NHS Digital Data Release Register - reformatted

University Of York projects

1048 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).


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

Centre for Health Economics, University of York, Programme Level Agreement — DARS-NIC-667040-B5T1X

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2023-11-16 — 2026-11-15

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. Civil Registrations of Death - Secondary Care Cut
  2. Community Services Data Set (CSDS)
  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 Critical Care (HES Critical Care)
  7. Hospital Episode Statistics Outpatients (HES OP)
  8. Improving Access to Psychological Therapies (IAPT) v1.5
  9. Improving Access to Psychological Therapies (IAPT) v2
  10. Mental Health and Learning Disabilities Data Set (MHLDDS)
  11. Mental Health Minimum Data Set (MHMDS)
  12. Mental Health Services Data Set (MHSDS)
  13. Patient Reported Outcome Measures (Linkable to HES)

Objectives:

The Centre for Health Economics (CHE) at the University of York requires access to NHS England data for the purpose of the following research programme:
“Centre for Health Economics, University of York, Programme Level Agreement”

The following is a summary of the aims of the research programme provided by or on behalf of the CHE:
The Centre for Health Economics is a research department of the University of York, dedicated to the study of the economics of health and health care. CHE's Research Strategy aligns with, and contributes to, the University Research Strategy, with one of the strategic aims of 'research with relevance and reach'. It also aligns with several of the University's Research Themes, in particular: Health and Wellbeing; Justice and Equality; Risk, Evidence and Decision Making; and Technologies of the Future. CHE produces policy relevant research and innovative methods that advance the use of health economics to improve population health. As the NHS continues to grapple with financial pressures, and the short and long-term impacts of the COVID-19 pandemic, research carried out in CHE aims to support decisions about where and how increasingly limited budgets are spent. CHE works closely with decision-makers at international, national and local levels to ensure that research is addressing their needs and priorities.

CHE’s research (http://www.york.ac.uk/che/research/) using NHS England data is organised into six priority areas (research themes):
1) Economic Evaluation and Health Technology Assessment
2) Health Policy
3) Equity in health and health care
4) Health and social care
5) Mental Health
6) Public Health

CHE's priority research areas are reviewed by the CHE Executive and the Departmental Research Committee every three years, as part of the Research Strategy (last review 2022). Projects or programmes of work are broadly aligned to a priority area, with cross-cutting research across these. For example, Economic Evaluation cuts across a number of themes, including Health and Social Care, Public Health, and Equity in health and health care; and Health Policy includes research in Health and Social Care, and Mental Health. Each priority area is led by a senior researcher, typically a Professor or Reader (Associate Professor), who are substantively employed by the University of York - Centre for Health Economics.

Case studies highlighting cross-cutting research:
- Allocating resources in the NHS (https://www.york.ac.uk/research/impact/allocating- resources-in-the-nhs/);
- Deciding which health and care treatments should be nationally funded (https://www.york.ac.uk/research/impact/funded-treatment-decisions/);
- How productive is the NHS? (https://www.york.ac.uk/research/impact/how-productive-is- the-nhs/)

The research undertaken using NHS England data informs health and social care policy and practice by identifying the effectiveness, efficiency, distribution, and quality of a wide range of services provided to the population. It produces insights that allow the maximisation of health gain and other measures of benefit from limited healthcare budgets, along with information on how health and health care is/can be distributed equally to meet the health needs of varying demographics. NHS England data potentially provides a view of health care utilisation for CHE to understand how effective delivery of care is distributed both nationally and locally, contributing to the delivery of new healthcare policy aimed at improving the quality of care.

RATIONALE FOR STRATEGIC PRIORITIES AND PROGRAMMES

1. Economic Evaluation and Health Technology Assessment:
Economic Evaluation and Health Technology Assessment focuses on research and training relating to the economic evaluation of health care programmes and interventions. CHE undertakes a range of methodological research in economic evaluation, and the design, conduct and analysis of applied economic evaluations. These include integrated economic and clinical randomised trials, decision analytic modelling studies and economic and statistical evaluation of observational and retrospective data sets. Additionally, CHE conducts economic evaluation in a number of cross-cutting themes, primarily: social care, public health, and global health.

Programmes of work include:
- NICE Technology Assessment Reviews;
- Policy Research Unit in Economic Evaluation of Health and Care Interventions (EEPRU, http://www.eepru.org.uk/);
- Supporting local decision makers (ARC-YH, https://www.arc-yh.nihr.ac.uk/);
- Health opportunity costs;
- Elicitation: capturing the uncertain beliefs of clinical experts in a quantitative form to use in further analysis using evidence synthesis (a process to combine evidence from multiple sources using appropriate statistical techniques);
- Personalised medicine;
- Research prioritisation.

2. Health Policy:
Health Policy undertakes applied and methodological economics research to critically appraise and evaluate organisational and incentive structures of the healthcare system. This covers the behaviour and performance of organisations and individuals within the healthcare system.

Programmes of work include:
- Contracting and reimbursement;
- Efficiency and Productivity;
- Workforce;
- Integrated Health & Social care;
- Measuring health & quality of care;
- Organisation and structure of health systems;
- NIHR Policy Research Unit in Economics of Health Systems and Interface with Social Care (ESHCRU, https://eshcru.com/).

3. Equity in health and health care :
There are substantial inequalities in health and health care outcomes between more and less socially disadvantaged people, which raise important concerns about quality of care and justice. CHE’s work in this area includes not only studies that aim to describe and understand such health inequalities, but also studies that aim to provide decision makers with information about the impacts of their decisions on health inequalities, such as distributional cost-effectiveness analysis, health equity measurement and monitoring for health care quality improvement, and quasi-experimental evaluation of policy impacts on health inequalities.

Programmes of work include:
- Distributional cost-effectiveness analysis (DCEA);
- NHS equity indicators; econometric methods and policy evaluation;
- The equity impacts of hospital competition;
- Inequality in waiting times;
- Primary care workforce distribution;
- Deliberative process for addressing equity concerns;
- Public preferences for reducing health inequality.

4. Health and social care:
Economic constraints on public sector budgets and improvements in care generally mean that people are living longer and there is an increased need for research on how best to allocate resources and deliver services that are efficient, equitable and offer good value for money.

Programmes of work include:
- ESHCRU (https://eshcru.com/)
- EEPRU (http://www.eepru.org.uk/)
CHE’s research on social care is developed under the programmes of these two Policy Research Units, both funded by the National Institute for Health Research (NIHR) on behalf of the Department of Health and Social Care. The NIHR School for Social Care Research (SSCR) is also a core funder of CHE’s economic evaluations of social care interventions.

5. Mental Health:
Mental health problems are the largest single cause of disability in the UK, representing a quarter of the national burden of ill-health, and are the leading cause of sickness absence.

Programmes of work in the area of mental health economics and policy include:
- Socio-economic determinants of mental illness; the nature of the treatment and the quality of care received by people with mental illness;
- Health inequalities in treatment rates;
- Mental health outcomes;
- The economic evaluation of mental health interventions and services;
- The organisation and funding of mental health services;
- The performance of mental health providers.

6. Public Health:
Public health services play a vital role in preventing ill health and reducing health inequalities. The Office for Health Improvement and Disparities launched in October 2021 with the intent to coordinate public health activities across central and local government, the NHS and wider society. CHEs research on public health is a cross-cutting research theme.

Programmes of work include:
- Socioeconomic determinants of health, health behaviour and health inequalities;
- Economic consequences of health and health inequalities;
- Economic evaluation of public health interventions;
- Evaluation of public health interventions through econometric methods and microsimulation (an alternative method which involves simulating the impacts of hypothetical and/or new programmes or forecasting the impacts of existing programmes in new contexts and over time);
- Local health and care research partnerships;
- Resource allocation and health inequalities;
- Commissioning public health services.

The methods for use of HES data and other NHS England datasets will vary from project to project, with data analysed in different ways and employing a variety of statistical methods. However, there are a number of common ways in which the data is used. This Agreement permits use of the data for the following:
• Assessing data quality, completeness, relevance and volume of data prior to and during undertaking research analysis
• Measurement of efficiency, effectiveness, and productivity of health and social care systems nationally, subnationally, and at the organisational level, e.g. health and social care providers, integrated care systems, public health providers etc.
• Evaluation of differences in the performance of health care providers in terms of the amount, cost and quality of provision and in patient outcomes including mortality and self-reported morbidity
• Evaluation of the impacts of health care policy, organisation, finance and delivery of NHS services and public health services and quantification of differences in health care utilisation, expenditure, morbidity and mortality over time, across geographic regions, health and social care providers, and among different patient groups
• Investigation of the level of and inequalities in access, outcomes, and costs of health services in England
• Evaluation of the interface between the different sectors and different organisations of the healthcare system, including the effects of quality and access of primary care on patient use and outcomes in secondary care; and the relationship between public health services, long term care, social care and secondary care utilisation
• Evaluation of the impact of the COVID-19 pandemic on the demand for services, and healthcare utilisation
• Evaluating the NHS budgetary impacts, resource use implications and morbidity and mortality effects of specific health care interventions, including screening, diagnosis, management and treatment to inform economic evaluation
• Exploring and evaluating patterns of comorbidity, pathways of care, health and social care use and cost, and health and care outcomes of specific populations to inform economic evaluation and decision analytic models
• Evaluating the impact of specific health service interventions on healthcare resource use, morbidity, survival and risk of further illness, to inform economic evaluation and decision models
• Evaluating the size and characteristics of populations impacted by specified health policies in order to estimate the level of burden and population distribution of costs and outcomes;
• Assessing changes over time in access, public health services, healthcare delivery and utilisation, diagnoses, treatment and patient characteristics to support evaluation of health policy impacts
• Evaluating changes in NHS expenditure, programme budget categories and resource use over time alongside impacts on morbidity and mortality to estimate the marginal productivity of the NHS.

PROJECT SCOPING AND RESPONSIVE ANALYSIS

CHE are implementing a new strategy to assess the viability of new projects. Across many of CHE’s strategic research priorities, analyses may be undertaken of NHS England data for scoping research and responsive analyses, as described below.

Scoping analysis:
When a new project idea or research question is conceived, it may be both beneficial and necessary to use NHS England datasets to carry out preliminary analysis prior to the submission of a research funding application. Such scoping analyses would support researchers in testing their proposed research questions to confirm feasibility, and allow researchers to generate relevant, accurate and high quality proposals with the confidence that the data can be used to generate the desired outcome and impact.

During scoping analysis, researchers may undertake the following types of assessment:
- Test whether key outcomes of interest are numerous enough.
- Check whether coding is consistent across organisations and geographic areas, and over time.
- Determine whether particular statistical methods would be appropriate for the questions being asked.
- Test whether CHE research would have the necessary statistical power to be able to make high quality conclusions.
- Assess the minimum level of data required for the purpose.

Scoping analysis is approved by the Data Access Request Group (DARG). Requests for scoping analysis are submitted by the individual(s) within a project team, and a record of requests and outcomes is kept in a register maintained by DARG. The request captures the aim of the scoping, data set(s) required, data fields and years necessary; and the approval date and person, outcome of scoping exercise, and status of data is recorded on the register. It also confirms that other sources of data have been considered prior to this request. Where the outcome of the scoping exercise is to proceed with a research funding application, the working dataset used for the scoping exercise will be kept, pending the outcome of the funding application. If the individual(s) conclude that the project is not feasible, or the funding application is unsuccessful, the working dataset used will be erased. No member of staff will ever make copies of full NHS England datasets.

Responsive analysis:
The University of York - Centre for Health Economics holds several NIHR Policy Research Programme contracts. Some of these, such as the contracts for the Policy Research Units, include a requirement to undertake rapid response research. The aim of this responsive facility is to meet emerging needs of policy makers at the Department of Health and Social Care (DHSC) or its arm’s length bodies (e.g. NHS England). The evidence can be commissioned at short notice, and the nature of the requests depends on the issues and challenges facing the Department as priorities and policy evolve and develop. For example, the study team may be asked to provide evidence quickly in response to Parliamentary Questions; changes in priorities in the health or care system may lead to requests for a short piece of analysis to inform new policies; or evidence to support pressing analytical needs within the Department may be requested. These responsive requests are additional to the core, planned work undertaken under the auspices of the same contracts. Whilst the topic of some requests may fall within the broad priority areas outlined above, this cannot be guaranteed. Therefore, University of York wish to include an additional ‘responsive analysis’ purpose to ensure University of York have the necessary permissions in place to ensure the study team can respond to DHSC rapid requests in line with University of York's contractual requirements.

HOW DECISIONS ARE MADE ABOUT PROJECTS AND USE OF NHS ENGLAND DATASETS

This Agreement permits CHE to use the data for the purposes of projects undertaken within the work programmes described above, and which are conceived, planned, approved and initiated through the following process:
New projects are conceived in a collaborative process, drawing on the relevant specialisms and experience of researchers across the research themes. Research questions are developed, and appropriate potential funding opportunities are identified - i.e. internal sources (University of York/ Centre for Health Economics) or an external funding organisation (e.g. National Institute for Health and Care Research (NIHR), Medical Research Council (MRC), Economic and Social Research Council (ESRC), Department of Health and Social Care (DHSC), etc). Complying with the relevant funder’s requirements and application processes, the research team, led by the principal investigator, will ensure that:
• The project has a clearly defined objective that meets the scope and eligibility criteria of the funding call
• A detailed research plan is prepared, including data requirements, methods, project management, dissemination, outputs, anticipated impact and a project timetable
• The use of NHS England datasets is necessary to fulfil the aims and objectives of the project, and that use is proportionate,. This includes taking consideration of data minimisation, NHS England datasets requested, years requested, size of cohorts, and exclusion criteria applied.

All staff and postgraduate students are provided with information and support on Data Protection Impact Assessments (DPIAs) and are provided with screening questions to determine whether a DPIA should be undertaken. The Principal Investigator (PI) will determine whether a DPIA is required; and will seek guidance from the CHE Data Governance Group, or the University Data Protection Officer, where necessary.
An internal review of each funding proposal takes place, by two senior members of staff appointed by the CHE Department Research Committee Chair and Deputy Chairs. The purpose of this review is to: provide independent advice and guidance for principal investigators; ensure that all proposals being made in CHEs name meet CHEs quality standards, fit the research mission and maintain CHEs reputation; and ensure that the resources that are being requested are adequate to deliver the work. Upon completion of the review, the finalised proposal will be submitted to the funding body, and will undergo panel review (internal funding) or peer review (external funding).

A project ‘kick-off’ meeting is held to make all members of the team working on the project aware of its key features and planned pathway to completion, including key deadlines, required outputs, etc. The CHE principal investigator, and the research team, will be bound by the research plan, and committed to achieving the agreed deliverables of the project, and in line with the terms and conditions of the funder.

The following steps are required to apply for access to NHS England data:
1. Completion of ‘CHE Data Access Request Form - NHS England Data’ by researcher
2. Review of application by Data Access Request Group (DARG)
3. Centre for Health Economics: NHS England Data Access Register (internal and public facing) updated

The Data Access Request Group (DARG) provides oversight of all requests to access NHS England data in the Centre for Health Economics (CHE). As a companion group of the CHE Data Governance Group (DGG), the DARG will manage and review procedures and criteria for accessing NHS England data, and be responsible for the assessment and decision making on requests for access to these data.

DARG consider requests for access to data on the basis of the following criteria:
a. Purpose/ scope, and CHE research priority (theme)
b. Publicly available data
c. Sensitivity of data
d. Data minimisation
e. Legal basis for processing health data
f. Expected measurable benefits to health and/or social care
g. Public and Patient Involvement and Engagement
h. Ethics
i. Commercial purposes

The Programme Level Agreement does not permit use of NHS England data for commercial purposes. If a research project submitted to the DARG has a commercial benefit, it would require a separate data application to NHS England.

The DARG will meet monthly and will maintain a register of applications and decisions made. This will be publicly available on the CHE website for the benefit of participants and other researchers, and will include a lay summary of successful applications.

EXAMPLES OF PROJECTS:

1. Project title: Efficiency, cost and quality of mental health care provision
CHE Programme: Mental Health
Overview: This project looked at the efficiency, cost and quality of current mental health care provision, and how changes can be made to drive efficiency improvements. The team assessed which quality indicators are valued by service users and clinicians. These included aspects such as improvements in outcomes, better and more equitable access to care, and distance to providers. Quality adjusted life year (QALY) weightings were developed for each of these indicators in order to assess efficiency, using a QALY framework. These data were used to produce a cost-effectiveness plane for mental health trusts, to enable the team to identify high-quality, low-cost providers, and to further examine organisational factors associated with cost effectiveness. This information informed estimates of how resources can be reallocated to be more cost effective, and what input-mix (eg capital, labour) might be associated with improved cost effectiveness.

Data minimisation approach: The work used HES APC 2014/15- 2019/20; HES APC 2020/21 monthly data to and including September 2021; MHMDS 2013/14, MHLDS 2014/15-2015/16.
Duration: 2017 - 2021
Funder: Health Foundation. Ref. 57151

2. Project title: Partnership for Responsive Policy Analysis and Research (PREPARE) CHE Programme: Health Policy
Overview: This project is exploring the links between child health and child poverty, in particular the NHS hospital utilisation of children born into deprivation (using the indices of
deprivation (ID) as a proxy for poverty) in comparison with children who are not born in deprived areas. The study also explores whether any difference in hospital utilisation over the early life course has changed over time. To do this the project is creating a birth cohort of children born in NHS hospitals in England in specific financial years (2000, 2005, 2010, 2015, 2018), and is tracking their use of NHS services (inpatient, outpatient and A&E) over their life course (up to age 18 for those born in 2000). The analysts will then test whether the age-sex adjusted differential use of hospital services across children born into rich and poor neighbourhoods has changed over time.

Data minimisation approach: The work will use only HES APC 2000/01-2020/21; HES A&E 2007/08 - 2018/19; HES OP 2002/03-2020/21; Emergency Care Dataset 2017/18-2020/21. Duration: April 2020 - March 2025
Funder: NIHR Policy Research Programme. NIHR 200702.

3. Project title: Analysis of purchaser-provider contracts: modelling risk sharing and incentive implications.
CHE Programme: Health and social care: ESHCRU
Overview: The previous consensus regarding the development of contract arrangements (towards more fixed price, Payment by Results, National Tariff contracts) was subject to critical review (NHS England and NHS Improvement joint pricing team, 2019). Different arrangements are being developed for emergency and elective acute care, mental health services and a variety of ‘locally priced’ services (NHS England and NHS Improvement, 2019). The reformed financing would use a blended payment mechanism consisting of a two-part tariff, which comprises a fixed sum with payment either reduced or increased at a given fixed rate for treatments above or below a given threshold. This project develops a theoretical and empirical investigation of the impact of blended payment on emergency care provision. The overall aim is to provide relevant theoretical and empirical insight into the trade-offs, risks and benefits of different forms of contract on the provision of emergency care.
Output 1 investigates the variation of providers’ optimal proportion of patient to admit from an A&E attendance to major A&E Department.
Output 2 analyses the variation on number of A&E attendances to major A&E department across the purchasers of emergency services (Clinical Commissioning Groups).
Output 3 combines the estimates of Output 1 and 2 to examine how purchasers’ efforts to decrease A&E attendances to Major A&E Departments influence the providers proportion of patients admit from the same departments.
Output 4 analyses the variation of providers’ optimal proportion of psychosis patients to from the different referral sources.
The primary data source was the 2018/19 individual patient level A&E Hospital Episode Statistics (HES) data which was combined with General Practice (GP) level characteristics for the analysis in Outputs 1-3. For Output 4, the Mental Health Services Data Set (MHSDS) was used.

Note this is not an exhaustive list.

Data minimisation approach: The primary data source was the 2018/19 individual patient level A&E Hospital Episode Statistics (HES) data. Only individual level data essential to the Output 1 analysis is kept, as gender, age and ethnicity. The patients’ GP practice code recorded on the HES episode is used to attribute to each patient their GP practice characteristics (e.g. clinical quality, number of GPs, extended access offer) and the patients’ residence (LSOA) to attribute to each patient the distance to the AED and their area of residence characteristics (e.g. Index of Multiple Deprivation). In Output 2, the total number of A&E Major Department attendances at GP practice level was used. The study team collected the set of GP practice characteristics, e.g. patient list demographic and disease prevalence, clinical quality, number of GPs, extended access offer from the NHS England primary area hub. Output 3 analysis uses the information at provider and purchaser (Clinical Commission Group) level.
In addition, for the analysis on mental health the study team use the MHSDS data for 2018. Only individual level data essential to Output 4 analysis is kept, as gender, age, and ethnicity.
Duration: 24 months (2021)
Funder: NIHR PRP ESHCRU II (Policy Research unit in the Economics of Social and Health Care).

The above are a few examples of the way in which NHS England data is used under this Agreement only for illustrative purposes. University of York can provide, upon request of NHS England, all uses of NHS England data.

The following NHS England data will be accessed:
- Hospital Episode Statistics (HES): Critical Care, Outpatients, Admitted Patient Care (APC), Accident and Emergency (A&E) – necessary to provide a range of information on hospital admissions, risk associated with admission, describing hospital resource use of patients, cost estimations.
- Emergency Care Data (ECDS) [historically, HES Accident and Emergency] - · Emergency Care Data Set (ECDS) – necessary because this dataset replaced HES Accident & Emergency data
- Civil Registration (Deaths) - Secondary Care Cut (CRD SCC) – necessary to report rates of mortality and model risks of certain diseases.
- Patient Reported Outcome Measures (PROMS) - necessary to measure health benefits produced by the health system.
- Mental Health Services Data Set (MHSDS) [historically, Mental Health Minimum Data Set (MHMDS) & Mental Health and Learning Disabilities Data Set (MHLDDS)] - necessary to measure socio-economic determinants of mental illness; the nature of the treatment and the quality of care received by people with mental illness; health inequalities in treatment rates; mental health outcomes; the economic evaluation of mental health interventions and services; the organisation and funding of mental health services; and the performance of mental health providers.
- Community Services Data Set – necessary to enable CHE to explore the role of community services in helping prevent unnecessary hospital admissions and enabling speedier hospital discharges. As the population of England becomes older, there are more patients with chronic conditions that receive treatment in the community, so its use is more relevant now than it was previously. Therefore the use of this dataset will assist CHE when commissioned with policy research by the Department of Health and Social Care.
- Improving Access to Psychological Therapies (IAPT) Data Set – necessary for research on the relationship between mental health and economic outcomes such as labour market participation. Key variables in the data include employment, self-employed status, employment support advisor indicator, absenteeism, benefit receipt, disability code, and diagnosis. This data will be used to develop economic models and framework to evaluate cost effectiveness and productivity.

Different projects will have different requirements in terms of data dissemination frequency. For example, some projects will only require updated data annually whereas some projects will need more frequent data on a quarterly basis. Quarterly dissemination of data supports responsive projects, providing more timely evidence to policymakers - for example, to support the response to the COVID-19 pandemic.
The level of the data will be pseudonymised.

As part of the CHE data access request form, a data fields selection form is completed for every application requesting access to NHS England data which details the data fields and periods required. Each project requirements are different therefore data minimisation is applied to each project at inception. This could be limited to data periods or geographic regions however each project will be unique and all data minimisation will be reviewed by DARG.

The University of York is the Controller as the organisation responsible for ensuring that the data will only be processed for the purpose described above.
Though CHE may be commissioned by another organisation to undertake a project involving the processing of data under this Agreement, CHE will retain sole discretion for determining if and how the data would be used for any purpose.

Where the University of York - Centre for Health Economics is a partner in research collaborations, including (but not limited to) ESHCRU, EEPRU, and Applied Research Collaboration-Yorkshire and Humber (ARC-YH), the team within the University of York will be solely responsible for all decisions on how this research will be carried out including all decisions in respect of what data processing is required. Data provided as part of this Agreement will not be shared with collaborators. The University of York - Centre for Health Economics cannot be compelled by any third party to process the data for any purpose or in any way. The data will only ever be used for purposes that directly support the priorities of the University of York - Centre for Health Economics, as described in this Agreement.

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

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

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

The funding for all projects under this programme level Agreement comes from multiple sources. Current funders for ongoing projects include:
• National Institute for Health Research (NIHR)
• The Health Foundation

Funding to continue the work described will be sought on an ongoing basis.

CHE’s research is largely externally funded, with a broad potential funding base which could include (but not limited to): National Institute for Health Research (NIHR); European Union; Wellcome; and UK Research and Innovation (UKRI). In addition to external sources of funding, CHE may receive internal (University of York/ Centre for Health Economics) research funding.
The funder(s) will have no ability

Yielded Benefits:

Two examples of yielded benefits to date are as follows. These are not exhaustive examples of yielded benefits under this Data Sharing Agreements. The first project is work commissioned by the Department of Health and Social Care (DHSC) /National Institute for Health and Care Research (NIHR) and it relates to the production each year of an annual update of the national NHS productivity figures that incorporate the most recent financial year of data. Annual updates of NHS productivity growth rate figures were used by the DHSC both externally and internally in monitoring, informing policy debate, the annual spending review, and negotiations on budget setting. Under this project, CHE also provided data about the quality of NHS care to the Office of National Statistics that are used each year in the construction of the national accounts. Over the years, additional analyses of productivity growth have been carried out at the hospital-level, specialty level, and geographical level. Hospital-level productivity analyses also examined the factors underlying variation in productivity, which assist the DHSC in exploring how to get the best value from NHS resources. A second example relates to work carried out in 2017 on patient-assessed outcomes which was extended, by working with the Vale of York commissioning body, to generate a web tool to support discussions between patients and their GPs about whether to undergo planned surgery. CHE developed the online tool, aftermysurgery.org.uk to inform patients about their likely outcome of hip and knee surgery and groin hernia repair. This online tool uses Patient Reported Outcome Measures (PROMs) data to present, for each user of the tool, information on health outcomes experienced by other patients that have similar preoperative characteristics. The intention of this tool is for it to be used in primary care to facilitate shared decision-making between general practitioners and patients. Having an operation is a big decision and it is natural to wonder how you will feel after surgery. Many people in this situation would like to know how patients before them have benefited from surgery. This online tool shows what thousands of NHS patients have said about their own experiences. It can be used to see how patients of the same age and with similar health problems felt after they had their operation. Patients complete the survey before and several months after surgery. Prior to deciding on surgery this tool allows patients to compare themselves to people who are similar to them and see how much surgery helped them. It is hoped that this benefits patients by giving them a better idea of what to expect if they decide to have surgery, and helps them decide whether to go ahead with surgery or not, in conjunction with their GP. The toolkit remains live, and is an ongoing benefit from this project.

Expected Benefits:

Since 2009, CHE has used NHS England data to provide stakeholders with objective evidence and research to support decision making on health and social care, through analysing the effects that lifestyle choices have on health, and examining the costs and the benefits of policies - including both clinical effectiveness and cost-effectiveness - and the implications for equity.

Through its research using NHS England datasets, CHE hopes to inform and influence health and social care policy and practice, fulfilling its mission to provide evidence to policymakers to promote health and wellbeing through the effective, efficient and equitable use of scarce resources.

Examples include:
CHE aims to partner with practitioners, policymakers, and patient and public involvement & engagement (PPIE) groups, to support the Centre to produce policy relevant and impactful research that evolves in response to changing needs and policy priorities. CHE aims to provide stakeholders with objective evidence and research to support their decision making.

In evaluating the performance of health care providers, CHE aims to provide evidence to support national and regional policy-makers and providers with decision-making on the provision of services that offer the greatest value for money according to the benefits, aiming for a more efficient allocation of health care resources, through appropriate budget spend.

By investigating inequalities in healthcare access and outcomes, CHE hopes to help the NHS address its Public Sector duty under the Health and Social Care Act 2012 to reduce health inequalities. CHE has previously worked with NHS England’s equality and health inequalities team to disseminate equity indicators to local decision makers within the NHS, and help clinical commissioning bodies use them to address the NHS duty.
CHE’s work on efficiency, effectiveness and productivity aims to support the Department of Health and Social Care with exploring how to get the best value from NHS resources, in addition to monitoring, informing policy debate, the annual spending review, and negotiations on budget setting.

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

The use of the data could:
• help the system to better understand the health and care needs of populations.
• lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
• advance understanding of regional and national trends in health and social care needs.
• advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as obesity and diabetes.
• inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
• inform decisions on how to effectively allocate and evaluate funding according to health needs.
• provide a mechanism for checking the quality of care. This could include identifying areas of good practice to learn from, or areas of poorer practice which need to be addressed.

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

Processing:

The University of York currently hold pseudonymised data which were supplied under a different data sharing agreement with NHS England (DARS-NIC-84254), for the below datasets:
- Hospital Episode Statistics (HES): Critical Care, (2011/12-2021/22)
- Hospital Episode Statistics (HES) Outpatients, (2003/04-2021/22)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (1989/90-2021/22)
- Emergency Care Data (ECDS) (2017/18-2021/22) [historically, HES Accident and
Emergency (2007/08-2019/20)]
- Civil Registration (Deaths) - Secondary Care Cut (CRD SCC)
- Patient Reported Outcome Measures (PROMS) (2009/10-2020/21)
- Mental Health Services Data Set (MHSDS) [historically, Mental Health Minimum Data Set (MHMDS) & Mental Health and Learning Disabilities Data Set (MHLDDS)] (2011/12-2020/21)

Under this Agreement, the University of York are requesting latest available-2024/25 quarterly and annual disseminations of pseudonymised data for those datasets listed above, additionally the Community Services Data Set (2015/16-2024/25 and Improving Access to Psychological Therapies (IAPT) Data Set (2012/13-2024/25) are required.

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

NHS England data will provide the relevant records from the above datasets to the University of York. The data will contain no direct identifying data items. The data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.

The data will not be transferred to any other location.

The data will be stored on servers at the University of York Data Safe Haven and back up locations: onsite at the University of York, and offsite backup services provided by Amazon Web Services. Data will not be transferred to any other location. The data will remain on the servers at the University of York (and back up servers) at all times, and will not leave England at any time.

* Remote processing would be from secure locations within the territory of use identified within the agreement (UK and EEA)
* Remote access is via devices that are maintained by the controller
* All remote access is undertaken within the scope of the organisations DSPT (or other security arrangements as per this agreement), and complies with the University’s remote access policy
* No data will be held locally on the remote device.
* None of the above removes the conditions set out elsewhere within the agreement (eg: who may carry out processing, and for what purpose)

Access is restricted to employees or agents of University of York who have authorisation from the Principal Investigator.

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

The NHS England data will not be linked with other patient record level data.

The data will be linked with national and/or publicly available datasets; these include, but are not limited to:
• National Cost Collection data (previously National Reference Costs data)
• ONS area level statistics (eg. indices of social deprivation)
• aggregated census and other geographical data using the LSOA (Lower Super Outputs Area) variables
• Quality and Outcomes Framework and the Attribution Data Set using GP codes;
• accounts and organisational-level data using provider codes
• health and social care provider data
• primary and secondary workforce data
• social care workforce data

CHE will run the data through the Healthcare Resource Group (HRG) grouper and attach National Cost Collection data (previously National Reference Cost data) using HRG codes and will link HES APC with MHMDS/MHLDS/MHSDS using the bridging file.

Linking NHS England data with other datasets on healthcare costs, quality indicators, indices of deprivation, primary and secondary workforce, etc, does enhance the NHS England data, making them more useful to answer specific research questions, for example, inequity in waiting time or access to healthcare services, and/or when evaluating policy reforms aimed, for example at increasing the efficiency of hospital provision.
Should the Centre for Health Economics wish to undertake a project involving a specific cohort of patients for which a data linkage is required, a separate application to NHS England and, subject to approval, a separate Data Sharing Agreement permitting the processing will be required.

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

Researchers from the Centre for Health Economics will process the data for the purposes described above.

For data from the Mental Health (MHSDS, MHLDDS, MHMDS) data sets, and any Mental Health data linked to HES, the following disclosure control rules must be applied:
• National-level figures only may be presented unrounded, without small number suppression Suppress all numbers between 0 and 5.
• Round all other numbers to the nearest 5.
• Percentages can be calculated based on unrounded values, but need to be rounded to the nearest integer in any outputs.
• In addition, for Learning Disability data in Mental Health (MHSDS, MHLDDS, MHMDS), the England-level data also must apply the suppression of all numbers between 0 and 5, and rounding of other numbers to the nearest 5.

From the date this Agreement takes effect, the following separate Agreements between NHS England and the University of York will be terminated:
• DARS-NIC-84254-J2G1Q


End of Life Care for Infants, Children and Young People: a mixed methods evaluation of current practice in England — DARS-NIC-682554-L6G6Q

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2023-07-17 — 2026-07-16

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

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:

Unfortunately, around 450 children and teenagers in England with cancer will require end of life care each year. Currently, the provision of this care varies across the country and little is known about how this variation impacts on children and their families. There are growing numbers of specialist palliative (end of life) care services and children’s hospices in the UK, but there is little evidence to tell us how these services should be developed and what their role should be in supporting children and young people at the end of life. There is very little known about the costs of care and how best to use these limited resources to improve care for these children and their families.

There are studies from North America which have shown that many children with cancer receive high intensity treatments (for example intensive care, IV chemotherapy) very close to the end of their life but whilst palliative care specialists are involved in their care they receive less of these treatments and have more choice on place of care. There have been no recent studies of end of life care for children, teenagers and young adults in the UK.

The University of York requires access to NHS England data for the purpose of the following research project: End of Life Care for Infants, Children and Young People: a mixed methods evaluation of current practice in England.

The following is a summary of the aims of the research project provided by or on behalf of the University of York:

In this study the University of York wish to describe the use of high intensity treatments in children who have died from cancer and assess whether this varies according to the model of end of life care that was available in their treating service.

The University of York requests NHS England data for the purposes of a study to use data collected as part of clinical care, from around 4000 children, teenagers and young adults treated in cancer services in England who died between 2012-2020.

Using routinely collected data sources the University of York will assess whether the use of ‘high intensity’ treatments in children, teenagers or young adults who have died from cancer varies depending on the model of End of Life care that their service delivered.

The objectives of this study are:
1. Compile a comprehensive dataset of the hospital use, treatments and death records of all children, teenagers and young adults who have died from cancer in England from 2012-2020.
2. Describe the use of high intensity treatments and assess any change over time in the use of these ‘high intensity’ treatments
3. Assess whether the use of these ‘high intensity’ treatments varied depending on the model of End of Life care used by the treatment centre (using models identified from WS1 of main study).
4. Assess whether the use of these ‘high intensity’ treatments varied depending on any clinical or demographic characteristics of the children or young person used by the treatment centre.
5. Assess the resource use of the different models of End of Life care in children with cancer.

The following NHS England data will be accessed:
• Hospital Episode Statistics
- (NDRS) Admitted Patient Care
- (NDRS) Accident & Emergency
- (NDRS) Outpatients
• (NDRS) Cancer Registration
• (NDRS) Systemic Anti-Cancer Therapy Dataset (SACT)
• (NDRS) National Radiotherapy Dataset (RTDS)

The level of the data will be:

• Identifiable data received to NHS England by The Paediatric Intensive Care Audit Network (PICANet) & The Intensive Care National Audit Research Centre (ICNARC) – necessary solely for data linkage purposes by the National Cancer Registration Service (NCRAS) data production team.

• Pseudonymised data disseminated to the University of York from the NCRAS team at NHS England—for the purposes of data processing to fulfil the study objectives.

The data will be minimised and limited to data for a study cohort identified as per below.

• Patient age is between 0-25 who have died between 01 January 2012 to 31 December 2020 with a diagnosis of cancer in England.

For each individual patient, data will only be provided from the cancer start date [Diagnoses must have occurred from 01/01/1988 to 31/12/2020] and until Date of Death.

The quantum of data requested is the minimum necessary and could not be further reduced without impacting the ability to achieve the stated aims. The justification for the data products requested is that the study team need to acquire data which will provide the necessary information to quantify and assess the granular level of care this population received before death. This includes detail of hospital use, treatments and death records.

The University of York is only requesting data necessary to ensure that the aims of the study are achieved and to allow for comparability with previous scientific evidence. The research team at the University of York will only have access to pseudonymised data. The University of York is requesting data restricted to the minimum number of years required 01 January 2012 to 31 December 2020. Under 25’s receiving end of life care for a diagnosis of cancer is a relatively small sample size, as such a period of 8 years is indicated in order for the study team to capture a large enough cohort and to assess the change in care over time. There are no less intrusive ways to achieving the purpose of this project
The scope of the project and minimisation was peer reviewed by the National Institute of Health Research (NIHR) and subsequently reviewed by The Confidentiality Advisory Group (CAG) and Health Research Authority (HRA).

The University of York is the sponsor, sole data controller and responsible for ensuring that the data will only be processed for the purpose described above. The project is being funded by a grant provided by the NIHR Health Services and Delivery. Although NIHR is the study funder, NIHR will not carry out any data controllership activities.

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

The University of York is recognised as a public authority per Schedule 1 of the Freedom of Information Act 2000. Power is conferred upon the University of York by the University's Royal Charter "to advance learning and knowledge by teaching and research, and to enable students to obtain the advantages of University education."

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

In the University's capacity as a public authority, the research undertaken will be in the public interest as the outcomes of this study will quantify the effects of current healthcare practices, identify the impact on those affected and have the potential to improve healthcare by aiding service planning and provision.

The funding is provided by NIHR Health Services and Delivery. The funding is specifically for the project described. Funding is in place until Dec 31st 2024.

The University of York is the sole data processor. The University of York provide IT support for the purposes of data processing.

Co investigators from other organisations are involved and partly responsible for the delivery of the study. These include individuals from the University of Leeds, Banger University, Cardiff University Hospital, Bradford Royal Infirmary and Manchester University Hospital. The co-investigators will not be accessing or processing the NHS England data. The contract and responsibility of delivering the study is the University of York and they are sole data controllers.

12 paediatric oncology and haematology experts (consultants and nurses) were consulted in an advisory capacity to identify the definitions of high intensity care via a zoom event.

Organisations represented as part of the study steering committee include, NHS England, The Paediatric Intensive Care Society, Association for Paediatric Palliative Medicine and Together for Short Lives.

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

A parent advisory group was consulted regarding the collection of the data for the purposes described above. 6 parents as part of the parent advisory panel contributed to the design, management and dissemination of the overall study. Additionally, one of the co-investigators on this study is a bereaved parent.
The study design and use of patient identifiable data without consent was discussed with four bereaved parents of children who had cancer, these parents were from the Martin House Research Centre Family Advisory Board (www.york.ac.uk/mhrc) and PORT (Paediatric Oncology Reference Team). PORT is a team of parents in the UK who have direct experience of children's cancer. These parents were all supportive of the use of their child’s data without consent for linkage of these datasets.

“yes it is acceptable, no parent/carer would object a study that would lead to any service improvements. Personally, I would not want to be contacted now to request permission to use my child's data.”

“for me, it comes down to a question of whether the research needs to be done and, if so, what is the least invasive/ least amount of data sharing that can be done in order to do it. So, as someone whose child has died from cancer, I think that this research is important and so, for me, what option would be most acceptable for me and other people in this situation, given that (child’s name) data would be in this. I think that what you have suggested - that the data is identifiable only in order to link the datasets, sits ok with me. I would be happy with that. To me, it seems that you have tried to find a way of sharing as little data as possible with as few people as possible and I think that it is ok to do that without consent, because the research team won't have the identifiable data. If they did, I would want to be asked for consent.”

These parents also reviewed the text for the website and suggested some changes which have been made by the research team. There are existing opt out systems for the individual datasets used in this study. These opt outs will be adhered to for this study. A study specific opt out will be advertised via the University of York and Children Cancer and Leukaemia Group (CCLG) websites for 6 weeks prior to data extraction directing the parents to contact NHS England.

Expected Benefits:

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

The services provided to clients are expected to identify improvement opportunities which the client may then exploit by making changes to systems, processes, resources or infrastructure in order to improve patient experience and patient care.

Overall, the results from this study will hopefully yield new knowledge about inequalities in access. These results will feed into the revised NICE guidelines for End of Life (EoL) care for the study population and shape delivery of EoL care in order to utilise finite resources to maximise impact. This will ensure that there can be genuine progress in the ability of researchers, decision makers, the children and their families to contribute to an understanding of how we can ensure the limited funding for EoL care can be used for greatest benefit for the children at the end of their lives.

The study is expected to lead to recommendations that have an impact on the following areas:

1. Impact on children and families
The study will describe the use of high intensity treatments, assess, and changes over time in the use of these ‘high intensity’ treatments. It will also assess whether the use of these ‘high intensity’ treatments varied depending on any clinical or demographic characteristics of the children or young person used by the treatment centre. This knowledge is essential for recognising children and family needs, which has the potential to support healthcare provision. Ultimately, it may help to improve the quality of life for the children and their families, in particular the groups identified as receiving more high intensity treatment at EoL.

2. Impact on healthcare services and palliative care teams
Currently, the provision of this care varies across the country and little is known about how this variation impacts on children and their families. There are growing numbers of specialist palliative (end of life) care services and children’s hospices in the UK, but there is little evidence to tell us how these services should be developed and what their role should be in supporting children and young people at the end of life. By assessing whether the use of ‘high intensity’ treatments varied depending on the model of End of Life care the study will be able to provide better understanding on this topic.

Results will be valuable to inform service provision and planning, as it will provide better understanding of the population who may benefit from the services and the impact of the model of care received in their EoL.

3. Impact on commissioning: service and economic implications for the NHS
The hospital based paediatric palliative care services are NHS funded, with the majority of children's hospice services being provided by the voluntary sector. The data provided from this project hopes to ensure that the future provision of and planning of both NHS and voluntary sector services is based on robust data.

By assessing the resource use of the different models of End of Life care in children with cancer the study will facilitate understanding of what budget is needed to offer different forms of care, and importantly the role of inequality. Also, it will allow us to understand the likely benefits of additional funding in EoL care in terms of patients’ outcomes for the first time, facilitating a clear indication to budget setters.

Outputs:

The expected outputs of the processing will be:
• Submissions to peer reviewed journals such as Archives of Disease in Childhood, BMJ Supportive & Palliative Care, Palliative Medicine [approx. 12 months after receipt of data].
• Presentations at World Research Congress of European Association for Palliative Care; European Congress on Paediatric Palliative Care; Health Services Research UK Conference.
• Publication of reports on the Martin House Research Centre (MHRC) website (https://www.york.ac.uk/healthsciences/research/public-health/projects/martinhouse).

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.

All outputs will be aggregated data with small numbers suppressed, in line with the HES analysis guide. Conference and journal outputs hope to be available to clinicians, academics and members of the public.

The outputs will be communicated to relevant recipients through the following dissemination channels:
• Open-source frameworks such as being published on online webpages open to all,
• Email alerts to the key stakeholders,
• Briefing documents provided to the clinical leads in all the paediatric oncology centres, in the UK,
• Social media,
• Zoom-meetings,
• Journals,
• Public events such as scientific conferences.
All the study outputs will be available via the study website and via links from other websites. As well as email alerts to the key stakeholders a copy of the research briefing will be sent to the clinical leads in all the paediatric oncology centres, in the UK.

There may also be communication via emails, social media (@UoYmhrc) and/or virtual Zoom meetings.

The study team have identified the following local, national and international priority audiences for this study:
1. Parents
2. Clinicians
3. Healthcare managers and commissioners
4. Clinical membership bodies

The key influencers are:
1. Royal College of Paediatrics and Child Health
2. Paediatric Intensive Care Society
3. British Association of Perinatal Medicine
4. Association for Paediatric Palliative Medicine
5. Together for Short Lives

The key decision makers for this clinical area are NHS England and NICE (both part of the study steering committee).
The communication channels described in the following section will be used to update progress on the whole study and to disseminate the expected research outputs which will all be available to download from the study website (based on overall project results):

1. Logic model of EoL care for children.
2. Infographic representation of the typology of models of EoL care for children.
3. Research briefing for clinicians, setting out key findings and implications for practice and training+ animation.
4. Recommendations on future routine data collection.
5. Summary for commissioners provided to each ICS/STP.
6. Summary of findings for parents for distribution via parent facing organisations e.g. Together for Short Lives.
7. Estimates of the cost of EoL care in a paediatric population produced in this work will be submitted to the PSSRU Unit Cost of Health and Social Care Volume.
8. The wider clinical and academic audiences will be reached via conference presentations and academic articles.
9. Final report for the HS and DR journal.
10. Minimum of six journal papers (open-access).


The outputs are expected from early 2024. Final outputs will be towards the end 2024 – the peer review is expected to take up to 12 months so that is 3 years in total.

Processing:

This study involves data linkage with PICANet (Paediatric Intensive Care Audit Network) and ICNARC (Intensive Care National Audit & Research Centre) data to achieve its objectives.

PICANet and ICNARC will transfer identifiable intensive care data of both living and deceased children (no clinical data) to NHS England. The data will consist of identifying details (specifically NHS Number, Name, Date of Birth, Postcode, Gender, unique study ID, PID number) to be linked with NHS England data. This flow of identifiable data has approval from CAG.

NHS England will link the data received from PICANet and ICNARC to the relevant Cohort records. NHS England will then only flow back a Unique Study ID and a PICANet or ICNARC serial number. The data flowing back to PICANet and ICNARC from NHS England will contain no direct identifying data items nor will it contain any NHSE data. PICANet and ICNARC will then send their intensive care data containing/comprised of clinical and demographic data and the unique study ID via secure transfer to the University of York.

NHS England will identify the main study cohort using the inclusion criteria from the HES, SACT, RTDS and Cancer Registry. NHS England will generate a unique study ID. A pseudonymised linked dataset will be transferred via a secure electronic file transfer system (SEFT) to the University of York. The data will contain no direct identifying data items but will contain the Unique Study ID which can be used by the study team at the University of York to link the NCRAS data with the record level intensive care data (PICANet and ICNARC) held by the recipient.

The data will be stored on servers at the University of York Department of Health Sciences. No data is cached locally on end user devices.

No data is backed up off site at other locations.

This data sharing agreement permits the University of York to access NHS England data for 3 years. Any subsequent amendments or extensions will be subject to a new application. At the end of the study the University of York will apply to NHS England to seek approval for the data to be archived for 10 years. Data will be stored in the University of York in accordance with GDPR and the University of York guidelines. At the end of the default retention period (10 years) NHS England will issue a data destruction certificate where all data will be confidentially destroyed by a secure method.

The data will be accessed onsite at the premises of the University of York and by authorised personnel via remote access in line with NHS England's Remote access policy. The data will remain on the servers at the University of York at all times.

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

Employees or agents of the University of York are only permitted to access pseudonymised data including information derived from NHS England data. Such datasets will adhere to the relevant suppression rules.

PICANet and ICNARC will only flow identifiers from their intensive care datasets to NHS England and receive only a PID and study ID back from NHS England. PICANet and ICNARC are not permitted to access the pseudonymised record level study cohort derived from NHS England disseminated to the University Of York.

NIHR Health Services and Delivery (funder) and the institutions listed as key influencers are not permitted to access the data.

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

The data provided to the University of York will be combined with data derived from the NHS England datasets provided to PICANet and ICNARC.

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 the University of York will process the data for the purposes described above.

Once data linkage has been undertaken an assessment of data quality and completeness will be conducted for all the key clinical and demographic variables of interest. Data conflicts will be solved using
1) Qualitative assessment based on demographic data - removal of non-reliable/inconsistent data;
2) Count of missing data,
3) Use the most common recorded value (either exact or nearest for dates).
An assessment of missing data will be undertaken once the data are linked and multiple imputation using chained equations will be used where appropriate. If imputed datasets are used, then a sensitivity analysis comparing complete case and imputed analyses will be undertaken.

Derivation of Key variables:
Some of the key demographic variables will be obtained by combining different data sources e.g. ethnic group, deprivation score. In this situation if any conflict between data sources occurs, the study team will assign the most commonly recorded ethnic group (census 2011 categories) assuming that is not ‘unknown’. The study team will use standard small number suppression rules when publishing data (no cell size <10).

Descriptive Statistics:
Appropriate summary statistics, e.g. frequencies and proportions for categorical variables and mean (with standard deviation) or median (with interquartile range) for continuous variables will be produced for all the key variables to describe any variation.

Demographic (age, sex, ethnicity, deprivation score), and treatment information (geographical, hospital/trusts) data will be used for describing sample (objectives 2 and 4) and independent variables in the regression models (objective 3).

Primary outcome: any one of the following high intensity treatments: intravenous chemotherapy < 14 days from death (yes/no); more than one emergency department visit (yes/no); and more than one hospitalization or intensive care unit admission < 30 days from death (yes/no) (21).

Secondary outcomes: mechanical ventilation < 14 days from death, place of death (hospital, home, hospice, other) and any additional data items identified in the stakeholder workshop (see below) and considering the most updated definitions used in the scientific literature. The additional items identified in the stakeholder workshop were: Radiotherapy in last 14/30 days; any ICU admission in the last 30 days of life; >1 hospital admission in the last 30 days of life; HSCT in the last 100 days of life; >1 ED visit in the last 14 days of life; Oral or IV chemotherapy in the last 14 days of life; Mechanical ventilation in last 14 days of life; Hospital death.
Sensitivity analyses are planned to assess potential differences on the definition of the outcomes.

Analyses will evaluate and compare outcomes used in different End of Life care models (identified in WS1 of the study) using appropriate regression models. Each analysis will account for the multiple confounding factors in this population (age, underlying diagnoses, comorbidities, outpatient attendance, socioeconomic status (Index of multiple deprivation) identified using causal inference methods.

The health economic analyses will embed the estimation of the resource use of each package of care into the regression analyses of the retrospective data. The findings of these regressions will be used to inform a full costings analysis by combining with estimates of the unit costs of each resource use element. This analysis will explore the variation in the cost of the End of Life care models through extensive sensitivity and scenario analyses.


Modelling Healthcare-Evidence Responsive Behaviours (HERBs) in Doctors: A proof of concept study — DARS-NIC-205466-T2F7N

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: Other-The Medical Act 1983

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-05-01 — 2022-04-30

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

No data is to be disseminated under this version of the agreement.

The project aims to model how responsive different graduate groups of consultants are to the publication of new guidelines regarding the use of drug-eluting stents in percutaneous coronary intervention. It acts as a first step towards modelling actual clinical behaviour of consultants.

Drug-eluting stents were introduced in 2003 as an alternative to bare metal stents. Evidence emerged in 2006 regarding the potential harm of drug-eluting stents in some high risk patients. A second generation of drug-eluting stents were subsequently developed. Guidelines released in 2010 supported the use of drug-eluting stents in most patients, and guidelines released in 2014 recommended their use in nearly all patients.

University of York/Generl Medical Council aim to investigate how the use of drug-eluting stents changed in relation to the emergence of evidence against and subsequently in favour of their use. As such, the request is for data on all finished consultant episodes relating to percutaneous transluminal balloon angioplasty and insertion of stent into coronary artery' from 2006/2007 to 2016/2017 inclusive. 4 character information is needed on the procedure carried out to identify the type of stenting procedure performed. As it is not possible differentiate between drug-eluting stents and others using OPCS 4.2 codes, the sample is restricted to 2006 onwards when OPCS4.3 codings were introduced. Other data requested are thought to be potential confounding variables and as such need to be controlled for in the statistical modelling procedure.

This HES data extract will be linked to data held by the General Medical Council on the characteristics of doctors (the List of Registered Medical Practitioners), and then psuedoanonymised. From this combined dataset it will be possible to determine which consultants graduated in the UK, and which graduated outside of the UK. Modelling of this combined data set will allow us to answer the research question.

The request is funded by the University of York Research Priming Fund, 2018/19 round. The outputs of this work will feed into larger programmes of work.

The General Medical Council (GMC) will act as data controllers and data processors for this application. The GMC will receive the HES extract. The University of York is paying the GMC to link the HES extract to data held by the GMC. The data will then be psuedonanynomised and placed in the Health Informatics Centre safehaven hosted by the University of Dundee. As such, University of York researchers will not have access to identifiable consultant data. See section 5b for further details.

Expected Benefits:

No data will be disseminated under this version of the agreement.

The UK still heavily relies on overseas trained doctors and the impact of ‘Brexit’ will almost certainly impact the numbers of EEA trained doctors practising, which have already dropped by 6% over the last 5 years. Whilst there are plans to expand UK medical schools this won’t influence consultant numbers for over a decade. Thus, there are implications for workforce recruitment as well as medical regulation and education. Currently it is not known if clinical behaviour differs between different groups of doctors. This study will begin to investigate this. If differences are found, this study will lay foundations for a programme of research aimed at reducing unwarranted variation in medical practice, a topic of interest to the NHS via the 'Getting It Right First Time' (GIRFT) Programme.

Outputs:

No data will be disseminated under this version of the agreement.

This work is expected to produce one high-impact, open-access, peer-reviewed publication. Target journals include the BMJ or BMC Medicine.Target date for submission is Q3 2020. It is anticipated that publication of this work will be accompanied by a press release and social media activity.

University of York/GMC also anticipate significant findings will be presented at a number of local, national and international conferences and presentations.

Alongside coefficients from statistical models, outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide.

Significant research findings will be disseminated directly to relevant stakeholders (e.g. the GMC) to inform policy decisions going forwards. Findings will also be used to develop future funding proposals.

Processing:

No data is to be disseminated under this version of the data sharing agreement.

The University of York, in collaboration with the GMC, have determined the purpose and means of processing the data. The GMC will receive the initial HES data extract. This extract will be identifiable record level data, containing consultant code (i.e. GMC number) for each episode. Only the GMC will have access to this data, which will be stored in the secure servers of the GMC. Only basic patient level data will be requested. Access to the data at the GMC will be determined by job role. Only data analysts that work on the UK Medical Education Database (UKMED) will have access to the data. This is currently restricted to 4 people. The GMC will link the HES data extract to the List of Registered Medical Practitioners using the consultants GMC number. The linked data will then be pseudoanonymised by the GMC. If necessary, the data will be blunted to ensure non-identifiability.

The pseudoanonymised data will then be placed in the Health Informatics Centre (HIC) Safehaven hosted by the University of Dundee. The research team at the University of York will then be granted access to the pseudoanonymised data set remotely (see https://www.dundee.ac.uk/hic/hicsafehaven/) via the VMHorizon Secure Remote link. The University of York will not hold the data.

Statistical modelling will then be carried out in order to answer the research objectives outlined in the previous section. Only summary statistics and coefficients from statistical modelling can be extracted from the safehaven. No individual data are permitted to leave the safehaven.

The data processing will be performed under Article 6, section (e) of the GDPR. The Medical Act 1983 provides the legal basis for this flow of data. The GMC does not having specific data sharing powers in relation to research but relies on its broad statutory functions under the Medical Act 1983. These functions give the GMC the ability to use personal data for research purposes where it is proportionate to do so. The data which the GMC obtains for research are not used for other purposes within the organisation (for example, as part of fitness to practise investigations into individual practitioners). The data is only released to researchers in anonymised or pseudonymised research extracts, through a Safe Haven which prevents unauthorised identification or publication.

The GMC has the following functions and powers under the Medical Act:
Section 1 establishes the GMC’s overarching objective:
(1A) The over-arching objective of the General Council in exercising their functions is the protection of the public.

(1B) The pursuit by the General Council of their over-arching objective involves the pursuit of the following objectives—
(a) to protect, promote and maintain the health, safety and well-being of the public,
(b) to promote and maintain public confidence in the medical profession, and
(c) to promote and maintain proper professional standards and conduct for members of that profession.

Section 5 establishes functions related to undergraduate medical training:
(1) The General Council shall have the general function of promoting high standards of medical education and co-ordinating all stages of medical education.
(2) For the purpose of discharging that function the General Council shall -
(a) determine the extent of the knowledge and skill which is to be required for the granting of primary United Kingdom qualifications and secure that the instruction given in or under the direction of bodies or combinations of bodies in the United Kingdom to persons studying for such qualifications is sufficient to equip them with knowledge and skill of that extent;
(b) determine the standard of proficiency which is to be required from candidates at qualifying examinations and secure the maintenance of that standard;

Section 34H establishes functions relating to postgraduate medical training:
(1) The General Council shall—
(a) establish standards of, and requirements relating to, postgraduate medical education and training, including those necessary for the award of a CCT in general practice and in each recognised specialty; (b) secure the maintenance of the standards and requirements established under paragraph (a); and (c) develop and promote postgraduate medical education and training in the United Kingdom.
(2) In exercising their functions under this Part, the main objectives of the General Council, in addition to the over-arching objective, are—
(a) to ensure that the needs of persons undertaking postgraduate medical education and training in each of England, Wales, Scotland and Northern Ireland are met by the standards the General Council establish under subsection (1)(a) and to have proper regard to the differing considerations applying to different groups of such persons; and (b) to ensure that the needs of employers and those engaging the services of general practitioners and specialists within the UK health services are met by the standards the General Council establish under subsection (1)(a).

Schedule 1 part II allows the GMC to carry out broad activities conducive to its functions, and permits collaboration with related bodies:
9. It shall be within the capacity of the General Council as a corporation to do such things and enter into such transactions as are in their opinion incidental or conducive to the performance of their functions under this Act, including the borrowing of money.
9A (1) In exercising their functions, the General Council shall…

…(b) co-operate, in so far as is appropriate and reasonably practicable, with public bodies or other persons concerned with -
(i) the employment (whether or not under a contract of service) of provisionally or fully registered medical practitioners,
(ii) the education or training of medical practitioners or other health care professionals,
(iii) the regulation of, or the co-ordination of the regulation of, other health or social care professionals,
(iv) the regulation of health services, and
(v) the provision, supervision or management of health services.

The research proposal aims to address important questions about the links between medical training and clinical practice, and therefore supports the GMC’s statutory functions and can be used in the context of policy development.


MR1126a - Yorkshire and Humberside Haematology Network (YHHN) — DARS-NIC-390749-C4P0X

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, Anonymised - ICO Code Compliant, Identifiable, No (Mixed, Consent (Reasonable Expectation))

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), Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (Academic)

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

When:DSA runs 2019-02-01 — 2020-09-30 2017.06 — 2023.09.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF YORK, HULL UNIVERSITY TEACHING HOSPITALS NHS TRUST, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Accident and Emergency
  5. Hospital Episode Statistics Outpatients
  6. MRIS - Members and Postings Report
  7. MRIS - Flagging Current Status Report
  8. Cancer Registration Data
  9. Civil Registration - Deaths
  10. Demographics
  11. MRIS - Cohort Event Notification Report
  12. Emergency Care Data Set (ECDS)
  13. HES-ID to MPS-ID HES Accident and Emergency
  14. HES-ID to MPS-ID HES Admitted Patient Care
  15. HES-ID to MPS-ID HES Outpatients
  16. Civil Registrations of Death
  17. Hospital Episode Statistics Accident and Emergency (HES A and E)
  18. Hospital Episode Statistics Admitted Patient Care (HES APC)
  19. Hospital Episode Statistics Critical Care (HES Critical Care)
  20. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The aim of the study is to:
1) examine the disease management of haematological cancers and benchmark treatment with local and national guidelines
2) examine how illness and healthcare patterns among patients with haematological cancers differ from those of who do not have these cancers.

Patients with haematological cancers are from the Yorkshire and Humberside Haematology Network (YHHN). For clarity the cohort is referred to in this application as the YHHN cohort. It has been collected as part of the activities of the Haematological Malignancy Research Network (HMRN) www.hmrn.org.

YHHN is a specialised population-based register recording and analysing data on all patients diagnosed with haematological cancers (leukaemias, lymphomas and myelomas) in the Yorkshire and Humberside area. Since its start in 2004, YHHN has registered over 25,000 patients with haematological malignancies.

To examine disease management and treatment across the full patient pathway, the University now wish to link secondary care and mortality data to supplement the disease and treatment data collected in YHHN. Comparisons between YHHN patients and persons who do not have a haematological cancer will also be made using an anonymised sample from the general population (Covered under a separate data sharing agreement NIC-06759 )

The proposal has undergone peer review and is funded by Bloodwise (Reference 06001) and Cancer Research UK (CRUK grant number C9474/A18362).

Yielded Benefits:

A major aim of YHHN is to improve care and outcomes for patients, and data from our patient cohort has already impacted on the delivery of patient care across the 14 hospitals that serve the catchment population. Importantly, the YHHN area is representative of the UK as a whole in terms of both demography and clinical practice; meaning that results are generalizable and are of potential importance to national commissioning of cancer care services. Indeed, published information from YHHN has been used in NICE appraisals, as well as in the development of national guidelines. Recent publications have examined how patients with haematological malignancies present to secondary care, resource utilization and cost of treating haematological malignancies and the impact of novel therapies on outcome (https://www.hmrn.org/publications/papers). These findings all have the potential to help inform policy and commissioning of services directly impacting on patient care.

Expected Benefits:

Population-based data on clinically meaningful haematological malignancy subtypes (>60 subtypes) are not available elsewhere (cancer registries have difficulty in accessing diagnostic information systematically and tend to group into 4 main categories that contain a mix of diseases). Furthermore, the YHHN area is representative of the UK in terms of both demography and clinical practice, meaning that results are highly generalizable and are of potential importance to the commissioning of cancer care services at a national level.

YHHN is uniquely placed to utilise up-to-date diagnostic and treatment data to conduct research on these complex cancers. By linking the patient cohort to HES, the registry will extend its population-based data to include antecedent and post-diagnostic events in the healthcare setting. The uses of HES data will be multifactorial; and will be used to examine a number of questions along the patient pathway, including aetiological factors, routes to diagnosis, as well as healthcare utilisation patterns & costings (before diagnosis, around the time of diagnosis, and onwards into the survivorship phase).

With respect to measurable benefits these will, in large part, result from the provision of good quality data/information (to clinicians, patients, and commissioners) that are currently lacking. For example, some patients with aggressive cancers (such as diffuse large B-cell lymphoma) can be ‘cured’ but once in this survivorship phase, little is known about their healthcare needs. Precursor conditions such as monoclonal gammopathy of uncertain significance (MGUS) and monoclonal B-cell lymphocytosis (MBL), which can progress to their more aggressive counterparts myeloma and chronic lymphocytic leukaemia, are also linked to other serious morbidities; MGUS with osteoporotic fractures and thrombotic disease and MBL most notably with infections.

The University will investigate these, and many other, associations in-depth across the entire patient pathway. The healthcare patterns of haematological cancer patients will be put into context with a population of similar ages from a sample of the general population to compare with YHHN. In this context, ‘real-world’ population-based data that includes all health service contacts are required not only to inform aetiological hypotheses and plan future healthcare services, but also to monitor the impact of future therapeutic changes in the general patient population. The target date for expected measurable benefits to healthcare will be by the end of December 2019.

Outputs:

Haematological oncology is one of the most rapidly evolving areas of cancer research; and ≥ 60 clinically meaningful diagnostic groups are currently recognized in the latest World Health Organization (WHO) classification. Comprehensive reliable population-based information about the underlying occurrence and survival of patients diagnosed with these cancers, and their associated health care usage is limited – and linked register/HSCIC data provide a valuable UK resource for clinicians, patients and researchers.

Thus far the university have published accurate population-based information on the survival and prevalence of haematological malignancies, classified for the first time into clinically meaningful diagnostic groups1,2. Mortality data has also been used in conjunction with diagnostic, demographic and treatment data to examine survival by socio-demographic factors. Importantly, in this regard it was found that patients with chronic myeloid leukaemia living in less affluent areas had poorer survival than those living in more affluent areas, despite the fact that all patients had equal access to the daily oral medication required to control the disease3. Additionally, as part of a joint project with the National Cancer Equality Initiative established by the Department of Health, the university examined whether older people with haematological cancer were being under-treated. Using linked data, the university was able to show that patients with aggressive, but potentially curable, non-Hodgkin lymphoma who were fit enough to receive intensive chemotherapy were treated with curative intent, and that chronological age was not a major determinant of the decision making process. Critically, by linking to mortality data we also demonstrated that older, fitter patients who were treated showed the same survival benefit compared to younger people4. Linked register/HSCIC data have also been used to examine the treatment pathways and financial costs of haematological cancers; one example being that of diffuse large B-cell lymphoma, where we estimated that it costs the NHS £88-92 million annually to treat this disease 5. Below is a sample of outputs produced from register/HSCIC linked data -

1. Smith, A. et al. Lymphoma incidence, survival and prevalence 2004–2014: sub-type analyses from the UK’s Haematological Malignancy Research Network. Br J Cancer (2015). doi:10.1038/bjc.2015.94
2. Roman, E. et al. Myeloid malignancies in the real-world: occurrence, progression and survival in the UK’s population-based Haematological Malignancy Research Network 2004-15. Cancer Epidemiology (2016). doi:10.1016/j.canep.2016.03.011
3. Smith, A. G. et al. Determinants of survival in patients with chronic myeloid leukaemia treated in the new era of oral therapy: findings from a UK population-based patient cohort. BMJ Open 4, e004266 (2014).
4. Smith, A. et al. Impact of age and socioeconomic status on treatment and survival from aggressive lymphoma: a UK population-based study of diffuse large B-cell lymphoma. Cancer Epidemiology (2015). doi:10.1016/j.canep.2015.08.015
5. Wang, H.-I. et al. Treatment cost and life expectancy of diffuse large B-cell lymphoma (DLBCL): a discrete event simulation model on a UK population-based observational cohort. Eur J Health Econ 1–13 (2016). doi:10.1007/s10198-016-0775-4

As part of the partnership with the NHS, the Epidemiology and Cancer Statistics Group routinely conduct clinical audits across the study area. These audits use YHHN data to examine disease management, benchmarking treatment with local and national guidelines. Audit reports are discussed at biannual Network Audit Meetings attended by lead clinicians from the 14 hospitals, as well as patient representatives. In addition, audit reports are available to all Network clinical staff via the members website.

With respect to research outputs, traditional publication routes (peer-reviewed, open access publications and conference presentations) and the HMRN website (www.hmrn.org ) will be used to disseminate findings to local, national and international practitioners and academics. The peer-reviewed journals targeted are likely to be similar to those that we have already published in: British Journal of Cancer, British Journal of Haematology, Blood, British Medical Journal Open, Cancer Epidemiology, Journal of Clinical Oncology, PLoS One, and Value in Health among many others. To ensure accessibility, all peer-reviewed reports will be published under creative commons attribution 4.0 licence (CC BY); support for this is included in all grant applications.

In addition, findings will be disseminated at conferences; those that are regularly attended include the National Cancer Intelligence Network (NCIN) meetings, and conferences run by the American and British Societies of Haematology (ASH & BSH), European Haematology Association (EHA), National Awareness and Early Detection Initiative (NAEDI), and the Palliative Care Congress.

Outputs utilising the requested data will begin soon after data receipt, and will continue for a minimum of five years.

All outputs will follow guidelines on disclosure control and will only contain aggregated data with small numbers suppressed.

Processing:

YHHN has registered over 25 thousand cases of haematological malignancy and has approval which has permitted linkage of this cohort to ONS Mortality, cancer and HES data.

The University are interested in the causes of these diseases, as well as investigating and monitoring the short-term and long-term health care activity of patients with these complex cancers.

YHHN was established in 2004 specifically to provide robust generalisable data to inform clinical practice and research. It is predicated on NHS infrastructures, and is a collaborative venture between University researchers and clinicians. All diagnoses, including disease progressions and transformations, are coded to the latest WHO classifications by clinical staff at a single integrated haematopathology laboratory that contains all of the technology and expertise required for diagnosis and on-going monitoring; and all patients have full treatment, response and outcome data collected to clinical trial standards.

Data will be stored and processed at the Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York. Data will be stored on a Microsoft SQL server running on a secure Windows Server and will only be accessible by staff within the Epidemiology and Cancer Statistics Group. No identifiable data will be shared with third parties.


MR1126b - Yorkshire and Humberside Haematology Network (YHHN) — DARS-NIC-346859-C9J6J

Type of data: information not disclosed for TRE projects

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

Legal basis: 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)

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2019-02-01 — 2023-09-30 2021.09 — 2023.08.

Access method: Ongoing, One-Off

Data-controller type: HULL UNIVERSITY TEACHING HOSPITALS NHS TRUST, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. Cancer Registration Data
  2. Civil Registration - Deaths
  3. Demographics
  4. Emergency Care Data Set (ECDS)
  5. Hospital Episode Statistics Accident and Emergency
  6. Hospital Episode Statistics Admitted Patient Care
  7. Hospital Episode Statistics Critical Care
  8. Hospital Episode Statistics Outpatients
  9. MRIS - Cause of Death Report
  10. MRIS - Cohort Event Notification Report
  11. MRIS - Flagging Current Status Report
  12. MRIS - Members and Postings Report
  13. Civil Registrations of Death
  14. Hospital Episode Statistics Accident and Emergency (HES A and E)
  15. Hospital Episode Statistics Admitted Patient Care (HES APC)
  16. Hospital Episode Statistics Critical Care (HES Critical Care)
  17. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

This agreement is for the purpose of maintaining and updating the Yorkshire and Humberside Haematology Network (YHHN). This agreement will cover the Section 251 cohort of the study who were deemed too ill to provide consent. The Confidentiality Advisory Group has agreed to provide partial support under Section 251 to enable research nurses access to data in order to identify relevant patients from whom to seek consent. This approval also covers data extraction for deceased patients and for those too ill to provide consent, but the Group felt that consent should be sought from the ‘hard to reach’ groups and so the approval is limited in this respect.

NIC-390749 covers the patient cohort covered by consent.

NIC-06759 provides a comparison cohort for the study. Cohort Sizes;

NIC 346859 this agreement cohort size=25,000, NIC 390749 cohort size, 18,000 NIC 06759 contains a population cohort totalling 181,263 by the end of the agreement.

The University of York is the sole Data Processor and the joint Data Controller with Hull University Teaching Hospitals NHS Trust. YHHN is a collaboration with a clinical network, and the work is commissioned by Hull University Teaching Hospitals NHS Trust. All YHHN’s activities are agreed and monitored by the Haematology Network’s Audit Committee (the Yorkshire & Humberside Haematology Network Audit Committee). Bloodwise (a charity) and Cancer Research UK funds YHHN but are not involved in the conduct of the research. Only the University of York have access to data under this agreement.

Hull University Teaching Hospitals NHS Trust have been involved from the outset of the project as a member of the clinical network that comprises YHHN. It's involvement was formalised in 2009 in a previous approved version of the application to the Central Register, for mortality data (MR1126), where the Trust confirmed that YHHN was being run on behalf of an NHS organisation for the purposes of “public health provision or the management of health services.

The legal basis for processing personal data under GDPR, is to perform a task in the public interest. This is covered under Article 6(1)(e); - University of York and Hull University Teaching Hospitals NHS Trust are both public bodies, and it is in the public interest that work is done into providing details on cancer treatments and mortality rates. The personal data requested under this agreement includes information about a participants' health; these are considered as special category data, and therefore the legal basis for processing these data is processing required for scientific research purposes which is covered under Article 9(2)(j) of the GDPR.

YHHN is a collaboration between researchers at the University of York, specifically, the Joint Haematology Network Site Specific Group for the West Yorkshire and Humber, Coast & Vale Clinical Alliances (formerly known as the Cancer Networks of Yorkshire and Humber & Yorkshire Coast). The work is commissioned by Hull University Teaching Hospitals. This agreement requests updates of death notifications and cancer registrations, and HES records up to the latest financial year available. The objective of the project remains the same; namely to facilitate a greater understanding of the causes of haematological cancers, as well as their impact on the future health and healthcare needs of those who develop them.

Population-based data on clinically meaningful haematological malignancy subtypes (>100 subtypes) are not available elsewhere (cancer registries have difficulty in accessing diagnostic information systematically and tend to group into four main categories that contain a mix of diseases). Furthermore, the YHHN area is representative of the United Kingdom in terms of both demography and clinical practice, meaning that results are highly generalizable and are of potential importance to the commissioning of cancer care services at a national level. YHHN is uniquely placed to utilise up-to-date diagnostic and treatment data to conduct research on these complex cancers. By linking the patient cohort to HES, the registry will extend its population-based data to include antecedent and post-diagnostic events in the healthcare setting. HES data will be used to examine a number of questions along the patient pathway, including aetiological factors, routes to diagnosis, as well as healthcare utilisation patterns & costings (before diagnosis, around the time of diagnosis, and onwards into the survivorship phase).

It is not possible to reduce the number of years requested, as YHHN are looking at antecedent events prior to diagnosis and all post-diagnostic events to answer several important research questions, including the identification of potential aetiological factors and examination of health care utilization patterns along the whole length of the patient pathway.

In terms of identifying factors that may be causally associated with the subsequent development of a haematological cancer, contributing exposures and events may occur many years in the past – this holds true for the majority of cancers (e.g. smoking and lung cancer). For example, YHHN recent publication examining the impact of previous rheumatological disorders on subsequent lymphoma and myeloma development, observed effects for diffuse large B-cell lymphoma 10-years prior to cancer diagnosis. Likewise, going forwards, many years may elapse before the adverse effects of cancer treatment (e.g. cardiac problems) become manifest.

Appropriate safeguards are in place including data minimisation comprising of pseudonymisation, and the use of anonymised data where possible. In terms of dissemination outputs will only contain aggregated data, with small numbers suppressed in line with the HES Analysis Guide.

The overall aim is to facilitate a greater understanding of the causes of haematological cancers, as well as their impact on the future health and healthcare needs of those who develop them. Examples of how this will be achieved are below:

In relation to policy development, mortality data are required in order to identify whether the survival rates observed in clinical trials are replicated in the general patient population. This is particularly important for haematological cancers, where toxicity issues mean that clinical trials are often restricted to specific patient groups; for example, younger patients without existing co-morbidities. Hence population-based death data on the general patient population (YHHN) are essential in order to identify the impact of new treatments on the service, and to assess the potential impact on the service, for example a change in mortality.

Haematological oncology is one of the fastest moving cancer fields, and treatments and clinical guidelines are subject to rapid change. In this context it is critically important to be able to monitor whether or not a change in policy is delivering the expected improvements in outcome (mortality/survival) across the patient population as a whole (YHHN).

To facilitate providers/clinicians understanding of whether their own activity falls within the expected range, “observed” practice and mortality frequencies need to be compared with those that are “expected” on the basis of general and/or best practice rates. Monitoring mortality and health activity is required in order to identify whether or not expected changes associated with alterations in service delivery actually occur; and to quantify such change(s) and their impact on the wider service.

Background of study

YHHN’s cohort of patients with haematological cancers was established in 2004 to provide accurate population-based data on clinically meaningful cancer subtypes to inform aetiological hypotheses and plan health-care services, and also to monitor the impact of therapeutic changes in the general patient population. Patients enter the cohort when they are first diagnosed, and their molecular diagnostic/prognostic data are linked to clinical information in NHS medical records (paper and electronic).

NHS Digital supply the University of York with linked data on inpatient and outpatient Hospital Episode Statistics (HES) mortality data and national cancer registrations. These data complement the information collected from medical records. The present application requests an update to the latest death and cancer registrations, and HES records up to latest financial year. The objective of the project remains the same; namely to facilitate a greater understanding of the causes of haematological cancers, as well as their impact on the future health and healthcare needs of those who develop them.

This project is linked to (Yorkshire and Humberside Haematology Network Register (YHHN) Comparison Cohort); within which each patient in MR1126 diagnosed between 01/01/2009 and 31/12/2015 had 10 sex and year of birth matched individuals selected from persons alive and registered with a general practice in the YHHN study region at the time of their diagnosis to compare against. Pseudonymised HES, mortality and cancer registration data are supplied for these subjects from NHS Digital to the University of York.

Since September 2004, all patient's resident in the study area newly diagnosed with a haematological neoplasm or precursor condition have been included (~2,200 per annum). Yorkshire and Humberside Haematology Network (YHHN) cohort was initiated at a time when cancer care in England was co-ordinated through a series of area-based Cancer Networks. YHHN’s catchment covers two such adjacent Cancer Networks: the Yorkshire Cancer Network and the Humber & Yorkshire Coast Cancer Network. Health geography changed in April 2013 when Cancer Networks were incorporated into Strategic Clinical Networks, but YHHN’s boundaries were not affected. The Data Controller requires data on Hospital Episode Statistics (HES) namely: HES Admitted Patient Care (HES-APC) from 1997/98; Outpatients (HES-OP) from 2003/04 until the latest financial year available. Accident and Emergency (HES-A&E) from 2007/08 until 19/20 M12. The Emergency Care Data Set data 20/21 annual refresh. In addition, mortality, and cancer registration. Data are returned from NHS Digital using unique YHHN study numbers; meaning the data are pseudonymised. In order to examine aetiological factors and routes to diagnosis YHHN require information prior to the date of diagnosis, and information up to the latest financial year are required to explore healthcare utilisation patterns & costings (before diagnosis, around the time of diagnosis, and onwards into the survivorship phase).

To be included in YHHN, subjects have to be resident in the study area at the time of diagnosis. However, national linkage is required in order to comprehensively map hospital activity and cancer occurrence before and after diagnosis. Likewise, national linkage to deaths is required in order to examine survival. YHHN confirm there is no alternative, less intrusive way of achieving the purpose.

Only the variables necessary to perform the analyses required to address the purpose are requested. The University of York upload patient’s surname, forename, sex, date of birth and NHS number, along with their YHHN unique study number to NHS Digital’s Data Exchange Service (DES) for matching and subsequent linkage. Data are returned using YHHN’s unique study number only, minimising the use of identifiable data. The University of York is the sole Data Processor and the joint Data Controller with Hull University Teaching Hospitals NHS Trust. As outlined above, YHHN is a collaboration with the clinical network, and the work is commissioned by Hull University Teaching Hospitals NHS Trust. All YHHN’s activities are agreed and monitored by the Haematology Network’s Audit Committee (the Yorkshire & Humberside Haematology Network Audit Committee). Bloodwise and Cancer Research UK funds YHHN but are not involved in the conduct of the research.

Yielded Benefits:

A major aim of YHHN is to improve care and outcomes for patients, and data from the YHHN patient cohort has already impacted on the delivery of patient care across the 14 hospitals that serve the catchment population. Importantly, the YHHN area is representative of the UK as a whole in terms of both demography and clinical practice, meaning that results are generalizable and used in Health Technology Assessments; enabling organisations, including the National Institute for Health & Care Excellence (NICE) and the Scottish Medicines Consortium, to make decisions about the efficacy and cost-effectiveness of drugs for haematological malignancies. These decisions impact directly on patients’ survival, quality of life, and wellbeing, as well as commissioning within the NHS. Thus far YHHN data have been used in 10 NICE submissions evaluating treatment options for myelofibrosis, myelodysplastic syndromes, acute myeloid leukaemia, chronic lymphocytic leukaemia, follicular lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma. Evidence underpinning such assessment (i.e. clinical management and outcome by subtype) are usually extracted either from data derived from the website, peer-reviewed publications, or from clinical audits. Indeed, NICE recognises that YHHN findings are representative of UK clinical practice and increasingly recommends that these data are used to underpin decision-making. One example is approval of CAR-T therapy (axicabtagene ciloleucel) for relapsed diffuse large B-cell lymphoma (DLBCL). In this case, the NICE committee recommended: “NHS or UK standard of care data from the Haematological Malignancy Research Network should be explored to produce plausible estimates of survival for people having salvage chemotherapy.” Haematological cancers are at the forefront of targeted-therapy development and use. Importantly, the granularity of YHHN data permit evaluation of outcomes in diagnoses with molecular characteristics. For example, in acute myeloid leukaemia (AML: a cancer only treatable with intensive chemotherapy, which most patients cannot tolerate), it is recognised that patients with an internal tandem duplication in the FLT3 gene (FLT3-ITD), rather than a mutation in the tyrosine kinase domain (TKD), have poorer outcomes. Midostaurin is a new therapy for patients with FLT3-ITD and was recently considered for approval in the NHS by NICE. However, the main evidence presented originated from a phase 3 trial, which only included patients aged 18-60 years, although the median age of AML diagnosis is 72 years. To facilitate decision-making, the NICE committee requested that YHHN’s real-world data was used to characterise the general AML patient population by FLT-3 status and examine associated outcomes. YHHN was established to provide long-term, robust infrastructure, within which to generate evidence to inform and improve clinical practice, locally and national. This has undoubtedly been achieved; moreover, it has been accompanied by growing recognition of the study’s importance, relevance and uniqueness. Nationally, findings from YHHN-based work on routes-to-diagnosis of myeloma have been used by GatewayC (an online cancer education platform: https://www.gatewayc.org.uk/), in conjunction with CRUK, to develop training resources to promote early diagnosis among GPs and other primary care staff. YHHN’s published findings are also being used internationally, via online clinician education resources; for example: https://www.uptodate.com/contents/initial-treatment-of-mantle-cell-lymphoma. Furthermore, YHHN’s descriptive data are routinely incorporated into national cancer statistics and guidelines, as well as patient information leaflets and information produced by national charities (e.g. Cancer Research UK, Lymphoma Action, Bloodwise). The National Cancer Intelligence Network (NCIN), for example, commissioned YHHN to evaluate the quality of ascertainment of haematological cancers in English Cancer Registries. National rates were compared to those predicted from YHHN data. As a consequence of this report, information on incidence and outcome are now being presented by clinically meaningful groups. These data have been used by national organizations as a benchmark against which to evaluate the quality of their information gathering, and cancer commissioning services. Whilst substantial improvements in national cancer registration data for haematological cancers has taken place over the last 5 years, data on incidence and survival for some diagnoses are still not available. Accordingly, YHHN data (website: www.hmrn.org; and peer-reviewed publications) were used in current NICE guidance (2015: Haematological Cancers: Improving Outcomes) to ensure accurate and clinically meaningful descriptions of cases newly diagnosed each year, and survival.

Expected Benefits:

Population-based data on clinically meaningful haematological malignancy subtypes (>100 subtypes) are not available elsewhere (cancer registries have difficulty in accessing diagnostic information systematically and tend to group into 4 main categories that contain a mix of diseases). Furthermore, the YHHN area is representative of the UK in terms of both demography and clinical practice, meaning that results are highly generalizable and are of potential importance to the commissioning of cancer care services at a national level.

YHHN is uniquely placed to utilise up-to-date diagnostic and treatment data to conduct research on these complex cancers. By linking the patient cohort to HES, the registry will extend its population-based data to include antecedent and post-diagnostic events in the healthcare setting. The uses of HES data will be multifactorial; and will be used to examine a number of questions along the patient pathway, including aetiological (causing or contributing to the development of a disease or condition) factors, routes to diagnosis, as well as healthcare utilisation patterns & costings (before diagnosis, around the time of diagnosis, and onwards into the survivorship phase).

With respect to measurable benefits, these will, in large part, result from the provision of good quality data/information (to clinicians, patients, and commissioners) that are currently lacking.

For example, some patients with aggressive cancers (such as diffuse large B-cell lymphoma) can be ‘cured’ but once in this survivorship phase, little is known about their healthcare needs. Precursor conditions such as monoclonal gammopathy of uncertain significance (MGUS) and monoclonal B-cell lymphocytosis (MBL), which can progress to their more aggressive counterparts myeloma and chronic lymphocytic leukaemia, are also linked to other serious morbidities; MGUS with osteoporotic fractures and MBL most notably with infections. The University will investigate these, and many other, associations in-depth across the entire patient pathway. The healthcare patterns of haematological cancer patients will be put into context with a population of similar ages from a sample of the general population to compare with YHHN.

In this context, ‘real-world’ population-based data that includes all health service contacts are required not only to inform aetiological hypotheses and plan future healthcare services, but also to monitor the impact of future therapeutic changes in the general patient population.

Outputs:

Haematological oncology is one of the most rapidly evolving areas of cancer research; and more than 100 clinically meaningful diagnostic groups are currently recognized in the latest World Health Organization (WHO) classification. Comprehensive reliable population-based information about the underlying occurrence and survival of patients diagnosed with these cancers, and their associated healthcare usage is, however, limited - and YHHN’s linked register/NHS Digital data provide a valuable UK resource for clinicians, patients and researchers.

Thus far, data have been used to provide much needed information on mortality and survival for clinically meaningful cancer subtypes, and several peer reviewed papers and reports have been published. In addition to providing much needed baseline descriptive data, linked data have been used to tackle important topics relating to potential variations in survival with socio-economic status, mode of presentation, and age at diagnosis.

YHHN linked HES/Civil Registration (deaths) data have also been used historically to examine health economic issues requiring “real-world” information that cannot be obtained from clinical trials; for example, the cost of treatment across the whole patient pathway has been scaled to estimate national figures for several haematological cancers (including acute myeloid leukaemia and diffuse large B-cell lymphoma).

All published papers and reports, along with conference presentations, are available on the Haematological Malignancy Research Networks website (www.HMRN.org) – the umbrella Network under which YHHN sits. All peer reviewed articles are published under creative commons attribution 4.0 licence (CC BY) in well-respected journals; this far including the following:
• British Journal of Cancer,
• British Journal of Haematology,
• Blood, British Medical Journal Open,
• British Medical Journal Supportive & Palliative Care,
• Cancer Epidemiology,
• European Journal of Cancer,
• Journal of Clinical Oncology,
• PLoS One,
• Value in Health.

With respect to wider dissemination, the production and distribution of good quality descriptive information is a core YHHN objective and, in addition to feeding into reports and presentations, YHHN data underpin the statistics section of the HMRN website (www.hmrn.org/statistics); providing scalable up-to-date information on incidence, prevalence and relative survival for researchers, clinicians and patients, selection tools allowing users to pick specific disorders, and stratify by age and sex.

In addition, findings are, and will continue to be, regularly disseminated at conferences including the Public Health for England meetings, British Society of Haematology (BSH), American Society of Haematology (ASH), European Haematology Association (EHA), National Awareness and Early Detection Initiative (NAEDI), International Society for Pharmacoeconomics and Outcomes Research (ISPOR), and the Palliative Care Congress.

YHHN is also associated with an active patient partnership (https://yhhn.org/partnership ), and findings are regularly presented and discussed at a wide range of patient forums.

YHHN benefits from an established Patient Partnership (https://yhhn.org/partnership), which was established by us in 2009. Patients and carers can join the Partnership at any time, provide feedback about their experiences and become involved in YHHN research activities. The partnership currently comprises over 800 patients, all of whom have agreed to various degrees of involvement including completing questionnaires, reviewing YHHN literature and taking part in focus group discussions. The partnership has a Steering Group, comprised of YHHN patients and carers, local cancer user group leads, a clinical nurse specialist (and haematology user group lead), consultant haematologist and researchers.

The Steering Group meets at regular intervals to tackle any arising matters and discuss new studies, as well as the dissemination of recent findings and future research directions; its members are fully involved in YHHN and in the development of further collaborations/research projects and are currently designing a newsletter to send to members of the partnership to update them of YHHN research activities. This newsletter will be sent out once the University of York are receiving monthly flows of Mortality Data to ensure that the newsletter is not sent to anyone that is deceased and to avoid causing distress to the families concerned.

One example of a project where user involvement has been instrumental is the National Institute for Health Research (NIHR) funded project “facilitating patient choice in haemato-oncology”, which is predicated on the YHHN Register. This project was developed following discussions at patient focus groups; where concern was repeatedly expressed about the paucity of information available to assist patients in making decisions about their disease management. The project commenced in 2016, and users have played an active role in steering the project, both as applicants and as independent members of the steering committee.

To ensure accessibility, all reports will be published under creative commons attribution 4.0 licence (CC BY); support for this is included in all of the grant applications and the study’s websites will provide links to these open access publications, conference proceedings and copies of reports summarizing the findings. These outputs may be promoted through the study’s Twitter account (@HMRN_UK (190 followers)) and via YHHN's funders (@bloodwise_uk (28,800 followers), @CR_UK (310,000 followers)). Lay summaries of the findings will be provided on the YHHN patient/public website (www.YHHN.org) and may be presented at the study’s local user groups and in the newsletter. YHHN also engages with national charities who publish study findings on websites and in their magazines, and regularly invite researchers to present findings from the study at their meetings.

YHHN currently receives support from a variety of peer-reviewed sources. Core funding comes from a Bloodwise (formerly Leukaemia, Lymphoma Research – LLR) programme grant (April 2016 - Mar 2021; ref 15037; the epidemiology of haematological malignancies: determinants, prognostics, treatment and survivorship).

In addition, linked data from YHHN also form part of an NIHR program grant for applied research (Dec 2015-Nov 2019; ref RP-PG-0613-20002; facilitating informed decision-making in haemato-oncology) and a CRUK project grant (Oct 2015-Sept 2018); ref C9474/A18362; Quantification of antecedent events and outcomes in patients with haematological malignancies: analysis of a unique population-based matched patient cohort).
All funders receive annual interim reports, and final reports will be provided at the end of the funding periods Currently, several studies using HES/Civil Registration (deaths) data are either in progress or are planned.

With respect to the next 12 months, three specifics for examples are:

1. Investigate the impact of emergency admission on survival from a cancer that generally has good outcomes, Hodgkin lymphoma, to help explore why survival in a small proportion of patients is poor; this will involve linking HES/Civil Registration (deaths) data to YHHN’s diagnostic, prognostic and treatment data enabling a more thorough examination than is possible elsewhere.

2. Examine whether the mortality and healthcare utilisation return to that seen in the general population for patients who achieve remission with a potentially curable non-Hodgkin lymphoma - diffuse large B-cell lymphoma. This work will help support policies of when a patient no longer requires regular monitoring by haematology for disease re-occurrence.

3. Examine the relationship between previous joint replacement and subsequent diagnosis of specific haematological cancer. This work will help to identify whether joint replacements are a risk factor for developing certain subtypes of haematological cancers, or whether for certain subtypes (plasmacytoma/myeloma), the diseases could have been diagnosed at an earlier stage.

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. No record level data will be published or shared with any of the funders.

Processing:

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

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

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

The University of York will upload the following cohort details to NHS Digital’s Data Exchange Service (DES) for linkage
• patient’s surname,
• forename,
• sex,
• date of birth
• NHS number,
• YHHN unique study number

The University of York has supplied YHHN identifiers to NHS Digital previously. NHS Digital is responsible for linking and extracting data for YHHN subjects from administrative databases, and for returning the linked data to the University of York. The University of York is responsible for storing the returned data and processing the data for statistical analyses.

Data are returned to the University by NHS Digital with linked HES, cancer registration data and death certification; accordingly, as this information is about a participants' health these are considered as special category data.

All data flows involve patient level data. Data flowing from NHS Digital to the University of York will contain the YHHN unique identifier for each patient.

The University of York has supplied YHHN identifiers to NHS Digital and have confirmed that the linkage has been retained. NHS Digital use these identifiers to link to national death certification, cancer registration, and to HES-APC, HES-OP and HES-A&E.

The data extracted by NHS Digital will include all available years of HES, death notifications, and cancer registrations since the last data download; future extractions will be conducted on an annual basis and deaths will be updated on a monthly basis.

The information provided by NHS Digital will only be used to address the stated objective, namely, to facilitate a greater understanding of the causes of haematological cancers, as well as their impact on the future health and healthcare needs of those who develop them.

Data linkages to administrative datasets are conducted by NHS Digital, and only data for YHHN subjects are released to the University of York. Data received from NHS Digital will be incorporated into the YHHN research dataset at the University of York. YHHN stores data returned from NHS Digital in a research database that does not contain NHS numbers; these are held in a separate database accessible to a restricted number of staff all of whom know that they are held only for the purpose of linking to administrative datasets, and for no other purpose. The data will not be matched to publicly available data.

Data processing is only conducted by employees of the University of York who are engaged with the study; all of whom have received appropriate training in data protection and confidentiality. Data will only ever be processed by employees of the University of York.

All YHHN data are held in electronic format only and stored on a Microsoft SQL Server 2016 sp2 running on Windows Server 2016. Data are accessed on networked computers in a University of York office, located within the Department of Health Sciences; staff working on the project are granted access to the data by the Principal Investigator and their access is controlled by their username and password. NHS numbers, and data provided by NHS Digital cannot be accessed using a remote desktop server. No hard copies of the data exist, or will be made, and data are never stored on laptop computers or portable devices. All data supplied by NHS Digital for the YHHN are stored at the University of York, as named in the agreement.

All data are stored at the University of York on two servers in different locations to ensure there is a backup. These are situated in buildings on campus; the Department of Health Sciences, which is located on the West Campus and the Data Centre, which is on the East Campus. The DSPT for the University of York provides security assurance for both sites.


‘Your Tube’: the role of different diets in children who are gastrostomy fed — DARS-NIC-657422-S1K1C

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2022-07-08 — 2025-07-07 2022.12 — 2023.07.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

BACKGROUND:
There are increasing numbers of children with complex health care needs that require having all, or part, of their nutritional intake via gastrostomy feeds. The recommended feed for children via gastrostomy is a commercially produced formula. However, there is a growing body of parents who are interested in and/or choosing to feed their children home-blended meals. These parents often report benefits such as improved gastro-oesophageal reflux symptoms, less constipation and less distress in their child.

The need for further research in this area has come from a research prioritisation exercise, recent review of the literature and professional organisations e.g. British Dietetic Association.

The ‘Your Tube’ study is a consented cohort study which has recruited 180 children (aged between 6 months – 18 years) who are fed via a gastrostomy tube and follow them up for an 18 month period. 15 of the 180 participants recruited have been consented via consultee advice. The study participants have been consented for transfer of personal data to NHS Digital for the purposes of linking with their healthcare data. NHS Digital data will be used to assess healthcare resource use and complications from the children's gastrostomy and to assess agreement between parent reported healthcare use and HES data. This is important to assess the feasibility of a longer-term follow-up study using only HES data.

Please note that for some of the cohort (15 children), the parents of the child have consented. For example, consent forms completed by the parent for children aged 7-11 years and 12-15 years. A patient information sheet was provided in lay person terms to all participants regardless of age. Additional information on the project can be found here - https://www.york.ac.uk/healthsciences/research/public-health/projects/yourtube/

COHORT
Children who are gastrostomy fed and recruited in the Your Tube study (180 children). For analyses purposes the cohort will be split into two groups:
1. those who are fed formula feeds
2. those who are fed a home-blended diet.

The University of York are relying on GDPR article 6 (1)(e) and 9 (2) (j):

Article 6(1)(e) – as per the Charter of Incorporation, power is conferred upon the University of York “to provide instruction in such branches of learning as the University may think fit and to make provision for research and for the advancement and dissemination of knowledge in such manner as the University may determine”.

Article 9(2)(j) – this research is in the public interest as there are increasing numbers of children with complex health care needs that require having all, or part, of their nutritional intake via gastrostomy feeds. The outcomes of this research into gastronomy feeding will be of significant application to parents, young people and health professionals – particularly its focus on the use of home-blended diets.

The main research question for this study is:
What are the risks, benefits and resource implications for using home-blended food for children with gastrostomy tubes compared to currently recommended formula feeds?

Other objectives are:
1. To identify the important outcomes of gastrostomy feeding for parents, young people and health professionals.
2. To assess the safety of home-blended diets for children who are gastrostomy fed compared to liquid formula diets.
3. To identify and quantify the benefits of home-blended diets compared to liquid formula diets for children who are gastrostomy fed and their parents.
4. To identify and quantify the resources (family and statutory services) required to support home-blended diets compared to liquid formula diets.
5. To assess whether long-term follow-up of children who are gastrostomy fed is feasible using routine data sources.

DATA REQUEST:
The linked Hospital Episode Statistics (HES) data will be used to:
- assess healthcare resource use and complications from the child's gastrostomy
- assess agreement between parent reported healthcare use and HES data. This is important to assess the feasibility of a longer-term follow-up study using only HES data.

Individual linkage to HES inpatient care, emergency and outpatient data for each child in this cohort (180 children) from 2 years prior to recruitment to the study and for the 12-18 months duration of the study is needed to fulfil the study aims. Some children will have a lot of hospital use whilst others will have little, hence the need for the 2 years prior to the start of the study.

HES Admitted Patient Care (APC) and Emergency Care Dataset (ECDS) data are required to assess any planned or unplanned hospital use in relation to their gastrostomy, complications from their gastrostomy or their underlying condition. Although parents are being asked to report any hospital admission for their children they are likely not to recall the detail of reason for admission and length of stay which are important for assessing healthcare resources use.

The minimum amount of data is being requested that is required to achieve the stated study aims. i.e. only asking for data on the 180 children recruited to this study and only for the 2 years prior to recruitment to the study and the 12-18 month duration that they are in the study.

The University of York is the sole data controller who will also process the data. There are co-investigators on this study from the University of Leeds, Sunderland Hospitals Trust and South Gloucestershire. The co-investigators will not have any access to the NHS Digital data nor have any role in data processing and as such are not considered data processors. The co-investigators are not determining the means and purpose of the processing of the data and as such are not listed as data controllers.

The NIHR Health Technology Assessment programme fund this study but have no role in data analyses, data processing or data controllership.

The study protocol is published and available here https://bmjopen.bmj.com/content/9/10/e033831.

Expected Benefits:

The dissemination of these findings hope to inform national clinical guidelines including those of the British Dietetic Association.

At present there is little research evidence on which to base these guidelines, but more parents are feeding their children a home-blended diet. It is of public interest to have an evidence base on which to base clinical guidelines on the feeding of children with a gastrostomy.

Impact of children and families:
There are more than 10000 children in England who are currently fed via a gastrostomy. Understanding the safety and effectiveness of a home-blended diet may benefit children and their parents in terms of making an evidence-based decision about their child’s health. This may enable children and their family to have adequate support from their clinical team.

Impact of Healthcare professionals:
Understanding the role, safety and effectiveness of home-blended diet for these children is important in terms of supporting parents choices, ensuring that healthcare staff are aware of the study findings. These findings hope to enable clinical staff to support these children and families appropriately.

Impact on national guidelines:
At present there is little research on which to base clinical guidelines for home blended diet in children with gastrostomies. This study could provide evidence regarding safety that could enable these guidelines to be evidence based and robust.

Impact of NHS and commissioners:
This study could provide information on resource use and resource requirements for home-blended diets. This is important in terms of commissioning services for these children and families.

Outputs:

The outputs from this study will include:
1. Final report for the funder National Institute for Health Research (NIHR) Health Technology Assessment. This will be published on the NIHR journals website – target date August 2023 (submit to change due to COVID-19).

2. An academic paper will be submitted to Developmental Medicine and Child Neurology - target date August 2023 (submit to change due to COVID-19)

3. An abstract for conference presentation at the European Association for Childhood Disability – target date July 2022

4. Short Plain English summary that will be shared with policy makers (NHS England) commissioners and parents via professional and parent networks - target date August 2023

5. A plain English summary for parents in collaboration with the parent advisory panel, which will be made available here - see here https://www.york.ac.uk/healthsciences/research/public-health/projects/martinhouse/mh-publications/.

All outputs will be published at aggregate level with small number suppression rules applied in line with the HES analysis guide.

All outputs will be disseminated via twitter @UoYMHRC, professional email lists and the University of York website.

Processing:

DARS-NIC-657422-S1K1C is a sister application to DARS-NIC-334459-R9H4C. These applications have been split as a subset of the cohort are recruited under consultee advice, as such opt out will be applied to the those recruited under consultee advise (under NIC-657422) and not to those recruited under explicit consent (under NIC-334459).

The record-level cohort data to be sent to NHS Digital from the University of York will be personal data:
Date of birth,
NHS number,
Gender,
Postcode,
Study ID,
Recruitment date.

Data flow from NHS Digital to the University of York will be pseudonymised healthcare data with Study ID as the link to the 'Your Tube' cohort data. All other identifiers, barring Study ID will be removed.

Once received at the University of York, there will be no onward sharing of NHS Digital data. All data processing will happen at NHS Digital or the University of York by University of York employees who have undertaken University data protection training.

At the University of York, NHS Digital data will be linked to the clinical and demographic data collected from the cohort participants. University of York already have personal identifiable data on these cohort participants as this is a consented cohort. These data will not be linked with any other data sources.

DATA ACCESS:
Data will be stored securely on the department of Health Sciences server. Access will either be via on site PC or via secure remote VPN from home which does not allow any download of data.

The drive is only accessible to devices on the Health Sciences subnets which are behind a separate firewall from the rest of the university, or to devices on the University's network that have been whitelisted specifically. People are accessing those devices remotely using the University's virtual private network (VPN) which requires multifactor authentication for all users. After accessing the VPN separate credentials are required to connect to Health Sciences.

An accurate assessment of the healthcare use and safety cannot be undertaken without access to the requested data from NHS Digital. The data derived from the HES data will be added to the data collected from the parents/child/clinician for analyses to assess the following:

1. SAFETY OUTCOMES
The data on safety outcomes will be provided via parent questionnaires and via NHS Digital data (see next section).
• No. hospital admissions
• No. accident and emergency attendances
• Tube blockages
• No. of infections
• No. of gastrointestinal infections

Appropriate statistical models will be used to compare these outcomes between the two groups of interest at baseline at the end of the study. All models will adjust for the propensity score and any additional confounding factors in this population (age, underlying diagnoses, co-morbidities, outpatient attendance, parental factors, socioeconomic status). Propensity scores are a statistical model used to estimate the probability of participants receiving treatment/intervention by taking into account their individual characteristics. Study site will be added as a random effect to the models to allow for site-level variation.

2. HEALTHCARE USE OF DATA
The number of hospital admissions (emergency and planned) and length of stay will be calculated for each child. A comparison between the two analytical groups of interest at baseline will be undertaken using appropriate statistical models.

After final data collection at 18 months the number of hospital admissions and length of stay will be compared between the two analytical groups of interest using appropriate statistical models.

Appropriate statistical models will account for confounding factors in this population (age, underlying diagnoses, co-morbidities, outpatient attendance, parental factors, socioeconomic status).

An analyses of accuracy of reporting between parent report and HES data will be undertaken to assess whether long term follow-up of this cohort using routine data would be feasible and accurate.

The data processing will only be carried out by substantive employees of the University of York only (who are the Data Controller who also process the Data) and who have been trained in data protection and confidentiality.


‘Your Tube’: the role of different diets in children who are gastrostomy fed — DARS-NIC-334459-R9H4C

Type of data: information not disclosed for TRE projects

Opt outs honoured: Anonymised - ICO Code Compliant, No (Consent (Reasonable Expectation))

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2022-07-08 — 2025-07-07 2022.09 — 2023.07.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

BACKGROUND:
There are increasing numbers of children with complex health care needs that require having all, or part, of their nutritional intake via gastrostomy feeds. The recommended feed for children via gastrostomy is a commercially produced formula. However, there is a growing body of parents who are interested in and/or choosing to feed their children home-blended meals. These parents often report benefits such as improved gastro-oesophageal reflux symptoms, less constipation and less distress in their child.

The need for further research in this area has come from a research prioritisation exercise, recent review of the literature and professional organisations e.g. British Dietetic Association.

The ‘Your Tube’ study is a consented cohort study which has recruited 180 children (aged between 6 months – 18 years) who are fed via a gastrostomy tube and follow them up for an 18 month period. 15 of the 180 participants recruited have been consented via consultee advice. The study participants have been consented for transfer of personal data to NHS Digital for the purposes of linking with their healthcare data. NHS Digital data will be used to assess healthcare resource use and complications from the children's gastrostomy and to assess agreement between parent reported healthcare use and HES data. This is important to assess the feasibility of a longer-term follow-up study using only HES data.

Please note that for some of the cohort (15 children), the parents of the child have consented. For example, consent forms completed by the parent for children aged 7-11 years and 12-15 years. A patient information sheet was provided in lay person terms to all participants regardless of age. Additional information on the project can be found here - https://www.york.ac.uk/healthsciences/research/public-health/projects/yourtube/

COHORT
Children who are gastrostomy fed and recruited in the Your Tube study (180 children). For analyses purposes the cohort will be split into two groups:
1. those who are fed formula feeds
2. those who are fed a home-blended diet.

The University of York are relying on GDPR article 6 (1)(e) and 9 (2) (j):

Article 6(1)(e) – as per the Charter of Incorporation, power is conferred upon the University of York “to provide instruction in such branches of learning as the University may think fit and to make provision for research and for the advancement and dissemination of knowledge in such manner as the University may determine”.

Article 9(2)(j) – this research is in the public interest as there are increasing numbers of children with complex health care needs that require having all, or part, of their nutritional intake via gastrostomy feeds. The outcomes of this research into gastronomy feeding will be of significant application to parents, young people and health professionals – particularly its focus on the use of home-blended diets.

The main research question for this study is:
What are the risks, benefits and resource implications for using home-blended food for children with gastrostomy tubes compared to currently recommended formula feeds?

Other objectives are:
1. To identify the important outcomes of gastrostomy feeding for parents, young people and health professionals.
2. To assess the safety of home-blended diets for children who are gastrostomy fed compared to liquid formula diets.
3. To identify and quantify the benefits of home-blended diets compared to liquid formula diets for children who are gastrostomy fed and their parents.
4. To identify and quantify the resources (family and statutory services) required to support home-blended diets compared to liquid formula diets.
5. To assess whether long-term follow-up of children who are gastrostomy fed is feasible using routine data sources.

DATA REQUEST:
The linked Hospital Episode Statistics (HES) data will be used to:
- assess healthcare resource use and complications from the child's gastrostomy
- assess agreement between parent reported healthcare use and HES data. This is important to assess the feasibility of a longer-term follow-up study using only HES data.

Individual linkage to HES inpatient care, emergency and outpatient data for each child in this cohort (180 children) from 2 years prior to recruitment to the study and for the 12-18 months duration of the study is needed to fulfil the study aims. Some children will have a lot of hospital use whilst others will have little, hence the need for the 2 years prior to the start of the study.

HES Admitted Patient Care (APC) and Emergency Care Dataset (ECDS) data are required to assess any planned or unplanned hospital use in relation to their gastrostomy, complications from their gastrostomy or their underlying condition. Although parents are being asked to report any hospital admission for their children they are likely not to recall the detail of reason for admission and length of stay which are important for assessing healthcare resources use.

The minimum amount of data is being requested that is required to achieve the stated study aims. i.e. only asking for data on the 180 children recruited to this study and only for the 2 years prior to recruitment to the study and the 12-18 month duration that they are in the study.

The University of York is the sole data controller who will also process the data. There are co-investigators on this study from the University of Leeds, Sunderland Hospitals Trust and South Gloucestershire. The co-investigators will not have any access to the NHS Digital data nor have any role in data processing and as such are not considered data processors. The co-investigators are not determining the means and purpose of the processing of the data and as such are not listed as data controllers.

The NIHR Health Technology Assessment programme fund this study but have no role in data analyses, data processing or data controllership.

The study protocol is published and available here https://bmjopen.bmj.com/content/9/10/e033831.

Expected Benefits:

The dissemination of these findings hope to inform national clinical guidelines including those of the British Dietetic Association.

At present there is little research evidence on which to base these guidelines, but more parents are feeding their children a home-blended diet. It is of public interest to have an evidence base on which to base clinical guidelines on the feeding of children with a gastrostomy.

Impact of children and families:
There are more than 10000 children in England who are currently fed via a gastrostomy. Understanding the safety and effectiveness of a home-blended diet may benefit children and their parents in terms of making an evidence-based decision about their child’s health. This may enable children and their family to have adequate support from their clinical team.

Impact of Healthcare professionals:
Understanding the role, safety and effectiveness of home-blended diet for these children is important in terms of supporting parents choices, ensuring that healthcare staff are aware of the study findings. These findings hope to enable clinical staff to support these children and families appropriately.

Impact on national guidelines:
At present there is little research on which to base clinical guidelines for home blended diet in children with gastrostomies. This study could provide evidence regarding safety that could enable these guidelines to be evidence based and robust.

Impact of NHS and commissioners:
This study could provide information on resource use and resource requirements for home-blended diets. This is important in terms of commissioning services for these children and families.

Outputs:

The outputs from this study will include:
1. Final report for the funder National Institute for Health Research (NIHR) Health Technology Assessment. This will be published on the NIHR journals website – target date August 2023 (submit to change due to COVID-19).

2. An academic paper will be submitted to Developmental Medicine and Child Neurology - target date August 2023 (submit to change due to COVID-19)

3. An abstract for conference presentation at the European Association for Childhood Disability – target date July 2022

4. Short Plain English summary that will be shared with policy makers (NHS England) commissioners and parents via professional and parent networks - target date August 2023

5. A plain English summary for parents in collaboration with the parent advisory panel, which will be made available here - see here https://www.york.ac.uk/healthsciences/research/public-health/projects/martinhouse/mh-publications/.

All outputs will be published at aggregate level with small number suppression rules applied in line with the HES analysis guide.

All outputs will be disseminated via twitter @UoYMHRC, professional email lists and the University of York website.

Processing:

The record-level cohort data to be sent to NHS Digital from the University of York will be personal data:
Date of birth,
NHS number,
Gender,
Postcode,
Study ID,
Recruitment date.

Data flow from NHS Digital to the University of York will be pseudonymised healthcare data with Study ID as the link to the 'Your Tube' cohort data. All other identifiers, barring Study ID will be removed.

Once received at the University of York, there will be no onward sharing of NHS Digital data. All data processing will happen at NHS Digital or the University of York by University of York employees who have undertaken University data protection training.

At the University of York, NHS Digital data will be linked to the clinical and demographic data collected from the cohort participants. University of York already have personal identifiable data on these cohort participants as this is a consented cohort. These data will not be linked with any other data sources.

DATA ACCESS:
Data will be stored securely on the department of Health Sciences server. Access will either be via on site PC or via secure remote VPN from home which does not allow any download of data.

The drive is only accessible to devices on the Health Sciences subnets which are behind a separate firewall from the rest of the university, or to devices on the University's network that have been whitelisted specifically. People are accessing those devices remotely using the University's virtual private network (VPN) which requires multifactor authentication for all users. After accessing the VPN separate credentials are required to connect to Health Sciences.

An accurate assessment of the healthcare use and safety cannot be undertaken without access to the requested data from NHS Digital. The data derived from the HES data will be added to the data collected from the parents/child/clinician for analyses to assess the following:

1. SAFETY OUTCOMES
The data on safety outcomes will be provided via parent questionnaires and via NHS Digital data (see next section).
• No. hospital admissions
• No. accident and emergency attendances
• Tube blockages
• No. of infections
• No. of gastrointestinal infections

Appropriate statistical models will be used to compare these outcomes between the two groups of interest at baseline at the end of the study. All models will adjust for the propensity score and any additional confounding factors in this population (age, underlying diagnoses, co-morbidities, outpatient attendance, parental factors, socioeconomic status). Propensity scores are a statistical model used to estimate the probability of participants receiving treatment/intervention by taking into account their individual characteristics. Study site will be added as a random effect to the models to allow for site-level variation.

2. HEALTHCARE USE OF DATA
The number of hospital admissions (emergency and planned) and length of stay will be calculated for each child. A comparison between the two analytical groups of interest at baseline will be undertaken using appropriate statistical models.

After final data collection at 18 months the number of hospital admissions and length of stay will be compared between the two analytical groups of interest using appropriate statistical models.

Appropriate statistical models will account for confounding factors in this population (age, underlying diagnoses, co-morbidities, outpatient attendance, parental factors, socioeconomic status).

An analyses of accuracy of reporting between parent report and HES data will be undertaken to assess whether long term follow-up of this cohort using routine data would be feasible and accurate.

The data processing will only be carried out by substantive employees of the University of York only (who are the Data Controller who also process the Data) and who have been trained in data protection and confidentiality.


MR1325 - Yorkshire and Humberside Haematology Network Register (YHHN) Comparison Cohort — DARS-NIC-06759-X5V7P

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, Identifiable, No (Section 251 NHS Act 2006)

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), , Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Academic)

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

When:DSA runs 2019-03-01 — 2020-09-30 2017.06 — 2023.04.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF YORK, HULL UNIVERSITY TEACHING HOSPITALS NHS TRUST, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Flagging Current Status Report
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Critical Care
  6. Hospital Episode Statistics Outpatients
  7. Demographics
  8. Cancer Registration Data
  9. MRIS - Cohort Event Notification Report
  10. Civil Registration - Deaths
  11. Emergency Care Data Set (ECDS)
  12. HES-ID to MPS-ID HES Accident and Emergency
  13. HES-ID to MPS-ID HES Admitted Patient Care
  14. HES-ID to MPS-ID HES Outpatients
  15. MRIS - Members and Postings Report
  16. Hospital Episode Statistics Accident and Emergency (HES A and E)
  17. Hospital Episode Statistics Admitted Patient Care (HES APC)
  18. Hospital Episode Statistics Critical Care (HES Critical Care)
  19. Hospital Episode Statistics Outpatients (HES OP)
  20. Civil Registrations of Death

Objectives:

The overarching aim of the study is to examine how illness and healthcare patterns among patients with haematological cancers differs from that of those who do not have these cancers.

The study cohort consists of patients with haematological cancers from the Yorkshire and Humberside Haematology Network (YHHN). The YHHN patient cohort lies at the centre of research carried out by the Haematological Malignancy Research Network (HMRN - www.hmrn.org), and for consistency and clarity YHHN (as opposed to HMRN) is referred to throughout this application.

With 14 hospitals and a catchment population of around 4 million people, YHHN is a specialised “real-world” population-based register recording and analysing data on all patients diagnosed with haematological cancers in the Yorkshire and Humberside area. Since its start in 2004, YHHN has registered over 25,000 patients with a haematological malignancy. The University now wish to create a control cohort linked to the same routinely collected health data as YHHN for the following reasons;

• to study potential risk factors and comorbidities for haematological malignancies including, for example, autoimmune conditions, chronic infections, and medical procedures;
• to investigate whether illness patterns among patients with precursor haematological malignancies, such as monoclonal B-cell lymphocytosis (MBL) and monoclonal gammopathy of uncertain significance (MGUS), differ from those seen in the general population, both before and after their diagnosis.

For comparative purposes, the control sample will be selected from the general population by the HSCIC. For each YHHN case diagnosed 2009-15, 10 people with the same year of birth and sex will be randomly selected from among those alive and registered with a GP practice in the study region during the year the case was diagnosed.

This 'control' sample of approximately180,000 people, will be linked to HES records, as well as demographic, cancer registration and mortality data.

The proposal has undergone peer review by Cancer Research UK and has been awarded funding (CRUK grant number C9474/A18362).

Yielded Benefits:

The dataset was received in March 2017 and as such, the first analyses comparing the YHHN patient cohort to the general population comparison cohort are currently being conducted. YHHN anticipate that the first reports of findings using the data to be published over the next 12 months. A major aim of YHHN (MR 1126) is to improve care and outcomes for patients; and data from the patient cohort have been used in NICE appraisals and impacted on the delivery of patient care. Importantly, the YHHN area is representative of the UK as a whole in terms of both demography and clinical practice; meaning that results are generalizable and are of potential importance to the national commissioning of cancer care services. The YHHN control cohort, which is the focus of the present application (MR 1325), is key to the provision of further benefit since it allows meaningful comparisons to be made across the life-course between patients with cancer and those without. With respect to timelines, the linked control cohort dataset was received from NHS Digital in March 2017 and, following internal checks, the initial analyses are now nearing completion. The first report by the University of York has been submitted to the International Journal of Cancer – “Mature B-cell malignancies and rheumatological disorders: a report on risk and survival from the UK’s Haematological Malignancy Research Network” examines patterns of secondary care among individuals with lymphoma, comparing them to that seen among their matched controls. This is important since patients with lymphoma are known to be at increased risk of certain other co-morbidities, but the size of the risk(s) and their potential impact on outcome has not been previously examined in the UK. This publication is ready to be submitted to the International Journal of Cancer – “Mature B-cell malignancies and rheumatological disorders: a report on risk and survival from the UK’s Haematological Malignancy Research Network” Other analyses, several of which are likely to impact on patient care, are ongoing; and YHHN envisage that at least two other reports on this topic will be published in the next 12 months.

Expected Benefits:

Population-based data on clinically meaningful haematological malignancy subtypes (>60 subtypes) are not available elsewhere (cancer registries have difficulty in accessing diagnostic information systematically and tend to group into 4 main categories that contain a mix of diseases). Importantly, the YHHN area is representative of the UK in terms of both demography and clinical practice, meaning that results are highly generalizable and are of potential importance to the commissioning of cancer care services at a national level.

YHHN is uniquely placed to utilise up-to-date diagnostic and treatment data to conduct research on these complex cancers. By linking the patient cohort to HES, our registry will extend its population-based data to include antecedent and post-diagnostic events in the healthcare setting. The use of HES data will be multifactorial; and will be used to examine a number of questions along the patient pathway, including potentially aetiological factors and routes to diagnosis, as well as healthcare utilisation patterns & costings (before diagnosis, around the time of diagnosis, and onwards into the survivorship phase).

There is a dearth of “real-world” information for patients diagnosed with haematological malignancies, and measurable benefits will include the provision of good quality population-based data to inform clinicians, patients, and commissioners. For example, some patients with aggressive cancers (such as diffuse large B-cell lymphoma) can be ‘cured’ but once in the survivorship phase, little is known about their healthcare needs. Likewise, precursor conditions such as monoclonal gammopathy of uncertain significance (MGUS) and monoclonal B-cell lymphocytosis (MBL), which can respectively progress to their more aggressive counterparts myeloma and chronic lymphocytic leukaemia, have been linked to other serious morbidities; MGUS with osteoporotic fractures and thrombotic disease and MBL most notably with infections. Again, however, there is currently a paucity of good quality information on these topics.

The researchers will investigate these, and many other, putative associations in-depth across the entire patient pathway. Having a sample of HES data for the general population is essential in order to put the healthcare patterns of patients with haematological cancers into context, in much the same way as relative survival takes account background mortality levels. In addition, ‘real-world’ population-based data that includes all health service contacts are required not only to inform aetiological hypotheses and plan future healthcare services, but also to monitor the impact of future therapeutic changes in the general patient population. The target date for expected measurable benefits to healthcare will be by the end of December 2019.

Outputs:

Outputs utilising the requested data will begin soon after data receipt, and will continue for a minimum of five years. Specific outputs are described below.

The University will use traditional publication routes (peer-reviewed, open access publications and conference presentations) and the HMRN website (www.hmrn.org) to disseminate findings to local, national and international practitioners and academics.

The peer-reviewed journals targeted are likely to be similar to those that have already published in: British Journal of Cancer, British Journal of Haematology, Blood, British Medical Journal Open, Cancer Epidemiology, Journal of Clinical Oncology, PLoS One, and Value in Health among many others.

To ensure accessibility, all reports will be published under creative commons attribution 4.0 licence (CC BY); support for this is included in all of the grant applications.

Likewise, findings will be disseminated at conferences; those that are regularly attended include meetings of National Cancer Intelligence Network (NCIN), American and British Societies of Haematology (ASH & BSH), European Haematology Association (EHA), National Awareness and Early Detection Initiative (NAEDI), and the Palliative Care Congress.

Outputs will follow guidelines on disclosure control and will only contain aggregated data with small numbers suppressed.

Processing:

In order to conduct this research, the University needs to compare the healthcare experiences of individuals in the patient cohort to that of the general population. For these analyses, the researchers are looking to source a comparison population selected from the general population to link to HES, ONS and Cancer Registrations.

Selection of the control population sample and linkage to HES will be carried out by HSCIC. All personal identifying data will remain at the HSCIC and will not be made available to the University. Data for the comparison population, whether from ONS or HES, would be pseudonymised and will not contain personal sensitive fields.

For each YHHN patient, 10 people with the same year of birth and sex will be randomly selected from among those alive and registered with a GP practice in the study region during the year the case was diagnosed. The study region at the core of YHHN covers the Primary Care Trusts of Bradford and Airedale, Calderdale, Kirklees, Leeds, North Yorkshire and York, Wakefield District, East Riding of Yorkshire, Hull, North Lincolnshire, and Northeast Lincolnshire Care Plus. In order to match the control sample to YHHN cases, HSCIC will use the demographic details of the YHHN cohort already flagged in the patient tracking service under MR1126.

The choice of 10 “controls” for each YHHN patient will ensure adequate statistical power for the comparative analyses; and, with around 18,000 patients diagnosed between 2009 and 2015, the University would be requesting data extracts on 180,000 non-YHHN subjects.

Data requested from HSCIC for all controls are:

• Sex
• Year of birth
• Identifier of matched case - HSCIC holds the anonymised identifiers for YHHN cases for patient tracking and control selection under MR1126. As an example, if a case has an identifier of 01-0001, then the first control would be 01-0001-01, the second 01-0001-02, etc. This will allow the University to know which controls were matched to each case.

Analyses will examine health events that have happened in the past (before diagnosis/pseudo-diagnosis) and follow events that occur in the future; and some analyses will require data to be censored in the event of death or subsequent cancer. For this purpose, HSCIC will ‘flag’ the general population sample for future death and cancer registration; the extracted control cohort will be flagged in the patient tracking service and the following data, extracted from death and cancer registrations, supplied:

• Month and year of death
• Underlying cause of death
• Month and year of cancer diagnosis
• Cancer type

HSCIC will link the control cohort to HES records. Data required for this linkage which will not be released to the applicants are:
• NHS number
• Sex
• Date of birth
• Postcode

HSCIC will link the control sample to HES records and supply a pseudonymised HES output.

Data received from HSCIC for this research will not include any patient identifiable information.

Data will be stored and processed at the Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York. Data will be stored on a Microsoft SQL server running on a secured Windows Server and will only be accessible by the Epidemiology and Cancer Statistics Group staff. No identifiable data will be shared with third parties.


Economic Analyses of Health and Social Care -Evaluation of differences in the performance of health care providers in terms of the amount and cost of provision and in patient outcomes including mortality and self-reported morbidity; — DARS-NIC-84254-J2G1Q

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

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

When:DSA runs 2019-01-01 — 2021-12-31 2018.06 — 2023.01.

Access method: One-Off, Ongoing

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. Mental Health Services Data Set
  2. Patient Reported Outcome Measures (Linkable to HES)
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Critical Care
  5. Hospital Episode Statistics Accident and Emergency
  6. Hospital Episode Statistics Outpatients
  7. Civil Registration - Deaths
  8. HES:Civil Registration (Deaths) bridge
  9. Emergency Care Data Set (ECDS)
  10. Mental Health and Learning Disabilities Data Set
  11. Civil Registration (Deaths) - Secondary Care Cut
  12. Mental Health Minimum Data Set
  13. HES-ID to MPS-ID HES Accident and Emergency
  14. HES-ID to MPS-ID HES Admitted Patient Care
  15. HES-ID to MPS-ID HES Outpatients
  16. Civil Registrations of Death - Secondary Care Cut
  17. Hospital Episode Statistics Accident and Emergency (HES A and E)
  18. Hospital Episode Statistics Admitted Patient Care (HES APC)
  19. Hospital Episode Statistics Critical Care (HES Critical Care)
  20. Hospital Episode Statistics Outpatients (HES OP)
  21. Mental Health and Learning Disabilities Data Set (MHLDDS)
  22. Mental Health Minimum Data Set (MHMDS)
  23. Mental Health Services Data Set (MHSDS)

Objectives:

The Centre for Health Economics (CHE), based at University of York is requesting data for the following projects involving economic analyses of health and social care. Please note that for each of the following projects CHE staff will analyse individual level data from the various datasets. Only aggregated results will be published and disseminated.

Almost all of these projects are funded, at least in part, by the Department of Health (DoH) via a major programme of work funded as a Policy Research Unit (PRU) in the Economics of Health and Social Care Systems (http://eshcru.ac.uk/). The aim of the PRU is to inform and guide policy-making in the health and social care sectors by undertaking high quality, robust and policy-relevant research, based on the discipline of economics, thereby helping to improve the health and well-being of the population, reflecting distributional concerns and population diversity. A detailed work programme for the next two years of programme funding is developed in advance in collaboration with both a DoH Stakeholder Group and the PRU’s Advisory Group with meetings being held with each Group every six months. Approximately 20% of funding is reserved for the PRU to respond to short-term responsive requests for research. This process ensures that the work programme can be shaped to reflect enduring and emerging policy concerns. For some projects, additional funding has been secured to enable extended or deeper analyses of the research topic.

Under previous Data Sharing Agreements ONS date of death data was supplied. To further reduce the amount of potentially identifiable data items being processed, this data item will no longer be required and the data item previously supplied will be destroyed. In its place, the CHE will retain derived information indicating whether or not the patient was alive 7, 30, 90 and 365 days after admission. In new data, CHE requires flags added to the HES APC data indicating, for each admission, whether or not the patient was alive 7, 30, 90 and 365 days after admission. Under no circumstances will any attempt be made to backward engineer the date of death, and staff will be reminded that such action is prohibited and would be in breach of CHE’s data sharing responsibilities.

All of the work involves analysing the data in different ways. For example, an analysis under project 1 may focus on particular specialties, comparison of productivity across hospitals, or may be a broader assessment of national productivity. Many of the statistical methods to be employed require longitudinal data to investigate how changes in patient outcomes (including morbidity, mortality, emergency readmissions, length of stay, admissions for conditions that could be managed in primary care, inpatient admission rates after A&E attendance) are related to changes in policy (including payment policies and incentives), changes in market configurations, changes in organisational structure, and changes in patient characteristics. Pseudonymised patient level information is required to allow for the influence of past utilisation, for demographic factors, for socio-economic factors (e.g. deprivation) linked to the small area in which patients live, and patient distance from hospitals, social care providers, and general practices. It is also essential in investigating the equity implications of policies, market structure, and organisational arrangements. The Principal Investigators and Project Leads are responsible for determining what analyses will be undertaken and what data will be used for each analysis in support of the objectives agreed with the funding organisations.

Project 1 - Measurement of efficiency, effectiveness and productivity in the delivery of health care system nationally, sub-nationally and among hospitals;

The purpose of this project is to produce information for the Department of Health (DoH) and Secretary of State for Health on efficiency, effectiveness and productivity. In the current economic climate it is particularly important that changes in efficiency and productivity can be identified and monitored. This helps ensure accountability to the public for how the annual NHS budget is spent and to identify opportunities for better use of resources devoted to the NHS. This project provides numerical answers and context for, among others, House of Commons Health Committee, the Public Accounts Committee, Public Expenditure Inquiries, and DoH submissions in support of annual Spending Reviews. The work also contributes to the measurement of productivity of the health service in the national accounts, compiled by the Office of National Statistics.

Funder:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001). CHE Lead: Andrew Street

This project will use only the following data: HES APC 1998/99-2015/16; A&E 2007/08 - 2015/16; Critical Care 2011/12 – 2015/16; Outpatient 2011/12-2015/16; PROMs 2009/10 – 2015/16. Most of the work undertaken under this project involves measurement of productivity over time, hence the need to hold the data from 1998/99. It is also necessary to construct aggregated measures of NHS output and quality based on what has happened to each individual patient in whatever setting care has been delivered, hence the need for patient-level information. The project also requires use of the sensitive PROMs data as measures of the quality of health care.

Project 2 - Evaluation of differences in the performance of health care providers in terms of the amount and cost of provision and in patient outcomes including mortality and self-reported morbidity;

The purpose of this project is to produce information for National and local decision makers, such as the Department of Health (DoH), Clinical Commissioning Groups (CCGs) and Local Authorities (LAs), to assist decisions regarding the provision of services that offer the greatest value for money according to the benefits achieved. Delivering appropriate, high quality, health care services to patients, in the most cost-effective way, are important priorities in any health care system. Advancing these priorities requires the analyses of such things as variations in practice and of the relationship between patient outcomes and hospital and consultant workload; which dimensions of performance are most important to patients; and the extent to which financial incentives motivate best practice. Ultimately this project informs the assessment of the most efficient and cost-effective way of delivering a particular service. This helps ensure accountability to the public for how the annual NHS budget is spent and to identify opportunities for better use of resources devoted to the NHS. The project is designed to develop a more systematic evidence base that will allow policy-makers, providers and commissioners to develop policies to achieve efficiency and outcome-based commissioning; to publish information on performance in formats that are most useful to the intended stakeholders, and to redeploy resources to produce more efficient mixes of services both within and across the health and social care sectors.

Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001). CHE Lead: Andrew Street
• National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (CLAHRC YH) (Ref NIHR CLARHC YH II 14653)
• NIHR SDO Information and Value Based Commissioning - explaining the variation and causes of hospital activity and outcomes (Ref 11/1022/19). CHE Lead: Martin Chalkley
• NHS England - Economic evaluation of the Fragility Hip Fracture Best Practice Tariff. CHE Lead: Nils Gutacker
• EuroQol Research Foundation (Ref 2016450). The role of EQ-5D value sets based on patient preferences in the context of hospital choice in the national PROM programme in England. CHE lead: Nils Gutacker

The work for all these funders will require the sensitive PROMs data to measure patient outcomes.

The project will use only the following data: HES APC 1989/90 - 2015/16, Sensitive field: Consultant Code; HES Outpatient 2002/03 - 2015/16; PROMs 2009/10 – 2015/16.

Project 3 - Evaluation of the impacts of health care policy, organisation, finance and delivery of NHS services and quantification of differences in health care utilisation, expenditure, morbidity and mortality over time, across geographic regions, health providers, and among different patient groups;

The purpose of this project is to produce evidence to inform the Department of Health’s decisions on resource allocation and the design and direction of future policy regarding the health and social care sectors, with CHE’s advice and analyses being sought to feed into White papers and specific government reviews. This project includes understanding which type of “market” for health and social care services – from highly regulated internal markets to fully decentralised market models – best achieves strategic goals. It also includes evaluations of payment policies (including financial incentive schemes) and changes to the organisation of services (e.g. co-location of general practitioners alongside emergency departments) that seek to encourage good quality, cost-effective care and/or facilitate access to timely care. The main aims are to: analyse the potential for use of markets and payment mechanisms in health and social care to improve overall performance; analyse the impact that different payment policies or market configurations can have on prices, outputs, quality and outcomes; explore how the best payment systems and market configurations could be implemented in practice; and establish the effect of innovative organisational forms on costs and quality of care.

Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001). CHE Lead: Hugh Gravelle
• NIHR HS&DR 10/1011/22 and NIHR HS&DR 13/54/40 Relationships between quality of primary care and secondary care outcomes for people with mental illness. CHE Lead: Rowena Jacobs
• Wellcome Trust [ref: 105624] through the Centre for Chronic Diseases and Disorders (C2D2) at the University of York: Finance and organisation of mental health services. CHE Lead: Rowena Jacobs
• Health Foundation [ref: 57151] Efficiency, cost and quality of mental healthcare provision. CHE Lead: Rowena Jacobs
• NIHR HS&DR (Ref DRF/2014-07-055): Doctoral Research Fellowship - Measuring & explaining variations in general practice performance. CHE Lead: Rita Santos.
• NIHR HS&DR (Ref 15/145/06): General Practitioners and Emergency Departments (GPED) Efficient Models of Care. CHE lead: Nils Gutacker

The project will use only the following data: HES APC 1998/99 – 2015/16; A&E 2007/08 – 2015/16; Outpatient 2002/03 – 2015/16; PROMs 2009/10 – 2015/16; MHMDS 2011/12 – 2013/14; MHLDS 2014/15 – 2015/16; HES APC Sensitive Psychiatric Fields: Detention category (DETNCAT), Legal group of patient (psychiatric) (LEGALGPC), Legal status classification (LEGLSTAT)

The work for all funders will require the use of the sensitive PROMs data to measure morbidity over time.

This project will also require use of MHMDS & MHLDS data linked to HES data in order to carry out analyses into the economics around mental health and mental health care provision. CHE is requesting sensitive MHMDS/MHLDS fields and sensitive HES psychiatric fields (Legal group of patient, Legal status classification, and Detention category). These relate to the legal category / legal status of the patient which is an important indicator of patient severity. CHE will need these sensitive data items to accurately control for the impact of detention on resource use and utilisation. CHE needs to check data consistency between HES and the MHMDS/MHLDS and therefore requires sensitive data on legal status in both datasets.

Project 4 - Investigation of variation and inequalities of access, utilization, costs, patient outcomes, clinical practice, choice of provider, competition and concentration of health care services across England.

The purpose of this project is to produce information that the Department of Health and Office of National Statistics will use to address the NHS’ duty under the Health and Social Care Act 2012 to consider reducing health inequalities. CHE has recently developed new methods of local health equity monitoring for health care quality assurance, which NHS England adopted in 2016. In collaboration with analysts at NHS England, CHE will refine and use these methods and related measures to monitor the progress of national and local NHS organisations in reducing inequalities in healthcare access and outcomes, to gain insight into the determinants of inequalities, and to evaluate the equity impacts of local new models of care. The work will also assist the ONS to conduct distributional analyses of NHS spending for use in constructing statistics about in-kind social transfers.

Funder:
• NIHR TCC (Ref SRF-2013-06-015) Health equity impacts: evaluating the impacts of organisations and interventions on social inequalities in health. CHE Lead: Professor Richard Cookson
• ONS Update of current methodology for allocating social transfers in kind. CHE Lead: Miqdad Asaria

The project will use only the following data: HES APC 1989/90 – 2015/16; A&E 2007/08 – 2015/16; Outpatient 2002/03 – 2015/16; Critical Care 2011/12 – 2015/16; PROMs 2009/10 – 2015/16.

The work requires the use of sensitive PROMs data to measure patient outcomes in secondary care.

Project 5 - Evaluation of the interface between the different sectors of the health care system, including the effects of quality and access of primary care on patient use and outcomes in secondary care; and the relationship between long term care, social care and secondary care utilisation.

It has long been understood that health and social care services frequently provide treatment and care for the same individuals, so ensuring that these are ‘joined up’ or well co-ordinated has been an important and long-standing policy objective. In practice, however, both the services and approaches to monitoring these have developed separately, with potential implications for the efficiency and effectiveness of both health and social care. The purpose of this project is to produce evidence that will be used by the Department of Health and commissioners to inform discharge arrangements and the design of integrated care arrangements and to identify opportunities for substitution of different types of health and social care services. CHE shall also be developing an online web tool to inform patients about their likely outcome of surgery to impact on shared decision making in primary care in York.

Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001) CHE Lead: Andrew Street
• ESRC Impact Accelerator Account - developing an online web tool (Ref A0158801) CHE Lead: Nils Gutacker

The project will use only the following data: HES APC 1989/90– 2015/16; A&E 2007/08 – 2015/16; Outpatient 2002/03 – 2015/16; Critical Care 2011/12 – 2015/16, PROMs 2009/10 – 2015/16.

This project requires the sensitive PROMs data to measure patient outcomes in secondary care.

Project 6 - Evaluating the development of medical revalidation in England and its impact on organisational performance and medical practice.

In the past, once they had qualified, health professionals were subject to little or no scrutiny during their career unless their performance gave cause for concerns or there were complaints about them. But in 2012 the General Medical Council introduced a new requirement for all doctors to be “revalidated” at least once every five years while they hold a licence to practise. The purpose of this project is to measure the effect of medical revalidation on patient outcomes, including mortality, emergency re-admission and PROMs for several tracer conditions such as AMI, hip replacement etc., as well as to identify any unintended effects on the supply of medical labour in the English NHS. This project requires HES data to examine the impact of revalidation and related systems for managing medical performance in NHS acute care, looking at individual level and organisational level effects.

Evidence on the effectiveness of revalidation will allow policy makers to modify the current system and/or encourage its wider roll-out to other health professions to improve the quality of care provided, thereby benefitting patients in the English NHS and elsewhere.

Funder:
• Policy Research Programme (reference PR-R9-0114-11002). CHE lead: Nils Gutacker.

The project will use only the following data: HES APC 2007/08 - 2015/16; A&E 2007/08 – 2015/16; Outpatient 2007/08 – 2015/16; PROMs 2009/10 – 2015/16.
This project requires the sensitive PROMs data to measure organizational performance and the Consultant Code to assess differences in medical practice.


CHE confirms that the data under this application would only be used for the six projects listed, and any additional project (whether as part of the DH programme or otherwise) would require a separate approval. Equally individuals working on each project will only be permitted to access the data relating to that project, as identified within this application. Access is granted for each project only to the named individuals associated with that project under authorised user names. Such access is password controlled (with a password reset required on a regular refresh).

The controls enable a single copy of the data to be held, reducing security risk associated with multiple copies being provided per project. This model is aligned with similar arrangements for other sizeable research institutions.

The access procedures are set out in the University of York’ System Level Security Policy (October 2016), as follows:
“Logical measures for access control and privilege management

“Permissions to access the data are managed using Window’s Active Directory. Access to datasets is granted to named users only, as approved in the data sharing agreements. Users can store derived datasets in their personal user folders or in shared project folders, where access is granted to individuals working on the respective projects. Users are only allowed to store derived data in project folders if all users who can access the folder also have permission to access the source data according to current data sharing agreements.

“Access rights and permissions are reviewed for each data application and re-application. The ADACX IT manager configures user permissions once authorisation has been granted in writing from the CHE liaison officers, who maintain a list of user permissions.”

Further, access to data is administered and monitored by the CHE liaison officers through a registry. The registry lists all the projects with relevant Principal Investigator (PI) for which a valid Data Sharing Agreement issued by NHS Digital is in place. Every member of staff working on a project(s) is requested to sign a non-disclosure form on an annual basis. The purpose of this form is to ensure compliance to the Centre for Health Economics and the University of York’s data protection policies, adherence to the Data Protection Act and all its principles, and to the Centre for Health Economics System Level Security Policy. Members of staff who fail to return a signed form by the deadline provided will be excluded from access to the data until a signed form is returned.

Yielded Benefits:

Project 1 - The primary output from this ongoing project is the production each year of an annual update to national NHS productivity figures that incorporates the most recent financial year of data. In 2017, the annual update was produced for the DHSC and, as in other years, was used by them externally and internally in monitoring, informing policy debate, the annual spending review and negotiations on budget setting. Under this project, CHE also provided data about the quality of NHS care to the Office of National Statistics that are used each year in the construction of the national accounts. In 2017, additional analyses on hospital level productivity were produced for the DHSC, examining the factors underlying variation in productivity, which assists the DHSC in exploring how to get the best value from NHS resources. Project 2 - This project has produced a range of evidence that allows the DHSC and other organisations to understand and plan expenditure on different aspects of NHS care, to account for changes in activity and also to understand aspects of quality of care, First, it has investigated the drivers of health care expenditure, disentangling the influence of age, morbidity and proximity to death on the level of expenditure. This has important implications for the DHSC in terms of planning and for the design of the resource allocation formulae used to distribute the healthcare budget, Second, it has explored several aspects of activity in the secondary care sector, for instance, policies for moderating growth in elective admissions and how the provision of specialist rehabilitation services affect the duration and costs of hospital care. It has also looked at the use of patient valuation of the care they receive as a way of measuring the quality of hospital care. Third, it has investigated in-hospital mortality trends over time. These insights allow policy makers to appreciate the costs and the benefits of NHS provision and to plan for more effective and efficient care. Project 3 - The research in this project has evaluated whether the way in which the NHS is organised can affect the costs and outcomes of services to help inform policy decisions. In 2017, the research has produced evidence on a number of issues in mental health, including the relationship between costs and quality and socio-economic inequalities in access to care. Other examples include the production of evidence on the impact of the electronic booking system on referrals and non-attendances and the investigation of reasons for delayed discharges from hospital, both of which are important in terms of planning future organisation of care. Project 4 - This project has continued to investigate inequities in access, costs and outcomes and in 2017, further evidence relating to access, avoidable emergency hospital admissions and in-hospital mortality has been produced. This has added to knowledge of how to improve health and healthcare for vulnerable populations. Project 5 – In 2017, previous research on patient-assessed outcomes has been extended (by working with Vale of York CCG) to generate a web tool to support discussions between patients and their GPs about whether to undergo planned surgery. The on-line tool, aftermysugery.org.uk can be used by patients and their GPs, who input basic demographic data and fill in a pre-operative health status questionnaire. The webtool then returns a predicted post-operative health status, together with national comparator data, displayed in various visual formats. This information is designed to a) help patients decide whether they feel the expected health improvement is sufficiently high to make having the operation worthwhile, b) inform patients about the likelihood of a negative outcome, and c) provide information about which hospitals secure better outcomes for their patients. Other strands of the research have established that incentive payments made to GPs to increase the numbers of individuals diagnosed with dementia have been effective, but that there may have been unintended consequences on patient experience and access.

Expected Benefits:

The benefits are to be delivered on an ongoing basis in accordance with CHE’s funding agreements, and accessible from CHE’s website: http://www.york.ac.uk/che/. For all of the above projects, various funders have commissioned the work as evidenced by letters supplied. The expected benefits include:

Project 1 – to December 2017

The Department of Health uses CHE’s work on of efficiency, effectiveness and productivity to provide numerical answers and context for, among others, Parliamentary Health Committees, the Public Accounts Committee and Public Expenditure Inquiries. By detailing the amount and quality of care secured from NHS resources this work provides evidence about what the NHS is doing with the budget it receives and helps identify opportunities for better use of funding. This supports public accountability and transparency, and helps ensure that the NHS receives the budget it needs to meet health care demands and makes best use of taxpayers’ money.

Strong productivity growth for the economy as a whole is important because it increases tax revenues and helps improve wages and living standards. The Office of National Statistics draws heavily on CHE’s work in producing the national accounts, having adopted CHE’s methodological approach to measuring the contribution made by the NHS to national Gross Domestic Product (GDP) and, in assessing this contribution, by accounting for quality of NHS care using measures that CHE constructs from the data supplied by NHS Digital. Given that much government policy is designed to influence GDP, accurate measurement is essential to ensuring that policy is correctly focused and the government is properly held to account for its policies. CHE disseminates the work through various media to inform the public about NHS productivity. For example, this blog in The Conversation (https://theconversation.com/nhs-outpaces-the-uk-economy-in-productivity-gains-53899) has been widely cited to counter misconceptions that NHS productivity is poor. In fact, NHS productivity growth has outpaced that of the economy as a whole since the 2008 recession. Ensuring that the public is fully informed of this fact helps bolster support for the NHS, thereby making it more likely that the government provides the NHS with the funding required to meet the health care needs of the population.

Project 2 – to December 2017

CHE’s projects evaluating the performance of health care providers provide evidence to inform national and regional (Yorkshire and Humber – Y&H) policy-makers and providers about the scope and focus of performance improvement and outcome measures, tariff design, and patient choice. The project will assist decisions regarding the provision of services that offer the greatest value for money according to the benefits achieved. In due course this will translate to a more efficient allocation of health care resources, through appropriate budget spend. Where resources are allocated, according to the maximum benefits achieved, with a particular target condition, health benefits ensue. In addition, by working with local decisions makers, to promote the use of evidence based medicine and prospective evaluation, this will increase the potential for future decisions to be grounded on economic principles and consideration of the tradeoffs between choices made. In the short term, the work conducted to inform the NYH Major Trauma Network meeting will help to establish an appropriate, affordable, major trauma rehabilitation service in Y&H. This will translate to patients benefits associated with appropriate rehabilitation, as well as gains to the health service, in terms of reduced length of stay. It is anticipated that the work looking at the care hubs implemented in Y&H, will similarly be used to support commission/de-commissioning decisions regarding the future use of such services.

Project 3 – to December 2017

CHE’s evaluations of the impacts of health care policy, organisation, finance and delivery of NHS services are used to inform resource allocation arrangements and the design and direction of future policy regarding the health and social care sectors with CHE’s advice and analyses being sought to feed into White papers and specific government reviews. The main benefits from the projects will be to make better informed policy choices on issues related to: the design of payment systems, including financial incentives; the viability of small hospitals, and the implications from closing them e.g. in terms of restricted patient choices; the case for and against further expansion of private sector providers within the NHS; the usefulness of competition policies to improve access to hospitals (in the form of reduced waiting times); the likely impact of the introduction of the waiting times standards in mental health services, and supporting policymakers (e.g. NHE England and NHS Improvement) to improve the finance, organisation and quality of mental healthcare provision for the benefit of service users.

Project 4 – to December 2017

CHE’s projects investigating inequalities in healthcare access and outcomes are helping the NHS address its duty under the Health and Social Care Act 2012 to reduce health inequalities. Following extensive stakeholder involvement and knowledge transfer activity in 2016, our methods were adopted by NHS England in August 2016, as reported in The Guardian, The Independent, and various health media. Indicators of local inequality in potentially avoidable emergency hospitalisation based on our work have been incorporated in the CCG Improvement and Assessment Framework and the associated RightCare information packs distributed to all CCGs, and the NHS Equality and Health Inequalities Team is now actively promoting the use of these indicators by CCGs as part of the NHS quality assurance process for evaluating the equity impacts of local new models of care.

As part of our public and stakeholder engagement work for this project, we have developed various visualization tools and public-facing dissemination materials, which are collected together at this website: http://www.york.ac.uk/che/research/equity/monitoring/

Project 5 – to December 2017

The main beneficiaries of the aftermysurgery.org.uk webtool are local patients, their GPs and the Vale of York CCG, which commissions NHS services in the local area. Patients using the webtool will be better informed about the likely effect of surgery on their health, thus allowing them to make informed decisions about their healthcare choices and engage more with their GPs during the consultation. GPs benefit by being able to have a more informed and structured discussion with their patients about their healthcare options. The web tool can help to illustrate the likely impact of surgery on patients’ health, thus helping GPs communicate expectations about the effectiveness of surgery for individual patients. GPs can also draw on the data on local hospital quality to suggest a healthcare provider to the patient. The local Vale of York CCG benefits financially if the information communicated via the webtool leads to reductions in elective hospital activity. This would happen if patients that do not consider surgery to be sufficiently beneficial decide not to undergo the operation but seek other ways to manage their condition (e.g. medical management, physiotherapy). Furthermore, the web tool helps the CCG fulfil its obligation to help communicate information about hospital quality to patients and their GPs. The webtool is to be launched officially by the Vale of York CCG in January 2017. Its usefulness will then be evaluated, allowing for refinement of the interface, and national roll-out in late 2017.

Project 6 – to November 2016

The rationale for the project is to assess the economic arguments surrounding the issue of doctor re validation with particular emphasis on measuring changes to medical performance and assessing the cost-effectiveness of the programme in terms of not only increased health related quality of life for the population but also public assurance. We also directly address the extent to which the arguments outlined in the DH pre-programme impact assessment which was used to support the adoption of revalidation are being realised.

Outputs:

The outputs from all of the projects will include peer reviewed papers in academic journals, reports for funders, lay summaries such as newsletters and blogs, and conference and seminar presentations to academic, policy, professional and public audiences. The Centre for Health Economics has a long-established track record in delivery of policy research that utilises HES data, as recognized by the award of the Queens Anniversary Prize in 2007. Examples of recent publications arising from the above projects that have employed the HES data can be found here http://eshcru.ac.uk/publications/index.htm and http://www.york.ac.uk/che/publications/in-house/.

Reports will be produced containing aggregate results that show trends over time, differences across providers, commissioners, geographical areas and by patient subgroups and patient characteristics. The results will contain estimated correlations showing associations between patient outcomes and patient characteristics, hospital, institutional, geographic and environmental factors. Statistical results will be presented in interactive spreadsheets or “Dashboards” (e.g. similar to http://health-inequalities.blogspot.co.uk/ which uses QOF data and only contains aggregated data which can be interrogated), tables and maps of aggregate statistics summarising patient characteristics. Reporting will comply with ONS guidelines on disclosure of potentially patient identifiable data i.e. no small numbered cells and figures will be reported.

The outputs from each project will be delivered in accordance with CHE’s funding agreements, which run to different timelines with various milestones for each. The key milestones and timelines for each project (including 2015 publications) are:

Project 1 - The primary output from this project is the production of an annual update to national NHS productivity figures that incorporates the most recent financial year of data. Under this project, CHE has demonstrated that NHS productivity growth is meeting the requirements of the Five Year Forward View and outpaces that of the economy as a whole. CHE’s figures are widely used to inform policy discourse, with the DoH relying on the information for internal monitoring purposes and for external reporting and response purposes, such as to inform annual Spending Reviews. Under this project, CHE also provides data about the quality of NHS care to the Office of National Statistics that are used in the construction of the national accounts. In 2016, CHE presented productivity figures to the House of Commons Health Committee on the Impact of the Spending Review on health and social care and to the House of Lords committee on the long-term sustainability of the NHS.

In addition to the annual update of national figures, CHE also undertakes analyses of variation in hospital productivity and produces short reports or memorandum for the DoH to address specific questions about NHS productivity. CHE presents the work regularly to various audiences, including politicians, policy makers, academics, health professions and the general public through seminars, conference presentations and media appearances.

CHE has produced the following outputs during 2016:

Bojke C, Castelli A, Grašič K, Howdon D, Street A. Productivity of the English NHS: 2013/14 update. Centre for Health Economics, University of York; CHE Research Paper 126, January 2016.

Bojke C, Castelli A, Grašič K, Street A, Productivity growth in the English National Health Service from 1998/1999 to 2013/2014, Health Economics, 2016 DOI: 10.1002/hec.3338.

Bojke C, Castelli A, Grašič K, Howdon D, Street A. Did NHS productivity increase under the Coalition government? In: Exworthy M, Mannion R, Powell M. Dismantling the NHS? Evaluating the impact of health reforms. Policy Press, 2016.

Aragon Aragon M, Castelli A, Chalkley M, Gaughan J. Hospital productivity growth in the English NHS 2008/09 to 2013/14 Centre for Health Economics, University of York; CHE Research Paper 138, October 2016.

Street A, Grašič K. NHS outpaces the UK economy in productivity gains. The Conversation, 29 January 2016.

Bojke C, Castelli A, Grašič K, Mason A, Street A. Measurement and analysis of NHS productivity growth: adjusting for the quality of healthcare output. Centre for Health Economics, University of York; draft report to DoH, September 2016.

Bojke C, Grašič K, Howdon D, Street A. Alternative sources of primary care data for productivity calculations. Centre for Health Economics, University of York; draft report to DoH, July 2016.

Bojke C, Castelli A, Grašič K, Howdon D, Street A. Productivity of the English NHS: 2014/15 update. Centre for Health Economics, University of York; draft report to DoH, November 2016.

Project 2 - This project will produce a range of outputs, including reports to support policy decisions and peer reviewed publications. Where appropriate, analysis will also be disseminated to national and local decision makers at formal and informal meetings, including strategic commissioning groups.

CHE has produced the following outputs during 2016:

Duarte A, Bojke C, Richardson G, Bojke L. Final reports on commissioning of rehabilitation services in Yorkshire and Humber region, produced for York CCG. Delivered January 2016 and June 2016. Both of these reports were presented at NYH Major Trauma Network - Network Rehabilitation Strategy Group Meetings.

In 2017 CHE will deliver:
• a final report on commissioning of care hubs in the Yorkshire and Humber region, produced for York CCG, expected June 2017;
• a final report on the Vanguards delivered in Harrogate, produced for Harrogate and Rural CCG to be delivered June 2017;
• publication of analysis undertaken to inform commissioning of rehabilitation services in Yorkshire and Humber region, to be submitted to Rehabilitation journal in February 2017;
• publication of analysis undertaken to inform commissioning of care hubs in Yorkshire and Humber region, to be submitted to HSJ journal in September 2017; and
• completion of analysis undertaken to determine the use of multiple versus stated stenting in elective PCI, to be submitted to a cardiovascular journal, such as the British Journal of Cardiology.

Chalkley M, Aragón MJ. Demand Management for Elective Care: System Reform and other Drivers of Growth: An examination of the factors affecting the growth of elective hospital activity in England from 1998 to 2012 and the implications of those for managing demand for elective activity. Chapter 2 in “Elective hospital admissions: secondary data analysis and modelling with an emphasis on policies to moderate growth", to published in 2017 (https://www.journalslibrary.nihr.ac.uk/projects/11102219/#/).

Project 3 - During 2016 the following outputs have been produced as part of the ESHCRU workstream on markets and organizational structures in health and social care markets, and as part of NIHR HS&DR 13/54/40 and Wellcome Trust [ref: 105624] through C2D2:

Jacobs, R., Chalkley, M., Aragón, M.J., Böhnke, J.R., Clark, M., Moran, V. & Gilbody, S. (2016) Funding of mental health services: Do available data support episodic payment? CHE Research Paper 137, Centre for Health Economics: University of York.

Moran V, Jacobs R, Mason A. Variations in performance of mental health providers in the English NHS: An analysis of the relationship between readmission rates and length-of-stay. Administration and Policy in Mental Health and Mental Health Services Research Jan 2016. 20110.1007/s10488-015-0711-4

Gutacker, N., Siciliani, L., Moscelli, G., Gravelle, H. Choice of hospital: which type of quality matters? CHE Research Paper 111 and Journal of Health Economics, 2016, 50, 230-246.

Gaughan, J., Gravelle, H., Siciliani, L. Delayed discharges and hospital type: evidence from the English NHS. CHE Research Paper 133. To appear in Fiscal Studies.

Moscelli, G., Sicilliani, L., Gutacker, N., Gravelle, H. Location, quality and choice of hospital: evidence from England 2002/3-2012/13. CHE Research Paper 123 and Journal of Urban and Regional Economics, 2016, 60, 112-124.

Moscelli, G., Gravelle, H., Siciliani, L. Market structure, patient choice, and hospital quality for elective patients. CHE Research Paper 139. Centre for Health Economics: University of York.

During 2017, CHE will produce reports on ongoing work on quality of NHS versus private hospitals (March 2017), quality of small hospitals (December 2017), effects on patients of hospital closure (December 2017), competition and quality in general practice (March 2017), the effect of competition on hospital waiting times, and waiting time inequalities across (eg due to different quality) and within hospitals (December 2017), as well as papers on mental health funding (December 2017).

Project 4 - The primary output from this project in 2016 was the final report to NIHR HS&DR (Ref DRF/2014-07-055) submitted in January 2016 and published in Health Services and Delivery Research in August 2016; together with the annual progress report to NIHR TCC (Ref: SRF-2013-06-015) in December 2016.

CHE has produced the following outputs during 2016:

Cookson, R., Asaria, M., Ali, S., Ferguson, B., Fleetcroft, R., Goddard, M., Goldblatt, P, Laudicella, M, and Raine, R. (2016). Health Equity Indicators for the English NHS: a longitudinal whole-population study at the small-area level. Health Services and Delivery Research, 4 (26). https://dx.doi.org/10.3310/hsdr04260

Asaria M, Cookson R, Fleetcroft R, Ali S. Unequal socioeconomic distribution of the primary care workforce: whole-population small area longitudinal study. BMJ Open 2016;6(1):e8783 doi: 10.1136/bmjopen-2015-008783

Asaria M, Ali S, Doran T, Ferguson B, Fleetcroft R, Goddard M, Goldblatt P, Laudicella M, Raine R, Cookson R. How a universal health system reduces inequalities – Lessons from England. Journal of Epidemiology and Community Health 2016; doi: 10.1136/jech-2015-206742

Asaria, M., Doran, T. & Cookson, R. (2016). The costs of inequality: whole-population modelling study of lifetime inpatient hospital costs in the English National Health Service by level of neighbourhood deprivation. Journal of Epidemiology and Community Health. Accepted 19 April 2016. doi:10.1136/jech-2016-207447

Sheringham, J., Asaria, M., Barratt, H., Raine, R., & Cookson, R. (2016). Are some areas more equal than others? Socioeconomic inequality in potentially avoidable emergency hospital admissions within English local authority areas. Journal of Health Services Research & Policy. doi:10.1177/1355819616679198 first published on November 15, 2016

Cookson, R. A., Propper, C., Asaria, M., & Raine, R. (2016). Socio-Economic Inequalities in Health Care in England. Fiscal Studies 37(3-4), 371–403. DOI: 10.1111/j.1475-5890.2016.12109

Fleetcroft, R., Asaria, M., Ali, S., & Cookson, R. (2016). Outcomes and inequalities in diabetes from 2004/2005 to 2011/2012: English longitudinal study. British Journal of General Practice. DOI: 10.3399/bjgp16X688381

Gutacker, N., Siciliani, L. and Cookson, R., 2016. Waiting time prioritisation: evidence from England. Social Science & Medicine, 159, pp.140-151

Project 5 - Under this project CHE, working alongside Vale of York CCG, has generated a web tool to support discussions between patients and their GPs about whether to undergo planned surgery. This uses the APD and PROMs data to underpin an online web tool accessed at aftermysugery.org.uk. This tool can be used by patients and their GPs, who input basic demographic data and fill in a pre-operative health status questionnaire. The webtool then returns a predicted post-operative health status, together with national comparator data, displayed in various visual formats. This information is designed to a) help patients decide whether they feel the expected health improvement is sufficiently high to make having the operation worthwhile, b) inform patients about the likelihood of a negative outcome, and c) provide information about which hospitals secure better outcomes for their patients.

Work under this project has also established that payments made to GPs as part of the Quality and Outcomes Framework (QOF) dementia review have helped reduce the risk of long-term care home placement following acute hospital admission and that hospital patients discharged to the community have significantly shorter stays if they are cared for by general practices that reviewed a higher percentage of their patients with dementia. This demonstrates that the dementia review can improve the health and well-being of those with dementia and their carers.

CHE has produced the following outputs during 2016:

Online webtool: aftermysurgery.org.uk inviting prospective patients to “Find out how people like you felt after surgery”

Goddard M, Kasteridis P, Jacobs R, Santos R, Mason A. Bridging the gap: The impact of quality of primary care on duration of hospital stay for people with dementia. Journal of Integrated Care 2016; 24:15-25.

Kasteridis P, Mason A, Goddard M, Jacobs R, Santos R, Rodriguez-Sanchez B, McGonigal G. Risk of Care Home Placement following Acute Hospital Admission: Effects of a Pay-for-Performance Scheme for Dementia. PLoS ONE 2016; 11:e0155850.

Goddard M, Mason AR. Integrated Care: A Pill for All Ills? International Journal of Health Policy and Management 2017; 6:1-3. 10.15171/ijhpm.2016.111 (epub: 13 Aug 2016)

Project 6 - final report for Department of Health (reference PR-R9-0114-11002) due April 2017.

All products are available free of charge and available to the public via CHE’s website http://www.york.ac.uk/che.

Processing:

Whilst the nature of detailed analysis in relation to each project varies, the broad context of processing is consistent. The following processing activities apply to all of the projects listed above.

Data storage: Data will only be stored on the CHE data analysis server and the backup server and will only be accessible within the Centre for Health Economics to individuals who are substantively employed by the University of York. Access to data is restricted to specific individuals according to role and project. Access to sensitive data is also restricted to only those individuals working within projects that are authorised to use sensitive data.

Data analyses: CHE will use standard software (e.g. STATA, SAS, R) to analyse the data, derive descriptive statistics and apply multiple regression models to explore the relationships between variables.

Data linkage: CHE will run the data through the HRG grouper and attach Reference Cost data using HRG codes and will link HES APC with MHMDS/MHLDS using the bridging file. The data will then be linked:
• to aggregated census and other geographical data using the LSOA (Lower Super Outputs Area) variables;
• to Quality and Outcomes Framework and the Attribution Data Set using GP codes; and
• to accounts and organisational-level data using provider codes.

For the revalidation project CHE will use the consultant code to link with General Medical Council (GMC) register data on consultant age, gender, specialty and date and outcome of revalidation. The consultant code is a sensitive code and therefore access will be restricted to researchers involved in the revalidation project. Once linkage is performed for that project CHE will pseudonymise the consultant identifier. None of the linkages CHE perform will enable re-identification of any patients.

No data will be linked to record level patient data.

Data processing: Analyses of the HES and MHMDS/MHLDS data will involve estimation of statistical and econometric models using software including Stata, SAS and R. The analyses will take account of
1) patient demographic and socio-economic information such as age, gender, ethnicity, carer support, deprivation measures;
2) patient diagnostic information such as diagnoses (co-morbidities), Charlson score, psychiatric history, HRG or PbR care cluster;
3) treatment information such as admission type, specialty of provider, use of the Mental Health Act, community and inpatient services received by patients;
4) quality and outcomes such as PROMs, 30-day survival, HoNOS scores, waiting times, readmissions, and social outcomes such as employment and accommodation status;
5) service level factors such as number of contacts with staff, and delayed discharge.

For all projects the data will be used to undertake both cross-sectional and longitudinal analyses, allowing analyses within-year variations and of changes over time.


Personality disorder, socioeconomic factors and psychiatric hospital admissions: assessing the evidence — DARS-NIC-382364-Y0F4F

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, 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(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 2020-11-23 — 2023-11-22 2020.12 — 2021.05.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

The aim of this study is to identify whether there are relationships between key socioeconomic factors and psychiatric inpatient admissions for people with a personality disorder diagnosis. Evidence suggests that there are disproportionately high numbers of people with a personality disorder diagnosis in psychiatric inpatient settings (Evans et al., 2017). Difficult social circumstances have a longstanding and well-documented relationship with negative mental health outcomes (Marmot, 2010), but little is known about whether this association extends to people with a personality disorder diagnosis. This research seeks to understand whether socioeconomic factors are implicated in psychiatric hospital admissions for people with a personality disorder diagnosis. Analysis using the 2014 Adult Psychiatric Morbidity Survey will achieve these aims by enabling an exploration of the relationship between key socioeconomic factors, mental health conditions and mental health outcomes, each of which are available as indicators in the dataset.

People with a diagnosis of personality disorder currently experience a mortality gap of twenty years compared to the general population, almost one in ten will commit suicide and 50-70% of psychiatric inpatients have this diagnosis (Fok et al., 2012; Appleby et al., 2018; Dale et al., 2016; Evans et al., 2017). Economic costs resulting from healthcare service use and unemployment for people with personality disorders are estimated at over £7.9 billion annually with a projected rise to £12.3 billion by 2026 (McCrone et al., 2008). Specialist community service provision has been improving over time but remains patchy, and people with a personality disorder diagnosis are frequent users of accident and emergency, psychiatric inpatient, and primary care services (Evans et al., 2017).
To date, research has focused on individual treatment interventions but has not explored the potential impact of upstream social determinants on the health and healthcare use of people with a personality disorder diagnosis. Socioeconomic deprivation has longstanding links with poorer mental and physical health (Marmot, 2010), and for people with a diagnosis of personality disorder, who may already be socially excluded and unable to access appropriate support, difficult social circumstances have the potential to worsen symptoms. More evidence is needed to determine how socioeconomic factors affect psychiatric hospital admissions for people with personality disorders.
Using the APMS will offer an opportunity to study these relationships using a nationally representative dataset for the first time. This study will therefore contribute to the currently limited evidence base on personality disorders, with the aim of improving policy and clinical practice for people with this diagnosis.

All individuals who participated in the APMS were consenting adults living in England and Wales and, as such, the processing of their data for the purposes described here should not come as unexpected.

The greater understanding of these relationships and the potential for more effective healthcare interventions generated via the study findings means that it is in the public interest, as such the University of York intend to process personal data under Articles 6 (1) (e) and Article 9 (2) (j) of the GDPR for the purpose of carrying out this research. Furthermore, it is in keeping with the University of York’s ‘public task’ which states ‘the objects of the University shall be to advance learning and knowledge by teaching and research, and to enable students to obtain the advantages of University education’. As such, the basis for dissemination of this data falls under Sections 261(1) and 261(2)(b)(ii) of the Health and Social Care Act 2012.

The research is funded by a University of York/Wellcome Trust post-doctoral research fellowship. The funders will have no influence over the analysis or findings.

The University of York is the sole Data Controller who also processes the data for the purposes described in this Agreement.

Expected Benefits:

It is anticipated that the research will contribute to evidence on the socioeconomic risk and protective factors for psychiatric hospital admissions in people with a diagnosis of personality disorder. Understanding of the factors that precipitate mental health crisis in people with personality disorders is currently very limited and this study will therefore inform policy, clinical practice and the development of effective interventions. In turn, the evidence will seek to support reductions in the number of psychiatric hospital admissions, which are disruptive and upsetting for individuals and their families, in addition to being costly for health services when compared to effective community-based interventions. Findings will be shared with policy and healthcare stakeholders and people with lived experience of this diagnosis in 2021. The research will additionally be used to inform future personality disorder research priorities.

Outputs:

Findings will be submitted to peer-reviewed journals as one or more research papers, examples of which include the British Journal of Psychiatry and Social Science and Medicine. It is anticipated that the articles will be written in early 2021 and published mid-to-late 2021. The findings will also be presented at several events planned for March and April 2021 at the University of York, designed to raise awareness about the experiences of people with a personality disorder diagnosis. The findings will also be discussed with health and social care services and used to inform the development of care pathways. The study results may also be submitted for presentation at academic conferences. To avoid any potential risk of re-identification, all published results will be at the aggregate level and only used to draw population-level inferences.

Processing:

There will be no flow of data from the University of York to NHS Digital.

The 2014 APMS data set is held on behalf of NHS Digital by the UK Data Service (UKDS) (www.ukdataservice.ac.uk) and the UKDS is responsible for dissemination under direction by NHS Digital. The University of York will receive the pseudonymised APMS data set. There is no facility to select individual variables. The University of York will be able to download the data set from UKDS for the period specified within the Data Sharing Agreement and must securely destroy all local copies of the data set when the Agreement expires and notify NHS Digital in line with standard procedures. This 2014 version of the data set available has been redacted on Disclosure Control Procedure advice to minimise the likelihood of individuals being able to identify anyone taking part in the survey.

UKDS will transfer the pseudonymised APMS data to University of York. No other organisations will be involved in the flow of data

Data obtained in pseudonymised form will be stored on the University of York secure computer network. It will be accessed only by those involved in carrying out the research, all of whom are substantively employed by the University of York and have received adequate data protection and confidentiality training.

This research will involve both descriptive and multivariate analyses to determine whether associations exist between socioeconomic factors and psychiatric hospital admissions. Analysis will be undertaken for respondents with a personality disorder diagnosis only, alongside comparative analysis with respondents who report other mental health conditions - for example, psychosis, depression - and no mental health condition. As hospital admissions could to some extent be a function of health service capacity, for example, availability of beds, other indicators of a mental health crisis including previous self-harm or suicidality will also be used as outcome variables.

The full, pseudonymised 2014 dataset will be required due to the exploratory nature of the analysis. To avoid any potential risk of re-identification, all published results will be at the aggregate level and only used to draw population-level inferences. The data will not be linked as there are no identifiers to link on.
Data have been requested for three years to enable analysis to take place in the first instance, an extension may be requested at this point to allow for further analysis to take place if required.



Does the transition from paediatric to adult healthcare lead to increased healthcare usage for young people with a life limiting condition? A quasi-experimental study — DARS-NIC-331607-P4J8H

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Section 251 NHS Act 2006)

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

Purposes: No (Academic)

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

When:DSA runs 2020-08-20 — 2023-08-19 2021.01 — 2021.02.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

The research aims to determine whether there is an increase in healthcare use (particularly emergency healthcare use) when children with life limiting conditions transition from children's to adult services. The term Life limiting conditions (hereafter LLC) includes both life limiting conditions (these are conditions that invariably lead to premature death, e.g. Duchenne muscular dystrophy) and life-threatening conditions (these are conditions that may lead to premature death but may be cured, e.g. cancer). Examples of included conditions (the 282 most commonly recorded life limiting diagnoses in a previous extract of Hospital Episodes data) are provided to NHS Digital.

The research is in the public interest under Article 6 1(e) and Article 9 2(j) as it aims to quantify the effects of current healthcare practices and identify the impact on those affected. The research has the potential to improve healthcare for these individuals by identifying best practices.

The risks from dissemination are potential identification of individuals due to small numbers of individuals in some possible groupings (some conditions considered are very rare). This will be mitigated by following NHS Digital disclosure guidelines - either combining small groups of fewer than ten persons or censoring results for these groups. All dissemination for LLC will be at the level of diagnostic groups (e.g. cancer, neurological conditions) rather that at individual diagnosis level.

The requested data are essential for achieving the research objectives - outpatient data are needed to determine the point of transition from children's to adult services. Inpatient and A&E data are required to measure healthcare use. Death data (month and year of death only) are needed to remove individuals from the cohort after death.

The research is part of an NIHR funded fellowship looking at the transition from children to adult services for children with life limiting conditions, which commenced in 2019. The proposed analyses are a key part of the programme of research.

The broad objectives of the fellowship are:
1. Review the evidence for (quantitative evidence) a change in emergency hospital care and GP consultations during and after transition and (qualitative evidence) the reasons for this.
2. Quantify changes in care during and after the transition for young people with life limiting conditions, as compared to those with other chronic or no long term conditions:
a. in hospital care
b. in primary care
3. Determine what factors are associated frequency of GP consultation and emergency hospital care for young people with LLC during the transition.
4. Quantify the costs of any increase in emergency hospital care at the transition, for providers and patients and their families and compare these with costs of GP consultations.

Objectives 2a and 4 will be addressed using the data requested in this application. Objective 1 has been addressed through a systematic review of the literature and objectives 2b and 3 will be addressed by data requested from the Clinical Practice Research Datalink (CPRD), discussed in more detail further down in this agreement.

CPRD is a real-world research service supporting retrospective and prospective public health and clinical studies. CPRD is jointly sponsored by the Medicines and Healthcare products Regulatory Agency and the National Institute for Health Research (NIHR), as part of the Department of Health and Social Care. CPRD collects fully-coded patient electronic health records from GP Practices using the Vision® or EMIS® software systems. More detail on CPRD can be found here - https://www.cprd.com/services

The objectives addressed using data requested in this application can be further split as follows:
(i) determine basic demographic information - sex, age in each year, ethnic group, deprivation category and geographical region
(ii) Divide the population in HES experiencing transition from paediatric to adult care into three groups: those with a life limiting condition, those with a non-life limiting chronic condition and those without a long term condition. For those with a life limiting condition, category of condition will also be determined (e.g. cancer, neurological condition). Life limiting conditions will be determined from an ICD-10 (International Classification of Diseases, 10th Edition) LLC coding framework comprising 777 diagnoses.
(iii) Determine the age of transition of individuals in these groups
(iv) quantify changes in healthcare use at the transition, including (a) A&E visits per person per year, (b) Emergency inpatient admissions per person per year, (c) all inpatient admissions per person per year, (d) length of stay and (e) bed days per person per year and compare these between the groups identified in (i) and (ii)
(v) estimate the change in costs, to the NHS and to individuals/families of after transition compared to before the transition and compare these between the groups identified in (i) and (ii)

DATA REQUESTED
Pseudonymised data from the Inpatient Hospital Episodes Statistics (HES), Outpatient and A&E HES will be extracted by NHS Digital. This dataset which will include records for all individuals who were aged between 12 and 23 years at any point between 1 April 2007 and 31 March 2019. The University of York will only request data for those individuals in financial years 2006/07 (2007/08 for A&E) to 2018/19 and when they were aged 0-23 years at the first record in the year (see provided figure on cohort logic). Data should be provided in complete years - i.e. if an individual has records aged both 23 and 24 within a single financial year then all those records should be included (records when aged 24 years, within that financial year should not be excluded).

To provide some illustrative examples:
• An individual turns 23 on 20 January 2006. This person was not aged 12-23 at any point in financial years 2007/08 to 2018/19 and no data is requested for this person and he is not included in the study.
• An individual turns 12 on 1 April 2007. This person was aged 12-23 for the whole 2007/08 to 2018/19 study period (aged 23 in the 2018.19 financial year). Her records are requested for years 2007/08 to 2018/19 in A&E data and from 2006/07 to 2018/19 in APC and outpatient datasets (as she was included in the cohort and was aged 11, within ages 0-23 in 2006/07). Outcomes (objective iv and v) are analysed in all years 2007/08-2018/19, when she was aged 12 to 23. Categorisation under objectives (i to iii) takes place from years 2006/07 to 2018/19 when she was aged 11 to 23.
• An individual turns 12 on 30 May 2000. She was aged 12-23 years of age for the years 2007/08 to 2011/12. Data are requested for her for years 2007/08 to 2011/12 in A&E and from 2006/07 to 2011/12 in APC and outpatient data. Outcomes (objective iv and v) are analysed in years 2007/08-2011/12, when she was aged 19-23. Categorisation under objectives (i to iii) takes place from years 2006/07 to 2011/12, when she was aged 18 to 23.
• An individual turns 12 on 1 Junes 2018. He was aged 12 to 23 years for the 2018/19 year only. Data are requested for him for years 2007/08 to 2018/19 in A&E and 2006/07 to 2018/19 in APC and outpatient data. Outcomes (objective iv and v) are analysed in 2018/19 only, when he was aged 12. Categorisation under objectives (i to iii) takes place from years 2006/07 to 2018/19, when he was aged 0-12.

The study period of 1 April 2007 onwards has been chosen to match availability of A&E data (A&E visits are a key outcome). An additional preceding year of data (2006/07) is requested for APC and outpatient datasets as this will enable identification of diagnoses and transition points prior to the start of the study period.

Objective (i) requires all data possible for individuals included in the study, to pick demographic information that is not recorded in each record or often recorded inconsistently (e.g. ethnic group). Therefore data prior to age 12 are requested for included individuals.

For objective (ii), the additional data requested (i.e. data prior to age 12) are required to put individuals in one of the three groups identified, based on presence or absence of diagnostic codes and also patterns of hospital care - for example, an individual lacking diagnoses defined a-priori as indicative of a LLC or chronic condition but nonetheless having frequent outpatient appointments or inpatient admissions may be excluded from the group with no long term conditions. To form the three groups, data on all individuals in HES within the requested age range during the study period are required.

Objective (iii), determining transition points from paediatric to adult care, requires data from the APC and outpatient datasets, as transitions will be determined from the main speciality of consultants providing care. A year of data (2006/07) is required prior to the main study period to identify whether individuals in adult care in 2007/08 have just undergone transition or transitioned at an earlier point.

Objectives (iv) and (v) require records from the APC and A&E datasets as the outcomes of interest (hospital admissions and A&E visits are contained within these datasets). The 12-23 year age group is chosen to provide at least four years of data on each side of transition, assumed to take place from 16 to 19 years of age.

The years requested will enable the identification of any trends over time and also maximise power in the analyses in the smallest group, those with life limiting conditions.
Data are required at record level as there is a need to determine transition points for individuals and also to count individual events over time so that individual points of transition can be linked to individual hospital healthcare use. It is not necessary to identify individuals, but pseudonymised data are necessary to link healthcare events for the same individuals within and between the datasets requested. All records, even those that may initially be considered less relevant, such as maternity records for the cohort, are relevant to objectives (i) and (ii) and can also be used to determine presence of individuals in England within a year for time at risk calculations in the analyses.

National data are required to (a) maximise the sample size for those with life limiting conditions, enabling analyses of subgroups, (b) identify any regional variations that may indicate areas of best practice to inform future provision of services and (c) capture any regional variations in conditions - focussing on a particular region may give results that are not representative of the national situation. The results will be used to inform care nationally.
There are no other data available that provide information on hospital care for individuals in the number required (CPRD can provide HES data, but in much smaller quantities, with marginal power to meet the objectives (iii) and (iv) and insufficient power to analyses particular conditions or categories of LLC).

DATA MINIMISATION
The data requested have been minimised in the following ways:
Cohort definition: data are only requested for individuals aged 12-23 years at some point during the study period. This is the minimum age range needed to provide individuals aged to provide sufficient data either side of transition (i.e. 4 years either side of transition, which is expected to take place between 16 and 19 years of age for most individuals).

Datasets: the requested data sets are only those essential to the research: admitted patient care and A&E data as these contain the outcomes of interest (healthcare use as inpatient admissions, A&E visits etc), admitted patient care to identify life limiting and chronic conditions, and outpatient and admitted patient care datasets to determine the point of transition. Mortality data are requested as it is essential to know when individuals are no longer alive to calculate time at risk for the various outcomes - omitting these data would mean that any individuals dying outside of hospital would be retained in the analyses even though they had no further risk of inpatient admission or A&E visit. Other datasets, such as adult critical care, have not been requested to minimise data. Anonymised data are not suitable as it is necessary to link data for individuals between datasets - i.e. to link A&E and inpatient admissions for an individual with that individuals date of transition based on outpatient and inpatient data. Pseudonymised data have been requested to minimise data as the research does not require knowledge of whom each pseudonymised individual is, nor does it require linkage to datasets beyond those requested (i.e. no linkage will be made to other data, for which full identification would have been necessary).

Years/Study period: this is restricted to 2007/08-2018/19, i.e. not extending the study period back further than the point where all outcomes are available as these years would have lower research value due to limited outcome data (A&E data only available from 2007/08). An additional year of data (2006/07) is requested for APC and outpatient datasets to enable grouping of individuals for objectives (i) and (ii) prior to analysis of outcomes for those individuals within the study period (if data for 2006/07 were not requested, data from the first year of the study period, 2007/08 would have to be used to group individuals into the LLC and comparator* groups). Any reduction in years requested would reduce the statistical power to identify differences between subsets of individuals based on condition or geographical differences, one of the objectives of the research, important to identify areas of good practice with a lower impact of transition.
*Comparator groups are defined as (i) those with long term but not life limiting conditions to provide a comparison group for changes in healthcare at transition ages for conditions for which the transition is believed to be well managed and (ii) those with no known long term conditions to determine any changes in healthcare use not related to transition (e.g. due to other life changes such as transition from education to employment or from secondary education to further/higher education).

Filtering: The data cannot be narrowed by geography as the research question is relevant to care across England and one of the objectives is to identify any regional variations which may point to best practice that can be applied elsewhere. Restrictions have been applied by demographics, specifically age, limiting the data requested to that on individuals within four years of age of the expected range of transition ages - all these individuals can contribute useful data to the analyses, those younger or older cannot and their data have not been requested. The data cannot be minimised by clinical factors such as diagnosis as an objective is to compare care use around the transition for those with life limiting conditions, those with chronic conditions and those with no known long term condition - i.e. whole population in the relevant age ranges.

Episodes: The data have been minimised by only requesting episodes within the study period and up to age 23 years (in combination, these provide for an absolute limit of 13 years of data for any person; many included persons will have fewer years of data due to either being first observed in Hospital Episodes Statistics later than 2006/07 or reaching age 24 prior to 2018/19). All types of episode are required as they can be used to determine diagnoses - these include maternity episodes as some of the life limiting conditions are commonly recorded pre- or immediately post-birth (and these records also provide additional sources of demographic information, e.g. ethnic group, and evidence of presence in England at that time). Elective and non-elective episodes are of interest for determination of diagnoses, determination of transition points and as outcome data (evidence of healthcare use).

Data fields requested: Only ͚general͛ data items have been requested (i.e. no ͚high risk͛ or ͚identifiable͛ data fields) and possible duplicate information/fields with similar function have been removed in consultation with NHS Digital.

The request for death records has been limited to date of death, at month and year level only to minimise data requested and the risk of identification. This is because it is only necessary to be able to remove individuals from the analyses on death (and it is sufficient to do this from the start of the month of death) not to know the precise date of death.

The research is in support of a NIHR funded doctoral fellowship (DRF-2018-11-ST2-013) and so has further benefits in research training and increasing future research capacity in the field of research into children and young people with life limiting conditions, particularly around service provision and economic evaluations for this group.

RELATIONSHIP TO WIDER RESEARCH (INCLUDING CPRD DATA REQUEST) AND HANDLING OF DATA
As set out above, the research using the requested data is part of a wider fellowship looking at the transition for children and young people with life limiting conditions. It forms a vital part of this work, particularly for objectives 2a and 4.

To address objective 2b and 3 of the fellowship, a data request has also been submitted to CPRD for all primary care records in their GOLD dataset for individuals aged 12-23 years within the study period and linked pseudonymised HES data (linked by CPRD using their own pseudonymised HES extracts; not linked to the data presently requested from NHS Digital). The data requested from CPRD serves a particular purpose in meeting objectives 2b and 3 as, unlike the data presently requested from NHS Digital, it includes primary care records. Previous work has suggested that regular contact with the same GP is associated with reduced emergency hospital care and it is intended to test whether this is true for the population going through transition.

CPRD and NHS Digital data will be held separately in separate secure databases and will be analysed entirely separately with no attempt to link the two datasets.
The research is entirely contained within the University of York Department of Health Sciences, they are the sole Data Controller who also process data. There are no other organisations involved in the data processing.

Young people, their parents and their carers at Martin House Children's Hospice will be involved in the research through patient and public involvement through the Martin House Research Centre's Family Advisory Board. Members of this group have already fed into the research proposal through an earlier research prioritisation exercise and will be consulted before and after data processing to help focus the analyses on outcomes of particular interest and to interpret the findings. This involvement will be at the level of discussing research plans and aggregated outputs and the Family Advisory Board members will have no access to the datasets. Martin House Research Centre and Martin House Children's Hospice are not involved in any activity which could lead them to being considered a joint data controller. They were not involved in decisions on how or why the data provided by NHS Digital will be processed, they will only be involved through patient and public involvement to help focus the finalized analysis into meaningful outputs.

Martin House Children's Hospice is a charity providing family-led care for children and young people with life-limiting conditions, supporting families from across West, North and East Yorkshire. More details can be found here - https://www.martinhouse.org.uk/About-Us.

Martin House Research Centre is a multi-disciplinary centre for research on the care and support of children and young people with life limiting conditions or medical complexity, their families and the workforce that care for them. The Centre is holistic in its scope, recognising that the care and support needs of children and families span clinical/medical, social, psychological, parenting/caring, spiritual, financial and practical domains. More details can be found here - https://www.york.ac.uk/healthsciences/research/public-health/projects/martinhouse/

The research is funded by the NIHR under a Doctoral Research Fellowship. The NIHR had no input into the research design.

BENEFITS
The purpose of the research is to provide a number of benefits to young people with life limiting conditions and the organisations that provide their care:
• Under objective (iv) the research will identify changes in healthcare use at transition. This could, for example, be a sharp increase in A&E use among individuals with a LLC at the transition with high associated costs of care (objective v). This would make the case to commissioners to explore alternative forms of service provision, such as extending the paediatric model of care beyond the current age of transition.

• Under objectives (i and iv), the research will also identify groups that are more or less impacted by transition and so enable local services, such as Children’s and adult Hospices to better target their resources to assist these groups.

• Under objectives (ii and iv) , the research will highlight whether there are condition groups for which the transition appears to have fewer impacts, identifying possible best practice that can be replicated by service providers elsewhere

Expected Benefits:

The research will identify changes in healthcare use at the transition. If, for example, it identifies a sharp increase in A&E use among individuals with a LLC at the transition with high associated costs of care (objectives iv and v), this would make the case to commissioners to explore alternative forms of service provision, such as extending the paediatric model of care beyond the current age of transition. The conferences and, particularly, the directly targeted lay summaries will be the main way of reaching this audience, backed up by the detailed evidence contained within the journal papers. These impacts will be available from the date of distribution of lay summaries, i.e. by December 2021.

The research will also identify groups (objectives i and iv) that are more or less impacted by transition and so enable local services, such as children’s and adult Hospices to better target their resources to assist these groups. These impacts will come about mainly through the lay summaries distributed to hospices nationally and through hospice practitioner attendance at conferences. These impacts will also be available by December 2021.

Finally, in comparing the transition across different regions and different diagnostic groups within the population with LLC and with those with chronic conditions, the research will highlight whether there are any groups for which the transition appears to have fewer impacts (objectives ii and iv), identifying possible best practice. This will be highlighted in summaries to local commissioners so that they can look to adopt practices elsewhere that appear to work well. Again, this information will be available by December 2021.

The research outputs will provide quantitative evidence on the impacts of transition, the need for which has been highlighted in reports from the Chief Medical Officer, the Care Quality Commission and in NICE guidelines. It is intended that they should be included in future versions of such reports and NICE guidelines. These benefits will be available from the publication of peer-reviewed papers, expected from December 2021 or earlier.

Outputs:

The research will aim to provide the following outputs:
• At least two conference abstracts by December 2021, presenting the results of objectives (iv) and (v) to palliative care and general medical audiences. The targeted conferences will encompass clinical and academic audiences: the Royal College of GPs’ annual conference and the European Association of Palliative Care Congress .
• At least two papers submitted to peer reviewed journals by December 2021, presenting the results of objectives (iv) and (v) and again targeting clinical/academic journals e.g. British Medical Journal and Archives of Disease in Childhood.
• Lay summaries of the research findings, to be distributed to Children’s Hospices across England (and their representative associations such as Together for Short Lives), parents (and their representative groups such as Contact), specialist services commissioners, local NHS commissioners and the National Institute for Health and Care Excellence (NICE), also by December 2021.

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

At least two conference abstracts by December 2021, presenting the results of objectives (iv) and (v) to palliative care and general medical audiences. The targeted conferences will encompass clinical and academic audiences: The Royal College of GPs annual conference and the European Association of Palliative Care Congress. These conferences are attended by senior commissioners and so this will provide a means to bring the research to their attention, helping to realise the first of the benefits in section 5(d) - i.e. making the case to commissioners to explore different forms of service provision. Details on groups most affected will also be presented and the European Association of Palliative Care Congress is particularly relevant here to reaching hospice practitioners, helping them to identify groups in most need of extra support at transition, helping to realise the second of the benefits in section 5(d).

At least two papers submitted to peer reviewed journals by December 2021, presenting the results of objectives (iv)and (v) and again targeting clinical/academic journals e.g. British Medical Journal and Archives of Disease in Childhood. These provide a secondary means of reaching commissioners and hospice practitioners as outlined above and will provide them with additional detail, helping to realise the first two benefits set out in section 5(d). However, the main benefits from these outputs will be forming part of the evidence base for inclusion in future Chief Medical Officer reports and NICE guidelines, the latter of which routinely reference published research as the basis of new policy (as set out in section 5(d)). Inclusion in NICE guidelines is another way of influencing healthcare commissioning and service provision and so is relevant to all three benefits set out in section 5(d). The papers are also directly relevant to the benefit of supporting a PhD research study set out in section 5(d).

Lay summaries of the research findings, to be distributed to Children’s Hospices across England (and their representative associations such as Together for Short Lives), parents (and their representative groups such as Contact), specialist services commissioners, local NHS commissioners and the National Institute for Health and Care Excellence(NICE), also by December 2021. This is the quickest, most accessible and direct route to reaching this audience and will help to realise the benefits of changes in service provision and targeting of extra support to the most affected groups and implementation of established best practice - i.e. all three benefits in section 5(d).
All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

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

The data will be accessed and processed only by employees of the data controller (University of York) who have undergone data protection training. No other individuals will have access to the data. Individuals accessing the data are all substantive employees of the University of York (none on honorary contracts). One individual is currently registered as a PhD student at the University as they are completing this work within an NIHR Doctoral Research Fellowship, but they are also a substantive employee of the University (and hold a prior PhD and have substantial experience of using HES data). The data will be accessed at the University from offices located behind locked doors on password-protected machines or via password protected virtual private network remote desktop access with data located on network drives and/or databases. No further linkage will take place beyond that performed by NHS Digital in creating the data extract (i.e. linkage between APC, A&E, outpatient datasets and death data). There will be no matching to publicly available data, other than comparisons at aggregate level. There will be no attempt to identify individuals.

There are no data flows into NHS Digital. Data will flow out of NHS Digital to the University of York at record level (pseudonymised). These will be health data. Subsequent flows of data will be only within the University of York's systems (these will also be record level pseudonymised health data).
All data storage/access/processing and analyses will be undertaken in the University of York and no data will be provided to third parties that isn't fully aggregated with small number suppression in line with NHS Digital guidelines. All outputs will be at aggregate level with small numbers suppressed in line with NHS Digital guidelines.

DATA MANAGEMENT
The following section makes reference to objectives set out in the 'Objective for processing' section.
In line with objective (i) demographic data will be determined for the cohort. The start age recorded at the first hospital episode in each year will be used to assign the age category for each individual. Sex will be coded as male, female or not known. Individuals with more than one recorded sex will be assigned the most commonly recorded sex. Ethnicity is reported by census groups in the HES data. Individuals with more than one ethnicity will be assigned the most commonly reported ethnicity unless the most common ethnicity is ͚not known͛͘. For statistical analysis the diagnoses for the group with LLC will be categorised into sub-groups based on the main ICD10 chapters (ICD10 is the coding system currently used to record diagnoses in HES and is organised into chapters). Diagnoses for those with non-LLC long term conditions will be also be grouped. Each individual will be assigned a local authority and government office region (GOR) based on the lower super output area (LSOA, a small geographical area used by the government to group individuals for assessments of deprivation and other measures) of residence. These assignments will be done per year and if an individual moved address within that year the first local authority/GOR reported that year will be used. This will allow the individual to be assigned new geographical area over the time period but not within a year. An index of multiple deprivation score (the government's preferred measure of deprivation in England) will be assigned to each individual based on the LSOA of residence.

In line with objective (ii) individuals will be assigned to one of three groups – those with life limiting conditions (LLC, using a previously defined coding framework), those with other long term conditions (based on diagnoses and/or numbers of outpatient/inpatient episodes) and those with no known long term conditions (those not in the other two groups, possibly with further restrictions on numbers of hospital records).
In line with objective (iii) the point of transition from paediatric to adult services will be determined for each individual (based on consultant main specialty in outpatient and APC datasets - consultant main specialty indicates the primary competence of the physician and includes adult and paediatric specialties). For the group with no long term conditions (with sparse records in HES) the transition will be assumed to take place at 16 years of age.

STATISTICAL ANALYSES
The number of A&E visits, inpatient admissions, emergency inpatient admissions, length of stay and bed days will be determined per person per year and compared across the groups determined in objectives (i) and (ii).

The above measures of healthcare use will then be compared across the transition for the three groups identified in objective (ii). These comparisons will use statistical methods for comparing levels of a measured variable on either side of a cut-off in time – such as an intervention such as transition – or in other variables such as age. As not all individuals will transition at the same age, techniques will be used that account for this. These analyses will be repeated, split by the groups identified in objective (i). These analyses together will meet objective (iv).

Finally, costs of care will be estimated. This will be done using details of patient conditions and actions performed recorded in the inpatient data and using standardised costs for A&E visits. In this way, costs of care per person per year can be estimated and also analysed for differences across the transition using the techniques outlined above.

OUTPUTS
Outputs will be based around the main objectives of the present study. will summarise age of transition (objective (iii) numbers of A&E and emergency and all inpatient admissions, length of stay and bed days and changes in these at transition (objective iv) and estimated costs (objective v). These will all be at aggregate level (grouped with no fewer than ten individuals in each group) with grouping into condition types (objective ii) and demographics (objective i). Coefficients of regression models (i.e. the techniques discussed above: regression discontinuity, interrupted time series and difference in difference) may also be disseminated.


United Kingdom Childhood Cancer Study (UKCCS) — DARS-NIC-147884-R7CBN

Type of data: information not disclosed for TRE projects

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

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

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

When:DSA runs 2017-03-16 — 2022-01-31 2016.04 — 2020.10.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Cause of Death Report
  3. MRIS - Scottish NHS / Registration
  4. MRIS - Bespoke
  5. Hospital Episode Statistics Admitted Patient Care
  6. Hospital Episode Statistics Outpatients
  7. Hospital Episode Statistics Accident and Emergency
  8. MRIS - Members and Postings Report
  9. Civil Registration - Deaths
  10. Demographics
  11. Cancer Registration Data
  12. MRIS - Flagging Current Status Report
  13. Hospital Episode Statistics Accident and Emergency (HES A and E)
  14. Hospital Episode Statistics Admitted Patient Care (HES APC)
  15. Hospital Episode Statistics Outpatients (HES OP)
  16. Civil Registrations of Death

Objectives:

To examine the long-term survival of UKCCS case children and factors, or combinations of factors, that might effect survival including treatment, demograpics and various exposures. The analyses will provide important insights into possible improvements in treatment of children with cancer and the identification of children at increased risk of relapse.

Yielded Benefits:

A major aim of UKCCS is to improve care and outcomes for those who have recovered from cancer following a diagnosis as a child. UKCCS, an internationally recognized study with a longstanding record of informing on the possible causes of childhood cancer, is ideally placed to provide national data about the long-term health and healthcare needs of these participants. Indeed, the study has already provided population-based data on childhood cancer outcomes, its variation by cancer type, and socio-demographic characteristics. Importantly, the UKCCS is truly population-based and results are generalizable. Hence, findings have the potential to help inform policy and commissioning of services. The study is not in support of a PhD/post graduate research study.

Expected Benefits:

By linking UKCCS to secondary healthcare data, the study will extend its population-based data to include post-diagnostic events in the healthcare setting. The uses of secondary healthcare data will be to examine a number of questions concerning the health and healthcare utilisation patterns of those who have recovered from cancer as children (see Section 5a, point 6a). In this context, real-world population-based data that includes all health service contacts are required to plan future healthcare services.

This UKCCS proposal aims to inform the public health agenda of the future by providing information about the health and healthcare needs of adults diagnosed and treated for cancer as children.

Conducted nationwide, UKCCS not only registered all children under 15 years diagnosed with a cancer, but also a representative population sample of similarly aged individuals, meaning that results are highly generalizable and are of potential importance to the commissioning of cancer recovery services at a national level.

The study will benefit participants and those who have been diagnosed with cancer as children in the future by delivering evidence about their health (good or bad) that could be used to help develop guidelines and improve clinical practice (e.g. monitoring) and wellbeing. Diagnosed in the early 1990s with their cancer, and currently aged 23 to 42, participants health from childhood through to early adulthood can be examined with the current dissemination. Since some adverse events may only become evident at older ages, the wider aim of the project is to continue following the cohort's health and healthcare patterns as they age.

As a longstanding study, the UKCCS has connections to networks that will provide a framework for dissemination. Having worked with many partners since the study’s initiation, predominantly clinicians and researchers in the field, and a range of funders including the current, Bloodwise, - as well as having connections to charities/organisations through UKCCS and other studies- UKCCS are well-placed to disseminate findings on the long-term health of those who have recovered from a childhood cancer diagnosis to relevant parties including, participants, current cancer patients and their parents, as well as clinicians, policy makers and other stakeholders. UKCCS seek support or advice on the dissemination framework of the aggregated outputs but the overall decision making capability and processing responsibilities of the NHS Digital data lie with University of York.

With respect to measurable benefits, these will, in large part, result from the provision of good quality data/information (to clinicians, patients, and commissioners) that are currently lacking. For instance, little is known about those who were diagnosed with childhood cancers' healthcare needs or their development of other serious morbidities, particularly in the context of what occurs among a population of similar ages. The University of York anticipates a target date for expected measurable benefits within 3 years of data receipt.

Outputs:

Over the first 3 years following receipt of the data, diagnostic-specific comparisons between those who had cancer as a child and those who did not will be used to examine a range of questions, as described above. The University of York will produce several outputs on these topics, publishing the findings as papers and reports, as well as conference presentations. All peer-reviewed articles will be published in well-respected journals such as the British Journal of Cancer, International Journal of Epidemiology, Cancer Epidemiology, European Journal of Cancer, Cancer, and PLoS One. In addition, findings will be disseminated at conferences including UK Childhood Cancer Conference, National Cancer Research Institute Conference, and Pan-European Network for Care of Survivors after Childhood and Adolescent Cancers Conference.

Across all outputs, data are only ever made available in aggregated form, with small numbers suppressed

As part of this study, UKCCS will be initiating an active patient partnership, in line with all studies conducted by UKCCS research group (see https://yhhn.org/partnership as an example). A committee will oversee the patient partnership, whose members will be drawn from among clinicians, patients and their relatives, specialist nurses, and researchers with relevant experience. Preliminary discussions with paediatric oncologists and nurse consultants as well as a number of people diagnosed with cancer in their childhood (who are now young adults) have already taken place; the committee will be formalised following approval of this application. Headed by an independent chair, this advisory group will advise on UKCCS public/patient facing profile, including the production of literature and development of the website.

Core funding comes from a Bloodwise programme grant (Apr 2016-Mar 2021; ref 15037; the epidemiology of haematological malignancies: determinants, prognostics, treatment and survivorship). Funders receive annual interim reports, and final reports will be provided at the end of the funding periods. It is anticipated that the findings of the study could be used to help develop guidelines and improve clinical practice (e.g. monitoring) and those who have recovered from cancers' wellbeing; with outputs on survivorship dependent on this agreement it is worth noting that previous UKCCS research are referred to in NICE guidelines, two examples being signs and symptoms of brain tumours (https://www.nice.org.uk/guidance/ng12) and whether intramuscular administration of vitamin K at birth is linked to childhood cancer (https://www.nice.org.uk/guidance/cg37). Impacts will be maximised via national/international dissemination to NHS practitioners/commissioners, politicians/policy makers, and third-sector organisations.
To ensure accessibility to peer-reviewed outputs, all reports will be published under creative commons attribution 4.0 licence (CC BY); support for this is routinely included in all grant applications and the study’s website (www.ukccs.org) will provide links to these open access publications, conference proceedings, and copies of reports summarizing the findings.

The UKCCS website (www.UKCCS.org) will be updated to reflect all activities, and will contain information about the current proposal (e.g. fair processing, the data linkages and the purpose of these) and ongoing research; and social media forums (e.g. a study-specific twitter feed) will be established. Outputs will be promoted through the study’s Twitter account (to be established) and via the study’s funders (@bloodwise_uk (31,900 followers)). Lay summaries of findings will be provided on the UKCCS website (www.ukccs.org). UKCCS also engages with national charities who publish study findings on websites and in their magazines; an example of such dissemination can be found on the following link: https://bloodwise.org.uk/blog/children-less-affluent-backgroundsmore-likely-die-following-treatment-leukaemia-uk.

The UKCCS is an observational epidemiological study and as such, there are no plans- nor are any foreseen- to develop algorithms, testing and development of tools and new technologies.

Target dates for outputs on the topics is within 3 years of receiving the data. By way of example, the analysis on therapy-related associations will commence three months after receipt of the linked data and will continue for 26 months when findings will be ready for submission to scientific journals.

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

Processing:

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

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

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

The UKCCS is an observational epidemiological study where risks/burdens relate to the use of data. The UKCCS has kept up-to-date with confidentiality requirements to meet current ethical standards; in particular, paper copies of study forms have been destroyed, and individual identifying information, including names and addresses, have been removed from the study database. Study data are stored with pseudonymised study serial numbers in the research database on a secure computer system within the Department of Health Sciences at the University of York. The key individual identifying information retained by the study are NHS numbers at recruitment (old style, pre-1996); these are kept alongside the UKCCS study number in a separate secure database, and are used solely for the purposes of linkage to routine NHS administrative databases. Data returned from NHS Digital only contain the study serial numbers and are stored in the study research database. The returned data are accessible to a restricted number of University of York staff all of whom are substantive employees. Trained in data protection and cannot be accessed remotely or by third parties. In published results, anonymity is guaranteed by presenting frequencies and statistics only, and avoiding the reporting of small numbers. UKCCS researchers adhere to guidelines on disclosure risk, as defined in Office for National Statistics (ONS) guidance: (https://www.ons.gov.uk/methodology/methodologytopicsandstatisticalconcepts/disclosurecontrol/healthstatistics).

The University of York has previously supplied NHS Digital with identifiers for the 4430 UKCCS cases and 9758 controls (pre-1996 NHS numbers, date of birth and sex) along with their UKCCS unique serial numbers. These data are blinded to their cancer status.

NHS Digital will continue the linkage of all UKCCS subjects to cancer registrations and death notifications as well as exits/emigrations and re-entries to the NHS. NHS Digital will link the identifiers to Hospital Episode Statistics (HES Outpatients, Admitted Patient Care, and Accident and Emergency).

NHS Digital will return cancers, causes of death and movements in/out of NHS (with the month and year of these events) to the University of York. In addition, NHS Digital will return to the University of York, dates of appointments, ICD10 codes, procedure codes, consultant specialty codes, and methods of admission and discharge, all of which are contained in the HES data sets. The requested data will be supplied on an annual basis and returned with the UKCCS serial numbers supplied by the University of York.

All data flows will involve patient level data. Data flowing from NHS Digital to the University of York will contain the UKCCS serial numbers for each patient. University of York has already supplied NHS Digital with the identifiers needed for data linkage.

The University of York has supplied UKCCS identifiers to NHS Digital. NHS Digital is responsible for linking and extracting data for UKCCS subjects from administrative databases, and for returning the linked data to the University of York. The University of York is responsible for storing the returned data and processing the data for statistical analyses.

The University of York has supplied UKCCS identifiers to NHS Digital, under a previous iteration of this agreement and have confirmed that the linkage has been retained. NHS Digital will use the UKCCS identifiers to link to national death certification, cancer registration, movements in/out of the NHS, and to HES-APC, HES-OP and HES-A&E; and will extract, for UKCCS subjects, data in the requested variables. NHS Digital will return the extracted data for UKCCS subjects from these administrative datasets to the University of York along with the study serial numbers. University of York will store the returned data in its dedicated research database and process the data for the proposed analyses. The first data extraction will include all available years of HES, and deaths, cancer registrations and NHS movements that occurred since the last data download; future extractions will be conducted on an annual basis.

Data linkages to administrative datasets are conducted by NHS Digital and only data for UKCCS subjects are released to the University of York. The University of York holds data on the primary cancer diagnoses for those who had cancer as a child (cases); and for cases and controls, sociodemographic status. Data received from NHS Digital will be linked using the study serial numbers to these data for the analyses being conducted by the university.

UKCCS stores data returned from NHS Digital in a research database, that is separate from the NHS numbers (pre-1996). Only a restricted number of staff have access to the NHS numbers and know that these personal identifiers are held only for the purposes of linking to administrative datasets and not for any other purpose. Furthermore, when s251 support was granted - this was an explicit recommendation from the Confidentiality Advisory Group that hte original identifiers were obtained.

The data will not be matched to publicly available data.

Data received from NHS Digital will be returned to the University of York with the UKCCS serial numbers, and as such data are pseudonymised. There is no requirement to re-identify individuals and there will be no attempt to do so.

Data will only ever be processed by substantive employees of the University of York.

All UKCCS data are held in electronic format only, and stored on a server and accessed on network computers within the University of York. Staff working on the project are granted access to the data by the Principal Investigator and their access is controlled by their username and password. NHS numbers, and data provided by NHS Digital cannot be accessed using a remote desktop server. No hard copies of the data exist, or will be made, and data are never stored on laptop computers or portable devices.

The data supplied by NHS Digital for the UKCCS are stored at the University of York, and are managed by the Epidemiology and Cancer Statistics Group (ECSG), a research group in the Department of Health Sciences at the University of York.


National Audit of Cardiac Rehabilitation — DARS-NIC-12881-L1H2B

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2018-05-17 — 2021-05-16 2017.09 — 2020.03.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

This request for data is to enable the National Audit of Cardiac Rehabilitation (NACR) to report accurately on cardiac rehab so that commissioners can make informed decisions about the performance of services they fund. The same data helps the NACR team to report on performance against national clinical standards and patient outcomes at CCG and local clinical cardiac rehabilitation programme level.

The NACR aims to generate data on cardiac rehabilitation to help inform commissioning decisions and drive up the quality of provision and outcome for patients attending cardiac rehabilitation. The NACR has, for the past two years, produced audit reports that represent to a variety of organisation levels and readerships. In 2015, the audit reported at both Strategic Health Authority and anonymised programme level. In 2016 we produced named local reports which included one patient outcome. The 2017 report will continue to generate local and named reporting as well as at both Strategic Health Authority and Sustainability and Transformation Partnerships level.

As the NACR carries out more multi-factor analysis the numbers of patients in these analyses, in any one year, starts to become very small. For instance, five or more condition types are split and factored in (e.g. elective PCI, MI, MI+PCI, CABG and heart failure) plus gender, ethnicity and three age categories. This can result in fewer than 100 patients per group for any of the eight patient outcomes the University report, (QoL, physical activity status, fitness, HADs, BMI, waist circumference, BP, chol). In order to enable these important analyses, data from previous years needs to be combined with the new data.

Yielded Benefits:

Working with national associations such as the British Association for Cardiovascular Prevention and Rehabilitation (BACPR) and the British Heart Foundation (BHF) the University has, through data reporting, helped increase uptake to rehab services across England. The ability to report on the extent by which programmes recruit from the total eligible population (presently at 51%) is helpful but the University also needs to focus our efforts on helping programmes innovate around meeting the needs of the 49% of patients that presently don't take up the offer of cardiac rehab. Last year this represented 65,344 actual patients missing out on cardiac rehab which is known to add quality years to life. The introduction of the national certification programme has already seen an improvement in programmes performance and participation of a greater number of NICE Guidance informed patient populations. The success and widespread utilisation of the annual statistics report and research that the NACR has produced has resulted in the audit becoming registered on the NHS England Quality accounts, inclusion in NHS England CCG reporting and a non-mandatory best practice tariff. The audit has also produced close to 20 primary research articles using the NACR dataset. The NACR data is quickly becoming known, internationally, as a leading source of data for real-world Cardiac Rehabilitation research. This is the culmination of over ten years of work and the essential part that the HES data plays in the produce from the yearly annual report.

Expected Benefits:

NACR continues to monitor and improve the quality of service delivery which was shown to be of inferior quality by the RAMIT study - West RR, et al Heart 2011;98:637-44). Rehabilitation After Myocardial Infarction Trial.

The NACR report highlighted many of these shortfalls in 2015 at Strategic Clinical Network (SCN) level and provided further important detail at local level which allows individual programmes to see how they are performing against clinical minimum standards. This new reporting approach will deliver the required detail and enable CCGs and hospitals to see how they are performing against clinical minimum standards. The BACPR, in collaboration with the NACR, is running a national certification programme which aims to ensure that all CR programmes are working to agreed clinical minimum standards supplied at a programme level, to help make judgements about their level of achievement.

There will be tailored audit reports, national certification programme and key performance measures for local service accountability. The ability to report locally will not only enable commissioners and providers to make decisions based on the same high quality data but will also enable programmes to apply for national certification against clinical minimum standards. The overall aim is not to close CR programmes but is instead to drive up quality of delivery and optimise outcomes for patients.

The NACR and the University of York expect to see these improvements within 12 months of the analysis and reports. We are working with the BACPR with a shared aim of having at least 50% of programmes working to published minimum standards by June 2018.

Outputs:

This data will be reported at organisational level in the 2017 National Audit of Cardiac Rehabilitation (NACR) Annual Report with Strategic Clinical Network (SCN), and local reporting of key performance indicators and aggregated patient outcomes - expected publication December 2017. Also as part of a wider dissemination in peer reviewed journals, including BMJ Heart (Do patients characteristics determine the performance of CR programmes), International Journal of Cardiology (What factors determine uptake to CR in patients undergoing PCI) and European Journal of Preventative Cardiology (Does service timing matter for psychological outcomes in cardiac rehabilitation? Insights from the National Audit of Cardiac Rehabilitation). There will also be four papers in Heart Open (Is patient’s diabetic status a factor that influences taking up the offer of CR) (Does the mode of delivery of cardiac CR influence the psychosocial outcomes of CR patients?) (Is there an association between patient's COPD status and the outcomes they derived from CR?) (Is the rehabilitation and outcomes received in Heart Failure patients the same as traditional in scope patients). The paper investigating patients with Diabetes uptake will use the Hospital Episode Statistics as the eligible population, this will use aggregated data similar to that in the NACR annual report and will not be linked in anyway to the NACR data.

Only aggregated data will be reported. Small numbers will be suppressed in line with the HES analysis guide. The report will also be available online with open access and circulated via email to cardiac rehabilitation programmes. There will be no charge for this.

NACR also use HES data, at a local programme level, to characterise high and low performing CR programmes based on the extent to which they meet the British Association for Cardiovascular Prevention and Rehabilitation (BACPR) minimum standards. As part of this approach, they seek to establish degree by which CR attendance influences hospital readmissions which is becoming increasing important for commissioners, providers and patients. Since 2015, the NACR has published eight primary research articles using the audit data. These have ranged from assessing service quality (Salman), factors affecting uptake (Al Quait) and influences on patient outcomes (Doherty, Harrison, Fell). These papers are the start of a large evidence base using the NACR dataset. We hope to, using the HES data, build on this evidence by producing some peer reviewed articles, these will be assessing the rate of uptake, for specific subgroups or geographic regions.

Processing:

NHS Digital will send pseudonymised, non-sensitive data to the University of York. The British Heart Foundation (BHF) funded team supporting the NACR (employed by and based at the University of York) has substantial analytical skills and infrastructure with a proven record in managing the national audit. The audit team support over 1000 NACR users and are supported by the University of York analysts in cleaning and validating the data and carrying out basic and advanced statistical analyses using SPSS and Stata (software packages to enable statistical analysis of the data) which are licenced through the University of York.

The funders will not have any influence on the outcomes of the analysis.

The 1000 NACR users are multidisciplinary cardiac rehabilitation staff who work within hospital or community programmes. They enter patient-level data on the individual patients that they see and have access to this data through the NACR platform. No attempt is made to link the data entered by the 1,000 NACR users to the record level HES data supplied by NHS Digital.

No record level data will be shared with a third party. Record level HES data is not linked to any other data

The data is analysed by grouping the patients and the conditions to produce summaries by both region and CCG. This is then compared to the NACR data to generate uptake figures.


The effectiveness of modern day cardiac rehabilitation: A study from the National Audit of Cardiac Rehabilitation — DARS-NIC-68774-M1W6Q

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

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

When:DSA runs 2019-12-12 — 2021-07-11 2019.10 — 2019.10.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

Cardiac rehabilitation (CR) has been shown, through systematic reviews and meta-analyses, to reduce premature cardiovascular death, all-cause mortality and improve health related quality of life following an acute myocardial infarction (AMI) or after coronary re-vascularisation. As such National, European and International guidelines recommend the provision of prevention and rehabilitation programmes. In recent years the evidenced benefits of CR have been challenged. The RAMIT study, a pragmatic multi-centre randomised controlled trial (RCT) in the UK, questioned the value of CR when no effect on mortality, morbidity risk factors, health related quality of life or activity was found following UK based CR. Although the validity of the trial was questioned the results of this study in addition to the aging evidence base supporting CR means an important issue has been raised. Namely whether the efficacy demonstrated in historic CR RCTs is evident in modern day CR practice.

To investigate the effects of modern CR in routine practice an observational approach is required. The objectives of the project are as follows:

* To characterise eligible CR attenders and non-attenders

* To determine mortality rates (all cause and cardiovascular related) over a 5-year period for eligible CR attenders and nonattenders

* To determine healthcare utilisation, through re-admission, for eligible CR attenders and non-attenders

* To compare health care costs associated with re-admission in CR attenders and non-attenders and estimate the costs of providing CR

* To determine re-occurrence of AMI over a 5-year period for eligible CR attenders and non-attenders

* To investigate the role of co-morbidity on death, hospital re-admission and re-occurrence of heart attack

* To identify the characteristics of successful CR programmes

The results of this project will address an important issue in terms of future commissioning of cardiac care in the NHS i.e. whether CR in its current format should continue to be funded. With over 80,000 patients in the UK accessing CR services each year the findings of this project could have a profound impact on future patient care.

The project aims to answer whether modern day cardiac rehabilitation as delivered in routine practice across the UK remains beneficial for patients. The project will achieve this aim by investigating whether mortality, re-admission and re-occurrence significantly differs between cardiac rehabilitation attenders versus non-attenders. The results of this project will address an important issue in terms of future commissioning in the NHS for cardiac care i.e. whether cardiac rehabilitation in its current format should continue to be funded. With over 80,000 patients in the UK accessing cardiac rehabilitation services each year the findings of this project could have a profound impact on future patient care.

To answer this question an observational approach is required to accurately investigate the effects of CR delivered in routine practice. In addition a new RCT of routine CR practice could not be tested when CR is standard therapy and thus withholding such care from a control group would be unethical.

Yielded Benefits:

There have been no yielded benefits to date due to the delays in processing by the University of York. In any future version of this agreement - University of York must provide details of the benefits yielded to date from the data provision.

Expected Benefits:

The results of this project will address an important issue in terms of future commissioning of cardiac care in the NHS i.e. whether CR in its current format should continue to be funded. With over 80,000 patients in the UK accessing CR services each year the findings of this project could have a profound impact on future patient care.

The findings of this research will either provide ‘current’ and clear support for the continued investment in cardiac rehabilitation services. It is already apparent from data in the NACR statistical report and dialogue with national CR services that this extended period of austerity has impacted CR services, i.e. running shorter programmes which are not evidence supported. Thus, it is vitally important that relevant and current knowledge is obtained to remove doubts of efficacy and support evidence based practices. If more negative results are realised then significant changes to CR service provision may be required. The initial next step in the programme of research would be to undertake detailed interrogation of the data to identify what makes particular modern CR programmes effective or not.

There is a need in the cardiac rehabilitation (CR) community to conduct this analysis. The last Randomised Controlled Trial (RCT), the RAMIT study, which tested the efficacy of routine CR in the UK was commissioned due to doubts that ‘modern’ CR was still effective given advances in medicine, changes to how CR programmes are run and changes in the patient profile receiving care. The results of this RCT were contentious in that no benefits were found from CR, however conduct of the RCT was heavily criticised which led to doubts over the validity of the study findings.

As this RCT could not adequately address the question of efficacy and ethically a new trial of routine CR could not be run as CR is routine therapy and could not be withheld from a control group a different approach is required. Thus an analysis of observational data can be used to address this question.

Outputs:

A number of outputs are expected from this project, various routes will be used to permit free access to the findings for both clinicians, policy makers and patients as follows:

Journal publications: the authors will be targeting leading cardiac journals with high impact factors such as Circulation and Heart. Depending on when access to the data is achieved publications are anticipated during 2019.

Conferences: Cardiac related conferences will be targeted regarding the results of this project such as the European Society of Cardiology Congress. Conference attendance is anticipated during 2019.

Cardiac support Groups: The research team at the University of York, the University of Leeds and The Farr Institute have well established links to a number of regional cardiac support groups for patients. The project team will be liaising with these groups to decide an appropriate route to present the findings of this project to patients i.e. attendance at support groups, dissemination through newsletters.

The PI for the project is a leading clinician in the area of cardiac Rehabilitation. He has held leading roles in national and European cardiology societies: former president of the British Association of Cardiovascular Prevention and Rehabilitation (BACPR) and a former chair of the European Society of Cardiology, and is still heavily involved in both organisations. He also has close ties to the British Heart Foundation, a national cardiac charity, as the Director of the British Heart foundation National Audit of Cardiac Rehabilitation in the UK. He has contributed to health care policy with regard to Cardiac Rehabilitation, developing the Departments of Health Cardiac Rehabilitation Commissioning Pack and developing programmes which drive service excellence through the development of the Cardiac Rehabilitation certification programme with the British Association for Cardiovascular Prevention and Rehabilitation (BACPR). The PI is an active member of the UCL National Institute for Cardiovascular Outcomes Research Group, which manage other cardiology audits in the UK. Overall the team is positioned to engage with the individual CR programmes, cardiology societies, cardiac charities and has evidence of developing service change through health policy. This will maximise engagement of these study findings with the health care and cardiovascular community and ultimately drive service improvement.

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

Processing:

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

The University of York (via their data processor for the audit the Clinical audit and Registries Management Service (CARMS based in NHS Digital), will share with the data access request team at NHS Digital study ID, NHS number, sex and postcode to enable the data linkage to the HES and mortality data.

NHS Digital will create HES cohort based on distinctive diagnosis and Operating codes this will then produce a single cohort containing everyone in the Clinical Audit cohort and any additional people found in HES.

A pseudonymised data set will be released to and accessed by University of York. The data will be linked to the clinical audit data by the University of York via a study ID within that data set there will be no held identifiers.

Data for the analysis will be stored at the University of York where the analysis will be conducted.

Only those with substantive employment at the University of York will have access to patient level data.

Co-investigators from the University of Leeds and the Farr Institute will be providing support in terms of assisting with the project and supporting the development of the analysis they do not have any influence of the direction of the work they are acting in an advisory capacity only and have no controls over the manner by which the data is being used. Only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide, will be shared with the co investigators at Leeds and the Farr institute.

All outputs will be restricted to aggregate data with small numbers supressed in line with the HES Analysis Guide.

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


English Indices of Deprivation 2019 - Health Deprivation and Disability Domain indicators — DARS-NIC-219055-K4F8R

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-02-21 — 2020-02-21 2019.04 — 2019.04.

Access method: One-Off

Data-controller type: THE MINISTRY OF HOUSING, COMMUNITIES AND LOCAL GOVERNMENT, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

The Centre for Health Economics (CHE), based at University of York, is requesting pseudonymised episode-level data for the purposes of calculating and validating indicators of health deprivation for each lower-layer super output area (LSOA) in England.

A Lower Layer Super Output Area (LSOA) is a geographical area. Lower Layer Super Output Areas are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.

The resulting health deprivation indicators will form part of the English Indices of Deprivation 2019 and will be published as official statistics by the Ministry of Housing, Communities & Local Government (MHCLG) who are joint data controllers and funders. The calculations will follow the same process as for the English Indices of Deprivation 2015.

The work being undertaken by both University of York and the Ministry of Housing, Communities & Local Government is being done so under their GDPR legal basis for processing data in pursuant of their task in the public interest. MHCLG is the UK Government department for housing, communities and local government in England. It was established in May 2006 and is the successor to the Office of the Deputy Prime Minister, established in 2001. MHCLG has a responsibility as part of their public task to produce statistics and analysis the data being requested under this agreement supports that function. University of York are supporting MHCLG in carrying out their responsibility as part of their public task to produce statistics and analysis the data being requested under this agreement supports that function.

Ministry of Housing, Communities and Local Government have commissioned the OXFORD CONSULTANTS FOR SOCIAL INCLUSION LIMITED to run the commissioning process to find an organisation to do the work resulting in University of York being appointed to deliver the research.

OXFORD CONSULTANTS FOR SOCIAL INCLUSION LIMITED have no role in determining the outputs or use of the data being disseminated by NHS Digital under this agreement and research programme they deliver. University of York agree with MHCLG through the consultancy agreement the means by which the data should be processed.

The requested data will only be used

1) to calculate two indicators of health deprivation (separately for the periods April 2011 to March 2013 and April 2015 to March 2017) for each English LSOA:

- acute morbidity: the rate of emergency hospitalisations per LSOA, directly standardised using the age-sex distribution of the relevant ONS mid-year population estimates.

- mood and anxiety disorders: the rate of hospitalisation with a diagnosis of severe mental health problems relating to anxiety or depression per LSOA, directly standardised using the age-sex distribution of the relevant ONS mid-year population estimates.

2) to explore the reasons for observed changes in LSOA level rates for each indicator from 2011-2013 to 2015-2017. Specifically, University of York will examine whether marked changes in indicators between these two periods are the result of changes in coding, population composition, or local admission thresholds.

3) to disseminate the health deprivation indicators at LSOA level, with suppression for small cells (<5 individuals) being applied to ensure that individuals cannot be identified.

The University of York confirms that the data under this application would only be used for the purposes stated above. Only aggregate statistics with small number suppression, in line with the HES Analysis Guide at the LSOA level will be published.

Yielded Benefits:

In general terms, the Indices of Deprivation (IoD or IMD) are widely used by central and local government, the voluntary and community sector, and academics and researchers. Uses of the Indices include identifying places for prioritising resources and targeting funding, setting local strategies and monitoring progress. The output data of the IMD 2019 will be used across government departments, local government and public health. It is an update of the Indices of Deprivation 2015, the website for which is by far the most frequently visited MHCLG statistics page with 125,000 page views in the 12 months to end July 2017. It is the second most visited statistics page on the GOV.UK website (second only to the unclaimed estates list). Some of the multiple uses to of the indices are as follows: 1) Examples of use within MHCLG a) Funding allocation: i) The Fair Funding Review is considering the use of the Indices in funding formulae for the local government finance settlement. This allocates around £30 billion annually. b) To inform eligibility for Government policies: i) Used within bids by areas applying for the Controlling Migration Fund which will allocate £140 million; ii) Used to assess bids for the Coastal Communities Fund which allocates around £20 million per year; along with other local growth initiatives; iii) Used in the Neighbourhood planning funding process, to identify priority areas for higher funding. Allocates around £7m per year. iv) Used within bids by areas applying for the Future High Streets Fund which will allocate £675 million c) Policy analysis: i) The Indices have been used to assess the extent to which 2015-16 MHCLG spending on priority programmes was targeted towards the most deprived areas. This covers around £5.7 billion of spending; ii) Used to inform the Northern Powerhouse programme in briefings and contextual analysis. £3.4 billion of Local Growth funding has been awarded to Northern Powerhouse Local Enterprise Partnerships; iii) Used to identify pockets of high deprivation for the Thames Estuary Regeneration programme. 2) Examples of use by other government departments (OGDs) a) Funding allocation: i) Funding formula for Dedicated Schools Grant (DSG) uses deprivation as a compulsory factor. This is either measured from free school meals statistics or the Income Deprivation Affecting Children Index (IDACI), which is a supplementary indices created as part of the indices of deprivation. In 2017-18, 123 local authorities are using IDACI. The total amount of funding allocated through DSG was £40 billion in 2016-17; Distributing funding to Youth Offending Teams - £65 million per annum (Youth Justice Board); ii) The IDACI data is also an integral part in both the schools and high need formulae. IDACI data is used as a proxy for deprivation on High Needs NFF; HN NFF distributes a total of roughly £6bn in funding, of which around £270m goes through the IDACI factor. IDACI data is also used as a proxy for deprivation on Schools NFF; Schools NFF distributes around £33.5bn of funding, of which around £1,342m relates to IDACI. iii) The IMD is an important tool informing funding decisions by the Big Lottery Fund which reached 6.2 million people with £713m in 2016-17; iv) The Indices are currently used in allocation of European Regional Development Funds (ERDF). Community-Led Local Development is a mechanism for responding to barriers to growth in local areas and those in the 20% most deprived areas are prioritised. The UK Shared Prosperity Fund, which will assume a similar role after the UK exits the EU, is also considering the use of the indices as part of its funding methodology. v) The Home Office will need the Index of Multiple Deprivation 2019 to support any work in reviewing the police funding formula, the mechanism that allocates the government’s police core grant of £7 billion. This money is the majority of income for the 43 police forces in England and Wales. In recent months, the Parliamentary Accounts Committee, the Home Affairs Select Committee, and the National Audit Office, have all published reports that say the police funding formula needs to be reviewed urgently and the IoD2019 will form the key basis of this review. b) To determine eligibility for Government policies: i) Under 'Carbon Saving Communities', households in the most deprived 15% of neighbourhoods are eligible for insulation measures from energy companies. The English Indices were used alongside the Welsh and Scottish Indices for this purpose (BEIS). c) Policy analysis: i) The Indices are used to analyse the two overarching indicators in the Government’s Public Health Outcomes Framework: to reduce differences in life expectancy and healthy life expectancy between communities (PHE). ii) The indices forms a key component of the Department for Health and Social Care’s Slope Index of Inequality (SII). This is a measure of the difference in life expectancy between the most and least deprived sections of the local population. Included in the proposed NHS Public Health Outcomes Framework, it is used as part of the assessment of health inequalities and the 2019 update will be compared to the time points used in previous indices iterations. 3) Examples of local government use a) Local Growth: i) The Indices are also used in supporting local growth such as in Local Economic Assessments. Hackney Council has carried out a thorough analysis of the borough on all domains of deprivation in their LEA; ii) Leeds City Council has used the Indices in analysis of their economy for the Leeds Growth Strategy. This highlighted particular areas for improvement such as the Living Environment and Crime. b) Health and wellbeing: i) The Indices are widely used in Joint Strategic Needs Assessments (JSNAs). Responsibility for producing the JSNA lies with Health and Wellbeing Boards. These bring together local authorities and Clinical Commissioning Groups (CCGs) to develop understanding of the health needs of their communities and to develop Joint Health and Wellbeing Strategies (JHWS) to address those needs; ii) Surrey Health and Wellbeing Board have used the Indices to identify pockets of deprivation in a county which is one of the least deprived. In particular high levels of deprivation for some LSOAs has been found in Education, Skills and Training, Barriers to Housing and Services, Income Affecting Children and Income Affecting Older People. Practitioners use deprivation maps presented in the JSNA to inform initiatives to target services to vulnerable groups; iii) The Indices are also used by sustainability and transformation partnerships (STPs) led CCGs to inform their plans. The partnerships are formed by NHS and local councils in 44 areas covering all of England to develop proposals to improve health and care. iv) The Indices have been used, for example, to inform the North Central London Sustainability and Transformation Plan to understand where deprivation exists in the area. This is reflected in the plans focus areas such as in understanding and reducing variation in performance of primary care practices and hospitals. v) Analysts at NHS Digital make use of the indices across a number of indicator sets such as the Compendium of Public Health Indicators, NHS Choices and Summary Hospital Mortality Indicator (SHMI) primarily relating to mortality or emergency readmission measures. vi) The 2015 iteration feeds in to Public Health England’s Inequalities in life expectancy analysis, which brings together data pertinent to life expectancies and inequalities in life expectancy and makes it available to users via their website. c) Tools/advice: i) The Indices are used to identify areas of rural deprivation and were used recently in the LGA Health and Wellbeing in Rural Areas publication. This stated that pockets of rural deprivation can often be masked by higher level statistics so the Indices are a particularly valuable tool. The report recommended that rural local authorities ensure they make the best use of small area based data to identify areas of deprivation; ii) The Indices are also a primary source of data in the LGA’s online tool LG Inform which enables users to create reports, charts and maps for all local authorities in England.

Expected Benefits:

The English Indices of Deprivation are widely used by the UK Government, ministries and their arm's length bodies, researchers, charities, and individual citizens to describe and measure inequalities in health and social care. The Equality Act 2010 established equality duties for all public sector bodies which aim to integrate consideration of the advancement of equality into the day-to-day business of all bodies subject to the duty. The English Indices of Deprivation serve this purpose and allow public sector bodies, such as NHS England, to plan and deliver targeted interventions to reduce health inequalities.

The data requested under this agreement will help the Ministry of Housing, Communities & Local Government to produce the English Indices of Deprivation by Summer 2019, with the benefits of this information accruing subsequently.

Outputs:

The specific outputs expected are:

- Two directly standardised, precision-weighted indicators of health deprivation for each LSOA in England; published as official statistics by the Ministry of Housing, Communities & Local Government in e.g. Excel format.

- A technical report detailing the steps taken to create the indicators that form part of the English Indices of Deprivation

The target date for both outputs is Summer 2019.

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

Outputs from the 2015 work have been published on the MHCG website here;
https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015

Processing:

Data flow: Pseudonymised episode-level HES data will be transferred from NHS Digital to The University of York via secure digital transfer. After the analyses have been completed, The University of York will transfer non-identifiable LSOA-level aggregate health domain indicator data to the Oxford Consultants for Social Inclusion (OCSI), who will combine the health domain indicator with other domain indicators (all at LSOA level) to create the Indices of Deprivation. MHCLG will then publish the Indices of Deprivation at LSOA level. No episode-level data will be released by The University of York to any other party.

Data storage: Pseudonymised episode-level data will only be stored on the The University of York data analysis server and the backup server and will only be accessible to individuals who are substantively employed by the University of York. Access to data is restricted to substantive employees of University of York.

Data analyses: The University of York will use standard statistical software to clean and recode the episode-level data, derive descriptive statistics, calculate directly standardised health deprivation indicators, apply precision-weighting, and explore reasons for inter-temporal variation in indicators at LSOA-level.

Data linkage: The episode-level HES data will not be linked to any other dataset. To calculate directly standardised health deprivation indicators at LSOA level, counts of admission per age-sex group in each LSOA will first be calculated and these are then linked to ONS mid-year population estimates.

Data processing: Analyses of the HES data will involve statistical modelling using standard software (e.g. Stata, SAS, R). The analyses will take account of 1) patients' age and gender, 2) patient diagnostic information such as main and secondary diagnoses; and 3) treatment information such as admission and discharge date and type, and provider of care.

The data will be used to undertake both cross-sectional and longitudinal analyses, allowing analyses of within-period variations in health deprivation across LSOAs, and of changes in health deprivation over time.

Disclosure control: All outputs will contain only data that is aggregated to LSOA-level with small numbers (<5) suppressed in line with the HES Analysis Guide. This will ensure that the aggregate data released to OCSI, MHCLG and, ultimately, the public domain does not permit identification of individuals.

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

There will be no data linkage undertaken with NHS Digital data provided under this agreement.

Record level data will only be accessed and processed by substantive employees of The University of York.


Life Limiting conditions in children and young people in England: Prevalence and Survival — DARS-NIC-379681-D6L7G

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 – s261(1) and s261(2)(b)(ii), Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007; Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii), Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 ; Health and Social Care Act 2012 – s261(7)

Purposes: No (Academic)

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

When:DSA runs 2018-03-01 — 2020-02-28 2018.06 — 2018.09.

Access method: Ongoing, One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

University of York requires data for the purpose of a study which looks at the survival of children and young people with life-limiting conditions.

There are currently little robust data available on the survival of children and young people with Life-limiting conditions (LLC) in England and therefore the length of time that they would benefit from children’s palliative and hospice services. Therefore planning the current and future need for these services is difficult. Although previous analyses of HES data (for a different project – i.e. RU451 which has been destroyed) showed an increasing prevalence of children with a LLC in England, data is requested for this specific project to fully investigate the current and future services needs of this population.


The aims of this study are to:
1. Assess survival until death from any cause for children and young people with life-limiting conditions in relation to demographic and clinical profiles (this can only be undertaken by having death certificate data).

2. Update the prevalence of children and young people with Life-limiting conditions in England (2000-2014).

3. Describe the trends in prevalence of Life-limiting conditions within ethnic minority groups within England (2009-2014).

A pseudonymised dataset of all hospital admissions (HES) for children with a LLC was requested from the Health and Social Care Information Centre (HSCIC) (now known as NHS Digital). This data is linked to the ONS death data if the child has died, the fields requested will include date of death, place of death and cause of death.


The long time series of these data are required in order to adequately assess survival in this population of children. National data is required as these are still relatively uncommon conditions. These data will not be used for benchmarking.

The project was funded from May 2014-October 2015 but due to the delay in accessing the data the analysis has been delayed therefore the study have identified that the data would be required for a further year from now to allow for changes to manuscripts after peer review.

Martin House have been funding a programme of research since 2008 and they have a genuine interest in funding research which provides a robust evidence base for the development of children’s palliative care services. Martin House will not be able to suppress or alter research findings. And do not have access to any of the data which has not been aggregated with small numbers suppressed in line with the HES analysis guide.


Amendment 2018

The University have acknowledged that to increase the utility of the data analyses that they have undertaken to date under this agreement seeking an update to the data (3 further years of data) will allow them to develop and test a prevalence model that will estimate the prevalence of LLC in children and young people in England (objective 4). Planning services is a complex task and given the difficult funding climate being able to estimate the change in prevalence, and therefore need for services, would be very valuable to commissioners and service providers. Assessing changing place of death in the last year of life in this population will also assist in service planning. Both of these are included in the research protocol and supported by the ONS approval gateway.

Yielded Benefits:

The benefit to date is that whilst analysing the current data, conversations with services and policy makers have enabled the study to realise that in order to plan services effectively the study need to be able to project these prevalence data into the future, not simply analyse historical data. The main benefits that will be yielded need the study to disseminate the final figures, report and papers. This can only be done once the study have updated the analyses with the new data and added the prevalence modelling. The data on future prevalence on the number of children with life-limiting conditions will be available to all services and commissioners by Feb 2020. This will enable more detailed service planning and ultimately more children accessing the services that they require, when they need them. These data will also be used by national charities to lobby for policy and funding changes.

Expected Benefits:

Objectives 1,2 & 3

The results of this study will be very important for service planning and resource allocation for paediatric palliative care/children’s hospice services as the study will provide details of the number of patients who require these services and how long they require this service for. The hospital based paediatric palliative care services are NHS funded with the majority of children’s hospice services being provided by the voluntary sector. The data provided from this project will enable future provision and planning of both NHS and voluntary sector services to be based on robust data.

The addition of objective 4 will ultimately make the data more usable for services, if services need planning then a forecast into the future will be of benefit. As such that benefit of better planned services for this cohort will improve the experience and potentially lead to better outcomes.

Outputs:

All outputs will be aggregated and anonymised with small numbers suppressed, in line with the HES analysis guide.

Conference and journal outputs will be available to clinicians, academics and members of the public who attend the conferences.

The final report will be disseminated to specialist commissioners, hospital trusts and the voluntary sector. Email alerts to highlight key outputs from the study will be coordinated through professional networks (Association for Paediatric Palliative Medicine and the RCPCH) and third sector organisations which the applicants are linked in to (e.g. Together for Short Lives).

Martin House as a funder will receive the final research report which Martin House will make available as a pdf download to other interested parties, patients, carers and the public.

Objectives 1,2 & 3
1. A final summary research report will be prepared for the funding body i.e. Martin House (Target date Feb 2020). This pdf report will be available via their website free of charge.

Objectives 1,2 & 3
2. The study results will be prepared to present at an appropriate national or international clinical conference. e.g. the 3rd Congress in Paediatric Palliative Care (Target date Nov 2018) or Palliative Care Congress (Target date Oct 2019)

Objectives 1,2 & 3
3. This study results will be compiled for an academic publication in a peer review clinical paediatric journal. E.g. “archives of disease in childhood” (Target date Oct 2019)

The conference and journal outputs will be available to clinicians, academics and members of the public who may attend these conferences.

The final report will be disseminated to specialist commissioners, hospital trusts and the voluntary sector. Email alerts to highlight key outputs from the study will be coordinated through professional networks (Association for Paediatric Palliative Medicine and the RCPCH) and third sector organisations which the applicants are linked in to (e.g. Together for Short Lives).

Objective 4
The initial prevalence analyses using data up to 2014 has been completed and an abstract submitted to the international conference on childrens palliative care. The academic paper would be improved with the additional three years of data that we are requesting in this amendment.

An additional academic paper will be submitted for publication from the prevalence modelling work (Oct 2019).

The final report for the funding body will be produced by Feb 2020 and will include all the analyses.

Processing:

Objectives 1,2 & 3
NHS Digital has provided University of York with HES Admitted Patient Care data where a relevant ICD10 code is specified. A previously developed ICD10 coding framework was developed to identify children with a life-limiting condition (LLC), therefore all HES episodes for any individual who has ever had one of these ICD10 codes will be requested (aged 0-25 years at start of the episode).

The linked ONS death data has been provided for deceased individuals identified as having a LLC in HES. The mortality data will not be used to identify individuals.


Data Analysis – Undertaken by University of York
Objectives 1,2 & 3
Age category is assigned to each individual by using the start age recorded at the first hospital episode in each year. Age will be categorised into six groups: less than 1 year, 1 to 5 years, 6 to 10 years, 11 to 15 years, 16 to 20 years and 21-25 years.

Gender is coded as male, female or not known. Individuals with more than one recorded gender are assigned the most commonly recorded gender.

Ethnicity is reported by census groups in the HES data. An index of multiple deprivation (IMD) score is assigned to each individual based on their Lower Super Output Area (LSOA) of residence.


Objectives 1,2 & 3
For statistical analysis the diagnoses is categorised into 11 groups based on the main ICD10 chapters: neurology, haematology, oncology, metabolic, respiratory, circulatory, gastrointestinal, genitourinary, perinatal, congenital and ‘other’. No attempt is made to prioritise multiple diagnoses for individuals therefore individuals may have more than one life-limiting diagnosis.


Prevalence
Objectives 1,2
Population data from the Census is used as the denominator for prevalence calculations. Prevalence and 95% confidence intervals is calculated for the total number of children and young people with LLC for each year, for each age group, for each major diagnostic group, ethnic group and deprivation category. Trends over time will be assessed.
Survival

Objective 1
The data from the ONS Death Certificate data is used to identify patients who have died (date of death, place of death, cause(s) of death). Cause of death is assigned as related to the LLC or not related to the LLC. Survival is analysed using Kaplan Meier and Cox proportional hazards models. The main variables of interest in these models are survival by major diagnostic group, ethnic category and socio-economic status. The censoring date for the survival analyses is the date of data extraction.

Objective 4.
Data on the prevalence of children and young people with LLC from 2000-2012 will be used to build a prevalence model to predict the prevalence of children and young people with LLC up to 2018. This model will be assessed with the real data from the updated HES/ ONS extract. Once validate this model will be used to predict the prevalence of children with LLC in England until 2030 with appropriate confidence intervals.

The changing location of death ( home, hospital, hospice) over time will be assessed to look at patterns of where children with specific conditions tend to die over time using the coded place of death data from the ONS dataset.

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

ONS Terms and Conditions will be adhered to.

There will be no attempts made through the provision of the Data of Death shared under this agreement to identify any individuals.


Hospital use by babies enrolled in the PREVAIL randomised controlled trial — DARS-NIC-73974-P0L1Z

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, Informed Parental Consent, Health and Social Care Act 2012 – s261(2)(c); Other-Informed Parental Consent

Purposes: No (Academic)

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

When:DSA runs 2017-10-01 — 2020-09-30 2018.03 — 2018.09.

Access method: One-Off

Data-controller type: UNIVERSITY COLLEGE LONDON (UCL), UNIVERSITY OF LIVERPOOL, UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

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

Objectives:

The University of Liverpool (UoL), the University of York (UoY) and University College London (UCL) jointly require HES data and demographic data (date of death) for use in the PREVAIL study.

The PREVAIL study is a randomised controlled trial to determine the clinical and cost-effectiveness of using antimicrobial and antifungal impregnated peripherally inserted central venous catheters (AM-PICC) in very preterm babies compared with standard-PICC. It is funded by the National Institute for Health Research - Health Technology Assessment (NIHR-HTA) programme

Recruitment started in August 2015 and finished on the 11th January 2017 following which 828 babies were recruited via their parent(s)/guardian(s) throughout England. The parents or guardians of the babies taking part in PREVAIL gave informed consent for the PREVAIL research team to collect information on their baby's routine records from the birth to up to 6 months following their inclusion in the study.

The trial has three Data Controllers. UCL, UoY and UoL collaborate in deciding the appropriate methods for analysis and on the interpretation of the results. The co-principal investigators are (respectively) an employee of UCL and an employee of Bradford Teaching Hospitals NHS Foundation Trust who is the honorary chair at the Hull York Medical School (a partnership between the University of York and the University of Hull). The UoL runs the PREVAIL trial and will conduct the effectiveness analysis. The UoY will conduct the health economic analysis. UCL, as co-PI, will oversee the study.

The UCL co-PI will only have access to results in the aggregate form, but will collaborate in the planning of the analyses. Given this involvement, UCL is a co-data controller although not data processor of the patient level data. The individual at UCL will only have access to aggregated results from the data processors within this agreement which may contain small numbers. Bradford Teaching Hospitals NHS Foundation Trust will have no input in the planning of the analyses but will assist the team in the interpretation of the results. The input of the Bradford Teaching Hospitals NHS Foundation Trust co-PI in the PREVAIL project is centred in the conceptualisation, supervision and design of the clinical trial, interpretation of the results and application to clinical practice. Bradford Teaching Hospitals NHS Foundation Trust will have no access to the data and will only have access to the aggregated results which are suppressed in line with the HES Analysis Guide. For these reasons, Bradford Teaching Hospitals NHS Foundation Trust is not a data controller nor a data processor.

The University of Liverpool (UoL) and University of York (UoY) are co-data processors because they will have access to the individual level data and conduct the analysis. The UoL will send the identifiers of the babies enrolled in PREVAIL to NHS Digital, since it runs the PREVAIL trial and stores the personal data. Neither UCL nor the UoY have access to this personal information on the PREVAIL babies.

The PREVAIL study has 3 major work streams:
1. Randomised controlled trial comparing AM-PICC with standard-PICC in reducing infections and associated complications during the stay at the neonatal care unit. This is led by the University of Liverpool (UoL).
2. Health economic analysis comparing AM-PICC with standard-PICC in improving health-related quality of life and life expectancy, and their impact on costs of health care. This is led by the University of York (UoY).
3. Generalisability analysis to understand the risk of infection across the various neonatal units. This is led by the University College London (UCL).


The PREVAIL study will use NHS Digital data to inform Workstream 1, namely about whether the type of PICC affects the risk of death, and Workstream 2, namely on the cost of hospital care and risk of death by type of PICC.


The UoL will evaluate whether AM-PICC avoids infections (primary outcome) and other adverse health outcomes, including risk of death and time to death, compared with standard-PICC. In order to compare risk and time to death over the 183 days of follow-up, the UoL requires information on the date of death over the 183 days of follow-up. It is necessary to identify the date of death to calculate the effect of AM-PICC vs standard-PICC on mortality using appropriate time to event methods. Comparing risk of death between groups using information on whether death occurred, without access to the exact date of death, will risk diluting any effect between the two groups, and increases the risk of bias.

The UoL will obtain the data on date of death from PDS and date of discharge (if method of discharge is death) from HES. Previous studies suggest that there are some inconsistencies in routine records. Having the two sources indicating date of death will allow the UoL to determine whether there are inconsistencies in records, and if inconsistencies are found, to present a sensitivity analysis on the effect of AM-PICC vs. S-PICC on the time to death under each data source. The purpose of this sensitivity analysis is to understand whether using a different data source has an impact on the effect of AM-PICC vs. S-PICC.


The UoY will conduct the health economics analysis. The health economic analysis will evaluate the costs to the NHS of using AM-PICC or standard-PICC, and compare those with the health outcomes, namely health-related quality of life and life expectancy, under the two options. The objective of the health economic analysis is to inform a decision of whether the NHS should invest in AM-PICC for all or some preterm babies.

Health economic evidence is important for the NHS so that clinicians can make an informed decision on whether a specific type of PICC is good value for money. The NHS runs a fixed budget, therefore if additional funds are invested in some interventions, other interventions cannot be funded. This means that an intervention is good value for money if it is better for health and cost saving, or it increases costs but this cost increase is offset by the gains in health, compared to other interventions that could have been funded.

The health economic analysis has five components: (a) comparison of health care costs between trial groups, (b) decision modelling to extrapolate the health outcomes and health care costs from the 183 days follow-up to the expected lifetime of the babies, (c) value of information analysis on the key sources of uncertainty, (d) value of implementing the cost-effective intervention in the NHS, and (e) estimate of the costs of a blood stream infection to the NHS. Component (a) and (b) are relevant to this application.

Component (a) cost comparison will identify the health care resource use recorded for each PREVAIL baby and cost it using the appropriate unit cost. It will indicate whether there are differences in cost between trial group over the 183 days of follow-up and the magnitude of any differences. Component (b) decision modelling will simulate the health outcomes (health-related quality of life and life expectancy) and health care costs of the PREVAIL babies to their expected lifetime. The decision model requires information on the costs of the PREVAIL babies by trial group and health status at follow-up; this includes whether babies had a blood stream infection, whether death occurred within the 183 days of follow-up, and its date, so that UoY can calculate survival by trial group and infection group. Hence, the UoY request for data on health care resource use held in HES, and date of death, between randomisation and 183 days post-randomisation, held in PDS.

Expected Benefits:

The PREVAIL study will provide evidence on whether AM-PICCs reduce infections and are cost-effective in improving health of preterm babies. The question of the effectiveness and cost-effectiveness of AM-PICC for preterm babies is of high importance to the NHS. Survival in neonatal care has improved remarkably over time, but the rates of permanent neurological disability in preterm babies are high and have not improved. For example, more than 1 in 4 babies with gestations of less than 26 weeks have a serious disability at 2 years. Infections increase the risk of neurological disability. Consequently, interventions that reduce the risk of infections will reduce the burden of disability in premature babies.

AM-PICCs have been shown to be effective in reducing blood stream infections in children and adults. However, there is no evidence on their benefits in preterm babies, who are at a high risk of infection. Given the lack of evidence on benefit, and their increased costs compared to the standard PICC, NHS neonatal units currently use the standard PICC. The PREVAIL study will address this gap in knowledge by finding out whether AM-PICCs reduce the risk of infections in preterm babies, and whether this reduced risk justified their increased cost.

The findings of the study will help hospitals and policy makers to decide whether to implement AM-PICCs or not. To inform these decisions, PREVAIL will present the results to policy makers in a report to NIHR-HTA in which explicit research recommendations and implications for practice will be made. Findings and recommendations for practice will also be presented at clinical conferences and in peer reviewed journal publications. Findings will be fed back to neonatal units and regional neonatal networks through the Neonatal Data Analysis Unit.

It is expected that, thanks to the PREVAIL study, hospital managers and clinicians will have evidence about how good are AM-PICC vs. standard-PICC in preventing infections, improving health and reducing costs. This evidence will inform their decisions about which type of PICC to buy for neonatal care units. We expect this evidence to be used as soon as it is reported, in late 2018 – early 2019.

Parents will have a better understanding about the risks of infection and their consequences to the health related quality of life and life expectancy of their babies, and how the different types of PICC can prevent infections. PREVAIL will disseminate this information via Bliss, as soon as the results are available in late 2018 – early 2019.

PREVAIL cannot give a date for benefits to the NHS being realised as any benefits depend on many factors, including the results of the study (i.e. whether there are any clinical and cost benefits or harms of AM-PICCs and how large and certain these effects are) and on other barriers and facilitators of implementation. PREVAIL’s aim is to disseminate and explain the findings of the study to policy makers, service providers and the public so that decisions can be made about impregnated CVCs and any benefits to the NHS can be realised.

If results show an important benefit of AM-PICCs, PREVAIL will advocate for the findings to be incorporated into national guidance to neonatal units and to be reviewed by NICE. PREVAIL will also advocate for national monitoring of uptake of AM-PICCs (if findings are positive) through national audits.

Outputs:

The outputs of this research are an understanding about whether AM-PICC compared with standard-PICC reduces the risk of infections and their complications, including death, and the consequences for NHS costs. The NHS makes decisions on which interventions to fund based on information on their effect on health and costs. Cost is important given that the NHS has limited funds; hence cost savings can release funds to be invested in other interventions that improve health. Conversely, interventions that are more costly may warrant funding if they improve health over and beyond the health forgone due to disinvesting in other interventions. In this context, hospitals should invest in AM-PICCs if AM-PICC prevent infections and are good value for money. In other words, AM-PICCs may be cost-saving, in that preventing infections may reduce the cost of hospital care; or AM-PICC may be more costly, but their additional costs are offset by the gain in health-related quality of life and/or life expectancy, from the prevention of complications due to infection.

The UoY, UoL and UCL will report the output of the study in a number of outlets; the outputs will be produced in an appropriate way depending on the audience:

• NIHR Health Technology Assessment monograph detailing the methods and the results of the PREVAIL study. This includes: the methods of the PREVAIL trial, the methods and results of the effectiveness analysis on the risk and short term consequences of infection, and the methods and results of the economic analysis on the short and long term consequences of infections in health-related quality of life, life expectancy and costs. The expected publication date is 2019, and it is open access to all readers.

• Peer-reviewed publications in high impact journals such as Paediatrics. PREVAIL envisage that the UoL will lead at least one peer-reviewed publication on the effectiveness analysis and that the UoY will lead at least one peer-reviewed publication on the economic analysis. These will be submitted by the end of 2018. Given the long lead times required for peer-reviewed papers, publication will likely be in 2019 but unfortunately this cannot be more specific. The publications will be deposited in each institution open access repositories or published in open access format.

• Presentation of the results at national and international conferences and neonatal network meetings, throughout 2018 and 2019.

• The results will be shared with the parents of the PREVAIL babies, through the PREVAIL website.

• The results will also be shared with user representatives such as via the Bliss parent newsletter, website and social media. Examples of this kind of summary can be seen here http://www.bliss.org.uk/achievements

The NIHR monograph, peer-reviewed publications, and presentations at conferences are targeted at clinicians, hospital managers, and researchers. Clinicians make decisions about which type of PICC to use, and therefore are ideally placed to use this research to directly inform the care provided to preterm babies. Clinicians and hospital managers are involved in purchasing decisions about which type of PICC to obtain. Researchers can take the results of this study forward and continue to improve the understanding about infections in preterm babies and how to prevent them. Health economists working in the area of neonatal care, for example, may be interested in PREVAIL’s methodological approach.

The outputs disseminated by Bliss are targeted at parents and aim to improve their understanding about the risks and consequences of infection and whether AM-PICC vs. standard-PICC can prevent them. This will help parents understand the evidence on which decisions affecting their baby are based on.

The output of the analysis will be reported only in aggregate format as average and standard deviation of the cost of hospital care. Small numbers will be suppressed in line with HES analysis guide.

Processing:

The UoL holds the PREVAIL trial database. This includes identifying details of the participating babies plus details of blood stream infections, resistance, antibiotics and feeds. Other than the Date of Birth, no identifiers would be shared with the UoY or UCL, nor linked with the data to be supplied by NHS Digital.

The UoL will send to NHS Digital the babies’ identifiers (NHS number, date of birth, post code and the PREVAIL trial ID). The UoL will also send the time period for which the data is requested for each baby, which is between the date of randomisation and 183 days post randomisation.

NHS Digital will extract the relevant HES records for each baby and, if applicable, the date of death sourced from the Personal Demographic Service (PDS). NHS Digital will link the hospital records and date of death to the PREVAIL trial ID and strip the dataset of any personal identifiers (date of birth, NHS number, postcode).

NHS Digital will securely transfer the dataset to the Centre for Health Economics (CHE) at UoY. The data will be saved directly to the CHE data server via secure LAN connection. The data transfer across the LAN network is not encrypted but secured by the internal network security of the UoY (data is not copied outside of the internal network).

Additionally, the UoL receives data from the Paediatric Intensive Care Unit (PICU) linked to the PREVAIL trial ID from the Paediatric Intensive Care Audit Network (PICANet). This data contains HRG code and length of stay at the PICU plus date and type of discharge. UoL will provide pseudonymised data from PICANet linked to the PREVAIL trial ID to the UoY.

The UoY also receives pseudonymised Neonatal data linked to the PREVAIL trial ID from Public Heath England’s Neonatal Data Analysis Unit (NDAU). This data contains HRG code and length of stay at the neonatal unit plus date and type of discharge.

The UoL will also supply to the UoY a copy of the data collected during the PREVAIL clinical trial (PREVAIL dataset). This copy of the PREVAIL dataset will contain a minimum of personal information about the PREVAIL babies: trial ID, date of randomisation, date of birth, and relevant data that has been collected by the UoL from the recruiting hospitals during the trial. Date of randomisation is required to calculate the hospital length of stay during the PREVAIL trial follow-up period. Date of birth is required to ascertain whether there are differences in costs or health outcomes by the baby’s age at randomisation and trial enrolment.

From the HES and PDS data supplied by NHS Digital, the UoY will create a dataset containing the PREVIAL trial ID, date of death if death occurred, and date of discharge if method of discharge from hospital is death. This dataset will be encrypted and sent via the access controlled UoY drop-off service to the UoL. These are the only data fields sent from the UoY to the UoL.

Using the trial ID, the UoL will link this dataset to a copy of the PREVAIL trial dataset stripped of personal identifiers. The UoL will use these data to determine the effect of AM-PICC vs. standard-PICC on the risk of death and the time to death.

The UoY will use the NHS Digital dataset to calculate the cost of inpatient care, outpatient care and accident and emergency care for each PREVAIL baby.

The UoY will link the cost of inpatient care, outpatient care and accident and emergency care, and date of death to the data supplied by UoL (described above). The UoY will use the date of death to calculate survival over the follow-up period. This will inform the calculation of survival and quality-adjusted survival in the decision model.

The three cost elements (cost of inpatient care, the cost of outpatient care and the cost of accident and emergency care) do not constitute the full cost of hospital care for the babies enrolled in the PREVAIL trial (the PREVAIL babies). In order for the total cost of hospital care to be calculated, data is required on the hospital stays by the PREVAIL babies in the neonatal care unit and in the paediatric care unit. Data on hospital stays in the neonatal care unit is held in the National Neonatal Research Database (NNRD). Data on hospital stays in the paediatric care unit are held in the Paediatric Intensive Care Audit Network database (PICANet).

The PREVAIL babies have all had stays in the neonatal care unit, since it is the setting where the trial took place and the babies were recruited. Babies in the neonatal care unit may transfer to the inpatient wards if surgery takes place, such as surgery for retinopathy, which is common in preterm babies. Therefore, if some babies have had surgery, this additional resource use will be recorded in HES inpatient rather than in NNRD. Babies can be hospitalised in a paediatric unit, which is recorded in PICANet, if they were discharged home but have required more hospital care subsequently. This is because babies, if readmitted to hospital, are admitted to the paediatric unit, rather than the neonatal unit, due to concerns about infections. Babies may be seen at outpatient appointments for follow-up, and this data is stored in HES outpatients. Accident and emergency admissions, if they occurred, will be recorded in HES accident and emergency.

For these reasons, the UoY, the UoL and UCL have sought hospitalisations data from NNRD, PICANet and HES. In summary, for each PREVAIL baby, the UoY will calculate:
• The cost of neonatal care, from data held in NNRD.
• The cost of paediatric care, from data held in PICANet.
• The cost of inpatient care, from data held in HES inpatient.
• The cost of outpatient care, from data held in HES outpatient.
• The cost of accident and emergency care, from data held in HES accident and emergency.
Summed up together, these 5 cost elements constitute the cost of hospital care for each PREVAIL baby.

The UoY will link the 5 cost elements to the PREVAIL dataset. The objectives are:
• To determine whether having an AM-PICC or a standard-PICC has a direct impact on costs, whether infection has a direct impact on costs, and if so, in which cost element.
• To estimate the cost of hospital care by whether infection occurred and by whether death occurred, which will inform the decision model for the health economic analysis.
• To investigate clinical characteristics that may predict the cost of hospital care and their risk of infection. These characteristics will be used to determine whether there are babies for whom AM-PICCs or standard-PICCS are more beneficial and more cost-effective, and therefore should be prioritised for receiving them.

UCL will not have access to the individual level data, but it will collaborate in the development of the methods and interpretation of the results.

The UoY will store and archive the data used for the health economic analysis. Of the NHS Digital data, this is date of exit for reason of death, and hospitalisations recorded in HES inpatient, HES outpatient and HES accident and emergency. The NHS Digital data will be stored for the duration of the analyses. The derived data (e.g. hospital cost for each baby) will be held for minimum of 10 years, in line with data retention requirements stipulated by the funder (NIHR) and a maximum of 15 years, in line with contractual agreements with the study sponsor (UCL). These data will be encrypted and held on central IT system with backup provided by IT services.

The UoL will store and archive the data used for the effectiveness analysis. Of the NHS Digital data, this will consist of the the date of death (derived from the PDS date and/or HES discharge dates) of the PREVAIL babies who died within the follow-up period of the PREVAIL trial (183 days). The personal information on the PREVAIL babies is stored by the UoL on a networked filestore with access restricted (via ACL’s) to central IT staff who maintain the filestore and CTRC staff with a demonstrated need to access the files.

The persons at the UoL and at the UoY conducting the processing activities involving data from NHS Digital have no access to the personal information on the PREVAIL babies other than date of birth, date of randomisation, and, if applicable, ‘date of death’ (i.e. PDS date and/or HES discharge date). There will be no attempt to identify the PREVAIL babies from this data. The UoL holds the PREVAIL patient identifiers separately and unlinked, and will not link the identifiers to the data received from NHS Digital.

The individuals with access to the data are all direct employees of the UoL or UoY.
For UoY, only members of CHE who are authorised to access sensitive data by the CHE liaison officers and who require access to these data as part of their research activities will be allowed to use the system. All staff members are required to comply with the department’s and the university’s policies on the handling and usage of research and sensitive data.
For UoL, all staff members are required to comply with CTRC SOP’s and University Policies on the handling and usage of research and sensitive data. All CTRC staff members undertake data protection training on an annual basis.

The data requested will only be used to calculate the cost of hospital care, risk of death and time to death for each baby taking part in PREVAIL over the 183 days follow-up period as detailed. The data will not be used for any commercial or marketing purposes; the data will not be provided in record level form to any third party. The outputs of the analysis will be reported in aggregate form as average and standard error. All outputs will be aggregated with small numbers suppressed (in line with the HES Analysis Guide).


Evaluating the cost-effectiveness of the Best Practice Tariff for hip fracture — DARS-NIC-50329-G1L1P

Type of data: information not disclosed for TRE projects

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

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(5)(d)

Purposes: No (Academic)

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

When:DSA runs 2019-01-01 — 2020-08-05 2017.06 — 2017.08.

Access method: One-Off

Data-controller type: UNIVERSITY OF YORK

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Outpatients
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Admitted Patient Care
  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. Bespoke Cohort: MPS_ID Linkage
  8. Civil Registrations of Death - Secondary Care Cut
  9. Emergency Care Data Set (ECDS)

Objectives:

The objective of this research is to assess the cost-effectiveness of the hip fracture Best Practice Tariff (BPT) from an NHS perspective by measuring its impact on process quality and outcomes and comparing it to its cost implications. To this end, the University of York, Centre for Health Economics (CHE) will explore how the introduction of the hip fracture BPT and subsequent changes to it and the national tariff have affected:

1. Achievement on the incentivized process quality standards,

2. Patients’ health outcomes (i.e. mortality and quality adjusted life years) and the occurrence of adverse events (infections, readmissions),

3. Cost to the purchaser of care.

Furthermore, to explore why producers may respond differently to the BPT, CHE will explore:
4. How the BPT has affected providers’ unit costs,

5. How improvements on specific quality standards correlate with patients' health outcomes,

6. How improvements on specific quality standards correlate with costs and how this relationship changes as achievement levels improve,

7. Which elements of the BPT for hip fracture are the hardest to achieve (i.e. their level) and offer most scope for improvement (i.e. provider variability in average achievement),

8. Whether providers with a positive profit margin (tariff and BPT bonus net of unit costs) are more responsive to the BPT than those with a negative profit margin.


CHE's proposed research project differs from previous evaluations of the BPT in that it evaluates both short- and long-term effects of the introduction of the BPT and explicitly links improvements in process quality to outcomes and costs. This allows, for the first time, a full evaluation of the cost-effectiveness of the BPT.

Yielded Benefits:

CHE have performed a comprehensive review and evaluation of the Best Practice Tariff for hip fracture. CHE results shows the scheme has been successful overall, both in terms of the uptake as well as by directly increasing patients benefit by improving outcomes. CHE are currently writing up the results and preparing manuscript for publication. CHE's work and results have been discussed at regular meetings with NHS England, who are very supportive of CHE's work. It is expected that the final results of CHE's research will directly impact the future development of the BPT tariff for hip fracture. Due to the timing of the final report, the findings of this project are likely to feed into the development of the 2020/21 NHS Tariff at the earliest.

Expected Benefits:

With more than 60,000 admissions every year and an estimated annual cost of two billion pounds in direct healthcare costs alone, hip fractures represent a large proportion of the total NHS activity.

The Best Practice Tariff (BPT) for hip fracture aims to improve the quality of care for this patient group by incentivising treatment according to best clinical practice. It rewards the achievement of a number of specific care standards, for example time to surgery, prevention of falls and involvement of a geriatrician throughout the care pathway.

Financial incentives, such as those provided by the BPT, have the potential to influence provider behaviour and can be instrumental in improving quality of care and reducing costs. The current evidence base is insufficient to help inform decisions about future refinements and roll-out of BPT tariffs. If BPTs are not cost-effective, then the associated resources might be better invested elsewhere.

CHE's proposed comprehensive review and evaluation of the effectiveness and cost-effectiveness of the hip fracture BPT will help ensure the efficient use of resources in the NHS, by demonstrating whether the benefits of the programme measured by patient health outcomes outweigh its opportunity cost.

The work is commissioned by NHS England, and will provide the policy makers with the evidence of cost-effectiveness of the BPT tariff for hip fracture. This will include the information on whether the BPT works as intended, whether the tariff is set appropriately or there should be changes made to it. CHE will give recommendation for future development of the BPT tariff, not only for the hip fracture, but also for other clinical areas.

NHS England will be able to use the information to update their BPT pricing policies and create better incentives for hospitals to provide high quality care to patients.

Outputs:

CHE shall be providing quarterly reports to NHS England for the duration of the project, with a final report due in December 2017. This will be converted into a scientific article for publication in an international journal, the hope being that this is published during 2018.

The quarterly reports will show CHE's progress with the research project and are likely to contain preliminary results as well as description of methodology. Research will be done in different steps, for example, data preparation, descriptive statistics, econometric modelling. CHE will convey these steps in an interim report.

Different methodology will be used in the work (for example different econometric methods, Ordinary Least Squares (OLS), Generalised Linear Model (GLM)) and the results are likely to change according to the method used.

All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide.

In the article, CHE will report the methodology of the research project, including details on econometric method. CHE will provide the readers with descriptive statistics of the data as well as give an overview of the findings.

Processing:

The University of York’s CHE holds a set of pseudonymised HES data for the years 1989/90 to 2015/16 plus vital status at 7, 30, 90 and 365 days post-date of admission derived from ONS mortality data. This data was provided under a separate Data Sharing Agreement DARS-NIC-84254-J2G1Q. CHE will extract and utilise a subset of this data for use in this project. Once extracted, the subset will not be relinked with the ‘master’ dataset.

The Royal College of Physicians (RCP) hold the National Hip Fracture Database (NHFD) at Crown Informatics. Crown Informatics will extract the identifiers of each patient whose data is held in the NHFD and will assign a unique patient ID to each patient (ID#1). This ID will not be present in the NHFD and will not be retained by RCP once transferred.

Crown Informatics (on behalf of RCP) will securely transfer a file containing the patient identifiers (NHS Number, DoB, Postcode & Sex) plus the unique patient ID (ID#1) to NHS Digital. No clinical data from the NHFD will be supplied to NHS Digital.

NHS Digital will link the identifiers to its HES patient index and extract the matching HESIDs (ID#2). The HESIDs will be encrypted using the same encryption key as used for DARS-NIC-84254-J2G1Q [the other DSA].

NHS Digital will produce a bridging file matching the NHFD ID (ID#1) with the encrypted HESID (ID#2). Additionally NHS Digital will assign to each patient a unique study ID (ID#3) that is not common to the data supplied under DARS-NIC-84254-J2G1Q [the other DSA]. NHS Digital will supply the bridging file to CHE.

Crown Informatics will flow pseudonymised NHFD data containing the unique NHFD ID (ID#1). Crown Informatics will then destroy any record of the unique NHFD ID (ID#1) that could be used to relink that ID to identifiers held in the NHFD. NHS Digital will require HQIP to ensure that ID#1 is destroyed by Crown Informatics once the audit data has flowed to the University of York and NHS Digital could seek confirmation from HQIP that this condition has been imposed.

CHE will use the encrypted HESID (ID#2) to extract the relevant pseudonymised HES + ONS/derived mortality data from the ‘master’ dataset.

CHE will use the bridging file to link HES data (and linked derivations) to the pseudonymised NHFD data.

CHE will remove from the linked dataset both the NHFD ID (ID#1) and the encrypted HESID (ID#2) leaving only the unique study ID (ID#3) as a remaining patient identifier ensuring the data is pseudonymised and cannot be relinked back to identifiers by CHE, RCP, or Crown Informatics.

The linked dataset will be held and maintained separately to the data provided to CHE under DARS-NIC-84254-J2G1Q and will not be linked with any other data.


This is a retrospective observational study using a regression-based case-control approach. The cost-effectiveness of the hip fracture BPT will be evaluated using an interrupted time series approach, as well as a difference-in-difference approach with two control groups:

1. Non-English providers that are not subject to the BPT but are included in NHFD

2. High vs low performing providers in previous years
All analyses will employ appropriate econometric techniques such as Hierarchical Generalised Linear Modelling to isolate the effect of BPT achievement from that of patient characteristics (i.e. case-mix). These models are appropriate for the non-normal distribution of outcomes and clustering of patients in providers. Data quality and completeness will be assessed prior to analysis. If more than 5% of data on key outcome or explanatory variables are missing CHE will employ multiple imputation techniques to address the effect of the missing data.



Changes in mortality will be translated into quality-adjusted life years (QALYs) using life expectancy data, population health related quality of life (HRQoL) data, and utility values from the UK general public. This will allow CHE to express all patient health outcomes in terms of a common metric, QALYs.

The cost of care to the purchaser will be calculated as the sum of HRG base tariff payment for hip fracture treatment, BPT bonus and cost of additional activity in post-acute care not covered by the BPT, including A&E attendances, outpatient appointments, and emergency readmissions.

The costs to hospitals and other providers of delivering care according to BPT requirements will be assessed using reference cost data and length of stay data.

Cost-effectiveness will be calculated as incremental cost per QALY. CHE will determine the probability that the hip fracture BPT is cost-effective over a range of different valuations for a unit gain in QALY.


Authorised users
Only substantive employees of CHE will have access to the data and will access the data only for the purpose set out in this application.


Project 19 — DARS-NIC-03452-G8Z1V

Type of data: information not disclosed for TRE projects

Opt outs honoured: N ()

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)

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2016.08.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Outpatients
  4. Hospital Episode Statistics Accident and Emergency
  5. Mental Health and Learning Disabilities Data Set
  6. Patient Reported Outcome Measures (Linkable to HES)

Objectives:

Centre for Health Economics (CHE), based at University of York are requesting data for the following projects involving economic analyses of health and social care. Please note that for each of the following projects CHE staff will analyse individual level data from the various datasets. Only aggregated results will be published and disseminated.

Project 1 - Measurement of efficiency, effectiveness and productivity in the delivery of health care system nationally, sub-nationally and among hospitals;
In the current economic climate it is particularly important that we are able to identify and monitor changes in efficiency and productivity. The purpose of this project is to produce information for the Department of Health and Secretary of State for Health on efficiency, effectiveness and productivity and to provide numerical answers and context for, among others, Health Select Committees, the Public Accounts Committee and Public Expenditure Inquiries. The work also contributes to the measurement of productivity of the health service in the national accounts, compiled by the Office of National Statistics.
Funder:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001)
This project will use only the following data: HES APC 1998/99-2014/15; A&E 2007/08 - 2014/15; Critical Care 2011/12 – 2014/15; Outpatient 2011/12-2014/15; PROMs 2009/10 – 2014/15; ONS mortality 1998/99 – 2014/15,
The project also requires use of the sensitive PROMs and ONS mortality data as measures of the quality of health care.


Project 2 - Evaluation of differences in the performance of health care providers in terms of the amount and cost of provision and in patient outcomes including mortality and self-reported morbidity;
The purpose of this project is to produce evidence to inform national and regional (Yorkshire and Humber) policy-makers and providers about the scope and focus of performance improvement and outcome measures, tariff design, and patient choice. The project is designed to develop a more systematic evidence base that will allow policy-makers, providers and commissioners to develop policies to achieve efficiency and outcome-based commissioning and to redeploy resources to produce more efficient mixes of services both within and across the health and social care sectors.
Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001)
• National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (CLAHRC YH) (Ref NIHR CLARHC YH II 14653) This project will use the sensitive field Consultant Code which is required to assess variations across consultants in measures of patient safety, quality and patient reported outcomes.
• NIHR SDO Information and Value Based Commissioning - explaining the variation and causes of hospital activity and outcomes (Ref 11/1022/19)
The work for all three funders will require the sensitive PROMs and ONS mortality data to measure patient outcomes including mortality and self-reported morbidity.
The project will use only the following data: HES APC 1989/90 - 2014/15, Sensitive field: Consultant Code; HES Outpatient 2002/03 - 2014/15; PROMs 2009/10 – 2014/15; ONS mortality 1997/98 – 2014/15.


Project 3 - Evaluation of the impacts of health care policy, organisation, finance and delivery of NHS services and quantification of differences in health care utilisation, expenditure, morbidity and mortality over time, across geographic regions, health providers, and among different patient groups;
The purpose of this project is to produce information that will be used by the Department of Health to inform resource allocation arrangements and the design and direction of future policy regarding the health and social care sectors with CHE’s advice and analyses being sought to feed into White papers and specific government reviews. This project includes understanding which type of “market” for health and social care services – from highly regulated internal markets to fully decentralised market models – best achieves strategic goals. The main aims are to: analyse the potential for use of markets in health and social care to improve overall performance; analyse the impact that different configurations of markets can make on prices, outputs, quality and outcomes; explore how the best configurations could be implemented in practice.
Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001)
• NIHR HS&DR 10/1011/22 and NIHR HS&DR 13/54/40 Relationships between primary care and secondary care outcomes for people with mental illness
• Wellcome Trust [ref: 105624] through the Centre for Chronic Diseases and Disorders (C2D2) at the University of York: Finance and organisation of mental health services
The project will use only the following data: HES APC 1998/99 – 2014/15; A&E 2007/08 – 2014/15; Outpatient 2002/03 – 2014/15; PROMs 2009/10 – 2014/15; MHMDS 2011/12 – 2014/15, ONS mortality 1998/99 -2014/15.
The work for all three funders will require the use of the sensitive PROMs and ONS mortality data to measure morbidity and mortality over time.
This project will also require use of MHMDS data linked to HES data in order to carry out analyses into the economics around mental health and mental health care provision. CHE are requesting sensitive MHMDS fields and sensitive HES psychiatric fields (Legal group of patient, Legal status classification, and Detention category). These relate to the legal category / legal status of the patient and if CHE’s analyses are to be robust, are crucial for CHE’s models as an important indicator of patient severity. CHE will need all sensitive data items to accurately control for the impact of detention on resource use and utilisation. CHE need to check data consistency between HES and the MHMDS and therefore require sensitive data on legal status in both datasets.


Project 4 - Investigation of access to and socio-economic inequality in the use healthcare, patient outcomes, clinical practice, choice of provider, competition and concentration of health care services across England;
The purpose of this project is to produce information that the Department of Health will use to address the NHS’ duty under the Health and Social Care Act 2012 to consider reducing health inequalities. CHE will produce prototype NHS equity dashboards to help national and local NHS organisations to monitor performance in reducing inequalities in health outcomes and access to healthcare at different stages of the patient pathway.
Funders:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001)
• NIHR HS&DR (Ref 11/2004/39): Developing indicators of change in NHS equity performance
• NIHR HS&DR (Ref DRF/2014-07-055): Doctoral Research Fellowship - Measuring & explaining variations in general practice performance.
The project will use only the following data: HES APC 1989/90 – 2014/15; PROMs 2009/10 – 2014/15; ONS mortality 1997/98 – 2014/15.
The work for both funders will use the following sensitive PROMs and ONS mortality data to measure patient outcomes.


Project 5 - Evaluation of the interface between the different sectors of the health care system, including the effects of quality and access of primary care on patient use and outcomes in secondary care; and the relationship between long term care, social care and secondary care utilisation.
It has long been understood that health and social care services are frequently providing treatment and care for the same individuals, so ensuring that these are ‘joined up’ or well co-ordinated has in one form or another been a key objective of policy. In practice, however, both the services and approaches to monitoring these have developed separately, with potential implications for the efficiency and effectiveness of both health and social care. The purpose of this project is to produce information that will be used by the Department of Health to inform discharge arrangements and the design of integrated care arrangements and to identify opportunities for substitution of different types of health and social care services. CHE shall also be developing an online web tool to inform patients about their likely outcome of surgery to impact on shared decision making in primary care in York.
Funder:
• Department of Health to the Policy Research Unit in the Economics of Health and Social Care Systems (Ref 103/0001)
• ESRC Impact Accelerator Account - developing an online web tool (Ref A0158801)

The project will use only the following data: HES APC 1989/90– 2014/15; A&E 2007/08 – 2014/15; Outpatient 2002/03 – 2014/15; Critical Care 2011/12 – 2014/15, PROMs 2009/10 – 2014/15; ONS mortality 1997/98 – 2014/15.
This project requires the sensitive PROMs and ONS mortality data to measure patient outcomes in secondary care.


Project 6 - Estimation of resource use, costs and other parameters for cost-effectiveness analysis to support NHS and DH decisions.
The purpose of this project is to estimate the costs and health outcomes associated with a range of medical interventions identified as priorities by the Department of Health. These include interventions in oncology, cardiology and infectious disease. Only HES APC data for the years 1997/98 -2014/15 are required to estimate costs. Access to ONS mortality data, also limited to the years 1997/98 - 2014/15, are required to estimate impact on survival.
Funder:
• Department of Health to the Policy Research Unit in the Economic Evaluation of Health and Care Interventions (Ref 104/0001)
The project will use only the following data: HES APC 1997/98 - 2014/15; ONS mortality 1997/98 - 2014/15.
This project requires the sensitive ONS mortality data to measure cost-effectiveness.


Project 7 - To assess variability in uptake of treatments to inform the cost-effectiveness of interventions to increase uptake of high-value interventions.
The Department of Health is interested in the uptake of health care interventions which have been recommended by national guidelines. These interventions are identified by the Department of Health and include drug treatments for hepatitis C. The research is seeking to estimate uptake using HES APC data, for the years 1997/98 - 2014/15, as well as the implications for costs and survival (ONS mortality, limited to years 1997/98 - 2014/15) uptake falling short of national recommendations.
Funder:
• Department of Health to the Policy Research Unit in the Economic Evaluation of Health and Care Interventions (Ref 104/0001)
The project will use the following data: HES APC 1997/98 - 2014/15; ONS mortality 1997/98 - 2014/15.
This project requires the sensitive ONS mortality data to measure cost-effectiveness.


Project 8 - Evaluating the development of medical revalidation in England and its impact on organisational performance and medical practice.
This project requires HES data to examine the impact of revalidation and related systems for managing medical performance in NHS acute care, looking at individual level and organisational level effects.
Funder:
• Policy Research Programme (reference PR-R9-0114-11002). CHE lead: Dr Chris Bojke.
The project will use only the following data: HES APC 2007/08 - 2014/15; A&E 2007/08 – 2014/15; Outpatient 2007/08 – 2014/15; PROMs 2009/10 – 2014/15.
This project requires the sensitive PROMs data to measure organizational performance and the Consultant Code to assess differences in medical practice.


CHE confirms that the data under this application would only be used for the eight projects listed, and any additional project (whether as part of the DH programme or otherwise) would require a separate approval. Equally individuals working on each project will only be permitted to access the data relating to that project, as identified within this application. Access is granted for each project only to the named individuals associated with that project under authorised user names. Such access is password controlled (with a password reset required on a regular refresh).
The controls enable a single copy of the data to be held, reducing security risk associated with multiple copies being provided per project. This model is aligned with similar arrangements for other sizeable research institutions.

The access procedures are set out in our System Level Security Policy (November 2015), as follows:
“Logical measures for access control and privilege management Permissions to access the working data files are managed using Window’s Active Directory and the NTFS file system. Only members of CHE have access to the file store directory, and the HES data storage directory restricts access to only those staff with permission to access the HES data. The ONS data will be kept in compressed encrypted format within an ONS data directory with user permissions given only to ONS approved projects and users that have signed the ONS data usage form. Permissions are managed centrally with only John Galloway authorised to configure user permissions once authorisation has been granted in writing from Adriana Castelli or Katja Grasic.

Access to the CHE analysis server (ADACX) is controlled by Mark Wilson who is only authorised to provide access to users following written approval by Adriana Castelli or Katja Grasic. who provide confirmation that users have permission to analyse the HES data and are listed on the HES data user agreements with the HSCIC. The HES server will be used to analyse the ONS data which will be stored in encrypted format within a directory with user permissions set to allow only individuals named on the ONS data user agreement.”

Further, access to data is administered and monitored by Adriana Castelli through a registry. The registry lists all the projects with relevant Principal Investigator (PI) for which a valid Data Sharing Agreement issued by the Health and Social Care Information Centre is in place. Every member of staff working on a project(s) is requested to sign a non-disclosure form on an annual basis. The purpose of this form is to ensure compliance to the Centre for Health Economics and the University of York’s data protection policies, adherence to the Data Protection Act and all its Principles, and to the Centre for Health Economics System Level Security Policy. Members of staff who fail to return a signed form by the deadline provided will be excluded from access to the data until a signed form is returned. A copy of the non-disclosure form is attached.

The use of ONS mortality data is approved on an individual basis per project.

Expected Benefits:

The benefits are to be delivered on an ongoing basis in accordance with CHE’s funding agreements, and accessible from CHE’s website: http://www.york.ac.uk/che/. For all of the above projects, DH have commissioned the work as evidenced by letters supplied. The expected benefits include:

Project 1 – to December 2017
The Department of Health uses CHE’s work on of efficiency, effectiveness and productivity to provide numerical answers and context for, among others, Health Select Committees, the Public Accounts Committee and Public Expenditure Inquiries; the Office of National Statistics draws heavily on CHE’s work in producing the national accounts; CHE disseminate the work through various media to inform the public about NHS productivity.

Project 2 – to December 2017
CHE’s projects evaluating the performance of health care providers provide evidence to inform national and regional (Yorkshire and Humber) policy-makers and providers about the scope and focus of performance improvement and outcome measures, tariff design, and patient choice.

Project 3 – to December 2017
CHE’s evaluations of the impacts of health care policy, organisation, finance and delivery of NHS services are used to inform resource allocation arrangements and the design and direction of future policy regarding the health and social care sectors with CHE’s advice and analyses being sought to feed into White papers and specific government reviews.

Project 4 – to December 2017
CHE’s projects investigating socio-economic inequality will help the NHS address its duty under the Health and Social Care Act 2012 to consider reducing health inequalities. By the end of 2015 CHE will produce prototype NHS equity dashboards” (e.g. similar to http://health-inequalities.blogspot.co.uk/ which uses QOF data) that help national and local NHS organisations to monitor performance in reducing inequalities in health outcomes and access to healthcare at different stages of the patient pathway. An example of CHE’s work in this area is here: http://www.sciencedirect.com/science/article/pii/S0277953612001086

Project 5 – to December 2017
The projects assessing the interface between the different sectors of the health care system are used to inform discharge arrangements, design of integrated care arrangements and to identify opportunities for substitution of different types of health and social care services.

Project 6 and 7 – to December 2017
CHE’s projects examining cost-effectiveness will produce information which can be used to assist national and local decision making regarding an efficient use of healthcare resources in the NHS. CHE has an international reputation in undertaking cost-effectiveness analyses: http://www.eepru.org.uk/Publications(2353189).htm

Project 8 – to November 2016
The rationale for the project is to assess the economic arguments surrounding the issue of doctor re validation with particular emphasis on measuring changes to medical performance and assessing the cost-effectiveness of the programme in terms of not only increased health related quality of life for the population but also public assurance. We also directly address the extent to which the arguments outlined in the DH pre-programme impact assessment which was used to support the adoption of revalidation are being realised.

Outputs:

The outputs from all of the projects will include peer reviewed papers in academic journals, reports for funders, lay summaries such as newsletters and blogs, and conference and seminar presentations to academic, policy, professional and public audiences. The Centre for Health Economics has a long-established track record in delivery of policy research that utilises HES data, as recognized by the award of the Queens Anniversary Prize in 2007. Examples of recent publications arising from the above projects that have employed the HES data can be found here http://eshcru.ac.uk/publications/index.htm; http://www.york.ac.uk/che/publications/in-house/; and http://www.eepru.org.uk/Publications(2353189).htm.

Reports will be produced containing aggregate results that show trends over time, differences across providers, commissioners, geographical areas and by patient subgroups and patient characteristics. The results will contain estimated correlations showing associations between patient outcomes and patient characteristics, hospital, institutional, geographic and environmental factors. Statistical results will be presented in interactive spreadsheets or “Dashboards” (e.g. similar to http://health-inequalities.blogspot.co.uk/ which uses QOF data and only contains aggregated data which can be interrogated), tables and maps of aggregate statistics summarising patient characteristics and will comply with ONS guidelines on disclosure of potentially patient identifiable data i.e. no small numbered cells and figures will be reported.

The outputs from each project will be delivered in accordance with CHE’s funding agreements, which run to different timelines with various milestones for each. The key milestones and timelines for each project (including 2015 publications) are:

Project 1 - interim reports by September 2016, final report by December 2017 for Department of Health (Ref 103/0001).
Castelli A, Street A, Verzulli R, Ward P. Examining variations in hospital productivity in the English NHS. European Journal of Health Economics, 2015, 16 (3), 243-254; DOI 10.1007/s10198-014-0569-5.
Bojke C, Castelli A, Grašič K, Street A. Productivity of the English NHS: 2012/13/update. Centre for Health Economics, University of York; CHE Research Paper 110, 2015.
Aragon Aragon M, Castelli A, Gaughan J. Hospital Trusts productivity in the English NHS: uncovering the possible drivers of productivity variations. Centre for Health Economics, University of York; CHE Research Paper 117, 2015.

Project 2 - interim and final reports for Department of Health (Ref 103/0001) by July & December 2016 and 2017; interim and final report for NIHR SDO (Ref 11/1022/19) by June 2016 and December 2017.
Gutacker N, Street A. Multidimensional performance assessment using dominance criteria. Centre for Health Economics, University of York;CHE Research Paper 115. 2015.
Castelli A, Daidone S, Jacobs R, Kasteridis P, Street AD. The determinants of costs and length of stay for hip fracture patients. PLoS One 2015;doi:10.1371/journal.pone.013354
Gutacker N, Street A, Gomes M, Bojke C. Should English healthcare providers be penalised for failing to collect patient-reported outcome measures (PROMs)? Journal of the Royal Society of Medicine, 2015; 108: 304-316 DOI: 10.1177/0141076815576700
Gomes M, Gutacker N, Street A, Bojke C. Addressing missing data in patient-reported outcome measures (PROMs): implications for the use of PROMs for comparing provider performance. Health Economics, 2015;doi:10.1002/hec.3173.
Gutacker N, Bloor K, Cookson R, Garcia-Armesto S, Bernal-Delgado E. Comparing hospital performance within and across countries: an illustrative study of coronary artery bypass graft surgery in England and Spain. European Journal of Public Health 2015;25(suppl1):28-34.
Gutacker N, Bloor K, Cookson R. Comparing the performance of the Charlson/Deyo and Elixhauser co-morbidity indices across five European countries and three conditions. European Journal of Public Health 2015;(suppl1):15-20.
Jacobs R, Gutacker N, Mason A, Goddard M, Gravelle H, Kendrick T, Gilbody S. Determinants of hospital length of stay for people with serious mental illness in England and implications for payment systems: a regression analysis. BMC Health Services Research 2015;15:439.
Moran V, Jacobs R. Comparing the performance of English mental health providers in achieving patient outcomes. Social Science & Medicine 2015;140:127-136.
Bojke C, Grasic K, Street A. How much should be paid for Prescribed Specialised Services? Centre for Health Economics, University of York; CHE Research Paper 118, 2015.

Project 3 - final report for Wellcome Trust [ref: 105624] by March 2016; interim and final reports for Department of Health (Ref 103/0001) by July & December 2016 and 2017; progress and final NIHR HS&DR 13/54/40 NIHR HS&DR by, January and July 2016, January and July 2017, January and July 2018.
Santos R, Gravelle H, Propper C. Does quality affect patients’ choice of doctor? Evidence from the UK. The Economic Journal 2015;doi:10.1111/ecoj.12282.
Gutacker N, Siciliani L, Moscelli G, Gravelle H. Do Patients Choose Hospitals That Improve Their Health? Centre for Health Economics, University of York; CHE Research Paper 111, 2015.

Project 4 - interim and final reports for NIHR HS&DR (Ref DRF/2014-07-055) by July & December 2016 and 2017; interim and final reports for Funder NIHR, SRF-2013-06-015) by July & December 2016 and 2017 and 2018; Cookson R, Gutacker N, Siciliani L. Waiting time prioritisation: evidence from England. Centre for Health Economics, University of York; CHE Research Paper 114, 2015
Moscelli G, Siciliani L, Gutacker N, Cookson R. Socioeconomic inequality of access to healthcare: Does patients’ choice explain the gradient? Evidence from the English NHS. Centre for Health Economics, University of York; CHE Research Paper 112, 2015.
Cookson R, Gutacker N, Garcia-Armesto, S, Angulo-Pueyo E, Christiansen T, Bloor K, Bernal-Delgado E. Socioeconomic inequality in hip replacement in four European countries from 2002 to 2009 – area level analysis of hospital data. European Journal of Public Health 2015;25(suppl1):21-27.

Project 5 - interim and final reports for Department of Health (Ref 103/0001) due July & December 2016 and 2017.
Gaughan J, Gravelle H, Siciliani L. Testing the Bed-Blocking Hypothesis: Does Nursing and Care Home Supply Reduce Delayed Hospital Discharges? Health Economics 2015;24(S1):32–44.
Kasteridis P, Goddard M, Jacobs R, Santos R, Mason A. The Impact of Primary Care Quality on Inpatient Length of Stay for People with Dementia: An Analysis by Discharge Destination. Centre for Health Economics, University of York; CHE Research Paper 113, 2015.
Kasteridis P, Mason A, Goddard M, Jacobs R, Santos R, McGonigal G. The Influence of Primary Care Quality on Hospital Admissions for People with Dementia in England: a Regression Analysis. PLoS One 2015;10(3):e0121506.
Jacobs R, Gutacker N, Mason A, Goddard M, Gravelle H, Kendrick T. Do higher primary care practice performance scores predict lower rates of emergency admissions for persons with serious mental illness? An analysis of secondary panel data. NIHR HS & DR Journal 2015;3(16).

Project 6 - interim and final reports for Department of Health (Ref 104/0001) due December 2016 and 2017.

Project 7 - interim and final reports for Department of Health (Ref 104/0001) due December 2016 and 2017.

Project 8 - final report for Department of Health (reference PR-R9-0114-11002) due November 2016.

All products are available free of charge and available to the public via CHE’s
website http://www.york.ac.uk/che.

Processing:

Whilst the nature of detailed analysis in relation to each project varies, the broad context of processing is consistent. The following processing activities apply to all of the projects listed above.

Data storage: Data will only be stored on the CHE data analysis server and the backup server and will only be accessible to individuals associated with the Centre for Health Economics, University of York. Access to data is restricted to specific individuals according to role and project. Access to sensitive data is also restricted to only those individuals working within projects that are authorised to use sensitive data.

Data analyses: CHE will use standard software (e.g. STATA, SAS, R) to analyse the data, derive descriptive statistics and apply multiple regression models to explore the relationships between variables.

Data linkage: CHE will run the data through the HRG grouper and attach Reference Cost data using HRG codes. The data will then be linked:
• to aggregated census and other geographical data using the LSOA (Lower Super Outputs Area) variables;
• to Quality and Outcomes Framework and the Attribution Data Set using GP codes; and
• to accounts and organisational-level data using provider codes.

For the revalidation project CHE will use the consultant code to link with General Medical Council (GMC) register data on consultant age, gender, specialty and date and outcome of revalidation. The consultant code is a sensitive code and therefore access will be restricted to researchers involved in the revalidation project. Once linkage is performed for that project CHE will pseudonymise the consultant identifier. None of the linkages CHE perform will enable re-identification of any patients.

No data will be linked to record level patient data.

Data processing: Analyses of the HES and MHMDS data will involve estimation of statistical and econometric models using software including Stata, SAS and R. The analyses will take account of
1) patient demographic and socio-economic information such as age, gender, ethnicity, carer support, deprivation measures;
2) patient diagnostic information such as diagnoses (co-morbidities), Charlson score, psychiatric history, HRG or PbR care cluster;
3) treatment information such as admission type, specialty of provider, use of the Mental Health Act, community and inpatient services received by patients;
4) quality and outcomes such as PROMs, 30-day survival, HoNOS scores, waiting times, readmissions, and social outcomes such as employment and accommodation status;
5) service level factors such as number of contacts with staff, and delayed discharge.

For all projects the data will be used to undertake both cross-sectional and longitudinal analyses, allowing analyses within-year variations and of changes over time.


Project 20 — DARS-NIC-148035-41S3L

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y, N ()

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)

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2016.08.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration

Objectives:

To examine the survival of YHHN patients in relation to their presenting diagnostics, prognostics and treatment. The YHHN is a unique population based inclusive register, and the findings will have implications for the country as a whole.

Data access will be restricted to the research team mentioned in section 7 of this agreement. Any amendments will be notified to the NHS IC.

Expected Benefits:

With over 50 different sub-types and many pre-cursor conditions, haematological malignancies
comprise a heterogeneous group of cancers with widely differing treatments and prognoses
ranging from relatively indolent through to more aggressive forms. YHHN, an inclusive register of
patients newly diagnosed with a haematological malignancy whilst resident in the former Strategic
Health Authorities of West Yorkshire and North & East Yorkshire and Northern Lincolnshire, was
established on 1st September, 2004. With a population of just under 4 million, around 2000
people are newly diagnosed with a haematological malignancy in the region each year. These
patients are diagnosed by a central diagnostic laboratory, the Haematological Malignancy
Diagnostic Service (HMDS) (www.hmds.org.uk ) based at St James's Hospital, Leeds. HMDS is
part of the Clinical Haematology Network which covers the 14 hospitals (6 multi-disciplinary
teams) within the Network region. This partnership is further enhanced by formal links with the
Epidemiology & Genetics Unit (EGU) based at the University of York (www.egu.york.ac.uk). This
collaborative group is ideally placed not only to inform local clinicians and local audit, but also has
the potential to be developed as a resource for high quality population-based clinical research –
the findings from which are likely to have implications across the country as a whole.

Outputs:

10.0 Transfer of Data between the NHS IC and the Department of Health Sciences
Seebohm Rowntree Buliding University of York

• The Data is categorised as Restricted and will be treated by the NHS IC in accordance with NHS IC protocols for the transfer and use of NHS Restricted Data.

Electronic or disk
• Before transfer the NHS IC will encrypt the data using the required standard of '256-bit AES encryption' compatible with the receiving organisations systems with a password length of at least 12 characters which must include numbers, letters and symbols, and should be a mix of upper and lower case characters.
• The Data will be sent via secure electronic file to a Permitted User at the Department of Health Sciences Seebohm Rowntree Buliding University of York
• The password will be provided to the Permitted User at the Department of Health Sciences Seebohm Rowntree Buliding University of York taking responsibility for arrangements under this Agreement via telephone or e-mail.
• The named person must not share the Data or password with any other person at any time.
• Before transfer of data to the NHS IC the Department of Health Sciences Seebohm Rowntree Buliding University of York will encrypt the data using the required standard of '256-bit AES encryption' compatible with NHS IC systems.


Paper
• The NHS IC will arrange for secure courier of the data to Department of Health Sciences Seebohm Rowntree Buliding University of York
• Packages containing patient data are addressed specifically to the principal contact
• All packages are double-wrapped
• All packages are despatched by recorded delivery for which the recipient's signature is required
• Progress of delivery up to point of receipt will be tracked on-line using a unique tracking number


11.0 Storage of Data

Refer to the System Level Security Policy. This policy has been approved for this project by the DH Security Officer in conjunction with the application, NIGB and DMsG approval given for this project, detailed in section 4 of this agreement. Data Storage was covered in the policy.



Processing:

No contact will be made with any individual(s) that could be identified from the information supplied, except as specified in the protocol and associated letters agreed between the Department of Health Sciences Seebohm Rowntree Buliding University of York and the NHS IC.

Use of these Datasets are for the sole purpose set out above. The Data must not be shared with any other organisation or named individual not explicitly referred to within this agreement. If the information referred to herein is subject to an FOI or other request to share the Data, then agreement from the NHS IC must be sought before undertaking this.

The Dataset must not be shared with any third party in the format in which it is provided to you by the NHS IC.

Information tools derived from this Dataset will not be provided to any organisations without the specific consent of the NHS IC.

Any publications derived from this Data by any party must be subject to ONS confidentiality guidance on the release of Health Statistics:

http://www.ons.gov.uk/about/consultations/closed-consultations/disclosure-review-for-health-statistics---consultation-on-guidance/