodology to Identify Aortic Aneurysm Activity in England from Hospital Admissions Data.

The Programme Grant will feed into the process of reconfiguration of NHS vascular services in England, initially based upon data analysis and the development of organisational models for care and subsequently through the development of quality indicators that will become available and validated over the course of the research programme.

The benefits will begin to be realised from 2019 onwards.

The programme (including all work streams) has produced a number of outputs, which are available on the website:

Academic research papers: https://scharr.dept.shef.ac.uk/vascular-research/outputs-and-resources/academic-researchpapers/

Posters and other media: https://scharr.dept.shef.ac.uk/vascular-research/outputs-and-resources/posters-and-othermedia/

Presentations: https://scharr.dept.shef.ac.uk/vascular-research/outputs-and-resources/presentations/

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

Processing:

The original processing is continuing. Over the past two years detailed analysis and modelling protocols have been developed to define case mix groups and outcomes and incorporate these into models of service configuration. The University of Sheffield are currently awaiting NHS Digital data so that these models can be updated with more recent data. This data will be used to identify trends and variation in activity across vascular service provision areas in England. The analysis will focus on four main conditions (abdominal aortic aneurysms, peripheral arterial disease, carotid artery disease, varicose veins). Variations in case mix and comorbidity will be examined. Outcomes for which data are available will be examined (e.g. readmissions, surgical revision procedures, subsequent cardiovascular events). Linked mortality data will be a key indicator in relation to longer term outcome.

Data analysis will consider: trends in activity; identification of appropriate outcome measures; variations in practice, particularly in relation to new technologies (e.g. endovascular repair of aortic aneurysms, endovascular treatment of peripheral arterial disease, new treatment modalities for varicose veins) and differences in patient selection; travel distances to treatment centres and transfers between hospitals; relationships between service configuration, volume and outcomes.

Vascular case mix groups will be defined using a previous Health Technology Assessment (HTA) study (in 2000). An expert working group of specialists in general practice, vascular surgery, vascular radiology, nursing and other relevant specialties will be established to review the decision-making rules used to analyse the data. The group only have access to the aggregated outputs, with small numbers suppressed in line with the HES Analysis Guide. . It will review and modify the classification of case mix groups to take account of changes in coding and new procedures and investigations. It will also advise on potential quality indicators and outcomes from aggregated outputs. These outputs will be small numbers suppressed in line with the HES Analysis Guide.

Potential indicators and outcomes include extended hospital stay (criteria specified for individual procedural or diagnostic groups), readmissions and repeated procedures, early and late mortality after aortic aneurysm surgery, diagnosis of stroke after carotid surgery and amputation of a lower limb after arterial surgery.

The research requires data over a long time-period, to include periods of time before and after reconfiguration. The data also needs to be for the whole country in order to assess differences in practice and outcomes across the country.

The data will be stored on the University of Sheffield’s secure central computer machines managed by the University’s Corporate Information and Computing Services Department (CiCS). A firewall is operated and maintained by CiCS to protect the entire University of Sheffield campus network. The machine room is secure with shutters, access control, intrusion detection and early fire detection with fire suppressant facilities. CiCS carries responsibility for the physical and other security of the networked systems of the University, a requirement of the data handling commitments under data protection legislation.

Access to the NHS Digital data will be password protected and restricted to researchers working directly on the project.

The volume of data is large and processing is carried out on fast encrypted computers housed in University premises and connected to the University’s campus network.

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

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

There will not be data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the agreement.

Scaling up and improvement: To implement and evaluate a programme of Shared Haemodialysis care (dialysis self-management support). SHARE-HD — DARS-NIC-150780-W6W3Z

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-04-01 — 2020-03-31 2019.07 — 2019.07.

Access method: One-Off

Data-controller type: SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST, UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

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

Objectives:

The School of Health and Related Research (ScHARR) at the University of Sheffield requires HES admitted patient care (APC) and accident and emergency (AE) data for use in the evaluation work stream of the scaling-up and improvement implementation and evaluation of shared haemodialysis care (dialysis self-management support) project, known as SHAREHD.
There is evidence from a range of long-term conditions of a relationship between patient involvement in aspects of their own care and improved outcomes. Patients who receive dialysis treatment at centres do so in an environment that generally discourages patient involvement. Shared Haemodialysis Care (SHC) describes an intervention where patients treated with in-centre haemodialysis are given the opportunity to learn tasks relating to their own dialysis treatment. This study is a quality improvement collaboration, which is led by Sheffield Teaching Hospitals (STH) to scale up shared haemodialysis care for patients on centre-based haemodialysis. The study is a 24-month research study, nested within a 30-month quality improvement project, which aims to assess the effectiveness and economic impact of a structured programme to encourage patient involvement in centre-based haemodialysis.
STH are the sponsor of the main study and are responsible for the overall project management, recruited patients and obtaining ethical approval. ScHARR, University of Sheffield are responsible for the 24-month research study to evaluate the effectiveness of SHARE-HD.
Sheffield Teaching Hospital in collaboration with the NIHR Collaborations for Leadership In Applied Health Research and Care (CLAHRC) based at ScHARR at the University of Sheffield were successful in responding to a request from the Health Foundation Scaling Up Improvement Programme for taking successful health care interventions and delivering them to scale, and duly won the grant-funding Award from the Health Foundation. STH contributed towards the writing of the protocol for the evaluation study. The evaluation, which includes the design, analysis, and writing up of the evaluation is the sole responsibility of ScHARR at the University of Sheffield. The statistical and health economic analysis plan (SHEAP) was developed independently from the rest of the project by ScHARR at the University of Sheffield. The aims and direction of the evaluation study are the sole responsibility of the University of Sheffield. The data requested will be analysed by ScHARR, presented to the wider team and in the same way will be presented for reports and publications in aggregated summaries, complying with HES analysis guides requirements for small numbers.
This study, which began in January 2016, aims to evaluate the scaling up of shared haemodialysis care in 12 dialysis centres across England using an open pragmatic stepped wedge randomised controlled trial study design, with three steps each lasting 6 months. Recruitment of participants took place across the 12 sites, ranging from 27 at the lowest site to 80 at the largest. Following a 6-month baseline data collection period or step; the 12 clusters (hospitals) are randomised in two steps, of 6 hospitals at a time to either the SHAREHD programme or usual care. After the first six-month step 6 out the 12 hospitals will be delivering the SHAREHD intervention; and after the second six month step all 12 hospitals will be delivering the SHAREHD intervention.
The intervention at the centre of the programme is to give centre based haemodialysis patients the choice to undertake up to 14 treatment related tasks. These range from performing observations, preparatory hygiene, setting up the dialysis machine, securing dialysis access, programming the machine, initiating and completing treatment.
The primary aim of the study is to test the clinical effectiveness of the intervention (SHAREHD) compared to usual care on the ability of sampled haemodialysis patients to complete 5 or more out of 14 tasks at the 6 assessment points.
The secondary aims are set out below:
1. To test the impact of the intervention on the secondary outcomes (number of patients who choose to go home on HD or perform in centre dialysis independently; Patient activation, quality of life and renal symptom score; hospitalisation, infection, vascular access intervention, safety and adverse events).
2. To determine the resource use and costs of providing the SHAREHD intervention.
3. To examine the cost-effectiveness/cost-utility of the intervention
4. To examine the interaction between the effectiveness of the intervention and a range of patient-level characteristics, including age, ethnicity and socioeconomic deprivation.
5. To model the incremental cost-effectiveness of the SHAREHD if it was rolled out across England and Wales
The data requested is to identify the co-morbidities of patients which will be adjusted for in all analyses, to identify hospitalisations (secondary aim 1) and to derive resource utilisation information for the economic evaluation (secondary aims 2 and 5). Data will only be accessed by staff of ScHARR, University of Sheffield.
The primary analysis will compare those completing five or more tasks and the information to analyse this has been collected directly as part of the study. A multi-level generalised linear mixed (random-effects) logistic regression model will be fitted to the longitudinal data to answer this question.
The HES data will be used to identify the presence of co-morbid conditions previously proven to be prognostically significant in patients receiving renal replacement therapy. By identifying individual ICD10 and OPCS codes from the diagnosis and procedure code fields in HES from admitted patient care episodes from the two years prior to the start of the study, the presence of these co-morbid conditions will be assigned to patients at the start of the intervention. Previous applications of this method in this setting have found it to
a. reduce variation in outcomes
b. have similar accuracy to clinician coded co-morbidities (Fotheringham et al, 2014)

Sub group analysis will be performed regardless of the statistical significance on the overall intervention effect.
Multilevel logistic regression will be applied to explore the impact of serious adverse events, including hospitalisations (Secondary aim 1) Continuous secondary outcomes including quality of life and change in renal symptoms scores will be analysed using random effects linear regression. Other secondary outcomes including hospitalisations will be analysed using random effects logistic regression.
The base case cost-effectiveness analysis will compare SHAREHD with usual care. The primary cost-effectiveness analysis will be reported as incremental costs per quality adjusted life year (QALY) over the study period from an NHS and social care perspective. Secondary analysis will examine the cost-effectiveness from wider perspective (including participant and companion travel time and time away from usual activities), incremental costs per competency achieved/person achieving five or more tasks, the cost per home HD case achieved and the cost-effectiveness over an extended time horizon.
Data on resource use will be collected using; Hospital episode statistics (HES data), Your Health Survey and ethnographic observation. Each specific resource used will be valued using unit costs based on NHS reference costs and other national averages, to generate a total cost of national generalisability. The elements of total cost will include:
• Set up cost of the SHAREHD programme: including the cost of learning events (staff time (trainers)), printing materials, time (trainees),
• Cost of delivering the intervention: including the time spent delivering the SHAREHD programme to participants (ethnographic study)
• Impact of the intervention on the NHS: including hospital visits, treatments and investigations (HES data)

The cost per cluster / unit will be generated across the period of the trial (24 months post randomisation). This will be constructed from resource use aggregated at the unit level with the relevant unit cost applied. Costs (excluding research related costs) that do not vary by trial arm (e.g. recruitment of clusters) or by cluster within a trial arm (e.g. costs setting up SHAREHD) will be apportioned and added to each cluster. The cost per participant will also be generated and presented as incremental costs per quality adjusted life year.
The beyond-trial modelling of longer-term expectations for cost-effectiveness will reflect the health service use consequences of the SHAREHD programme, mean cost and QALYs will be modelled over a lifetime horizon for the intervention compared with usual care and will take the form of a cost-utility analysis (CUA) to estimate the incremental cost and the incremental effect of intervention compared with usual care. The model will have four states:
• Share HD/HD
• Home HD
• Transplant
• Death

For each state there will be distributions around the expected costs and utilities and time spent in each state, where costs and utilities will be a function of patient age at the point in the model. For the SHAREHD state patients the number of tasks a patient is likely to achieve independence on will also be generated and a patient’s utilities and costs will be estimated as function of this based on data observed in the SHAREHD study. Model outcome will be the represented as an incremental cost-effectiveness ratio (ICER).
Further details of the cost-effectiveness analysis can be found in the statistical and health economic analysis plan (SHEAP) and the study protocol, which have been included with this application.

This research is performed in the public interest for scientific research and statistical purposes, in compliance with Article 6(1)(e) and 9(2)(j) of the GDPR. Ethics approval has been granted for the purposes as described in the study protocol with which this agreement falls in line.

If it is proven that changing health behaviours leads to improvements in patient reported outcomes while being cost neutral and without demonstrable harm then this needs to be robustly demonstrated to gain traction for a wider implementation. The impact of this health intervention will be determined in the social care setting through the data collection instruments that have been developed. The wider cost implications of this will be modelled. All of the above statements are measurable from the analyses proposed. Sustainability and spread are being maximised through the programme and with the proposed supporting evidence these benefits will be realised within the lifecycle of the service specification documents for haemodialysis: authored by clinical reference groups, these mandate the specification of a dialysis unit and the services it offers, including shared haemodialysis care.

Expected Benefits:

SHAREHD is designed to empower patients to take more control over their chronic health condition. Kidney disease costs the NHS approximately 1.4 billion pounds a year, with the 57,000 renal replacement therapy patients consuming over half of this. Like many other interventions, there are benefits and risks, costs and savings, which need to be determined in order that the wide range of stakeholders can appraise and invest in SHAREHD. This study is able to achieve this through linkage to HES.

Quantifying the reductions in adverse events, hospitalisation and healthcare usage are powerful motivators: patients need this information to help them decide to undertake tasks relating to their dialysis. Clinical staff and policy makers can evaluate shared haemodialysis care and its benefits alongside other interventions which may compete for similar resources. Understanding the cost of implementing shared haemodialysis care and potential downstream savings, or cost-neutrality in the presence of increased quality is essential for managers, policy makers and clinical reference groups. Many evaluations of health interventions are not subjected to such rigorous cost-effectiveness evaluation, and the reporting of these analyses both informs evaluations being designed by others looking to improve healthcare, and also more widely argues the benefits of such interventions. It also supports evaluation using linked datasets including those offered by DARS.

The hazard of devolving aspects of haemodialysis care to patients should not be overlooked. However, demonstrating more broadly that patient involvement in their own care, not by choice or motivation, but through health improvement interventions is essential knowledge for the wider health and social care community. Much of the existing evidence has been conducted cross-sectionally (e.g. associative not causative) as articulated in the Health Affairs vol 34, no 3 article Green Hibbard et al: "When Patient Activation Levels Change Health Outcomes and Costs Change Too", or is based on individual testimonies.

Outputs:

Descriptive statistics for the cohort will be presented at summary level, for example mean with standard deviation for continuous variables and numbers with proportions for categorical data. Results will be presented in an aggregate form, for number and proportion of tasks performed, and graphical figures of this data will be presented in the same format. The time series analysis of this data is looking at trends over time before and after the implementation of the SHAREHD programme. Multi-level generalised linear models will be applied to analysis, whether a participant can complete five or more tasks, the number of tasks each patient can complete (supervised or independent), the number of tasks a patient can complete independently and whether a patient has home dialysis. Results and figures will again be presented at an aggregate level.

The economic analysis will follow guidance on cost-effectiveness analysis set out by the National Institute for Health and Care Excellence (NICE, 2013). Results will be presented as total costs to the NHS and the costs of the SHAREHD programme will be compared with costs of usual care as costs per QALY and cost per competency achieved and cost per home HD case achieved. Please see SHEAP, which is included as a supporting document (SD_9) with this application, for further details of all analysis,

A draft report was provided to the Health Foundation in December 2018. An article on the effectiveness and cost-effectiveness of SHAREHD will be prepared for an open access peer review journal, possible journals to be submitted to include: Nephrology Dialysis Transplantation or Medical Decision Making. A simplified version of findings will be published on SHAREHD study website and issued to study participants.

All outputs are aggregated with small number suppression in line with the HES Analysis Guide.

Because of the particular need for user engagement, newsletters are are issued regularly and the patient facing website, https://www.shareddialysis-care.org.uk, is constantly updated.

Processing:

Sheffield Teaching Hospitals will provide NHS digital with SHAREHD study ID, NHS number, date of birth, surname, forename and gender for the participants enrolled in the SHAREHD study who gave consent for the study team to request HES data from NHS digital. Data will be uploaded over the secure system provided by NHS digital.

NHS digital will return linked APC and AE data including the unique study ID and no other identifiers. No subsequent flows of data will take place.

ScHARR will store the data on a secure drive at the University of Sheffield on a (securely housed) networked virtual machine accessible only from within the campus network. This data is stored in a separate location to the participant identifiers. The two datasets will not be re-linked and the data will remain pseudonymised.

Data will only be accessed by six employees within ScHARR who have authorisation from SHAREHD study team to access the data for the purposes described, all of whom are substantive employees of the University of Sheffield. The employees who will have access to the data include: data-manager (1 senior and 1 junior staff member) who will be responsible for cleaning the data, statistician team (1 senior and 1 junior staff member) who will carry out the statistical analysis, and health economist team ((1 senior and 1 junior staff member) who will be responsible for the economic modelling. No record-level data will be shared outside of these substantive employees.

Data will be verified by confirming number of records provided matches that are specified in the HES extract covering letter provided by NHS Digital, data ranges will also be checked. Best-practice HES data cleaning scripts will be applied to remove duplicate and erroneous records.
A report will be produced and shared with the Health Foundation showing the results and will include a series of tables and figures summarising the case mix characteristics, primary and secondary outcomes (completion of five or more tasks, whether patients start home haemodialysis, patient activation, health related quality of life, renal symptoms and cost-effectiveness) information will be presented for baseline, and step 1 and step 2 of the stepped wedge design (see SHEAP). An open access journal article will be prepared and submitted to peer review journals reporting the effectiveness and the cost-effectiveness of SHAREHD.

Descriptive statistics for the cohort will be presented at summary level, for example mean with standard deviation for continuous variables and numbers with proportions for categorical data. Results will be presented in an aggregate form: for tasks performed this would be proportions achieving 5 or more tasks. Multilevel models will be used to account for the clustered nature of the data; multi-level generalised linear mixed/random effects models will be used for binary data and multi-level lineal mixed/random effects models will be used for continuous data. Results and figures will again be presented at an aggregate level. The economic analysis will follow guidance on cost-effectiveness analysis set out by the National Institute for Health and Care Excellence (NICE, 2013). Results will be presented as total costs to the NHS over the study period and modelled over a patient's lifetime using beyond-trial modelling. The costs of SHAREHD will be compared with costs of usual care for in-centre haemodialysis patients as a cost per quality adjusted life year gained. See Appendix C of SHEAP for examples of summary tables.
The data will not be linked with any record level data.

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

SHAREHD requests HES APC and HES A&E data for analysis. The data being requested is for the period 2014/15 to present, this data is for two years prior to the study commencing in order to identify existing patient co-morbidities as described in section 5a above and described in the article by Fotheringham et al provided with this application. The remaining period is for the observed period of the study. Data is requested only for those participants who have consented to take part in the SHAREHD study and who have consented for researchers to access this HES data. No sensitive data items are being requested.

The statistical package STATA will be used for all analysis.
The data will be analysed to establish the effectiveness of the SHAREHD programme and for estimating its cost-effectiveness (see specific outcomes for further details). All outputs are aggregated with small number suppression in line with the HES Analysis Guide. There will be no sharing of record-level data with third parties. No attempt will be made to re-identify the patients from the data. The data will not be linked or used for commercial purposes.

Life and Bladder Cancer : The Yorkshire Cancer Research Bladder Cancer Patient Reported Outcomes Survey — DARS-NIC-129819-V5P5Z

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251, Section 251 NHS Act 2006)

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

Purposes: No (Academic)

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

When:DSA runs 2018-04-01 — 2021-03-31 2018.10 — 2019.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF LEEDS, UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. MRIS - List Cleaning Report

Objectives:

Bladder cancer is one of the most common human cancers. Its treatment can affect the physical, psychological and sexual function of a patient, which reduces their overall quality of life. It is important to collect information about the experiences of patients as they reflect outcomes, identify areas of care that need improvement, and how to improve this care. These patient reported outcome measures (PROMs) are important measures of healthcare delivery and identify concerns that matter most to patients. The study team will develop a questionnaire that records these measures in patients with bladder cancer during and after treatment. The study team will survey all new and existing patients within Yorkshire and the Humber and will compare outcomes across the region, across the spectrum of disease states and treatments, and over the first 12 months since diagnosis. The study team will use this information to understand outcomes within the population, to identify gaps in care and barriers to care improvement, and to shape clinical care delivery.

The primary aims of the Life and Bladder Cancer (LABC) study are to describe the Health Related Quality of Life (HRQL) of patients living with bladder cancer diagnosed in Yorkshire, Humber, North Derbyshire and South Tees, to gain a deeper understanding of the variation in outcomes and to identify areas of unmet need.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall health related quality of life of individuals. One way of finding out about the impact of bladder cancer and its treatment on patient health related quality of life is by asking patients directly using Patient Reported Outcome Measures (PROMs). Although there have been some studies evaluating the health related quality of life of people with bladder cancer, many of these have been small scale or restricted to subsets of patients. In the main there is a dearth of large scale research examining PROMs in people living with and beyond bladder cancer. The importance of PROMs as healthcare measure is recognised, and with this is mind, the study will survey PROMs for patients with bladder cancer across Yorkshire, Humber, South Tees and North Derbyshire.

The LABC project has two complementary sub studies that use different patient cohorts in different designs:
1. Longitudinal survey of PROMs within the first year of diagnosis requires informed consent to be taken from participants.
2. Cross-sectional survey of PROMs within patients living with and beyond bladder cancer.
All patients alive within 10 years of a current or previous diagnosis of bladder cancer having been treated by one of the NHS hospitals in Yorkshire, Humber, South Tees and North Derbyshire will be invited to complete a single survey unless they have registered a type 2 objection. Patients with all types and stages of bladder cancer will be included. The study requires access to the NHS Digital list cleaning service to remove people who have registered a type 2 objection from this cohort, to check the most current address and to provide fact of death.

The NHS Digital list cleaning service will be used to carry out mortality checks and retrieve current patient addresses for those people in the LABC cross-sectional survey cohort (excluding any cross-sectional cohort participants who have raised type 2 objections) for the purpose of administering a PROMs survey of people diagnosed with bladder cancer in Yorkshire, Humber, North Derbyshire and South Tees.

No NHS Digital data is being shared under this agreement for the longitudinal survey.

Yielded Benefits:

Four hundred questionnaires have been sent out to bladder cancer patients as part of the cross sectional pilot send out. The questionnaires should have reached participants in week commencing 5th November 2018. A reminder will be list cleaned and sent out 3 weeks after the original to participants who have not responded. To date 2 online replies have been received.

Expected Benefits:

The benefit of the list cleaning with NHS Digital data has two key benefits:
1) Latest addresses are obtained so that follow-up has greater coverage, and
2) As far as possible, surveys are not sent out to addresses of patients who are deceased, which could cause distress.

Whilst the treatment of bladder cancer can affect the physical, psychological and sexual function of a patient, relatively little is known about the impact of the disease and its treatment upon the overall HRQL of individuals.

The primary aims of the LABC study are to describe the HRQL of patients living with bladder cancer diagnosed in Yorkshire, Humber, North Derbyshire and South Tees, to gain a deeper understanding of the variation in outcomes and to identify areas of unmet need.

Outputs:

Cross sectional cohort
All patients alive within 10 years of a current or previous diagnosis of bladder cancer having been treated by one of the NHS hospitals in Yorkshire, Humber, South Tees and North Derbyshire will be invited to complete a single survey unless they have registered a type 2 objection. Patients with all types and stages of bladder cancer will be included.
The study require access to the NHS Digital list cleaning service to:
1) Remove people who have registered a type 2 objection from this cohort. This will ensure that questionnaires are not sent out inappropriately to people who has asked not to be involved in research.
2) Provide fact of death. The list clean will remove people who have died from the cohort list. This will ensure that surveys are not sent to people who have died and avoid any potential upset to their relatives from receiving the mailing. Death checks will be carried out immediately prior to survey mail out (initial and reminder). However, it must be acknowledged that even with the most stringent checks, a small number of individuals may have died very close to the time of survey mailing and these will receive a survey.
3) To check the most current address for mailing the survey. By providing up-to-date addresses NHS Digital will help the study work towards achieving the highest possible response rate and therefore make the results more representative of the population.

The following outputs from the study are envisaged:

• Empirical knowledge of key clinical, socio-demographic and psychosocial factors that predict patients’ generic and cancer-specific health related quality of life ( HRQL). Findings will be disseminated through a series of reports, academic papers (open-access) and conference presentations, and all findings will be available on the dedicated study website.

• The electronic report and toolkit will be available to key stakeholders to provide detailed anonymised information. The toolkit will enable each NHS Trust, Clinical Commissioning Group and Strategic Clinical Network to visualise the results for their organisation and to compare them against the national ‘average’.

• A validated survey tool for the collection of health outcomes of bladder cancer survivors. This would be made available for use by other organisations and researchers (dependent upon appropriate conditions of use).

Processing:

There are a number of organisations involved in the LABC study, and their involvement is detailed below. However, data provided by NHS Digital will be received and processed only by Quality Health Ltd.

Quality Health Ltd
Quality Health is a Care Quality Commission (CQC) approved national contractor and works for 360 NHS Trusts throughout England on the National Patient and Staff Surveys. Quality Health are the LABC study data processor. They will send and receive the questionnaires, storing the survey mailing and response data on their systems. Quality Health will destroy identifiable data needed for mailing when the surveys have closed and the questionnaire information that is retained will only be identified by a unique identification number. The returned completed questionnaires will be stored in paper and electronic formats within the secure systems used routinely by Quality Health Ltd. The electronic version of the survey data will be encrypted and sent securely to the National Cancer Registration and Analysis Service.


National Cancer Registration and Analysis Service (NCRAS)
NCRAS is run by Public Health England and is responsible for cancer registration. NCRAS will identify the cohort for the cross sectional study and carry out future linkage. NCRAS will forward the dataset of pseudonymised questionnaire responses, disease and treatment information alongside a study identification number (only) to the study team in Leeds for analysis.


University of Sheffield
Joint study research location and sponsor of the LABC study. The CI and the Project Coordinator are based at the University of Sheffield. Only pseudonymised spreadsheets and case report forms will be held at the University of Sheffield.


University of Leeds and Leeds Institute for Data Analytics (LIDA)
Joint study research location. The CI and the PROMs design and statistical team members are based at the University of Leeds. The Leeds research team are also part of the Leeds Institute for Data Analytics (LIDA) The linked survey response data will be analysed by the study team at Leeds University (in pseudonymised format only). The cleaned and pseudonymised data will be sent to the University of Leeds using a secure transfer mechanism (Leeds Institute for Data Analytics (LIDA) web drop system) and stored securely on the LIDA integrated research campus (IRC) platform.



Study Methodology Summary
Quality Health require NHS Digital to perform a list-cleaning service and to provide the latest demographic details including fact of death and confirmation of address details for those patients who have not raised a type 2 objection.
Access to the data is limited to Quality Health Ltd and will only be used for the purpose of this agreement.
Quality Health will submit a data file to NHS Digital containing the following limited patient identifiable data fields for patients in the LABC PROMs cohort:
Name
Surname
Postcode
NHS number
Date of birth
Gender

The administration of the questionnaire (flowing the list cleaning) will be carried out by Quality Health who will act as a central data collection centre. NCRAS at Public Health England will transfer minimal patient identifiable information including names and addresses, post code, date of birth, NHS number and a unique LABC identifier to Quality Health. Quality Health will pass these details on to NHS Digital for list cleaning as described above.

Please note that if no response is received from the patient, or if consent is not given, no further information will be accessed in relation to this individual patient.

Following receipt of the completed questionnaires, Quality Health will clean and anonymise the data to remove any identifying information. The cleaned data is sent back to NCRAS using a secure transfer mechanism. NCRAS will link the questionnaire data back to the necessary patient, disease and treatment information contained within the cancer systems. NCRAS will forward the dataset of pseudonymised questionnaire responses, disease and treatment information alongside a study identification number (only) to the study team in Leeds for analysis. The data will be stored within the secure environment at the Leeds Institute for Data Analytics (LIDA).

Data from NHS Digital is only provided to Quality Health Ltd, and only for the purpose of administering the cross-sectional survey.

MR1452 - The Invasive Dentistry – Endocarditis Association (IDEA) Study: A study of the link between invasive dental procedures and critical medical events including infective endocarditis, myocardial infarction, stroke, pulmonary embolus and spontaneous pre-term birth. — DARS-NIC-116377-L5J9M

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Identifiable, Yes (Section 251, Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012 – s261(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(7); 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 2018-12-03 — 2021-12-02 2019.04 — 2019.04.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. MRIS - Bespoke
  3. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Sheffield requires Hospital Episode Statistics (HES) and patient-identifiable linking-data for use in the Invasive Dentistry - Endocarditis Association (IDEA) Study: A study of the link between invasive dental procedures and critical medical events including infective endocarditis, myocardial infarction, stroke, pulmonary embolus and spontaneous pre-term birth.

There is concern that bacteria entering the circulation during invasive dental procedures (IDP) could precipitate critical medical conditions including infective endocarditis (IE), myocardial infarction (MI), stroke (ST), pulmonary embolus (PE) and spontaneous pre-term-birth (PTB). Most concern has centred on IE, a heart infection with 30% first-year mortality where oral bacteria are the causal organism in 35-45% of cases. Indeed, before 2008 it was standard care for people at risk of IE to receive antibiotics before IDP (termed antibiotic prophylaxis). The effectiveness of antibiotic prophylaxis has never been proven, and in 2008 NICE recommended it should stop. However, the UK is the only country where antibiotic prophylaxis is not recommended for those at high-risk of IE, and a recent study found that UK IE incidence has risen since 2008. Much less is known about any causal link between IDP and MI, ST, PE or PTB, but these are serious conditions with high mortality/morbidity and where there is public and professional concern about the possibility that IDP could precipitate MI, ST, PE or PTB. It is important that we know if IDP precipitates them or not. The purpose of this study, therefore, is to determine if there is a link between IDP and IE, MI, ST, PE or PTB.

By looking back in time in the HES database (as far back as 2000) for IE patients only, the study team also aims to identify those IE patients who were at high risk of developing IE, by searching their previous inpatient data for certain specified 'high-risk' conditions and procedures, occurring prior to their admission for IE. This will enable the team to repeat the analyses to determine if there is an increased risk of IE following invasive dental procedures in individuals at high risk of IE, compared to individuals at lower risk for IE. Some of the diagnosis or procedure codes used to identify patients at high risk of IE may appear within the data relating to admissions several years prior to the main IE admission falling within the study period, so it is important to retrieve historical data from as far back as possible, where full data exists, in order to capture all the necessary prior admissions to be able to identify high-risk individuals (without missing any due to poor data completion). HES data has improved in completeness over time; January 2000 was selected as the earliest point at which the team could be confident that the historical data would contain all the relevant codes.

The study will use HES data to identify patients who develop IE, MI, ST, PE or PTB and, using personally identifying details, link this HES data to routinely collected dental data, to identify those patients who had an IDP in the period preceding their medical event. For patients with IE only, additional HES data from the period prior to the IE event will also be retrieved, in order to identify patients who were at high risk for IE before developing the condition. Patient identifiable data will be processed and linked by NHS Digital and NHS Business Services Authority Dental Information Services only, with pseudonymised HES and dental data then being passed to the research team at the University of Sheffield for analysis. The University of Sheffield will not receive patient identifiable data.

The study is important because if IE is linked to IDP there is potential to reduce the number of IE cases by using antibiotic prophylaxis. This could improve patient safety and reduce costs to the NHS. Identifying if IDP precipitates MI, ST, PE or PTB is also important for patient safety and could allow preventative action to be taken. Alternatively, if there is no causal link between IDP and any of these conditions, patients and their clinicians (doctors and dentists) can be reassured about the safety of dental procedures, and unnecessary prevention measures could be stopped.;'

Expected Benefits:

If the research confirmed an association between invasive dental procedures and IE, it is anticipated that this would be seen as further validation of the recommendation of the European Society for Cardiology, American Heart Association, and other international guideline committees advising that dentists give Antibiotic Prophylaxis (AP) before performing invasive dental procedures on individuals considered at high risk of developing IE. It would also provide further evidence that the current NICE guidelines, recommending no AP, may be wrong, could be putting patients at risk, and should be reconsidered.

Alternatively, if the research demonstrated no association between invasive dental procedures and IE, it would support the recommendations of NICE and provide evidence that the recommendation of other guideline committees around the world - to give AP - could be wrong and should be reconsidered. Such an outcome would also suggest that rather than focussing on AP to prevent the ~45% of IE cases caused by oral bacteria, prevention strategies should instead re-focus on improving oral hygiene in those at risk of IE in order to reduce the small but continuous risk that likely results from daily activities such as tooth brushing, flossing and chewing food, particularly in those with poor oral hygiene.

Whatever the outcome, therefore, the results of this study are likely to impact the guidance given by international guideline committees and therefore the care given by Dentists and Cardiologists for individuals at risk of developing IE.

Currently, although concern has been raised about the possibility that invasive dental procedures could precipitate MI, ST, PE or PTB, the real risk of this is not known and no preventative measures are advocated. A clear demonstration that there is no link between invasive dental procedures and MI, ST, PE or PTB would provide reassurance to clinicians and patients about the safety of dental procedures. Alternatively, if a link were demonstrated between any of these conditions and invasive dental procedures, research could be initiated to identify ways of reducing or eliminating this risk.

Because of the likely presentation/publication schedule, impact with regard to the IE data will likely come in 2020/21 and for the other conditions 2022/3.

Outputs:

The data will be presented at appropriate Cardiology and Dental scientific meetings and conferences, and in peer reviewed journals and a final report.

Published outputs will enable the data generated by this study to add to the body of evidence which is used to inform policy decisions, (e.g. of NICE), guidelines and care practice around the use of antibiotic prophylaxis for invasive dental procedures. NICE and other international guideline committees will only evaluate peer reviewed, published data when setting or revising their guidance. Hence the team's priority is publication in high impact peer reviewed journals. Nonetheless, if the team's findings are of urgent or critical importance, they will contact relevant guideline committees to give them advance notice of publication, and offer access to the data.

The final report to NIHR is mainly for the benefit of the funder, (NIHR), who will normally make the final report publicly accessible and usually bring important findings to the attention of interested public, patient and professional and groups.

Presentation of data at scientific meetings and conferences will increase awareness and dissemination of the results amongst the relevant professional and scientific communities, enable the team to test their results against peer opinion, and provide an opportunity for contesting of the results by interested clinical and scientific colleagues.

The data relating to the link between invasive dental procedures and IE the team will aim to publish in a major general medical journal such as the Lancet, New England Journal of Medicine, JAMA or BMJ alternatively, if that doesn’t prove possible, then a major international cardiology journal e.g. Circulation or the European Heart Journal. The anticipated date for first manuscript submission is September 2020. However, in the experience of the team it can take up to a year before such a paper is actually published i.e. 2021.

The team would also target presentation of the data at a major cardiology meeting (American Heart Association (AHA) meeting in November each year or the European Society for Cardiology (ESC) meeting in August each year) in late 2019 or 2020. The team will also consider presentation at the International Association for Dental Research (IADR) meeting in 2020.

The data for the link between invasive dental procedures and MI or stroke the team will present at an IADR meeting and publish in either a dental (possibly the Journal of the American Dental Association (JADA)) or cardiology journal (Circulation of European Heart Journal).

Data on any link between invasive dental procedures and pulmonary embolus or spontaneous pre-term birth will we will also present at an IADR meeting and most likely publish in JADA. These data will be analysed after the main IE study and so presentations will likely follow in 2021-2 and publication in 2022-3.

All outputs will only contain results in highly aggregated format and as statistical summaries and measures of association. Small numbers will be suppressed in line with the HES Analysis Guide. Record level information will not be released to any third party.

Processing:

The IDEA study will link national data on courses of dental treatment (NHS Business Services Authority Dental Information Services database) and hospital admissions for infective endocarditis (IE), myocardial infarction (MI), stroke (ST), pulmonary embolus (PE) and spontaneous pre-term birth (PTB) (Hospital Episode Statistics (HES) database) to investigate if there is a link between invasive dental procedures and the development of these conditions.

NHS Digital will identify within the HES inpatient admissions data all admissions with a diagnosis (primary or secondary) of IE, MI, ST, PE, or PTB between 1st April 2010 and 31st March 2016. NHS Digital will then undertake an internal linkage exercise with data from PDS, to produce a full set of patient identifying information for these admitted patients (NHS number, surname, forenames, date of birth, gender, full address, postcode). From the full HES and patient identifiable data, NHS Digital will create two datasets, linked by a unique study ID (encrypted HES ID) for each patient. These are described below.

Dataset 1: will contain a full set of patient identifiers for all study patients, and their encrypted HES IDs, but no HES clinical data. This dataset will be sent to NHS Business Services Authority (NHSBSA) Dental Information Services in Eastbourne, England. NHSBSA will use the supplied patient identifiers to identify study patients within their own database, and retrieve all their dental treatment records from 1st April 2009 - 31st March 2016. Included in these records will be the date of any course of dental treatment and whether the treatment included any extraction, endodontic treatment or a scale and polish. NHSBSA will create Dataset 3 by removing the patient identifiers from the retrieved dental treatment records (but retaining the encrypted HES ID provided by NHS Digital). NHSBSA will send Dataset 3 (pseudonymised dental treatment records) to University of Sheffield research team, and will securely destroy their copy of Dataset 1, (patient identifiers received from NHS Digital), notifying NHS Digital of data destruction.

Dataset 2: will contain clinical and operational HES data for all inpatient admissions between 1st April 2010 and 31st March 2016, where at least one of the diagnosis fields contains one of a list of specified codes indicating IE, MI, ST, PE, or PTB. It will also contain clinical and operational HES data for all inpatient admissions (with any diagnosis), between 1st January 2000 and 31st March 2016, relating to those patients from within the group identified above whose diagnosis code indicated IE; this is for the purpose of identifying patients who were at higher risk for IE. All records will include the patient's encrypted HES ID, but no patient identifying information. This dataset will be sent by NHS Digital to the University of Sheffield research team in Sheffield, England. The University of Sheffield research team will link Dataset 2 (pseudonymised HES inpatient data) to Dataset 3, (pseudonymised dental treatment data provided by NHSBSA for the patients identified in Dataset 1), using the encrypted HES ID for each patient; this data will be used for the study analysis.

The research team at the University of Sheffield will analyse the data according to the study protocol and report the results through peer reviewed journals, a report to NIHR and conference proceedings.

Identifiable patient data will only be used for linking purposes and it will only be transferred, in the form of Dataset 1, between the two NHS organisations (NHS Digital and NHSBSA). The research team at the University of Sheffield will not receive any patient identifiable data; they will only receive Datasets 2 and 3, from both of which all patient identifiable information will have been removed (and replaced with the encrypted HES ID to enable data linking).

The pseudonymised HES inpatient data will be stored and processed solely at the University of Sheffield. The data will only be accessed by individuals within the University of Sheffield study team - all of whom are working under appropriate supervision on behalf of the University of Sheffield and are subject to the same policies, procedures and equivalent controls as substantive employees of the University. The team's research collaborators at Taunton and Somerset NHS Foundation Trust, Guy’s and St Thomas’ NHS Foundation Trust, and The Carolinas Healthcare System (North Carolina USA) fulfil an advisory role only, providing valuable input during the selection of ICD-10 codes to use for condition identification, advice on the analysis of causal organism for infective endocarditis cases, (including identification of likely oral organisms and their associated ICD-10 codes), and additional clinical perspective on the study. Research collaborators do not have an active decision-making role, and will not have any access to the data.

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 comparative effectiveness and efficiency of Cognitive Behaviour Therapy (CBT), Counselling for Depression (CfD) and other High Intensity Therapies (HIT) in the treatment of depression in the Improving Access to Psychological Therapies (IAPT) service. — DARS-NIC-85465-H1W9F

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2018-05-11 — 2021-05-11 2018.06 — 2018.09.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Improving Access to Psychological Therapies Data Set
  2. Improving Access to Psychological Therapies Data Set_v1.5
  3. Improving Access to Psychological Therapies (IAPT) v1.5

Objectives:

The University of Sheffield (the School for Health and Related Research (ScHARR)), funded by the British Association of Counselling and Psychotherapy (BACP), aim to examine the effectiveness of High Intensity Therapies (HIT) in the Improving Access to Psychological Therapies (IAPT) programme by focussing on the three types of HIT which are most commonly offered in IAPT. As indicated in the most recent IAPT report, Cognitive Behaviour Therapy (CBT), Counselling for Depression (CfD) and “other high intensity therapies (not specified)” are the most widely available therapies in IAPT. The report includes figures that suggest a broad equivalence in outcomes between these three therapy types in IAPT.

However, the analysis of the IAPT data to date has not acknowledged the difference in related groupings (e.g. outcomes for patients of therapist A are likely to be related, as are outcomes of a particular service, as are outcomes of a particular CCG). Thus, a more rigorous statistical analysis ought to be conducted that accounts for the naturally nested nature of the data.

Using multi-level modelling techniques that address the nested nature of the IAPT data, would allow researchers to evaluate both the overall effectiveness of the three HIT as well as to evaluate how other variables (including service-level variables) impact therapy outcome. It is intended that the research will increase understanding of the variables that significantly impact the effectiveness of IAPT HIT interventions, which has the potential to lead to improvements in HIT outcomes for IAPT patients. It is important to undertake a rigorous analysis of this data in order to ensure that the psychological therapies available within IAPT are providing comparable outcomes for patients. Evaluating the contribution of service-level variables may suggest potential pathways for service improvement, for example if the impact of the service-level variables on patient outcomes is significant and/or very variable across services. Equally it is important to evaluate the importance of patient-level variables, for example to explore whether there is a differential effect of patient intake-severity or socio-economic status on outcomes in the three HITs. Again, a more nuanced understanding of the inter-relations between patient and therapy (intervention) variables can suggest pathways to service improvement and improved patient outcomes, for example by finding that some interventions are better suited to patients with more moderate levels of difficulties.

This work is also important as there is currently no research outside of the most recent IAPT report supporting the effectiveness of CfD and yet it has been rolled out across IAPT. There is clear evidence from randomised controlled trials as to the effectiveness of CBT, however, there is currently no such evidence for CfD. CfD was developed following the 2009 publication of the NICE guidelines for Depression in Adults in which there was some evidence in support of counselling, specifically from person-centred and emotion-focused approaches. CfD aimed to provide an evidence-based manualised form of counselling that could be delivered within IAPT.

Similarly, it is important to evaluate the effectiveness of the HIT labelled ‘other’, as while it is unclear exactly what these interventions consist of, they appear to be as effective as CBT and they are the second most available therapy provided within IAPT. The IAPT recommended therapy types have been designated as such based on empirically-informed recommendations thus it is in the interests of the public to evaluate also this ‘other’ therapy type if it is doing as well as the IAPT recommended therapies.

The University of Sheffield wishes to access the IAPT CBT, CfD and other HIT’s data in order to build on work examining outcomes for these therapies. Prior to this request the University has successfully requested and been granted access to IAPT data collected as part of the 2nd round of the National Audit of Psychological Therapies (NAPT). (Please note: this work was conducted on audit data only, accessed through HQIP).

The NAPT data was collected between 2012 and 2013, prior to the roll-out of CfD training in 2011 and therefore the counselling data included was that of generic counselling rather than CfD. The analysis of the available data demonstrated CBT and counselling to be equally effective in the treatment of depression and multi-level modelling applied to the data indicated site variability that was not effected by therapy type.

ScHARR wish to build on this work by conducting a similar analysis of more recent IAPT data, focusing on CfD as the NICE approved form of counselling available in IAPT.

The University of Sheffield is funded by The British Association for Counselling and Psychotherapy (BACP) in the production of outputs from the research. For researchers in the Mental Health Research Unit at ScHARR, analysis of the data will be a continuation of a programme of work using Multilevel Modelling (MLM) with large routinely collected data samples from different sources. In addition to answering the research questions, researchers are interested in the application and development of MLM methodology. The BACP has a commitment to championing the need of patients as well as the counselling professions broadly. As the largest professional body for the counselling professions in the UK their 45,000 members work across a wide range of therapeutic modalities, including within IAPT providing CBT, CfD as well as potentially the IAPT therapies designated as ‘other’.

Additionally, this work will complement a large scale randomised controlled trial (not using IAPT data provided in this agreement) currently underway which is comparing the efficacy and cost-effectiveness of Counselling for Depression with CBT as delivered within an IAPT service. No data will be linked to the record-level patient data provided under this agreement, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data.

The overall aim of the programme of work described here is to improve outcomes for IAPT patients by improving understanding of the ways in which different patient, therapy intervention and service-level variables impact patients’ outcomes in therapy.

Expected Benefits:

The planned analyses of the requested data will be of benefit to the health and social care system by informing patients, the public, practitioners and commissioners about the comparable effectiveness of psychological therapies currently available through IAPT services. Through dissemination of the study findings via a range of platforms including social media, press releases, dissemination to BACP members, and at academic conferences and peer-reviewed publications, patients and the public will be empowered to understand that there is a choice in the form of therapy they can access through IAPT and how the available psychological therapies compare to one another in terms of effectiveness for patients.

Through informing commissioners of the findings of this work, for example via the NHS clinical commissioners newsletter, commissioners will be well placed to determine which types of psychological therapy to make available to their patients by understanding the comparable outcomes of the available step 3 interventions. ScHARR will also build the knowledge base of counselling and psychotherapy practitioners by disseminating to the 45,000 BACP membership.

In addition to this, there is a benefit to scientific knowledge through publication of the study findings in a high-impact peer-reviewed journal.


Outputs:

All outputs stemming from this work would be presented at an aggregated level small numbers suppressed only, no record-level data will be disseminated.

ScHARR aim to publish a minimum of one key paper of the findings of this data in a high-impact peer-reviewed journal on an open-access basis. Researchers must be mindful that publications are not always accepted in the first journal of choice, and therefore a number of journals have been considered that ScHARR would consider in order of priority, should the paper not be accepted initially. The aim is to submit this paper within 12 months of receiving the data for analysis.
Journals may include: BMC Psychiatry, British Journal of Clinical Psychology, Journal of Applied Psychology
Following publication in a peer-reviewed academic journal researchers plan to disseminate the findings to different audiences as detailed below.

A full report would be disseminated internally within BACP and presented to the BACP Board of Governors. This would consist of aggregate data only.
An article would be written for the BACP practitioner journal ‘Therapy Today’ which is circulated to all 45,000 BACP members. A brief summary of the findings would also be included in the BACP E-bulletin which is disseminated to all BACP members.

Alongside the written dissemination of the findings the plan is to submit to present the findings at a number of academic conferences, including the BACP Annual Research Conference, the Society for Psychotherapy Research UK and International conferences and the British Psychological Society conference. Additionally, there may be other events that this work would be relevant to present at to reach a wide range of audiences.
ScHARR would also disseminate the findings of this research to various audiences including:
• Political briefings (eg parliamentarians, civil servants and Government departments)
• Public engagement
• The National Institute for Health and Care Excellence (NICE)
• Scottish Intercollegiate Guidelines Network (SIGN)
• Professional bodies and mental health charities (eg UKCP, BPS, Mind, Relate)
• Via social media platforms (eg Twitter)
• Commissioners
• Employers

Processing:

All record-level data will be stored and analysed at the School for Health and Related Research (ScHARR) at the University of Sheffield.

No data will be linked to record level patient data, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data.

Record-level data will be analysed at the University of Sheffield. The ScHARR team has expertise in multi-level modelling and has previously analysed data collected from individual IAPT services and the NAPT dataset in this manner.

Aggregate-level data (with small numbers supressed) will flow between the University of Sheffield and BACP in order to keep the full team informed as analysis progresses, however the pseudonymised record-level data will remain at the University of Sheffield. BACP's involvement will only be to support and collaborate on outputs from the research and nobody at BACP will have any access to the record-level data.

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).

An Evaluation of Alcohol Treatment Centres: Implications for Service Delivery, Patient Benefit and Harm Reduction — DARS-NIC-29100-R2S2F

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)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-03-21 — 2020-03-20 2017.06 — 2018.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

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

Objectives:

The evaluation of the diversion of alcohol related attendances is an National Institute of Health Research, Health Service and Research Delivery Programme funded research project to estimate the effectiveness, cost-effectiveness, efficiency and acceptability of alcohol intoxication management services (AIMS) in managing alcohol-related ED attendances. AIMS, also referred to as Alcohol Treatment Centres, Alcohol Recovery Centres, Alcohol Welfare Centres and, in the media, “Drunk Tanks”) are designed to receive, treat and monitor intoxicated patients who would normally attend Emergency Departments (ED) and to lessen the burden that alcohol-misuse, an avoidable healthcare cost, places on unscheduled care. AIMS offer the potential to mitigate some of the pressures on ED at times when it is experiencing a sustained increase in demand. At peak times (e.g. Friday and Saturday nights) most admissions to ED are alcohol-related and they cause the ED clinical environment to suffer, as well as staff morale.

This study, which began in January 2016, is a mixed methods study and this request is for NHS Digital HES ED and inpatient data to evaluate the effectiveness and cost-effectiveness of AIMS. The study follows a natural experiment where six cities have been recruited which have already implemented AIMS throughout England and Wales and will compare them to six control cities where no AIMS is present (ten cities in England, two in Wales). AIMS cities will be matched with control cities for similar demographic characteristics using Home Office iQuanta.

There are three focuses to the evaluation:
i) What is the impact of Alcohol Intoxication Management Services (AIMS) on the work practices and professional identities of frontline staff in managing the intoxicated and other related work activities?
ii) To what extent does AIMS implementation affect key performance indicators in ambulance and health services?
iii) What are the costs of setting up and running an AIMS and what cost savings may be realised elsewhere?
The aim being to provide evidence that informs local and national decision makers on opportunity for a national roll-out across UK cities and provide information about what works through the study of effectiveness, efficiency, processes, barriers and opportunities.

The School of Health and Related Research (ScHARR) at The University of Sheffield are responsible for work stream (aims) 2 and 3. This request is for data to enable the study to answer aims ii) and iii) and we are collecting data from ambulance services, AIMS service providers (NHS, charities, police services) in order to achieve the study aims. All data being requested is pseudonymised and will not be linked as in line with the HES analysis guide. (Note that on page 14 of the protocol it is stated that the study would explore the linkage of AIMS data to routine NHS data, the conclusion of this exploration is that it is not viable to link and therefore no linkage of datasets will be carried out).

The data requested is for work stream (aims) ii and iii only and will not be used in work stream i.data is only accessed by staff of University of Sheffield.

In order to assess key performance indicators (aim ii) it is aimed that it will be use in an interrupted time series approach to look at the impact on ED attendances (primary outcome), total time spent in ED, time to treat and alcohol related inpatient attendances. The project will evaluate the effectiveness by comparing attendance rates in control and intervention cites.

The study will also present AIMS activity data in terms of a summary of patient characteristics, and AIMS models (who provides the AIMS service and the type of service e.g. mobile or permanent services). This study will also conduct an economic evaluation to determine the costs required to set up and run an AIMS and estimate the cost savings to other health services. The costs of setting up an AIMS will be requested directly from the service providers (NHS, charities, police services) however, the study also needs pseudonymised data on services and treatments provided in the ED and for hospital admissions, including length of stay, and services provided in order to fully evaluate the cost-effectiveness of AIMS and its impact on the NHS.

Yielded Benefits:

There are currently no peer reviewed published outputs in relation to the data specified within this agreement and therefore no 'benefits'. The final report was submitted to NIHR on 4th March 2019. It is anticipated that the period of peer review and comments may include further interrogation of the data on which analyses are based. It is therefore imperative to retain and have access to the data as for a period of a further year (until 20.3.20).

Expected Benefits:

Management of the intoxicated in city centres is complex and involves partnerships between health, police and ambulance services. Further, AIMS services are typically commissioned by local governments, police, health care or other agencies in partnership. AIMS are being implemented or decommissioned by NHS Trustsservices throughout England and Wales without any evidence on their effectiveness or cost effectiveness. Further different types of AIMS in operation including mobile units verses permanent units and it is important to understand what works and what does not work in order to make recommendations. Further, there is an increased demand on EDs with approximately 70% of attendances being alcohol related at peak times.

This can create bottlenecks in the NHS system which can impact on health care. AIMS have the potential to alleviate this bottleneck by reallocating resources which can thus improve unscheduled care. However, existing AIMS services have not yet been evaluated and it is important to establish whether they are effective and if they are whether all types of services are effective or only specific types. This analysis will evaluate the system and identify what works well and make recommendations about where improvements can be made for local and national decision makers in the NHS. Clearly if AIMS are not cost effective this will highlight a need for action.

The potential benefits of AIMS to people seen in the ED and AIMS units are difficult to measure because of the complex nature of the problem. For the intoxicated the goal of AIMS units are to ensure they are treated in a safe environment and aim I of the project will seek to measure this, which is outside the NHS Digital request. By potentially addressing the bottleneck in EDs other non-intoxicated patients can be seen more quickly and treated sooner and the analysis of time to treatment, time to assessment and total time in ED and looking at the throughput of patients through an ED will answer these questions.

While our primary interest is in the area of health, we also recognise that learning could promote co-funding and thus decision makers across the three primary partners need to be involved in real-time. In this respect we aim to develop diffusion and dissemination strategies that both capitalise on stable links with local and national stakeholders and that are also able to encourage engagement in the face of rapid change. As such three diffusion mechanisms will be exploited: (1) A Policy and Impact Group led by project investigators that will be responsible for dissemination; (2) a Study Steering Committee (for preliminary membership see below), that includes a broad range of practitioners and (3) the formation and ongoing development of a Learning Community for practitioners who want to be involved with the project but are unable to commit sufficient resources to become more fully involved.

The Policy and Impact Group have experience of engagement and are directly involved with a number of policy and practice groups and key stakeholders (Department of Health, Primary Care Trusts, and Local Authorities). The group will continue to engage with policy impact groups and ensure the results are disseminated to these groups (e.g. UK Home Office, DoH, Welsh Government, NHS, Association of Chief Police Officers (ACPO), College of Emergency Medicine, Local Alcohol Action Areas (LAAAs)). In addition, the group will write a practitioner oriented report the aim of which would be to provide a summary of findings in such a way that the report can be used for service development across the UK.

One of the remits of the Study Steering Committee (SSC) is to ensure outputs are relevant and timely, and that can provide advice and decision making capacity to the research team, including guidance on dissemination of outputs. The SSC will be initiated in accordance with the NIHR HS&DR guidelines “Research Governance Guidelines: Study Steering Committee (SSC).” The SSC will provide the primary mechanism through which we will reach key decision makers and this diffusion mechanism will develop in parallel with Work Stream I. The SSC will identify both diffusion partners through which learnings can be best promulgated (e.g. ACPO, Community Safety Partnerships, Regional Leads for Public Health, the Welsh Government, and the regional commissioning boards for Clinical Commissioning Groups) and seek to target decision makers in Scotland, England, Wales and Northern Ireland.

The Learning Community will operate in parallel with Work Package I and SSC, and will seek to identify local and national parties that would be interested in learnings from the project but are unable to contribute to it. To facilitate engagement an online resource will be developed that makes use of social media such that interested parties are able to keep abreast of project developments with little effort. An online regular update will be published on a quarterly basis, promulgate this through email and twitter and encourage feedback to the Project Team. The recent "Have a Word" Knowledge Transfer Partnership (www.vrg.cf.ac.uk/Files/20140107_KTP_finalreport.pdf) that successfully engaged practitioners through social media, branding and media to encourage engagement in clinical and other staff will be used as a template. Formal and informal diffusion mechanisms will encourage practitioners and decision-makers to both contribute their views on managing the intoxicated and sign up to receive regular updates on project activity. These materials will also be made available to mainstream media and place quarterly updates on-line and encourage more general feedback from the public.

This analysis will be completed by January 2018 or six months after receipt of HES data from NHS Digital if later. The outputs will be shared in terms of a final report and five peer reviewed open access publications, one on the overall results and a further two related to work packages 2 and 3. Results of the analysis will be disseminated in an easy to digest and accessible online format that seeks to develop the co-production of guidance on best practice and will also be released as part of our study newsletter. In addition to the guidance on best practice further realistic actionable learning outcomes will be defined throughout the project in collaboration with the study management group, study steering committee and with feedback from the learning community.

A patient and public involvement ( PPI) group is actively involved in the project, owing to the complexity of the project there are PPI members from three different areas: a member of The Involving People Network, Wales sits on the project steering group, and members of The Sheffield Emergency Care Forum (SECF) who reviewed earlier drafts of the protocol and The Sheffield Addiction Recovery Research Panel (ShARRP) form a separate PPI advisory group. Whilst the group are primarily involved in work stream (aim) I which includes surveys they will form a crucial part in ensuring the results are disseminated appropriately.

Outputs:

The results of this study will be published in an NIHR HS&DR report, which is an open access publication. The report will be completed and sent to NIHR before the end of July 2018. The project also aims to publish the results in high quality peer review journals, the main outcomes work is likely to be submitted to a generic journal such as the BMJ or the Journal of Public Health. The health economic results will be submitted to a health economic journal series such as Medical Decision Making. A recommendation report will also be produced that will be made available to trusts, ambulance service, police services and charities that are interested in setting up AIMS services in their area.

Descriptive statistics for the cohort will be presented at summary level, for example mean with standard deviation for continuous variables and numbers with proportions for categorical data. Results will be presented in an aggregate form, for ED and inpatient admissions this would be number of admissions per day and graphical figures of this data will be presented in the same format. The time series analysis of this data is looking at trends over time before and after the implementation of this intervention. Survival analysis methods will be used to analyse time, such as time in the ED, time to treatment and time to assessment. Results and figures will again be presented at an aggregate level. A comparison will also be made between AIMS cities and matched (using Home Office iQuanta) cities that currently don’t have AIMS.

Resource use includes items such as the ED attendance, ambulance journey, inpatient stay and length of stay. The project will account for differences in key resource variables which expect to be in-patient admissions and length of stay as these are known to be expensive, by including them in the time series analysis as variables. The economic analysis will follow guidance on cost-effectiveness analysis set out by the National Institute for Health and Care Excellence (NICE, 2013). Results will be presented as total costs to the NHS and the costs of AIMS with be compared with costs of usual care as costs per ED admission avoided.

All outputs will contain data only in aggregated form (with small numbers suppressed in accordance with the HES Analysis Guide). No commercial requests will be worked on.

Processing:

Data will be stored on a secure drive at the University of Sheffield on a (securely housed) networked virtual machine accessible only from within the campus network. Four people (all substantive employees of the University of Sheffield) will have access to the data, data-manager who will be responsible for cleaning the data, one a statistician health economist, a health economist research assistant and a statistician research assistant. No record-level data will be shared outside of these substantive employees.

The data will be analyses to establish the effectiveness of AIMS services and for estimating the cost-effectiveness of AIMS (see specific outcomes for further details). If small numbers arise they will be suppressed in accordance with the NHS Digital HES Analysis Guide and will follow guidelines regarding sensitive conditions. There will be no sharing of record-level data with third parties. No attempt will be made to re-identify anyone from the data. The data will not be linked and will never be used for commercial purposes.

The primary analysis will explore whether the intervention impacts on the number of ED attendances and will use an interrupted time series analysis of this data is looking at trends over time before and after the implementation of this intervention. To conduct this analysis on data from each intervention city it is assumed that each AIMS is open twice a week and will have at least 104 observations over a minimum period of 365 days. Statistical tests including the Dickey-Fuller test, autocorrelation (ACF) and partial auto correlation will be used to establish seasonality, stationarity and differencing which are for establishing the presence of absence of patterns common to time-series data.

Other statistical tests will be carried out to establish the statistical model fit and model goodness of fit. Time series models will also include information on type of incident, age and diagnosis, investigations and treatments in order to examine the effect of AIMS on different patient groups.

Secondary outcomes include hospital admissions, ED key performance indicators (total time in the ED, time to treatment, time to initial assessment, those leaving the ED before being treated and re-attendances within 7 days). Survival analysis methods will be used to analyse time, such as time in the ED, time to treatment and time to assessment. The Kaplan-Meier test will be used to explore differences in total times in the ED, time to treatment and time to initial assessment and an appropriate statistical test will be used to examine differences between groups (for example if times are evenly distributed over time then a log-rank test would be used). If the data is of sufficient quality to examine the impact of time (total, assessment and treatment) on a number of variables then an appropriate semiparametric (Cox proportional hazard model) or parametric survival model will be fitted to the data. An appropriate regression model (ordinary least squares or generalised linear model) will be used to look at length of stay and proportions will be examined using the Chi-squared statistic and logistic regression analysis to allow for differences in case mix.

A comparison will also be made between AIMS cities and paired matched (using Home Office iQuanta) cities that currently don’t have AIMS. Poisson regression models will be used to examine the number of ED attendances between AIMS and control cities to allow for differences in case-mix.

Resource use includes items such as the ED attendance, ambulance journey, inpatient stay and length of stay. The study will account for differences in key resource variables which expect to be in-patient admissions and length of stay as these are known to be expensive, by including them in the time series analysis as variables. The cost of setting up and running an AIMS will be collected from each AIMS site, this will be collated at the aggregate (overall cost) level. The resource use related to the cost of ED services will be obtained from HES and ambulance dispatch data (this information will not be linked but a cost for each service obtained. Unit costs will be obtained from NHS reference costs for HES data. The mean costs of AIMS will be compared with the mean cost of usual care and results will be presented as mean incremental cost per ED admission avoided. The study will also look at the mean incremental cost per ambulance dispatch avoided. The economic analysis will follow guidance on cost-effectiveness analysis set out by the National Institute for Health and Care Excellence (NICE, 2013).

The statistical package STATA will be used for all analysis.

Project 19 — DARS-NIC-62448-Z8K5T

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:2017.12 — 2018.02.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

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

Objectives:

The data obtained from DARS (NHS digital) shall be processed solely for the CENTER-TBI registry work package (17). The CENTER-TBI study consists of 20 workpackages . The overall aims of the whole CENTER - TBI study are:
- To improve characterization and classification of TBI in Europe, with inclusion of emerging technologies.
- To identify the most effective clinical care and to provide high quality evidence in support of treatment recommendations and guidelines.
In summary, the core study will be recruiting TBI patients across Europe to collect high-quality clinical and epidemiological data with repositories for neuro-imaging, DNA and serum from patients with TBI in order to improve TBI characterisation and identify the most effective TBI treatments, The CENTER-TBI core study will be making comparisons in treatments and interventions across 80 centres and 21 countries. This approach is called Comparative Effectiveness Research (CER). Therefore, data shall be collected from these European centres to enable CER analyses of differences in clinical care and management pathways in TBI. A total of 5400 TBI patients will be recruited from acute hospitals; the emergency department, in-patient wards and intensive care. Data and blood samples will be collected but in some cases a non-invasive scan of the brain (MRI scan) will be done. Patients will be followed up for up to 2 years. Patients with a clinical indication for CT scan who present to the hospital within 24 hours of TBI injury shall be recruited to participate following consent. Follow-up will include questionnaires, repeat MRI scans (if done), blood samples and computer based tests of cognitive processes (CANTAB). However, data from NHS Digital will not be utilised for this part of the project.

The specific aims of the whole CENTER TBI study are
- To collect high-quality clinical and epidemiological data with repositories for neuro-imaging, DNA, and serum from patients with TBI.
- To refine and improve outcome assessment and develop health utility indices for TBI.
- To develop multidimensional approaches to characterization and prediction of TBI.
- To define patient profiles that predict efficacy of specific interventions (precision medicine).
- To develop performance indicators for quality assurance and quality improvement in TBI care.
- To validate the common data elements (CDEs) for broader use in international settings.
- To develop an open database compatible with the Federal Interagency Traumatic Brain Injury Research (FITBIR).
- To intensify networking activities and international collaborations in TBI.
- To disseminate study results and management recommendations for TBI to healthcare professionals, policymakers, and consumers, aiming to improve healthcare for TBI at individual and population levels.
- To develop a knowledge commons for TBI, integrating CENTER-TBI outputs into systematic reviews.

The co-ordinating centre for the CENTER-TBI project is based at Antwerp University Hospital, department of Neurosurgery, Belgium . The project commenenced on 1.10.2013 and will finish on 31.03.2020.

CENTER-TBI REGISTRY WORK PACKAGE (17)
The CENTRE-TBI registry work package (17) – the sole part of CENTER TBI to which this application pertains - will be determining whether the results obtained from the core study are generalizable to TBI across Europe (EXTERNAL VALIDATION or generalisability).

The CENTRE-TBI registry work package is being executed by the Centre for Urgent and Emergency Care Research (CURE) within the School of Health and Related Research, The University of Sheffield, UK. There are four other participants of this work package namely: Antwerp University Hospital (UZA), Belgium, Cologne-Merheim Medical Center (CMMC), Germany, Erasmus University Medical Center, Netherlands, and University of Melbourne Australia. The roles of these participants are advisory so none of the organisations listed above receive any raw data nor are they involved in data processing. Only analysed data summaries would be shared with participants of the registry work package.

The registry work package commenced in July 2015 with a completion date of December 2018.

The registry work package is based on pragmatic data collection of all patients with TBI seen in 21 countries namely: Austria, Belgium, Bosnia, Denmark, Finland, France, Germany, Hungary, Israel, Italy, Latvia, Lithuania, Moldova, Netherlands, Norway, Romania, Serbia, Spain, Sweden, Switzerland and UK . Data collection will be elementary and based on retrospective extraction from clinical records of data that are routinely collected clinically. No target recruitment number has been set for the CENTER-TBI registry.

Therefore the data obtained from NHS Digital shall be used to determine the representativeness of the CENTER-TBI core study population in the United Kingdom only, in terms of age, gender, mechanism of injury, severity of TBI, treatment and outcome for the period of 2015 and 2016. In other words, the data from NHS Digital would assist the registry work package in assessing the generalisability of the CENTER-TBI core population when compared to the traumatic brain injury population in the United Kingdom. NHS Digital data is essential in this latter stage of the work package where the external validation analysis is currently being conducted.

The findings from this work package and project as a whole, will hopefully impact the systems of TBI care and organisational aspects of TBI care delivery in the NHS. If WP 17 shows good generalisability of the CORE CENTER TBI data then new performance indicators and improved prognostic models will facilitate benchmarking and assessments of quality of TBI care by the NHS. Ultimately, by identifying more efficient and targeted TBI care for the NHS on this project, the project intends to achieve improved outcomes which will translate to reduced costs for the NHS.

The findings of the CENTRE-TBI registry work package may increase the commercial value of the CENTER-TBI core study data findings. However, the Centre for Urgent and Emergency Care Research (CURE), School of Health and Related Research will not in anyway be involved in commercial activities relating to data obtained for the purpose of use on this work package. Fusion IP is listed in the consortium agreement as being responsible for the commercial exploitation activities of the University of Sheffield. Work package 17 team is neither linked, in contact with fusion IP nor will any data (raw or aggregated) be transferred to Fusion IP.

Expected Benefits:

Previously, the introduction of the UK National Institute for Health and Care Excellence Guidelines for TBI management in 2003 was associated with a 12% reduction in TBI mortality. These guidelines were around improved access to imaging for early diagnosis of TBI as well as improved access to specialist neuroscience care for TBI. However, there have been no new therapies for TBI in the last 30 years. It is therefore reasonable to expect to realise similar benefits at the end of the CENTER-TBI project when the research team has achieved an improved understanding of the most complex disease in the most complex organ (TBI) and further identified new therapies through this comparative effectiveness research in addition to the current guidelines by NICE.

A cascade of benefits will follow the dissemination of findings from this research. For example, the findings should inform the next update of the NICE Head Injury Guidance. This would improve commissioning and provision of TBI care as well as assisting policy makers in the UK such as the Department of Health to make informed decisions about TBI prevention that will improve the health of the UK populace. It shall also serve as a guide for best practice among clinicians in treating TBI patients across the UK and Europe.

Another benefit of the outputs created from NHS Digital data will be improved TBI characterisation and stratification which will allow for more personalised, targeted and effective therapies, thereby improving UK patient outcomes after TBI and ultimately reduce healthcare costs.

CENTER-TBI is a generational opportunity to improve Traumatic Brain Injury care. TBI is the commonest cause of deaths in hospital trauma attendances; hence, University of Sheffield would anticipate the findings to save approximately 20,000 EU lives per annum in a predominately economically active population by year 2020 as well as reduce disability in survivors.

Outputs:

The HES data obtained from NHS Digital will only be used for the CENTER-TBI registry work package in order to report the external validity of the CENTER-TBI core study population in the United Kingdom.
Progress Reports of interim results will be provided to the CENTER-TBI reporting team in April 2018.

The final report of results will be submitted to the European Commission in October 2018. This will cover all findings of the study including a detailed assessment of the external validity of the CENTER-TBI core population and any likely biases in terms of demography and injury characteristics. Charts and tables will show the disease characterisation and comparative effectiveness analyses obtained from the core study in Europe. Once finalised, this will be submitted for publication in the Lancet Neurology or The Journal of the American Medical Association (JAMA) with an estimated publication date of December 2018. These are open access, peer-reviewed journals.

Research results will be integrated with living systematic reviews of TBI care. This means that high quality, up-to-date summaries of this research will be updated as new evidence becomes available.

In order to ensure knowledge transfer and bridge the evidence to practice gap, results of this research shall be disseminated at various levels:
1. CENTER-TBI is embedded within the framework of the International Initiative on TBI Research (InTBIR), a collaboration of the European Commission (EC), the Canadian Institute of Health Research (CIHR) and the National Institute of Health (NIH). Therefore dissemination to policy makers in the UK, Europe and the rest of the world would be in form of a published report at the end of the project presented to InTBIR,
2. Dissemination to healthcare professionals in the UK and all over the world would be at scientific conferences including the International Brain Injury Association (IBIA) meeting, International Conference on Emergency Medicine and European Congress on Emergency Medicine. The research from the work package will be published in high impact journals such as “The Lancet Neurology”, by open access to enable wide dissemination and access.
3. Dissemination to patients will be through:
• The Centre for Urgent and emergency care REsearch (CURE), School of Health and Related Research, The University of Sheffield which is strongly linked in with the Sheffield Emergency Care Forum, a patient-public Involvement (PPI) group which aims to make emergency care research accessible to the public
• Headway, a UK-charity that provides support, services and information to brain injury survivors, their families and carers, as well as to professionals in the health and legal fields.

Findings in form of data summaries will also be disseminated in the UK through TARN (Trauma Audit Research Network).

A simplified version of the findings will be disseminated to the work package collaborators:
1. German Trauma Registry, Germany
2. Trauma Audit and Research Network (TARN) (Copenhagen and UK)
3. National Healthcare Service Center (AEEK), Hungary
4. Centre for Disease Prevention & Control, Latvia
5. Institute of Hygiene, Health Information Centre of Institute of Hygiene, Lithuania
6. Paediatric Tertiary Trauma Center, Vilnius University Children’s Hospital, Vilnius, Lithuania
7. Consumer Safety Institute (VeiligheidNL) Netherlands.
8. Swedish Trauma Registry, Sweden
9. Norway Trauma Registry, Norway
10. NHS Digital Data Access Request Service, UK
11. Federal Public Service Health, food chain safety, environment, Belgium
12. Ministerio de Sanidad, Servicios Sociales e Igualdad. Instituto de Información Sanitaria. Registro de altas – CMBD, Spain
13. European Association for Injury Prevention and Safety Promotion (EuroSafe),

A short presentation/abstract will be developed to summarise the findings for a range of stakeholders, including; healthcare professionals, patient groups and/policy makers at the General Assembly of CENTER-TBI, International Brain Injury Association (IBIA) meeting and International Conference on Emergency Medicine and European Congress on Emergency Medicine. Findings will also be presented at the Centre for Urgent and emergency care REsearch (CURE) group meeting, School of Health and Related Research, The University of Sheffield in December 2018.

Dissemination of results of analysed data will be written into reports such as the EU commission Scientific Work package progress Report and CENTER-TBI External validity Report. External validation results shall also be disseminated at conferences such as the International Brain Injury Association World congress On Brain Injury 2018 and in high impact factor journals such as The Lancet Neurology, Journal of the American Medical Association (JAMA).

There will neither be any requirement nor attempt to identify individuals from the data obtained from NHS Digital. Also, data will not be made available to any third parties except in form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

The CENTER-TBI website will provide links to the open access papers . All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

In addition to pseudonymised HES data supplied by NHS Digital, pseudonymised routine raw TBI data/data summaries will be supplied to the Centre for Urgent and Emergency Care Research (CURE), School of Health and Related Research, The University of Sheffield by 13 European organisations namely:

1. German Trauma Registry, Germany
2. Trauma Audit and Research Network (TARN) (Copenhagen and UK)
3. National Healthcare Service Center (AEEK), Hungary
4. Centre for Disease Prevention & Control, Latvia
5. Institute of Hygiene, Health Information Centre of Institute of Hygiene, Lithuania
6. Paediatric Tertiary Trauma Center, Vilnius University Children’s Hospital, Vilnius, Lithuania
7. Consumer Safety Institute (VeiligheidNL) Netherlands.
8. Swedish Trauma Registry, Sweden
9. Norway Trauma Registry, Norway
10. NHS Digital Data Access Request Service, UK
11. Federal Public Service Health, food chain safety, environment, Belgium
12. Ministerio de Sanidad, Servicios Sociales e Igualdad. Instituto de Información Sanitaria. Registro de altas – CMBD, Spain
13. European Association for Injury Prevention and Safety Promotion (EuroSafe),

for the periods of 2015/2016 and 2016/2017 to enable comparisons with the core study in each country which is simultaneously collecting data for these periods. External validation analysis would be carried out separately for each country to assess the generalisablity of the core study population in that particular country. Only the Centre for Urgent and Emergency Care Research (CURE), School of Health and Related Research, The University of Sheffield has been commissioned to undertake this external validation work in the CENTER-TBI project.

UK TBI data will be required from NHS Digital (from HES) only for the CENTRE-TBI registry work package ,in order to assess the representativeness of the CENTER-TBI core study in the UK. Without using national data, it is not possible to guarantee that the selected CENTER TBI core UK patients sample are accurately representative of the United Kingdom TBI patients and, without this certainty, the analysis would be weakened and the findings less credible. Given the potential impact of the findings on TBI on patients, healthcare and policy, it is essential to minimise uncertainty. Therefore, data obtained from NHS Digital will used for this purpose without linking it to any other data. The NHS Digital data will only be used for the CENTRE-TBI registry work package.

Comparisons will be done with regards to age, gender, mechanism of injury, severity of TBI, treatment and outcome differentiated per stratum. The strata of patients are TBI patients hospitalised as a result of acute injury (<24hours prior to hospital attendance) and patients who attend and are discharged from the Emergency Department (not admitted) with TBI codes but a negative CT brain scan. Hence, Hospital Episode Statistics Admitted Patient Care, Critical Care and Accident and Emergency data is requested; specifically Traumatic Brain Injury ICD codes. Only the Center for Urgent and Emergency Care Research (CURE) within the School of Health and Related Research, The University of Sheffield, UK will have access to the record level data supplied by NHS digital.

Data security and access to data
Data obtained from NHS Digital and other sources will be stored on a secure drive at the School of Health and Related Research, University of Sheffield. Authorisation to the specific project system folder will be given strictly to the work package team.

The work package team consists fully of substantive employees of the University of Sheffield (with no staff on honorary contracts in the team) and are subject to the University of Sheffield Information Security Policies. This includes complying with the Data Protection Act 1998 and a legal requirement to not pass any data (personal or other) to a 3rd party, unless required by law or statutory obligation.

Data provided by NHS Digital will not be considered as being “Center-TBI Data”, and will not be shared with any third party. Any outputs from the analysis of NHS Digital data must be aggregated in nature, with small numbers suppressed in line with the HES Analysis Guide.

Simple cross tabulations and confidence intervals will be generated for age and gender variables. Comparison will be made between the core and the external registry of the same country within the same stratum using the statistical software to execute the external validation of the core CENTER-TBI study. For example: The UK core study population will be compared to the TBI population in the HES and TARN data. Consideration will be made concerning the appropriate use of statistical tests to determine statistical significance of the comparisons for each country. Hence, the generalisability of the core study population will be determined in the UK and across Europe. Furthermore, effective clinical care of Traumatic Brain Injury can be identified.

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).

Project 20 — DARS-NIC-315175-P8X6Z

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y, N

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)

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2017.02.

Access method: Ongoing, One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Cause of Death Report
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Critical Care

Objectives:

Purpose #1: PhOEBE Study

The PhOEBE research programme aims to identify, refine and test (using predictive models) a set of prehospital outcome measures that can be measured using information that is routinely collected. The measures will be used to measure the impact the care ambulance services provide has on patients, to assess the impact of services changes using before and after service change data and for benchmarking and across Trust comparisons.

A secondary objective is to test the feasibility of linking patient level ambulance data with subsequent health data, as this has not been done before.

The PhOEBE study has previously received the pseudonymised linked data of all patients whose contact with specific ambulance services during the first half of 2013 was recorded by an electronic Patient Report Form (ePRF) or who received telephone advice from the ambulance service. This data was used to identify and refine the outcome measures. A further linkage is required involving the patients whose ambulance contact was recorded during the second half of 2013. Their pseudonymised linked data will be used to test the measures, using predictive models where appropriate. This second set of data relates to a different time period and different patients.

Purpose #2: VAN Study

Increasing use of the emergency ambulance service has the potential to overload ambulance services and reduce the quality of service offered to life threatening emergencies. The task for services is to try and match their response to the clinical needs of callers. Alternative care such as advice over the telephone and referral to more appropriate providers (‘hear and treat’), treatment at scene (‘see and treat’), or transport to a lower level health care facility (‘see and convey elsewhere’) may offer better solutions for some calls. Non-conveyance is central to the recent Keogh review of urgent and emergency care because it is a way of offering patients care closer to home (NHS England, 2013).

Considerable variation exists in non-conveyance rates between the 11 ambulance services in England. Some variation may be warranted because of differences in population characteristics in geographical areas covered by ambulances services, some may be due to organisational differences between the services, and some may be the result of differences in the wider emergency and urgent care system and options available to manage care closer to home. There is a need to understand how to increase non-conveyance rates without compromising safety in terms of higher re-contact rates or other adverse consequences. The VAN study will explore the drivers of variation to help ambulance services to identify ways of increasing non-conveyance rates appropriately in the future.

The VAN study will use a subset of data supplied for the PhOEBE study. It will require the data only of patients who were ‘not conveyed’ to hospital following a call to the ambulance service. Due to time constraints, the VAN study will only use the data of patients whose ambulance contact was recorded during the first half of 2013. This data has already been linked and supplied to the University of Sheffield.

Expected Benefits:

Purpose #1: PhOEBE Study

The emergency ambulance service is the first point of contact with the NHS for over 6 million patients a year who have an urgent healthcare problem. Ambulance services in England have recently changed their service to meet the needs of patients in a timely and clinically appropriate way. For example, taking some patients directly to specialist care, providing care at home, or referring to other services. In order to assess how well these and any future changes are working in terms of improving care for patients the ambulance service needs to measure the impact they are having on the patients they attend. At present this is difficult as ambulance services have no information about what happens to patients once they have left their care. Using the outcome measures developed in the PhOEBE study, ambulance services will be able to measure the impact the care they provide has on patients, they will be able to assess the impact of services changes using before and after service change data and for benchmarking and across Trust comparisons.

The PhOEBE study is due to end inJanuary 2017. The benefits described are the result of the PhOEBE programme, which is a 5 year NIHR funded research programme.

Purpose #2: VAN Study

As the costs of ambulance care keep rising, due to the year on year increase in demand for ambulance services, it is important that the ambulance service develop better ways of managing this demand. This was highlighted in the recent Keough report.

As Ambulance services currently have no data or information about mortality or hospitalisation rates for people who they do not convey to hospital, there is very little evidence base from which this service design can be attributed to. The results from this study will provide ambulance services with key information about what happens to this group of patients and enable ambulance services to assess the safety, appropriateness and cost of different types of non-conveyance.

This study also has wider issues for the health care economy. For example, if patients who are not conveyed to hospital end up utilising more health services this can be detrimental to the wider health service. This information is important for service development and demand management and is of particular interest to ambulance service managers, policy makers, NHS England and service commissioners.

This analysis is being provided for academic publications that will be useful to policy makers and service providers in the UK and internationally.

The results of this study will be fed directly to ambulance trusts and also to NHS England. The media team will also arrange press releases and the study will work with Patient and Public Involvement groups to discuss/decide on further publications.

Outputs:

Purpose #1: PhOEBE Study

The primary output will be a final report to NIHR in January 2017.

Further dissemination of findings will be achieved via journal publications on the methodology and results of the 8 measures and how they could be used - the target journals are the Emergency Medical Journal and Annals of Emergency Medicine - and presentations during2016, 2017 and 2018.

Data will be presented by analysis groups e.g. patients with serious emergency conditions, patients who received telephone advice, comparisons of ambulance trusts. Outputs will contain aggregated data only. As the data sample is large the expectation is that there will be no groups will small numbers but if small numbers are present they will be suppressed in line with the HES Analysis Guide.

Purpose #2: VAN Study

Health Service & Delivery Research Programme (separate research stream from within NIHR – other is programme grants for applied research)
The primary output will be a final report to the NHIR’s Health Service & Delivery Research Programme (HS&DR) in January 2017.

Further dissemination of findings will be achieved via journal publications - the target journals are the Emergency Medical Journal and BMJ Open in 2017 – and the following presentations: Health Services Research Network (HSRN ) 2017 and 999 EMS research forum 2017. The 999 EMA research forum is attended by senior managers and policy makers.

All outputs will contain aggregated data only. Data will be presented within analysis groups, for example, type of non-conveyance.

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

Processing:

Purpose #1: PhOEBE Study

The PhOEBE team identified potential prehospital outcome measures from systematic reviews of the literature and patient interviews. The measures were developed and refined during a consensus process, using face to face consensus events and a Delphi study. The final output was a shortlist of 8 measures that can be measured using routinely collected information and represent a broad patient population and multiple clinical conditions.

Based on a prespecified 6 month time period, two study ambulance services identified and extracted all CAD and ePRF data for ambulance contacts where an electronic Patient Report Form (ePRF) was used. Ambulance service staff split the data files into a pseudonymised clinical file and an identifiable data linking file. The data linking file was sent to HSCIC using their SEFT system. The clinical file was sent to the PhOEBE study team at the University of Sheffield.

The HSCIC linked the data linking file (ambulance data) to HES APC, HES A&E and ONS data using their data linking algorithm. Where NHS numbers are not available within the ambulance data, with permission from CAG, HSCICs NHS number tracing service was used to ensure more accurate linkages.

Following completion of the data linking, HSCIC securely sent the linked data file to the PhOEBE study team. Prior to sending, HSCIC removed or pseudonymised identifiable data. For example, patient names were removed, date of birth was changed to age, etc.

Upon receipt of the linking file the PhoEBE team linked this to the clinical data using a unique key, which is a unique number for each record that is present in the clinical data and the linking data files.

Using this process, no patient identifiable data items was handled outside of NHS Trusts and organisations.

This process will be repeated using all CAD and ePRF data for ambulance contacts where an electronic Patient Report Form (ePRF) was used for a further 6 month period directly following the first period.

The PhOEBE study team will build and test predictive models using the measures and measures will be risk adjusted where appropriate. This analysis will be undertaken by statisticians within the PhOEBE project team.

The team also intend to track what happens to patients once they have left the ambulance service care. However this is at a system level rather than an individual patient level. The team will report on length of hospital stay, readmissions, EMS recontacts and mortality.

Outputs will not be at record level. Comparisons will be made across ambulance trusts and type of service received, (telephone advice; treated at home; conveyed to hospital).

The data will not be used for commercial purposes, not provided in record level form to any third party and not used for direct marketing.

Purpose #2: VAN Study

This application relates to Workpackage 3.2 of the study protocol 'final protocol published v2 LSOA 121214'. Workpackage 3.2, in this new study, aims to use the linked dataset constructed by HSCIC for the NIHR Applied Research Programme PhOEBE. In workpackage 3.2 of the VAN study, the University of Sheffield will use data from the PhOEBE study relating to people who were not conveyed to hospital to identify the mortality rate, the hospitalisation rate, and the Emergency Department (ED) attendance rate for non-conveyed patients. The focus of the study is understanding variation in non-conveyance. The VAN study will consider patient, crew and locality level characteristics only. This is because the team will have this data for two ambulance services only. East Midlands Ambulance Sevice (EMAS) covers five localities and Yorkshire Ambulance Service (YAS) linked data will only cover one locality.

The study has NHS ethic approval to use the PhOEBE data in the VAN study and the University of Sheffield have correspondence from CAG stating that additional CAG approvals are not required to reuse the PhOEBE data in the VAN study as the data is de-identified and there are no other data being used in the VAN study which could be used to re-identify the data.

Whilst the VAN study is collecting additional data within its other study workpackages, this data is different to the data that is being reused from the PhOEBE study and cannot be combined. VAN is collecting qualitative data from people working within and related to the ambulance service and routine ambulance dispatch data (CAD) from 11 ambulance services. Whilst the CAD data is patient level data, it relates to a different time period to the PhOEBE data and does not contain any patient identifiable data. Therefore it is not possible to link the PhOEBE data to the VAN data or to identify individuals from the data.

The same people are working on both the PhOEBE project and the VAN project. The data management and support officer works across both projects and will write a query using the software package R to extract the data required for VAN from the main PhOEBE dataset.

For the VAN study the team do not need full date of death or cause of death. They can request that the PhOEBE study review the data and provide date of death as a total rather than at patient level - i.e. number of deaths within 3 days or number of deaths within 7 days from interaction with the ambulance service etc. They also only need access to the cause of death to eliminate data for patients who would have died anyway e.g. those receiving palliative care.

The new dataset will be placed in a folder which is stored on a virtual machine (VM). VM data is only accessible from specific encrypted computers by specific named people. The research teams will guarantee that the 2 datasets, PhOEBE and VAN, will be kept separately. All approved staff work across both the PhOEBE and the VAN projects. The same analysts are working on both projects and no additional people require access to any data. The same security standards apply as for the PhOEBE project.

Outputs will not be at record level.

The data will not be used for commercial purposes, not provided in record level form to any third party and not used for direct marketing.

MERIDIAN 2-3 year follow-on Study (MR1402) — DARS-NIC-384618-N1H4Y

Type of data: information not disclosed for TRE projects

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

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2018-11-15 — 2023-11-14 2017.09 — 2017.02.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Flagging Current Status Report

Objectives:

The objective for receiving this data is to determine whether or not to approach the current study cohort (with the intention to re-consent for the follow-up study) and to refine the estimates of diagnostic accuracy made in the original MERIDIAN study.

In order to re-consent the cohort for the follow-up study, the University of Sheffield (UoS) first wish to identify whether the child is still alive before making contact with the family, to avoid any emotional distress or upset. Section 251 approval has been given for this purpose. If the data shows that the child is no longer alive, no contact will be made with the family.

The study already has consent from the cohort to be approached about future research regarding their child’s development.

The cohort are part of the MERIDIAN study (HTA 09-06-01) which is the largest iuMR study to date and assessed the diagnostic accuracy of in utero magnetic resonance (iuMR) imaging and ultrasound for the detection of fetal brain abnormalities. The MERIDIAN 2-3 year follow up study will incorporate additional follow-up of its participants, specifically: i) to incorporate longer term outcomes observed over the first 2-3 years of life, and ii) to undertake a detailed neurodevelopmental assessment of infants. It will recruit participants from the MERIDIAN cohort when the children are aged 2-3 years old. The study will update and refine the estimates of diagnostic accuracy from the original study using clinical data which is available when the children are aged 2-3 years. In addition the study will explore the functional development of the children which will be used to assess the prognostic capabilities of iuMR and ultrasound (US).

Yielded Benefits:

The initial outputs from NHS Digital allowed the University to approach the families from the MERIDIAN study and obtain renewed consent to complete the follow up study. The outputs were important to ensure that there was no contact with the family of a deceased child which may cause emotional upset and distress to the family. The results and associated outputs of the overall study provided an updated diagnostic accuracy assessment based on longer term follow up and additional prognostic information which helped clinicians when counselling women and their families during pregnancy. The results allowed the University to have a better understanding of brain abnormalities detected antenatally and how they may affect a child’s long term development, which is extremely important to clinicians and families. The study will benefit pregnant women in the future who have been told that their baby might have a problem with their brain development. It is anticipated that this study will improve understanding for both parents and health professionals. The results allowed the production of improved information leaflets aimed at pregnant women and their families to help them understand the screening process and the impact of the potential brain abnormality on the child’s development and the prognosis for the child. The findings were also made available on the study website which is easily accessible to ensure that wide range of audiences are reached. This information was made available to health professionals and the public from August 2017.

Expected Benefits:

The initial outputs from NHS Digital will allow the University to approach the families from the MERIDIAN study and obtain renewed consent to complete the follow up study. The outputs are important to ensure that there is no contact with the family of a deceased child which may cause emotional upset and distress to the family.

The results and associated outputs of the overall study will provide an updated diagnostic accuracy assessment based on longer term follow up and additional prognostic information which will help clinicians when counselling women and their families during pregnancy. The results will allow the University to have a better understanding of brain abnormalities detected antenatally and how they may affect a child’s long term development, which is extremely important to clinicians and families.

The study will benefit pregnant women in the future who have been told that their baby might have a problem with their brain development. It is anticipated that this study will improve understanding for both parents and health professionals. The results will therefore allow the production of improved information leaflets aimed at pregnant women and their families to help them understand the screening process and the impact of the potential brain abnormality on the child’s development and the prognosis for the child. The findings will also be made available on the study website which is easily accessible to ensure that wide range of audiences are reached.

This information is expected to be available to health professionals and the public from August 2017.

Outputs:

The study findings will be published in journals and presented at research conferences. Findings will be made available through the study website. The UoS will also use the information to design better information leaflet to parents. Outputs will be available to clinicians, academics and the public. Outputs are expected from August 2017. The final Health Technology Assessment (HTA) report will be due December 2017.

Multidisciplinary and general medical journals, such as The Lancet will be the target of the outputs as the results of the study will be of interest to a wide range of clinicians (foetal medicine, radiology, neonatology, paediatrics, pathology, clinical genetics and health service providers). Foetal medicine and neonatology conferences will be targeted for dissemination of the research findings.

Processing:

Once the research team at the UoS has received the data from the NHS Digital, it will be stored on the University secure network drive in a restricted access folder. The data will also be imported in to the study propsect database. The database is username and password protected. The information will be linked with the study information (mother's name, child's name, date of birth) already held (with consent) for this participant using the unique study identifier.
See below an overview of the data flow:
(1) Eligible participants from MERIDIAN who have consented to be approached about further research are identified through the MERIDIAN database and through a consent form audit
(2) The date of birth and NHS number of the child/children born during the MERIDIAN study will be sent by the UoS research team to NHS Digital, including the unique study participant identifier
(3) Data provided to be linked by NHS Digital to the Mortality Data Patient Tracking system
(4) Information returned to the research team at the UoS, using the unique study identifier, regarding fact of death where applicable (other identifiable information will be removed)
(5) Once received by the UoS the MERIDIAN database will be updated accordingly. No contact will be made with any families whose child is identified as no longer being alive
The data will be stored and processed within Clinical Trials Unit, School of Health and Related Research at the UoS. The pseudonymised data will also be accessible by the study chief investigator and appropriate members of the research team in the Academic Unit of Radiology, UoS.

There will be a requirement for the collaborating consultant neonatologist at the Newcastle upon Tyne NHS Foundation Trust to access the fact of death information to ensure that the child's family is not contacted to participate in the study as part of the screening process.

The fact of death will be entered directly onto a secure area of the MERIDIAN database which will have restricted access.

Impact of closing Emergency Departments in England — DARS-NIC-392342-C3Y7R

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2017-08-01 — 2020-07-31 2016.09 — 2016.11.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

  1. Office for National Statistics Mortality Data
  2. Civil Registration (Deaths) - Secondary Care Cut
  3. HES:Civil Registration (Deaths) bridge
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Admitted Patient Care
  6. Civil Registrations of Death - Secondary Care Cut
  7. Hospital Episode Statistics Accident and Emergency (HES A and E)
  8. Hospital Episode Statistics Admitted Patient Care (HES APC)

Objectives:

The University of Sheffield has been awarded funding by the National Institute for Health Research (NIHR) to identify the impact of closing Emergency Departments (ED) in England. The mission statement of NIHR is to “maintain a health research system in which the NHS supports outstanding individuals, working in world-class facilities, conducting leading-edge research focused on the needs of patients and public”.

The study was funded following a researcher led call from the Health Services and Delivery Research (HS&DR) Programme. Prior to the award being given, this study was reviewed by a HS&DR funding panel, external reviewers, and the HS&DR commissioning board.

In recent years a small number of EDs in England have closed for all or part of the day, usually because it has not been viable to provide senior cover 24/7. This is set against a period of increasing demand for emergency care in terms of rising ED attendances and NHS ambulance calls. This research will therefore focus on a key area of current health service re-organisation in the UK, addressing a highly topical and important question. There is considerable controversy in relation to the reconfiguration, and closure of EDs. Closing EDs is not viewed positively by the public and patients, as demonstrated by campaign groups which have formed to prevent these closures. However, closures may not have the negative impact on patient outcomes that campaign groups suggest. Evidence on the effects of ED closures is essential to inform public debate and policy given that further closures are planned.

In recent years a number of Emergency Departments (EDs) have closed, or been replaced by another facility such as an Urgent Care Centre. Currently, there is little research evidence to inform decision making about these closures. This study will look to identify if local populations and emergency care providers are affected by such closures, focusing on five EDs which closed between 2009 and 2011.

The University of Sheffield has been awarded funding to identify the impact of closing Emergency Departments (ED) in England. Five EDs which closed (or were downgraded) between 2009 and 2011 will be the focus of the analysis. The purposes of the analysis are the following:
- To identify any changes in the pattern of mortality in the local population following closures
- To identify any changes in local emergency care service activity, and performance following closures


The University of Sheffield has requested pseudonymised data sets with the exception of Date of Death via ONS Mortality. The data will be analysed and the analysis presented in research reports, peer reviewed journals and at academic conferences. All dissemination activity will report aggregated results across patient episodes and not individual patient episodes. In any reports, small numbers will be suppressed in line with the HES Analysis Guide.

All reasonable steps in processing will be taken to avoid re-identification of deceased patients from within the HES data. The full Date of Death will be used to derive vital status after 3, 7 and 30 days and month of death only. There is no other requirement for Date of Death apart from the derived vital status outputs listed. For the purpose of producing these derivations only Date of Admission from the HES data will be required so therefore there is no requirement for Date of Death to be linked with the HES data. Date of Death (full) will not be linked to the HES data and the two will not be stored in a database as linked data. Date of Admission (HES) will be the only field linked to Date of Death (ONS Mortality).


The dataset itself will not be released to any third party, including other staff within the University of Sheffield.

Yielded Benefits:

University of Sheffield have produced a set of robust indicators to measure the impact of Emergency Department closures on local populations and emergency care providers. Following the calculation of these indicators, using HES data, University of Sheffield have produced evidence which will help to inform any future re-organisation of emergency care. Any re-organisation of healthcare will impact on the wider local health community, and it is important that findings are shared to enable commissioners and providers to build the findings into any future re-organisation, if appropriate.

Expected Benefits:


The current evidence base regarding ED closures is lacking. The expected output (and therefore benefit) of this study is to produce robust information about a series of indicators which can measure the effect of an ED closure on a local population, and emergency care providers, and therefore has the potential to inform future decisions about which EDs are selected for closure. These indicators may be transferable to other evaluations concerning the emergency and urgent care system, and may be particularly transferable to any evaluation of the re-defining of A&E care, proposed as part of the current review of emergency and urgent care services.

The expected impact of this study is to inform the re-organisation of emergency care in England by providing the general public, the NHS, and policymakers with the evidence to enable them to make informed decisions.

As such, there is no specific target date when measurable benefits will be apparent: benefits from the study will be applicable in relation to any proposed closure following the publication of our findings. However, given that further closures/downgrades are currently planned or under consideration it is likely that our findings will be considered in the context of closures immediately after publication of our results (i.e. within 12 months of the publication of findings).
In summary, the expected benefits are the production of a set of accurate, reliable, and credible indicators which can measure the effect of an ED closure (‘what’), and can be used by local populations and the healthcare community (‘whom’) when faced with decision making about potential ED closures (‘when’).

Outputs:

The data will be used to produce outputs for academic conferences and journal articles in peer reviewed health-related journals. Examples of journals the University of Sheffield will seek to publish their work in include the British Medical Journal and the Emergency Medicine Journal. The University of Sheffield will seek to publish ‘open access’ articles, therefore allowing their findings to be freely available. The University of Sheffield plan to submit conference abstracts to annual conferences organised by the Health Service Research Network and the Royal College of Emergency Medicine. Such conferences are generally attended by researchers, healthcare providers/commissioners, and patient and public groups. The funding body also requires the submission of a final research report, which the funder will make publicly available via their website.

Outputs are expected to be 'in print' in 2017. All outputs will only contain results in aggregated format and as statistical summaries. It is likely that for each of the 17 indicators, summary tables/graphs will be produced in the intended outputs.

Any re-organisation of healthcare will impact on the wider local health community, and it is important that findings are shared to enable commissioners and providers to build the findings into any future re-organisation, if appropriate. To date, the University of Sheffield have not actively publicised the study in local areas given the sensitivities around ED closures. However, information about the study is publicly available on the NIHR’s website. The University of Sheffield have been contacted by a local provider in one of their sites, offering support if needed. Once in receipt of the data and once the study is in progress the University of Sheffield intends to publicise the study within local health communities affected by closures, and at a national level with relevant organisations (e.g. NHS England, Association of Ambulance Chief Executives, College of Paramedics, Royal College of Emergency Medicine, and Healthwatch). Communication will take the form of written summaries produced to introduce the study, and reporting the results from the study. The University of Sheffield will also make themselves available to attend meetings of these groups to communicate their findings.

In addition the University of Sheffield intend to hold a public event in each of the five areas affected by the closures, disseminating the findings. The event will be open to the public. Local commissioners and providers will be invited to these public events, planned to take place following publication of the final report to the funder. Events are likely to take place mid 2017, but this will be dependent on the NIHR publication date of the final report.

Processing:

To enable analysis, five control EDs will also be selected.

There are a number of stages involved in creating the outputs:

Stage 1: calculation of catchment populations
In the first instance, a resident catchment population will be identified for each target ED (both intervention and control). Catchment populations will be identified from Hospital Episode Statistics (HES) Accident & Emergency (A&E) attendance/ and (or) Ambulance Service CAD data and will be defined as the lower super output areas of residence from which the majority of attenders at any ED during the study period, used the target ED.

Stage 2: Calculation of indicators
The next stage of processing will be the use of routine data, to produce a series of population and emergency care indicators for each resident catchment population. In total there will be 17 indicators relating to ‘death’ and ‘risk of death’, ‘A&E attendances’, ‘emergency hospital admissions’, ‘condition severity’, and ‘ambulance service performance’. Data will be drawn from Office for National Statistics*, HES A&E attendance, HES Admitted Patient Care, and NHS Ambulance Service Computer Aided Despatch datasets. Data from NHS Ambulance Services will be drawn directly from each NHS Ambulance Service Trust and will not be linked to any other dataset.


The information below describes each indicator in detail (all numbers and proportions, etc. relate to residents of the catchment areas) and identifies the data source required.

Data source - ONS mortality data and HES-ONS linked mortality data
• The number of deaths from conditions (as identified by ICD code) identified as rich in avoidable deaths
• Case fatality ratio for conditions (as identified by ICD code) identified as rich in avoidable deaths

For patients in the University of Sheffield’s HES APC data who also hold a ONS Mortality deceased flag, the ONS Mortality Date of Death (along with encrypted HESID) will be linked to Date of Admission (HES Admitted Patient Care) to derive vital status after 3, 7 and 30 days, and Month and year if the calculation exceeds 30 days, by calculating the days lapsed from Date of Admission to Date of Death. For those patients that hold a ONS Mortality status but do not appear in the HES APC dataset, only month and year of death will be derived.
The ONS Mortality database (with full Date of Death) will be stored separately and unlinked and a updated ONS Mortality database with the derived vital status details (and for removal of doubt, does not contain full Date of Death) will be linked to the HES data.


Data source - HES Accident & Emergency attendance
• Total ED attendances
• Total ED attendances by mode of arrival (i.e. by patients brought in by ambulance and those identified as having an ‘other’ mode of arrival)
• The number of arrivals at ED discharged without treatment or investigations(s) that required hospital facilities.
• The proportion of attenders at ED who are admitted to an inpatient bed

Data source - HES Admitted Patient Care
• The number of emergency hospital admissions for any condition
• The number of emergency hospital admissions for conditions (as identified by ICD code) identified as rich in ‘avoidable admissions’
• Mean length of stay in hospital for those admitted as emergencies
• The numbers and proportions admitted to critical care medicine

Data source - Ambulance service CAD data
• Mean time from 999 call to ambulance on scene,
• Mean time from ambulance arriving on scene to ambulance arriving at hospital
• Mean time to hospital from time of 999 call
• Mean time from ambulance arriving at hospital to ambulance ‘clear’ time
• Total ambulance service call volumes
• Non conveyance rates
• The number of emergency hospital transfers between local hospitals


Stage 3: Primary analysis
For all the indicators, data will be analysed using a time series of monthly values (for a minimum of 48 months spanning the closure or downgrading of the ED). A simple time series will be fitted to the data including a linear time trend, a seasonal effect, step interruptions for any other major changes to the local emergency care system, and a step interruption at the time of the change to the ED. Control series will also be used. The control catchment areas will be for populations in similar areas not expected to be affected by the closures either directly (as a result of ED attendances being diverted) or indirectly (via any impact on the ambulance service). Analyses will estimate the step in the intervention series, and the difference in the step between the intervention series and the control series. The University of Sheffield will use the estimate of the size of any step to estimate the impact on the indicators following the closure of the ED.

Data processing
The data will be processed and analysed within a single department, the School of Health and Related Research (ScHARR) within the University of Sheffield. The data will not, in any circumstances, be released to a third party. The data will be accessed by a restricted number of authorised individuals within the study team by use of a networked project folder which will be password protected.

Access to the data requires authentication (username and password) on the university network; further and distinct authentication (distinct username and password belonging to specific user accounts and from specific on-site machines - sited in locked rooms - only) on a specific "virtual machine". This virtual machine is the only "entity" from which intelligible access to the data is possible.

Specific user accounts allowing access to the data requested on the ‘virtual machine' are only granted to members of the study team, who are employees of University of Sheffield.

Authorisation to the specific project system folder will be given to a strictly limited number of individuals. All these individuals work within the same department (ScHARR) at the University of Sheffield and are subject to confidentiality policies of the University of Sheffield. The data will be cleaned and analysed by the data manager and statisticians who have experience in dealing with large datasets of routine data. The dataset will then be analysed by the statisticians in line with the aims of the study detailed in the study protocol that was approved by the University of Sheffield Ethics.

The calculated data will be reported to the team members in the form of a report detailing the aggregated results in line with the HES analysis guide.

The study will take 21 months to complete. The data extracts from the datasets will be removed from the network folder and destroyed three years after receipt from NHS Digital, which will allow the University of Sheffield time to complete the study dissemination period.

Project 23 — DARS-NIC-378491-R6K9Y

Type of data: information not disclosed for TRE projects

Opt outs honoured: N

Legal basis: 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)

Purposes: ()

Sensitive: Non Sensitive

When:2016.04 — 2016.08.

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 Critical Care
  4. Office for National Statistics Mortality Data (linkable to HES)

Objectives:

In 2009 the University of Manchester was commissioned by the Department of Health to evaluate the expected cost effectiveness of regional trauma networks (RTNs) prior to their implementation using published data and information from long-term follow-up of seriously injured patients. This work was subsequently sub-contracted to the University of Sheffield, and the University of Manchester will not have access to the data. The aim of this initial study was to estimate the expected costs and consequences of regional trauma systems with a view to implementing them across England and Wales should they be shown to be cost-effective. When estimating the expected cost-effectiveness of regional systems it was necessary to make a number of assumptions about the system owing to the sparse reported evidence on trauma systems at the time. However, it was felt that there was sufficient evidence to recommend the implementation of regional trauma systems. NHS England want to evaluate regional trauma networks and HES-ONS data would be used to update mortality estimates which are the number of trauma cases per year and cost (resources) in the period up to discharge from hospital following a trauma incident. Data from long-term follow-up of serious injuries and Trauma Audit and Research Network (TARN) data will be used to populate other parameters in the model. No data linkage will be made between HES, TARN and long-term follow-up data.

TARN includes patients who arrive at hospital alive after injury and subsequently fulfil one of the following criteria:
(i) Death in hospital during the acute phase
(ii) initial spell of care > 72hours
(iii) requires High-Dependency Unit (HDU) or Intensive Care Unit (ICU) care
(iv) Requires an interhospital transfer for acute care

The aim is to run cost effectiveness models comparing the period prior to trauma networks existing, during the set up phase and after implementation of trauma networks for the whole of England and separately for each trauma network. Information will be used to update model parameters in the cost-effectiveness model and reported as overall number of cases, mortality or overall cost for trauma networks across England or per trauma network for each of the periods described.

Expected Benefits:

This will demonstrate the benefits and cost savings of RTNs to the NHS and provide evidence to support the implementation of RTNs in other areas, for example Northern Ireland. It is anticipated that the information will be evaluative in terms of identifying the degree to which the cost effectiveness of trauma care has changed over the period of RTN implementation. If cost effectiveness at the NICE threshold is not achieved this will highlight a need for action to NHS England. It is unlikely RTN’s will be abandoned unless case fatality is worse and there is already evidence from TARN that this is unlikely.

The modelling may indicate differences between regions although direct comparisons are difficult to make as some RTNs were only fully implemented in 2014. If there is a wide disparity between regions who implemented at the same time then this will be explored to the level of determining whether variation in clinical outcomes, or cost, explain discrepancies. This has the potential to highlight good practice to lead clinicians and decision-makers at NHS England.

The target date will be to approximately 6 months after receiving the data for the report with the target audience being policy makers at NHS England. The target audience for the open access peer review article will be clinicians and managers involved in trauma care in the NHS with possible journals including the BMJ or the Journal of Trauma and Acute Care Surgery

Outputs:

Information will be used to populate an economic model on the cost-effectiveness of RTNs. Model outputs will include: incremental cost-effectiveness ratios, costs of running trauma networks and quality adjusted life years, expected number of additional survivors from RTNs, total number of trauma cases and number of deaths. The model will be run over 10,000 simulations and the mean result with confidence intervals will be reported. Other outputs will include a cost-effectiveness plane and cost-effectiveness acceptability curve for model results. This will be completed six months after receipt of data from HSCIC.

In statistical analysis and health economic analysis there is uncertainty around summary estimates, typically reported with standard deviations or confidence intervals. In economic evaluations a number of parameters contribute towards cost-effectiveness analysis e.g. costs, survival, quality of life and each of these has uncertainty around it. Generally, all uncertainties can be accounted for simultaneously in a cost-effectiveness model by assigning (statistical/mathematical) distributions (e.g. the normal distribution) around the information that is included in the model and use computer simulations to account for the uncertainty. The economic model built at the University of Sheffield for the previous work uses computer simulations to account for the uncertainty.

The University have previously produced a paper in 2011 looking at the cost-effectiveness of RTNs.

Processing:

Data will be accessed for the purposes of providing model parameters for mortality and costs for a cost-effectiveness model of RTNs. Data will be inputted into the cost-effectiveness model at an aggregate level e.g. mortality from trauma across England. The cost-effectiveness model is a mathematical/statistical model that uses a simulation technique to estimate the cost-effectiveness of regional trauma networks based on a set of parameters. In the case of the trauma networks these are number or survivors, number of deaths, mean cost. There will be no sharing with third parties. Any small numbers will be suppressed in line with the HES Analysis Guide.

Safety and quality indicators for hospital performance: an observational study in English hospitals — DARS-NIC-12983-Y3L3K

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 2019-06-01 — 2021-05-31 2016.04 — 2016.08.

Access method: One-Off

Data-controller type: UNIVERSITY OF SHEFFIELD

Sublicensing allowed: No

Datasets:

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

Objectives:

There is considerable public interest in the early identification of poorly performing hospitals which are providing unsafe or poor quality care to the patient populations they serve. The analysis of large sets of routine, observational data will be used to assess and compare the safety and quality of care provided by different hospitals.

“Unsafe” hospitals may be characterised as those having higher numbers of serious adverse events in low risk admissions than could be expected just by chance. Although similar ideas have been examined previously, these studies have been based on low risk diagnostic groups rather than low risk patients. Based on this concept, and using different measures to assess expected and observed outcomes, the performance of hospitals will be compared. Evidence of validity will be gathered by triangulating with other sources of evidence about safety concerns; by examining face validity based on determining what sort of 'low risk' patients having adverse events are being identified; and examining temporal stability as an indication that a 'characteristic' of the hospital is being measured. While some of the differences between hospitals are a result of intrinsic patient related factors (such as differing levels of deprivation, geography, comorbidity, and age), other differences seem likely to be intrinsic to the service factors offered within each hospital.

This research complements the work done in the development and validation of the Summary Hospital-level Mortality Index (SHMI), and in the development of methods for risk standardization and performance measures. The index was commissioned by the department of health and developed by ScHARR using five years of HES patient data. The SHMI provides a simple numerical tool for use in the identification of hospitals in need of further investigation.

The project will provide evidence for policy makers and hospital administrators to target appropriate interventions relating to the quality and safety of treatment offered to patients.

The applicant is requesting a de-identified data set and will not request sensitive items of data. The data set requested will be analysed and the analysis presented at research meetings. It is envisaged that research publications in peer reviewed papers will also be generated from the data. These conference presentations and papers will report aggregated results across patient episodes and the results will be based on statistical analysis generated from the data (typically in the format of tables, graphical representations and text).

The dataset itself will not be released to any third party, including other staff within the University of Sheffield. The individual patient episodes within the data will not be disclosed at any stage in the reporting of the results.

Yielded Benefits:

University of Sheffield have identified two novel mortality indices, the SHMI-Q, and the SHMI-S, based on the SHMI methodology. The two indices are tailored to assess the performance of hospitals that is specifically related to the quality (SHMI-Q) and the safety (SHMI-S) of care provided to patients, based on the analysis of routinely collected data. University of Sheffield consider unsafe, care provided by hospitals who experience a higher than expected mortality in-patient admissions deemed of relatively low mortality risk. In contrast, quality of care is seen as care provided by hospitals who experience lower than expected levels of mortality in admissions deemed, of relatively high mortality risk. The five year HES data set requested from the NHS Digital was used to develop these indicators. Annual SHMI-Q and SHMI-S indices were computed to compare the performance of 136 acute general hospitals in England between April 2010 and April 2015, and identify trusts where Quality and Safety of hospital care was observed. Within this period, University of Sheffield identified 28 trusts where care deemed unsafe was present, and 32 trusts where care provided was deemed of quality. University of Sheffield hope this work will feed into the current mechanism for assessing and monitoring hospital performance, as a way to discern between trusts providing care that is deemed unsafe and trusts providing care that is not necessarily of quality, going beyond the current performance assessment that is purely based on the detection of higher than expected mortality.

Expected Benefits:

The number of patient deaths that has been associated with non-optimal medical practices under hospital care has been of increasing concern to health and social care policy makers and practitioners. An increased demand for accountability at the level of these institutions has spurred the need for better measures of performance, and of comparison, between health care services.

A significant variation in mortality rates according to week day of admission has been shown by several studies made in English hospitals. A greater understanding of this phenomenon would be of great benefit to administrators, regulators, and practitioners, as a potential problem locator identifying areas where further investigations and funding should focus into.

This study will provide updated measures of hospital performance, identifying outlying (underperforming) hospitals. The study will also provide a hospital specific analysis of possible association between admission’s day of the week and increased mortality rates. Both of these analyses will be examined for stability over time, and variability.

The potential benefits to accrue to the Health and Social care systems will be an evidence based, current report on the performance of non-specialist hospital in England, including an analysis of stability through time and trending. The report will be targeted at health and social care regulators and practitioners as well as scholars.

This application continues the work of SHMI and is being processed by the same SHMI team who have been addressing the issue of hospital performance since the early 2000s, and although not new, the issue is still very much relevant to the
current healthcare system. Many crucial questions regarding the understanding of the complexity of hospital safety and performance remain unanswered. The particular concern of the team is that an analysis of hospital performance should
distinguish between high and low risk patient groups, and should investigate its relation to other factors such as day of week of admission. The main output from this research will be a report of the analysis that can identify hospitals that
might need further investigation because of safety issues (identified by relatively poor performance in low risk patients) or quality issues (identified by relatively poor performance in high risk patients). If hospitals are identified that manifest
poor performance that cannot be explained by the data this report will be supplied directly to the Department of Health.

It is difficult to quantify how policy would be impacted by this work, but this research does continue the work done for SHMI, and so SHMI makes a good example. Around 60% of all deaths occur in hospital and preventing avoidable
deaths is an essential objective for health services. The Francis Report on the Mid-Staffordshire Hospital Trust in Feb 2013 showed that excess mortality for the Trust was associated with poor care. The Summary Hospital Mortality Index (SHMI) was developed as a direct result of research carried out at the School of
Health and Related Research (ScHARR). 14 hospitals (many of which were also identified ScHARRs report of 2011) were identified by the Department of Health as having unacceptably high mortality, over two years using the Sheffield SHMI,
amongst other measures. The consequence was that the Care Quality Commission sent teams in to investigate the care of patients at these hospitals and this was reported in the Keogh report (2013) which will ultimately impacted staff, patients and hospital systems with the aim of improving patient outcomes


Outputs:

The outcomes expected from this analysis include:

• a report of the analysis that can identifying hospitals that need further investigation, indicators of hospital performance will be produced for each non-specialist hospital (October 2017),
• a Master’s thesis ( though this should not be considered a major output within this project) with a target date of September 2017
• abstracts, posters presented at academic conferences (with a target date of June 2017),
• a journal article will be submitted to peer reviewed health-related journals (with a target date December 2017).

The project will provide evidence for policy makers and hospital administrators to target appropriate interventions relating to the quality and safety of treatment offered to patients.

The data set requested will be analysed and the analysis presented at research meetings. It is envisaged that research publications in peer reviewed papers will also be generated from the data. These conference presentations and papers will report aggregated results ACROSS patient episodes and the results will be based on statistical analysis generated from the data (typically in the format of tables, graphical representations and text).

Target journals for publication include the BMJ, the Journal of Health Services Research and Policy, the BMC Medical Research Methodology, and the BMC Health Services Research.

The dataset itself will not be released to any third party, including other staff within the University of Sheffield. The individual patient episodes within the data will not be disclosed at any stage in the reporting of the results. Furthermore, the report will not serve to identify any hospitals. It will be focused on the potential of the method to identify any hospitals where there are concerns about the relative safety of care. It will therefore be focused on potential users of the methods (i.e. Department of Health and HSCIC).

The report will be sent directly to contacts in the Department of Health and also the SHMI Technical Working Group at HSCIC, with whom ScHARR developed the SHMI model.

Processing:

Five years of routine population level data will be examined. The data requested expands over a period of five years to ensure that the standardised risk model detects trends and is
consistent with that of previous studies. The stability of the model through time is essential to ensure its validity; in particular, to ensure that meaningful changes can be detected and better distinguished from noise.

Data will be drawn from HES Admitted Patient Care for non-specialist hospitals in England. The processing will be undertaken within a number of stages as outlined below:

Stage 1: Secure management and analysis of hospital episode statistics (HES) for all in-patient admissions to non-specialist English hospitals.

Stage 2: Development and validation of hospital performance measures focused on patient safety, and statistical modelling to estimate the risk of adverse events (e.g. death in hospital) for every admission. Logistic regression models, using the covariates explored in the development of SHMI will be employed to estimate the risk of death (or other serious adverse event such as unexpected transfer to critical care) for each admission. Analysis of possible association between the day of the week an admission takes place and an increased risk of adverse outcome.

Stage 3: Comparison of risk adjusted event rates indicating unsafe performance between hospitals, and development of appropriate graphical methods such as funnel plots to compare casemix or risk adjusted event rates between hospitals.

Stage 4: Development of a critique of the methods to provide a clear interpretation of the results and their limitations.

Stage 5: Elaboration of a report on the findings, and potential submission to publication.

The data will be processed and analysed within a single department within the University of Sheffield, the School of Health and Related Research (ScHARR). The data will not, in any circumstances, be released to a third party, nor will it be used for commercial purposes.

The data will be accessed by a restricted number of authorised individuals within the study team. Authorisation to the specific project system folder will be given to a strictly limited number of individuals, namely the principal investigators and the statistician. All these individuals work within the same department (ScHARR) at the University of Sheffield and are subject to strict confidentiality and secure data management policies.

The data will be cleaned and analysed by the statisticians, the analysis of the dataset will be performed in line with the protocol approved by ScHARR Ethics committee, and will abide by the University’ of Sheffield’s Ethics Policy. The data will be reported to the other members the School in the form of a report detailing the aggregated results.

The project folder, containing the data, will be located in a networked PC that is username and password protected. Only specific users and specific on-site machines will be granted permission to the project folder. All access will be logged.

After the three year period the data will be destroyed under supervision of ScHARR IT staff.

The Department (School of Health and Related Research) has achieved IGTK approval level ‘satisfactory’ in order to process large routine data.