NHS Digital Data Release Register - reformatted

London School Of Economics And Political Science (lse) projects

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

🚩 London School Of Economics And Political Science (lse) was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. London School Of Economics And Political Science (lse) 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.

Effects of competition and incentives on productivity, quality and efficiency of NHS providers — DARS-NIC-354497-V2J9P

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

Purposes: No (Academic)

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

When:DSA runs 2018-11-01 — 2021-10-31 2017.12 — 2024.11.

Access method: One-Off, Ongoing

Data-controller type: LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. HES:Civil Registration (Deaths) bridge
  3. Hospital Episode Statistics Accident and Emergency
  4. Patient Reported Outcome Measures (Linkable to HES)
  5. Hospital Episode Statistics Outpatients
  6. Civil Registration - Deaths
  7. Civil Registration (Deaths) - Secondary Care Cut
  8. Hospital Episode Statistics Critical Care
  9. Civil Registrations of Death - Secondary Care Cut
  10. Hospital Episode Statistics Accident and Emergency (HES A and E)
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Critical Care (HES Critical Care)
  13. Hospital Episode Statistics Outpatients (HES OP)
  14. Emergency Care Data Set (ECDS)
  15. HES-ID to MPS-ID HES Accident and Emergency
  16. HES-ID to MPS-ID HES Admitted Patient Care
  17. HES-ID to MPS-ID HES Outpatients

Objectives:

The London School of Economics (LSE) research team undertaking this application comprises researchers from LSE Health and Social Care and the LSE Research Laboratory. The research team will principally use HES and PROMs data to make three distinct contributions to health and social care within a single programme of research. The first contribution is to analyse the impact that various aspects of policy-development and reform of the NHS have had on patient outcomes, waiting times, and provider behaviour. The second contribution compares health care system performance in several countries with the aim of spreading best practice across different countries, with specific focus on lessons that can be learnt for health and social care policy within the NHS. The third contribution develops and tests a range of multi-dimensional indicators of health care quality and outcomes. All project outputs are published at an aggregate provider level, and the identification of individual patients or clinicians is not possible nor their behaviour identifiable.

The purpose of receiving the data is to analyse the impact of on-going NHS reforms implemented between 2000 and the present day using pseudonymised non-sensitive patient-level data (further details of these reforms are provided below). These reforms were primarily associated with the introduction of two Acts of Parliament (the National Health Service Act 2006 and the Health and Social Care Act 2012) that changed both the organisational and payment structures of the NHS. The second Act of Parliament relies on on-going productivity gains to maintain efficient NHS output. The objective of the research programme is to assess the change in policy direction arising from these reforms, to better understand the impact of these reforms on patient outcomes and to improve the measurement of the impact of these reforms and to aid the efficiency with which they are implemented.

Research deliverables are currently underway to examine the impact of the following reforms to the NHS and include the following:

* Introduction of Payment by Results (2003-2006).
* Implementation of waiting time targets for first outpatient appointment, elective surgery, and A&E attendances (2001-2005).
* Introduction of the Quality and Outcomes Framework (2004).
* Introduction of patient choice of hospital and GP surgery (2006-2008).
* Introduction of independent care providers, both as part of the Independent Sector Treatment Centre programme and under the Any Qualified Provider programme (2003-2010).
* Introduction of Walk in Centres for ambulatory care (2000-2010).
* Introduction of the Alternative Provider of Medical Services GP contract (2004).
* Expansion of primary care provision under the Equitable Access to Primary Care initiative (2007-2010).
* Implementation of Equality and Human Rights Commission (EHRC) Memorandum of Understanding with the Care Quality Commission on equality and human rights in the context of healthcare (2011)
* Introduction of Clinical Commissioning Groups in place of Primary Care Trusts (2013-2014).
* Progress towards the Nicholson productivity challenge (2009-2015).
* Introduction of new signals of hospital outcomes and performance, including the NHS Choices website (2006), Patient Reported Outcome Measures (2009), the NHS Staff Survey (2003), and various NHS Patient Satisfaction and Patient Experience surveys.

Each of these research deliverables form part of the first contribution to health and social care outlined above, by analysing a specific policy introduced within the NHS over the last 15 years. Each of these deliverables also forms part of the third contribution to health and social care, in that each focuses on analysing the impact of a particular policy or reform on one or more multi-dimensional indicators of health care quality and outcomes.

The research team has primarily progressed the second contribution to health and social care – a cross-country comparison of health care system performance – via a project entitled “Do Financial Incentives Trump Clinical Guidance?: The case of Hip Replacements in England and Scotland” that examines the impact of financial incentives on clinicians’ decisions in England and Scotland. Making further progress toward this second contribution to health and social care will be a major focus of the research programme over the next 2-3 years.

Using patient-level data is necessary for risk adjustment at the patient level, thereby allowing control of confounding factors that will affect the analysis. Using patient-level data is also necessary to construct various inputs into the analysis -- for example, percentiles of distance from patient's GP surgery to hospital of admission, which are needed to construct indices that define hospital referral markets and potential areas of competition. All programme outputs produced thus far have been published at an aggregate provider level, where no individual patients or clinicians are identified or identifiable. The applicant intends to continue publishing project outputs on this basis, i.e. at the provider level and without identifying individual patients or clinicians.

Yielded Benefits:

The main outcome of the presentation at the Wennberg International Collaborative was simply to raise awareness about the research findings amongst policymakers and practitioners. Earlier work compiled by the research team (Cooper et al 2011) was cited by the then Prime Minister in support of the Health and Social Care Act 2012, which expanded choice and competition within the NHS. The research also helped to ensure that these reforms did not introduce price competition between health care providers, as had initially been proposed. The work on hospital quality and on choice and competition has been used by NHS Monitor (now NHS Improvement) in their measurement and analysis of hospital efficiency measurement, which forms one strand of their work to identify inefficient hospital trusts. The project lead has since worked with NHS Improvement to aid this strand of their work, and the work in this area also led to him becoming an adviser to the UK Competition and Markets Authority investigation into the Private Health Care Market which led to a range of measures being implemented in 2014 (see https://www.gov.uk/cma-cases/private-healthcare-market-investigation).

Expected Benefits:

The applicant’s research programme has multiple outputs that are being, and will continue to be, continuously disseminated to policymakers and policy analysts, via the types of channels outlined in the previous sections. The aim of the research is to benefit English health and social care by contributing to a better understanding of the impacts of past and existing health and social care policies. In so doing, the applicant hopes to contribute to more informed policymaking in the future.

Previous success in disseminating the applicant’s research to policymakers is attested to by the fact that, in 2012, Prime Minister Cameron referred to their research outputs in a speech in support of his reforms to the NHS. Further evidence of the success of these dissemination efforts in delivering benefits to health and social care is attested to by the attached letter of support (SD2) for the applicant’s research programme from the Prime Minister’s adviser for health and adult social care. Referring to the draft project output “Does Competition Improve Public Hospitals’ Efficiency? Evidence from a Quasi-Experiment in the English National Health Service”, as well as to past research outputs produced by the applicant, states that the applicant’s research “has informed the policy thinking at the highest levels of government and materially impacted policy formation for the better. His work serves as a prime example of how research can improve policy and make a positive impact that is felt outside of academia.”

LSE's research on NHS Walk-In Centres, which had already been disseminated to policymakers in Monitor and Department of Health at the time of last application, has now percolated down to CCG level and is being used to inform commissioning decisions. See for example:
• http://www.dorsetccg.nhs.uk/Downloads/aboutus/CCG%20Board/18%20March%202015/09.7%20x%20Appendix%201%20180315.pdf.
• https://www.bristolccg.nhs.uk/media/medialibrary/2016/09/bccg_front_door_rapid_evidence_review2016-09-16.pdf.

Outputs:

The expected outputs consist of research reports and published papers, and discussions and presentations to UK health and social care policymakers, policy analysts, and clinicians. A list of outputs to date is provided below. The primary target audience for these outputs is the health policy community. This includes policymakers, as a key aim of the research is to investigate the impact of recent changes to health and social care policy, with an ultimate objective of influencing future policy formation. However, it also includes other policy analysts, such as (but not limited to) those at the Health Foundation, the King’s Fund and Nuffield Trust, who, while they may not be directly involved in the policy formation process, do have an important influence over the terms under which health policy is debated and therefore formulated. A secondary target audience is the medical community that has been responsible for implementing many of the policy reforms analysed, and whose decisions therefore determine the success of these reforms, and the nature of their impacts.

The main outputs of these reports and papers will be estimates of the statistical relationship between different variables and the pursuit of establishing causal linkages between policy reform and health and social care outcomes. A few of these variables (e.g. whether a patient is discharged as dead) appear directly in HES, but most (e.g. length of hospital stay, patient severity aggregating across multiple diagnoses, hospital productivity, or intensity of competition to which a hospital is exposed) are constructed using multiple underlying HES variables. HES data will only feature directly in the outputs of these research projects in tables of summary statistics that report properties of key variables used in the analysis (such as minimum, maximum and average values). No data or datasets using HES data will be published or made available, either at the individual patient level or at an aggregate level, as part of this programme of work. The data will not be used for any commercial purpose.

Several project outputs are already available in draft form, and are expected to be published in final form over the next 1-5 years.

Draft project outputs already produced

In the applicant’s field of work, the life cycle of a research project can be roughly summarised as follows: work in progress presentation (slides only); unpublished mimeo; Working Paper (which may also be a final output, or may be progressed to academic journal submission); submission to academic journal (if relevant); and publication of final report or journal article. Irrespective of the final publication location, the research team takes substantial effort to disseminate its outputs by presenting findings to policymakers and policy analysts.

The following research projects are at work in progress stage (slides available on request):
• Ted Pinchbeck: “Taking care of the budget? Clinical decisions and Patient Outcomes under recent NHS reforms”.
• Tom O’Keeffe and Matthew Skellern: “Do altruistic hospitals and profit-maximising hospitals respond differently to competition?”.
• Zack Cooper and Stuart Craig: “Home for the Holidays: Evidence on the Relationship Between Prospective Payment, Length of Stay, and Patient Outcomes”.
The following research projects are at mimeo stage (papers available on request):
• Matthew Skellern: “The hospital as a multi-product firm: Measuring the effect of hospital competition on quality using Patient-Reported Outcome Measures”.
• Zack Cooper, Steve Gibbons and Matthew Skellern: “Independent Sector Treatment Centres in the English NHS: Effects on neighbouring NHS hospitals”.
The following research projects are at Working Paper stage:
• Ted Pinchbeck: “Walk This Way: Estimating Impacts of Walk in Centres at Hospital Emergency Departments in the English National Health Service”, SERC Discussion Paper 167, http://www.spatialeconomics.ac.uk/textonly/SERC/publications/download/sercdp0167.pdf.
• Zack Cooper, Stephen Gibbons, Simon Jones and Alistair McGuire: “Does Competition Improve Public Hospitals’ Efficiency? Evidence from a Quasi-Experiment in the English National Health Service”, CEP Discussion Paper 1125, http://cep.lse.ac.uk/pubs/download/dp1125.pdf.
The following papers are under review for academic journal publication (papers available on request)
• Alistair McGuire and Irene Papanicolas: “Do Financial Incentives Trump Clinical Guidance?: The case of Hip Replacements in England and Scotland” (for slides see: http://www.slideshare.net/OHENews/do-financial-incentives-trump-clinical-guidance-apr15).
• Alistair McGuire and Irene Papanicolas: “Measuring and Forecasting Hospital Quality”.

Dissemination of draft project outputs

This is a non-exhaustive list of the formal and informal methods by which the applicant has disseminated their draft project outputs to policymakers, policy analysts and clinicians.

The attached ESRC Outstanding Impacts Application Form by Professor Zack Cooper, a founding member of the applicant’s research team, outlines some of the applicant’s efforts to disseminate draft project outputs to policymakers through to 2013. Since 2013, draft outputs of the applicant’s research projects have been disseminated directly to policymakers in the following ways:
• Seminar presentation to Department of Health, July 2015.
• Seminar presentation to Office of Health Economics, May 2015 (attended by representatives from Monitor and Department of Health).
• Presentation of research to Department of Health group, March 2015.
• Meetings to report draft project outputs to representatives of Monitor and Department of Health.

In addition, the draft project outputs have been presented to the following fora that have been attended by policymakers and policy analysts:
• Health Economics Study Group (January 2015).
• Royal Economics Society Meeting (March 2015).
• LSE Spatial Economics Research Centre Conference (March 2015).
• LSE STICERD Work in Progress Seminar (October 2013) (attended by representatives of Royal College of Surgeons).
• Informal workshop involving representatives from King’s Fund, and former Prime Ministerial advisors and heads of regulatory bodies (June 2015).
• Joint LSE-Dartmouth College workshop on Medical Practice Variations (September 2014).

Finally, the research outputs have been reported in media sources widely read by health policymakers and thought leaders, including (this is a very incomplete list) the Health Services Journal, The Guardian, The New Statesman, The Daily Telegraph, and The Financial Times. For further details, see the attached ESRC Outstanding Impacts Application Form by Professor Zack Cooper. (SD1)

Update October 2016:
New Work in Progress
• Alistair McGuire and Victoria Serra-Sastre (2016), “The relationship between new technologies and workforce in English hospitals”, October.

New mimeos (available on request):
• Tom O’Keeffe and Matthew Skellern (2016), “Do altruistic hospitals and profit-maximising hospitals respond differently to competition?”, April.
• Tommaso Gabrieli, Mireia Jofre-Bonet, Alistair McGuire, and Matthew Skellern (2016), “Patients’ choice and hospital quality competition: Unintended impacts of the signals”, October.
• Jose-Luis Fernandez, Alistair McGuire, and Maria Raikou (2016), “Coordinating Hospital Discharges: Bed Blocking in England”, October.

New Working Papers:
• Ted Pinchbeck (2016), “Taking Care of the Budget? Practice-level Outcomes during Commissioning Reforms in England”, SERC Discussion Paper 192, February, http://www.spatialeconomics.ac.uk/textonly/SERC/publications/download/sercdp0192.pdf.
• Zack Cooper, Stephen Gibbons and Matthew Skellern (2016), “Does Competition from Private Surgical Centres Improve Public Hospitals’ Performance? Evidence from the English National Health Service” CEP Discussion Paper 1434, June, http://cep.lse.ac.uk/pubs/download/dp1434.pdf.

New academic journal publications:
• Irene Papanicolas and Alistair McGuire (2016), “Measuring and forecasting quality in English hospitals”, Journal of the Royal Statistical Society: Series A, May, ISSN 0964-1998.
• Irene Papanicolas and Alistair McGuire (2015) “Do financial incentives trump clinical guidance? Hip replacement in England and Scotland”, Journal of Health Economics 44, pp.25-36, ISSN 0167-6296.

Since the last application, LSE have disseminated their research to individual contacts in NHS Improvement, Department of Health, the Competition and Markets Authority, and the Health Foundation.

In 2015, LSE presented their research on the tension between financial incentives and clinical guidance to a Department of Health Seminar.

In September, LSE presented their research on the impacts of hospital competition to the Wennberg International Collaborative on unwarranted variations in health care utilisation and outcomes. This high-level forum included representatives from Monitor/ NHS Improvement, NHS England, and NHS Scotland, as well as senior representatives from other health care systems around the world.

In November 2016, LSE will be presenting their research on the impacts of hospital competition to a seminar at the Competition and Markets Authority. Our findings have a direct bearing on the CMA’s decision-making process concerning hospital mergers.

In November 2016, LSE will also be presenting their research on the impact of new medical technologies within the NHS to the Health Foundation. This will be a stepping stone to disseminating this research to widely to policymakers and the broader health policy community.

Processing:

Data provided by HSCIC is stored on a dedicated secure data server housed within the LSE that is only accessible to researchers authorised to use the data. Within the Secure Server, the raw HES data is stored on a SQL Server, providing a second layer of security. Researchers extract only the HES records they need using an ODBC connection that securely imports the required data into Stata. Statistical analysis conducted in Stata is also restricted to take place on the Secure Server -- the data remains on the Secure Server at all times. Individual project data are held in working files on the Secure Server. All data held on the Secure Server is encrypted; see the attached System Level Security Policy for further details.

Once final tables of results (e.g. regression tables, summary statistics) are produced, there is a monitored and highly restricted facility allowing researchers to remove such outputs from the Secure Server, to allow reproduction within reports and other deliverables. As noted above, these final outputs contain aggregate provider level data only, do not identify (or allow the identification of) any individual patients or clinicians, and comply with the HES Analysis Guidelines on suppression of small numbers. All data users are required to sign a data use agreement forbidding the removal of patient-level data from the Secure Server. All printing functionality on the Secure Server is disabled.

HES and PROMs data have been merged together at the individual patient level using the epikey field provided for this purpose by HSCIC. A small number of other publically available data sources (e.g. North West England unemployment rates) have been merged to the HES/PROMs data, in order to allow researchers to control for demographic or socio-economic characteristics of health care providers or geographical areas at a given point in time. These data sources include postcodes and latitudes/longitudes of health care providers to help define referral markets and areas of potential competition; area deprivation indices to aid in the risk-adjustment of outcomes; and hospital-level data such as annual admissions and NHS Staff Survey results. These data sources are only ever merged on the basis of provider-level fields (e.g. trust code, site code, region of England, or MSOA) and date fields (year, financial year, quarter or month). While it is not feasible to provide an exhaustive list of data sources that will be merged into HES/PROMs or of HES/PROMs fields that will be used for merging – on the grounds that research is fundamentally a discovery process and it may become desirable, in the future, to incorporate new data sources, merged on the basis of hitherto unused (for merging) HES/PROMs fields -- as the research progresses, merging of data will only be undertaken at the provider level and therefore will not compromise the anonymity of patients or clinicians. Any additional data sources used are always (with a single exception, noted in the next paragraph) fully anonymised, publicly available data that do not contain any individual-level information, but report average characteristics of large-scale geographical areas or health care providers at a given point in time. As such, the highly aggregated data that is merged into HES/PROMs cannot be used for patient identification, and cannot increase the risk of patient identification beyond the level of risk that is inherent to the pseudonymised patient level HES data itself.

In addition to the small number of fully anonymised, publicly available data sources that the applicant will merge to HES/PROMs, the applicant intends to merge one additional data source to HES/PROMs that is fully anonymised but is not publicly available – namely the World Management Survey or WMS (http://worldmanagementsurvey.org/) which was conducted for English NHS hospitals in 2006 and 2009. The WMS data consists of survey responses by individual hospital managers concerning hospital management practices. There are between zero and two survey responses per hospital trust, with one observation per survey response. This data source will be merged to HES/PROMs using the trust code field. The WMS data is fully anonymised in that it does not contain any personally identifiable information about the hospital managers that completed the survey, other than the trust code. It is not, however, publicly available, in the sense that a research application must be submitted and approved the WMS Oversight Committee in order to obtain the version of the data that contains trust codes.

No record-level data will be shared outside of the organisations named in the agreement.

Community Health Services: ESHCRU Policy Research Unit projects — DARS-NIC-409296-H4X9J

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2023-05-17 — 2025-06-30 2023.11 — 2024.09.

Access method: One-Off

Data-controller type: LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)

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. HES:Civil Registration (Deaths) bridge
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)
  6. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

The Economics of Health Care and Interface with Social Care II (ESHCRUII) is one of 15 Policy Research Units (PRUs) funded by the NIHR Policy Research Programme. Its predecessor, the Economics of Health Care and Interface with Social Care (ESHCRU) programme, was re-awarded for a further five years starting from January 2019. The aim of ESHCRUII 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. ESHCRUII involves researchers at the University of York and the Care Policy and Evaluation Centre (CPEC) within the London School of Economics (LSE).

For individual ESHCRUII projects, either LSE or the University of York takes the lead. University of York has no involvement in research projects conducted by LSE, and vice versa. For the purposes of processing requested in this data sharing agreement, University of York will not have access to the data provided under this agreement. LSE will be solely responsible for determining the purposes for and means of the processing of the personal data and decision-making related to the data and its analysis. University of York has no involvement in determining the purpose and means of processing and is not carrying out any data controllership activities.

The NIHR/DHSC has established an Oversight Group for ESHCRUII, as for each of the other PRUs, meeting twice a year to receive progress reports on projects and to discuss strategy. This Oversight Group does not in any way control the analysis of data. The NIHR/DHSC are funders of the research and as such are not directly involved nor responsible for decision-making related to the data, their analysis, and outputs.

Community health services (CHS) constitute an important part of the NHS. They provide community nursing, therapy and other services to people in the own homes or elsewhere in the community. They are important for the care of people living in the community with a range of health conditions that require nursing or therapy services in their own homes and can have a valuable role in promoting independence, preventing hospital admission, and expediting hospital discharge. This role is especially important as the NHS faces a huge waiting list for hospital elective care.

There is currently limited research evidence on the CHS, relative to evidence on primary care, hospital care and adult social care. The NIHR/DHSC has therefore outlined their support for ESHCRUII to conduct the two related projects for which NHS England data are sought through this application.

The purposes outlined in this data sharing agreement refer to two specific projects under ESHCRUII:

- PR-PRU-1217-20301(10): Demand for community health services for adults

This project will examine the use of the Community Health Services (CHS) by different groups of patients and by geographical area. The project will explore how service use varies by age, gender, ethnicity and health condition (broadly defined) and also by features of the patient’s area of residence, such as its rurality (that is, if the area is rural or urban) or how disadvantaged it is. This evidence can help policy makers to tackle health inequalities.

The project will also produce projections of demand for CHS nationally and locally for the next 10 years. It will involve analyses of linked data from the Community Services Data Set (CSDS) and Hospital Episodes Statistics (HES) and the production of a simulation model to produce the projections. The projections will reflect official populations projections on the numbers of people by age and gender and will not take account of future changes in policy or future patient preferences.

- PR-PRU-1217-20301(11): Interaction between community health services (CHS) and hospital care

This project will focus on examining use of the Community Health Services (CHS) by hospital inpatients and outpatients with health conditions for which their use is important. It will also consider the use of adult social care (ASC) by these patient groups by age and gender, drawing on findings from other studies (e.g. work conducted by the Adult Social Care Policy Research Unit – see https://www.ascru.nihr.ac.uk/), so that use of CHS and ASC can be compared.

The NIHR has stated that this is important work that will help inform central and local government policy on community
healthcare and having access to linked data is vital to further this understanding. This is also a sector the NIHR believe will play a crucial role in recovery from the COVID-19 pandemic.

The project will involve analyses of linked data from the Community Services Data Set (CSDS) and Hospital Episodes Statistics (HES). It will include consultation with clinical experts and policymakers about the health conditions for which use of the CHS is likely to be especially effective.

Since there is limited data available on the CHS in England, the CSDS is one of the few data sources on these services. It would be impossible to address the research questions in the public interest without use of CSDS data linked to HES data. The LSE researchers request access to pseudonymised, record-level data focused around use of community health services, admitted patient care, outpatient care, A&E attendances, service users’ characteristics (e.g. age, gender, employment status, receipt of social care, date of death where applicable) and area characteristics (e.g. deprivation and rurality).

The core data set LSE request is the Community Services Dataset (CSDS). CSDS is a nation-wide data set which includes care contacts recorded from a range of community settings. These activities could take place in settings such as health centres, community centres, mobile facilities, or a patient's own home. As such the scope of the CSDS is vast, for example, in 2021-22 in CSDS there were 17.7 million referrals relating to 8.2 million people. There were also 94.4 million care contacts.

LSE analysts request linked Index of Multiple Deprivation (IMD) data and rurality data to address a key objective of the first study, to examine variation in use of the CHS by deprivation and type of area. LSE request linked HES admitted patient care and outpatient data to address a key objective of the second study, examining the interrelationship with hospital services for those receiving CHS. For this purpose LSE request CSDS data for both those who use hospital inpatient or outpatient services and those who do not use these hospital services so that the study may examine how the characteristics and use of CHS varies between these two groups of patients.

The following NHS Digital data will be accessed:
• Community Services Dataset - necessary as this is the primary resource for assessing patient use of community services nationally.
• Hospital Episode Statistics Datasets & Emergency Care Dataset (ECDS) - necessary to provide information on use of secondary care services by patient included within the CSDS data.
• Civil Registration Mortality – necessary so that the project can be sure not to include people in the analysis after their deaths, which would adversely affect the quality of the analysis.

LSE have considered carefully the limitations feasible to the data requested without undermining the ability to conduct the analyses. LSE have limited the request in the following ways:
• Age group: LSE do not require data for children (aged under 18) as initial analysis will focus on adults use of CHS. The project currently focuses on use of CHS by adults only as the policy areas the project intends to support assessment and change within focus on those aged 18 & over and has been referenced within the projects NIHR funding grant.
• Years: will limit analysis to three years – 2019/20 to 2021/22 - which will suffice to achieve the studies’ objectives while including one pre-pandemic year,
• Variables: LSE have selected the appropriate variables specified in this agreement.
• Geography: LSE require national data as the analysis will include assessing the use of community services across different geographies.
• Episodes: LSE require access to all episodes within the periods selected in order to accurately assess patient use of CHS over time, view patient journeys through these services and capture any variations in the use of these services that will require indicating within the analysis.

LSE plan to investigate whether the use of community health services varies by age, gender and other patient characteristics and how the use of CHS is correlated with use of admitted care and outpatient care. These analyses are hoped to enable LSE to understand (1) what the future trends in the use of community health services will be as the demographic structure of England continues to change, and (2) to what extent the use of CHS interacts with, affects, or is affected by use of inpatient and outpatient hospital care.

The purpose of the research cannot be achieved in a less intrusive way, as record-level data is necessary to explore how use of services varies by individual-level characteristics. Conducting analyses on the basis of record-level data is a crucial step to understand variations in use of the CHS and interaction between the use of CHS and hospital services as well as a prerequisite to making projections of future service use. Without record-level data, the academic rigour of the analyses and projection modelling cannot be achieved.

This request to process NHS England data is made on the legal basis outlined in the GDPR, Article 6.1.(e). That is, the purpose establishes the legal basis for processing special categories of personal data being necessary for archiving purposes in the public interest, Article 9(2)(j) of the GDPR. The processing of sensitive personal data is in the public interest as the results of this work will help to identify the ways in which services can be improved and patients and treatments better matched, informing evidence-based health policy on how to improve the effectiveness of community health services in the UK.

Outputs:

The main outputs from the data analysis will be reports to DHSC and submissions to peer-reviewed academic journals. Target journals include the Health and Social Care in the Community and Health Economics (or similar journals). LSE will seek to publish in journals that have an open-access agreement with the LSE, as the research output is intended to be made freely available to the general public. If this is not possible and the journals require a fee from readers for access, LSE will make the full content available for free via LSE Research Online, as part of LSE’s commitment to the Open Access model. The target dates for the completion of reports and submission of journal articles will be between 31 December 2023 and 30 June 2024.

In addition to the reports and journal submission, the researchers plan to present research findings in seminars or workshops organised by the Care Policy and Evaluation Centre (CPEC) or the ESHCRU Policy Research Unit. The researchers also plan to present in national or international conferences such as the International Long-term Care Policy Network or Health Economists’ Study Group.

The researchers may also write blogs or other short articles summarising the findings to ensure they communicate the findings of the research to wider audiences, including policy makers, practitioners, service users, and other key stakeholders. These outputs will be published on the official website for this research project.

All figures produced will be aggregate figures. These figures will include proportions, total number of users, coefficients, means, standard deviations, and predicted probabilities. Each figure will contain a sufficient cell count in line with the HES Analysis Guide to avoid disclosure of patient data. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

LSE will consult public advisers, organisations supporting patients, and commissioners and providers of CHS, as LSE conduct the project. The findings will provide evidence to inform national and local planning of CHS. They hope to inform Spending Reviews, policy development and planning of CHS at national and local level, including informing ways for the CHS to address health inequalities. LSE will promote the impact of project findings through offering presentations and discussions to the Department of Health and Social Care, NHS England and relevant professional and voluntary sector organisations.

The findings of the research will also be communicated beyond academia to the public sphere and to citizens through engagement with politicians and stakeholders, seminars, blogs and articles, and social media.

Processing:

There will be no flow of data from the London School of Economics into NHS England. The following pseudonymised data will flow from NHS England to LSE:

• Community Services Dataset (CSDS)
• HES – Admitted Patient Care
• HES – Outpatient
• Emergency Care Dataset
• Civil Registration – Deaths (Secondary Care Cut)

The data will be processed by the LSE Secure Research Computing Governance Group. Amazon Web Services (AWS) provides the hosting environment of the LSE Secure Research Environment. All processing is done by the end user, namely researchers at the LSE who analyse data on the server after connecting.

Amazon Web Services (AWS) supply support to the system, but do not access the data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. The Amazon Web Services (AWS) datacentre provides cloud storage of the data only - the processing is conducted by individuals substantively employed by LSE only.

Once the data has been received at the LSE Secure Research Computing Governance Group, the data will then be analysed in the LSE Secure Research Computing Governance Group Environment, exclusively by the LSE researchers. All outputs produced will be aggregated with small numbers suppressed.

The data will be cleaned, with duplicate records removed. Outliers and impossible values will be carefully detected and checked before they are dropped. Researchers will take the following steps to examine missing values in the dataset. First, the proportion of missing values for each variable will be calculated. Second, analyses will be conducted to examine how the patterns of missingness are correlated with variables without missing values. This step will allow researchers to evaluate whether the mechanism of missingness is missing completely at random, missing at random, or missing not at random. Finally, examination of patterns will be used by analysts to substitute values for missing data. Researchers will build imputation models based on the missing mechanisms evaluated in the second step. Such a strategy will help researchers make full use of the information available and minimise the risk of biased estimates caused by missing values. The researchers will then implement the agreed methodology by applying the appropriate econometric techniques and using statistical software packages.

After the study has ended, the LSE would like to store the research data for a further 18 months. This will enable LSE to respond to any comments or peer-review suggestions on the research and to make the necessary adaptations. The data will be stored in the LSE Secure Research Computing Governance Environment with the usual security arrangements in place at all times, and only the researchers will have access to the data.

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

There will be no other data linkage undertaken with NHS England data provided under this agreement that is not already noted in the agreement. There will be no attempts made by the LSE to re-identify individuals involved in this project as there is no requirement to do so.

MR-1461 - Improving the experience of dementia and enhancing active life: living well with dementia - the IDEAL study (Data linkage extension) — DARS-NIC-29822-N0N7W

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2022-08-28 — 2025-08-27 2022.11 — 2024.06.

Access method: One-Off

Data-controller type: LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)

Sublicensing allowed: No

Datasets:

  1. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  2. Civil Registration - Deaths
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Outpatients
  6. Mental Health and Learning Disabilities Data Set
  7. Mental Health Minimum Data Set
  8. Mental Health Services Data Set
  9. Civil Registrations of Death
  10. Hospital Episode Statistics Accident and Emergency (HES A and E)
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Outpatients (HES OP)
  13. Mental Health and Learning Disabilities Data Set (MHLDDS)
  14. Mental Health Minimum Data Set (MHMDS)
  15. Mental Health Services Data Set (MHSDS)

Objectives:

BACKGROUND:

The 'Improving the experience of Dementia and Enhancing Active Life' (IDEAL) programme is led by the Centre for Research in Ageing and Cognitive Health (REACH) at the University of Exeter. The project website can be found here: http://www.idealproject.org.uk/about/ideal/. This application relates to the ‘Improving the experience of Dementia and Enhancing Active Life (IDEAL): Living Well with Dementia (Data Linkage Extension)’, which represents an arm of the wider IDEAL programme and is led by the Care Policy and Evaluation Centre (CPEC) at the London School of Economics and Political Science (LSE).

The following paragraph is a description of the wider IDEAL programme:

Living well with dementia, whether as a person with dementia or primary (usually family) carer, can be understood as maximising life satisfaction, reaching one's potential for well-being, and experiencing the best possible quality of life. This programme aims to understand what 'living well' means from the perspective of people with dementia and their carers. It hopes to identify what helps people to live well or makes it difficult to live well in the context of having dementia or caring for a person with dementia. To understand what 'living well' means to people with dementia and primary carers, the research team hope to explore how people adapt to the challenges that the condition presents over time. The research aims to explore factors important to adapting to dementia, such as the assets, resources and support networks people have available. The methods for questionnaire data collection and analysis are described in detail in the published protocol, available here - http://www.hqlo.com/content/12/1/164. A full description of the study’s data collection method, PDFs of all questionnaires and data dictionaries are available here - https://reshare.ukdataservice.ac.uk/854293/. The data dictionaries link is: https://reshare.ukdataservice.ac.uk/854293/3/854293_Data_documents.zip.

IDEAL Cohort Study Research questions
1. How do capitals, assets and resources, and adaptation in response to dementia-related and other challenges influence the ability to live well for people with dementia and carers, and what are the reciprocal influences between people with dementia and carers factors?
2. How do changes over time in capitals, assets and resources, dementia-related and other challenges, and adaptation affect evaluations of living well for people with dementia and carers?
3. What do people with dementia and carers believe helps or hinders the possibility of living well, and what factors are particularly important to them in terms of being able to live well with dementia?

COHORT

The IDEAL programme team carried out a two-year longitudinal cohort questionnaire study of 1545 people with dementia and 1285 carers living in the community in England, Wales and Scotland between 2014 and 2019. Interviewing of the IDEAL cohort began in March 2014 and ended in July 2018. Of the participants with dementia, 1385 members of this cohort are English. It is for this subset of the cohort for which NHS Digital data is requested.

The data collected in the study’s questionnaires have some limitations. While primary carers were asked to give information on service use by the participant with dementia, not all participants with dementia had a carer. In the latter case, the person with dementia was asked directly for the information. People with dementia may have difficulty recalling the services they used without a carer to help them. Also, because people find it more difficult to remember what services they have used in the more distant past, participants/carers were asked only to recall services used in the prior three months. As only three three-month snapshots of services were collected, there will be gaps in the information available for analysis. The questionnaire data are necessarily limited to the two-year duration of the study.

Participants (patients with dementia only) were recruited from NHS memory services and specialist clinics in Great Britain, and also from the Join Dementia Research portal. IDEAL researchers visited participants and asked them about factors that influenced their life satisfaction, well-being and quality of life at Time 1 (T1 includes consent as well as baseline data entry at 6 months, 12 months, 18 months).

IDEAL researchers revisited all participants on two more occasions, one year apart (Time 2 (T2); qualitative piloting, data collection, data entry and qualitative interviews and Time 3 (T3); data collection, data entry and qualitative interviews) to find out how things developed or changed over time and how any changes affected their life satisfaction, well-being and quality of life.

Participants with dementia (for some, the carers responded on the participants behalf) were asked to provide informed consent (where the participant lacked capacity to consent, personal consultees provided consent on behalf of the participant) to complete the study questionnaires at the baseline interview (T1) and subsequently were asked to provide consent to complete the study questionnaires at T2 and T3. At the second time point, researchers also requested consent from participants for linkage of questionnaire to health service and mortality datasets.

AIM & PURPOSE
CPEC-LSE requires Hospital Episode Statistics (HES), Civil Registration (Deaths) and Mental Health data for use in the IDEAL study. The proposed project under this agreement, an economic analysis, would link the administrative health and mortality data to the IDEAL cohort questionnaire data supplied to CPEC-LSE by the University of Exeter.

The current evidence base on use and costs of healthcare by people with dementia in the UK is sparse - for instance, studies have been based on small sample sizes and unconfirmed dementia diagnoses. The IDEAL survey data will enhance the evidence base on the economics of dementia; however there are limitations to the information that can be collected by self-report methods (e.g. discontinuous “snap-shot” data limited to the study period, drop-out). The data linkage component of the study hopes to provide important information on health service trajectories in this under-represented population.

The aims of the proposed economic analysis are:
• Compile statistics to describe trends on health services received by English participants with dementia, covering a continuous proposed period of 5 years prior to study entry and thenceforth to the end of the study’s two-year follow-up
• Calculate indicative health service costs for this population 5 years prior to study entry and over the two years of the study
• Carry out a longitudinal analysis of use of health care services and mortality for this population, examining trajectories of service costs and the relationship of those costs with living well outcomes.

The planned economic analysis of the IDEAL Questionnaire data examines the following questions:
1. What health and social care services are used by the person with dementia?
2. What are the costs to health and social care of supporting the person with dementia?
3. What are the costs to the person with dementia and the carer of supporting the person with dementia (e.g. out-of-pocket spend, lost income)?
4. Are health and social care services impacting on the ability of the person with dementia to live well and on the ability of the primary carer to cope with caring responsibilities?
5. What is the relative impact of different services on the ability of the person with dementia to live well, compared to the impact of dementia-related challenges and the inherent characteristics of the person with dementia and primary carer?
6. How does the ability of the person with dementia to live well change with different levels and combinations of services?
7. How does adaptation impact upon the relationship between service use and the ability of the person with dementia to live well over time?

The data requested may help to achieve the aim identified in the ‘Aim and Purpose’ section above by serving as the basis for:
• describing the cohort’s use of hospital and mental health services and associated costs over the study period and 5 years prior
• using modelling to examine trajectories of hospital and mental health service costs over the study period and 5 years prior
• using modelling to examine the relationship of hospital and mental health service costs with living well outcomes.

The economic analysis proposed in this agreement would use administrative data on health service utilisation for all English participants who have consented to data linkage. Linkage of the administrative and questionnaire datasets may improve the quantity and quality of data available for longitudinal analysis of use of health care services and mortality in the sample in the following ways:
• The research may have better temporal coverage than relying on the 3 snapshots of service available from the questionnaire data. This confers the benefit of improved tracking of rarer service use events such as hospital stays.
• The study may be able to collect data on the mortality of participants when other sources of information on loss to follow-up due to death are unavailable.
• Linkage could allow the examination of patterns of service use in terms of demographic characteristics such as stage and severity of dementia and dependence, which are not available in the administrative datasets.

Hospital Episode Statistics, Civil Registration (Deaths) and Mental Health data are required to describe the hospital and mental health services that participants with dementia used during the study and also before the study began. The data should be as comprehensive as is feasible to allow the lead IDEAL researcher at LSE to:
• Compile statistics to describe trends on health services received by people with dementia, covering a continuous proposed period of 5 years prior to study entry and thenceforth to the end of the study follow-up (2009-2018). The 5-year period prior to study entry encompasses a period over which prodromal change in the cognitive health of the sample may be expected and might be associated with higher use of health services. Statistics include: annual number of care contacts with mental health practitioners, numbers of general and mental health hospital admissions, numbers of outpatient clinic visits and numbers of deaths.
• Calculate indicative health service costs for this population over the proposed period.
• Construct summary variables of resource use and cost for use in longitudinal multivariate analyses
• Carry out a longitudinal analysis of use of health care services and mortality for an English sample of people with dementia, in the 5 years prior to the date of the baseline assessment, and then over the two intervening years from baseline to the last follow-up/time 3 assessment; and to examine trajectories of use with long-term living well outcomes.

The LSE researcher hopes to match unit cost information/price weights to indicators of acute hospital service receipt obtained from the data linkage, using the National Schedule of NHS costs (NHS Reference costs - https://improvement.nhs.uk/resources/national-cost-collection/. This should entail merging look-up tables of unit costs (aggregated, non-sensitive data) with the secondary care routine data, and attaching unit costs to the services used via statistical software code. The researcher hopes to produce derived variables for costs to use in the analysis.

ORGANISATIONS INVOLVED

The application is for LSE to control and process the data provided by NHS Digital. LSE will be the sole Data Controller, who will also process the NHS Digital data requested. The IDEAL researchers at the LSE will conduct the analyses of service use and costs data.

The University of Exeter are involved insofar as they supply LSE with the IDEAL cohort data (University of Exeter are the Data Controller for the IDEAL cohort data). An Academic Collaboration Agreement in place with CPEC-LSE agreeing to supply CPEC-LSE with the IDEAL cohort data. The University of Exeter have no responsibility in determining the means and purposes of the processing of NHS Digital data, nor will they have access to or process the NHS Digital data requested. They are therefore not a Data Controller or Data Processor for the purposes of this DSA.

A letter clarifying the roles of the University of Exeter and LSE in relation to data controllership and data processing has also been supplied to NHS Digital. The letter is signed by the Director of the Care Policy and Evaluation Centre at LSE and the Director of the Institute of Health Research at University of Exeter and has been reviewed and approved by the Data Access Request Service at NHS Digital.

Organisations that are involved in the wider project (but not processing NHS Digital data):
• The Centre for Research in Ageing and Cognitive Health (REACH) at the University of Exeter: lead centre for the IDEAL programme.
• Brunel University
• Cardiff University
• Innovations in Dementia Community Interest Company
• Kings College London
• RICE (Research Institute for the Care of Older People)
• University of Sussex
• University of New South Wales
• NWORTH (North Wales Organisation for Randomised Trials in Health) Bangor Trials Unit
• Newcastle University
• University of Bradford
• Betsi Cadwaladr University Health Board

The following organisations are partners in the IDEAL programme. They are not involved directly in the data linkage study:
• Brunel University
• Cardiff University
• Innovations in Dementia Community Interest Company
• Kings College London
• RICE (Research Institute for the Care of Older People)
• University of Sussex
• University of New South Wales
• NWORTH (North Wales Organisation for Randomised Trials in Health) Bangor Trials Unit
• Newcastle University

IDEAL questionnaire data were entered and checked by NWORTH CTU (North Wales Organisation for Randomised Trials in Health Clinical Trials Unit) at Bangor University. NWORTH CTU is responsible for the preparation of the questionnaire dataset for depositing to the UK Data Archive (https://www.data-archive.ac.uk/) on completion of the IDEAL study. However, NWORTH at Bangor University has no role in this application and will not have access to NHS Digital data disseminated under this agreement.

LEGAL BASIS FOR PROCESSING DATA

The justification for GDPR Article 6.1.e (processing of data is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller) is:

The proposed economic analysis hopes to examine the relationship between living well outcomes of people with dementia and their carers and patterns of paid and unpaid care, demographic and needs-related characteristics and other factors. As previously described, data linkage hopes to improve the quality and validity of the data available for analysis, serving to enhance the generalizability of the findings. The research may provide policy makers, commissioners, providers of health services and social care services, and people with dementia and their carers with: evidence of the patterns of service use and costs in a large sample of people with dementia; evidence of variations in costs related to socio-demographic and psychological characteristics; and evidence of the impact services (individual or combined) have on well-being outcomes. The existing UK evidence base on costs of care for people with dementia is currently limited. This information can be used for the purposes of planning better-targeted and more efficient services.

The scientific research processes justification for GDPR Article 9.2.j (archiving purposes in the public interest, scientific or historical research purposes or statistical purposes, necessary for scientific research processes) is:

The use of the administrative and questionnaire data is in proportion to the aims of the research as described above.

FUNDING

The first phase of the IDEAL study was funded by the Economic and Social Research Council (UK) and the National Institute for Health Research (UK) through grant ES/L001853/2 ‘Improving the experience of Dementia and Enhancing Active Life: living well with dementia’, from 2014 –2019.

The second phase, the IDEAL 2 study, is funded by the Alzheimer’s Society from 2018 –2022. This agreement is to link data collected during the IDEAL study, but the funding to permit the analysis of the data has been made available from IDEAL 2.

None of the above-named funders will have access to NHS Digital data disseminated under this agreement.

PATIENT AND PUBLIC INVOLVEMENT

At the stage of developing the consent forms and participant information sheets, the IDEAL study team consulted with the ALWAYS group (the study’s Public and Patient Involvement group, all members of which have been diagnosed with dementia and have carers) and revised the forms in line with their suggestions. The ALWAYS Group does not have access to or process NHS Digital data, nor will the group determine the aims or objectives of the project. Further information on the ALWAYS group is available here - http://www.idealproject.org.uk/takingpart/involvement/. When the results of the analyses of the linked data are available, they will be shared with the ALWAYS group to ensure that the communication of results is accessible and relevant and to focus recommendations for policy and research so that they reflect the priorities of people with dementia and their carers.

Expected Benefits:

The findings, including those about using health and social care services may be used to create an action plan setting out what can be done by individuals, communities, health and social care practitioners, care providers and policy-makers to improve the likelihood of living well with dementia.

The IDEAL team hope to:
- work with policy-makers to ensure the information the IDEAL researchers have produced could influence future policies.
- work with commissioners and providers of health services and social care services, and with practitioners in these areas, using the evidence the researchers have gained to improve the effectiveness of services.
- engage with the public to encourage a more constructive attitude towards dementia and make local communities more aware and dementia friendly.

The data may reflect the involvement of participants with dementia without a participating carer on hand, a seldom heard-from group whose needs and characteristics are under-researched. In addition, the data is hoped to be more robust in terms of understanding loss to the sample because the linked data may make it possible to track the mortality of participants when other sources of information on dropout due to death are unavailable.

The data linkage element of the study may improve data quality and validity, serving to enhance generalizability of the findings on service use and costs. There is a slim evidence base on service use by people with dementia in the UK. Typically studies have had small samples, or covered just one or two geographical regions, or involved populations with only one diagnosis of dementia (usually Alzheimer’s Disease) or with unconfirmed diagnoses. This means providers and practitioners have had little information to work from when planning how to meet the needs of people with dementia and their relatives. This research hopes to examine whether there are patterns of health service costs that vary in terms of, for instance, dementia sub-type; also, the research hopes to examine whether there are distinct trajectories of cost depending on different health profiles. The information from this research may be used to devise new interventions and to target existing local services more effectively.

IDEAL study impacts:
The researchers expect the IDEAL study to have a major impact on the lives and experiences of people with dementia and primary carers in the UK and internationally. The researchers expect the study to have a significant impact in three key areas:

First, the study may provide robust and high quality research evidence on what factors affect the capacity of people with dementia and primary carers to live well with dementia and could identify factors that are amenable to effective intervention in the short, medium and long term. The study may also have a significant impact on dementia research nationally and internationally by advancing methods in the area.

Second, the researchers hope to work closely with practitioners in health and social care so that the study makes a significant contribution to translating the improved knowledge and understanding of ways of addressing the capacity of people with dementia and primary carers to live well into effective health and social care practice.

Third, the research may provide insights and evidence to support the development of dementia-friendly communities. Focusing on the lived experience of dementia and the daily lives, social relations and physical and social environments of people with dementia and primary carers may allow the researchers to examine:
1) How everyday social interactions in a range of areas facilitate or present barriers to living well;
2) Ways in which public perceptions of dementia can be improved.

Throughout the study, the IDEAL researchers hope to utilise a range of impact pathways involving engagement with the public, practitioners, service providers and policy-makers. These include creating a prominent internet and social media presence (e.g. http://www.idealproject.org.uk, @IDEALStudyTweet), developing printed and online materials, contributing to training courses and programmes, conducting regional workshops linked to local research networks, presenting at events and conferences, and contributing articles to practitioner and academic journals.

The IDEAL researchers hope to hold a consensus meeting with the aim of synthesising the research findings to form an action plan identifying what individuals, practitioners, service providers, and policy-makers can do to enhance the potential for living well with dementia. This may be presented at an end-of-study conference the IDEAL researchers organise for practitioners, providers and organisations working in the field, in order to stimulate collaborative working with key stakeholders to address the aims of the action plan.

Through these activities, the researchers aim to have a significant impact by influencing public perceptions of dementia, enhancing the work of practitioners in health and social care, encouraging the development of new approaches and interventions, contributing to the development of cost-effective service provision, and improving policy.

Outputs:

The aim is to:
- publish a paper summarising the analyses of the health services and mortality data in 2023 in peer-reviewed publications such as the British Medical Journal; also chapters and policy papers, team working and discussion papers.

- present findings at one or more national and international conferences (Alzheimer’s Association International Conference July 2023; Alzheimer Europe October 2023; British Society of Gerontology 2023).

Publication outputs hope to present summary statistics (i.e. means, standard deviations, counts (but not of small numbers), percentages (but not of small numbers), and regression analyses estimates.

The analyses may contribute to the creation of an action plan setting out what could be done by individuals, communities, health and social care practitioners, care providers and policy-makers to improve the likelihood of living well with dementia. It is hoped that understanding patterns of health service use and costs in this population may provide much-needed information on where gaps exist and for whom.

The research team expect to have a broad impact on a wide range of academic disciplines through knowledge-exchange events. To do this the researchers hope to build upon existing academic and clinical research networks (e.g. The Dementias & Neurodegenerative Diseases Research Network, NISCHR CRC (Wales), Scottish Dementia Clinical Research Network). These outputs are expected to take place from 2023 onwards.

Further academic paper(s) may be published in open-access, high impact, peer-reviewed journals on methodology; multilevel analyses of hospital service use including acute inpatient admissions and lengths of stay.

The findings are hoped to be published on the IDEAL project website (http://www.idealproject.org.uk/about/ideal/). The IDEAL researchers hope to provide links to open access papers and provide lay summary information of the publications and findings.

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).

DATA FLOW:
1) University of Exeter (data controller of the IDEAL study questionnaire data) will securely transfer a file of identifiers (Study ID, NHS Number, Date of Birth, Surname, Forename, Gender and Postcode) to NHS Digital.

2) NHS Digital will identify corresponding records in the HES, Mental Health and Civil Registration (Deaths) data, using NHS Number, Date of Birth, Surname, Forename, Gender and Postcode for the years requested.

3) NHS Digital will generate a file of HES/Mental Health/Civil Registration (Deaths) data and the IDEAL Study ID.

4) NHS Digital will flow the linked, pseudonymised data to LSE.

5) LSE will store the data on a password-protected secure folder on its servers at LSE in London.

6) LSE will provide secure access to the data to the lead researcher at LSE.

7) The LSE lead researcher will link the records in the HES/Mental health/Civil Registration data files provided by NHS Digital to the corresponding records in the IDEAL Questionnaire dataset provided to them by the University of Exeter (henceforth the ‘linked dataset’) using the IDEAL Study ID.

Only the data of participants with dementia in England with documented consent or consent under consultee advice to data linkage will be requested for linkage (1385 participants).

The linked dataset will be used only for the purpose outlined under this agreement. Only the lead researcher employed by the LSE will have access to the full set of administrative data requested. No data received from NHS Digital will be transferred from the LSE to any other organisation.

The IDEAL team at University of Exeter stores the IDEAL participant identifiable information in a separate location to the participant questionnaire data. The database is only accessible to a limited number of research staff at the University of Exeter. All staff who access the database have completed the University’s data protection training. University of Exeter will not have access to the linked IDEAL cohort and NHS Digital data.

Recruitment to the IDEAL questionnaire study (excluding the piloting phase) began in July 2014. Baseline questionnaire recruitment proceeded until August 2016; re-contacting participants to seek consent to participate at subsequent follow-up timepoints started the same time one year later. Data linkage consent was sought at T2, the first follow-up point (starting between July 2015 and August 2017). People who dropped out after baseline (T1) would not have been asked for Data linkage consent (and so would not be included in the list of participants with data to be linked).

Final total numbers recruited to the IDEAL questionnaire study for each timepoint were:
At T1 (baseline) 1537 People with dementia (and 1277 carers) completed questionnaires (altogether 1545 being recruited).
At T2 1183 People with dementia (and 988 carers) completed questionnaires.
At T3 851 People with dementia (and 759 carers) completed questionnaires.

Of these, consultee advice was relied upon for very few participants:
At T2: 5 people
At T3: 3 people
Of these, 1 person used Personal Consultee advice at both timepoints.

So:
At T2, 1178 People with dementia gave consent to participate in the Questionnaire study on their own behalf, and 5 used a Personal Consultee
At T3, 848 People with dementia gave consent on their own behalf in the Questionnaire study, and 3 used a Personal Consultee

There is detailed supporting documentation for the IDEAL programme, available on the UKDA site - https://reshare.ukdataservice.ac.uk/854293/. The consent forms for data linkage, including the consultee form, are in a file available here - https://reshare.ukdataservice.ac.uk/854293/6/854293_Supporting_documents.zip.

Data security:
The LSE will have responsibility for applying sanctions in the event of a data breach. The LSE has in place a data assurance plan for the secure storage and processing of NHS Digital data at LSE, and storage and processing for data requested under this application will follow the same security arrangements. LSE has a strong commitment to information security, over and above what is laid out in this process for the safeguarding of data. LSE has senior level sponsorship of its information security policies and processes. Its top level Information Security Policy and its suite of information security policies has been approved by the LSE’s Information Technology Committee (ITC), which consists of Departmental Managers, Heads of Department and executive representatives from across the business. LSE has a wealth of experience conducting research using highly sensitive data that has been supplied by local and national governments, national and international agencies, police forces, other universities and other key providers. The researcher’s workstation is aligned to a central server, updated within a week of critical security patches being released, with updates pushed out through the Windows Server Update Services (WSUS) on a daily basis. All campus workstations run tamper-proof antivirus software, which is managed and updated centrally. End users and system administrators cannot disable antivirus software. All campus workstations are subject to Internet content filtering and application blocking policies. LSE servers and storage run anti-virus by default.

NHS Digital data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

There will be no requirement nor attempt to re-identify individuals from the data.

DETERMIND: DETERMinants of quality of life, care and costs, and consequences of INequalities in people with Dementia and their family carers. NHS Digital data-linkage (mortality data) request. — DARS-NIC-258494-J2Q5M

Type of data: information not disclosed for TRE projects

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

Legal basis: Health and Social Care Act 2012 – s261(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: Sensitive

When:DSA runs 2020-07-25 — 2023-07-24 2021.01 — 2023.06.

Access method: Ongoing

Data-controller type: UNIVERSITY OF SUSSEX

Sublicensing allowed: No

Datasets:

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

Objectives:

Mortality data is required from NHS Digital for the purposes of the DETERMIND study.

The DETERMIND (DETERMinants of quality of life, care and costs, and consequences of INequalities in people with Dementia and their family carers) study is a multi-centre research study funded by the Economic and Social Research Council (ESRC), led by the University of Sussex, who are the Data Controller. It will gather new data, longitudinally, from a cohort of 900 people with a recent diagnosis of dementia and their carers (total = 1800). The objective is to provide a range of quantitative and qualitative evidence, interpreted using a theory of change framework, about inequalities in experiences, outcomes and costs following a diagnosis of dementia. . The study includes 7 separate workstreams.

Workstream 1 covers the recruitment of, and fieldwork with, the cohort of 1800 comprising people with dementia and their carers and Workstream 7 covers the development of the theory of change framework, an inductive process drawing upon emerging evidence from all of the workstreams, to provide an overall ‘theory of change model’ mapping the causal chains between service inputs and outcomes of interest - and identifying the contextual factors that influence the pathways people take and the outcomes they achieve.

Workstream 2 considers access to services and the factors that may influence inequalities in service access.

Workstream 3 looks at service and other costs associated with different patterns of service access and with different levels of outcome.

Workstream 4 looks at the experiences, costs and outcomes of self-funders relative to other care users.

Workstream 5 considers the role of psychological factors on differential service access and/or outcomes.

Workstream 6 looks at how the timing of diagnosis impacts outcomes. The DETERMIND dataset will include many measures in order to identify factors associated with variation in access , costs and outcomes, using evolving theories about how factors may be linked - informed by complementary qualitative research, consultation with stakeholders and existing evidence - to develop a theory of change that is plausible and consistent with the evidence gathered during the study.

The project runs for 5 years from January 2019 to December 2023.

The requested mortality data is to support the conduct of research undertaken as part of the DETERMIND study. Mortality data obtained from NHS Digital will be used to support and augment the gathering of data from a cohort of 900 people with a recent diagnosis of dementia and their carers (total 1800 people). Specifically, it will be used for the following purposes:

a. to include fact of death, place of death and cause of death in pseudonymised individual-level research (survey) data-sets generated by the research project, thus allowing these variables to be systematically included in statistical analyses

b. to formally withdraw study participants and their carers from the research study in a managed and timely manner (the expectation is that approximately 10% of those with a diagnosis of dementia in the cohort will die each year, approx. n=90 per annum). As well as supporting effective project management, this is designed to avoid inappropriately including the deceased participant and bereaved carer for participation in further survey rounds, or approaching them to invite them to participate in associated qualitative research or linked studies.

c. to prompt administration of a carer bereavement survey to members of the cohort who have given their consent to be contacted about involvement in further research.

The University of Sussex is a public authority responsible for conducting scientific research for academic and public benefit. Data in the DETERMIND study is processed to enable the University of Sussex to perform its public task. University of Sussex rely on the following legal bases for processing data under the General Data Protection Regulation

• GDPR Article 6 (1) (e), public task and, for special categories of data (including health information and information concerning ethnicity and sexual orientation), Article 9.2(j), archiving, research and statistics.

Possible ethical issues have been considered and addressed throughout the process, including those considered by the HRA REC approval process.”

Eligibility and procedures for recruitment of the cohort includes anyone with a recent diagnosis of dementia in three participating NHS Foundation Trusts - (Gateshead Health NHS Foundation Trust, South London and Maudsley (SLAM) NHS Foundation Trust and Sussex Partnership NHS Foundation Trust) with no exclusion criteria. The cohort also includes their listed carers. There are no control groups, as the study has a non-experimental design.

University of Sussex request mortality data for the study cohort of 900 people with a recent diagnosis of dementia and their carers, (total 1800) covering fact of death, alongside information about place and cause of death.

The data requested is proportionate to the purposes set out above and there are no alternative, less intrusive ways of achieving these purposes. The extent and level of data will allow individual cohort members to be appropriately withdrawn and approached about involvement in further research studies (as relevant), and to allow fact of death, place of death and cause of death to be included in multi-variate analyses of pseudonymised individual-level survey data gathered from the cohort.

As a multi-centre study, the project team includes a range of research partners in addition to the University of Sussex (the Data Controller). All of these research partners are co-applicants on the research grant (not sub-contractors) and their roles and responsibilities are consequently governed by DETERMIND’s governance framework (including the study protocol and ethical approvals). The majority of these research partners are data processors (i.e. for primary data gathered in the course of the study for the 900 people with a recent diagnosis of dementia and their carers (total 1800)). The roles and responsibilities of research partners (and co-applicants to the research grant), and their involvement in processing mortality data obtained from NHS Digital, are as follows.

South London and Maudsley (SLAM) NHS Foundation Trust; Gateshead Health NHS Foundation Trust; Sussex Partnership NHS Foundation Trust (SPFT)
Local Principal Investigators and Research Assistants in three local NHS memory assessment services (located within NHS Foundation Trusts) will work, on behalf of the University of Sussex and the project as a whole, to recruit the cohort of 900 people with a recent diagnosis of dementia (300 in each NHS Foundation Trust) and their carers (1800 in total) and will administer a face to face survey (at baseline assessment and annually for a further three years) to members of the cohort.

The Local Principal Investigators are clinical consultants within the NHS Foundation Trusts and are co-applicants on the research grant, thus included within the governance framework for the project (e.g. protocol and ethical approvals). To facilitate the involvement of the NHS Trusts in research, each Local Principal Investigator holds an academic affiliation in addition to their clinical roles within the NHS Trusts; the Local Principal Investigator at SPFT has an affiliation with the University of Sussex, the Local Principal Investigator at SLAM has an academic affiliation at King’s College London (KCL) and the Local Principal Investigator at Gateshead Health NHS Foundation Trust has an academic affiliation at Newcastle University. The roles of the three NHS Foundation Trusts are described fully in the study protocol. There are, additionally, associated capacity and capability agreements/ statement of activities in place for the participating NHS organisations.

The University of Sussex, the London School of Economics and Political Science (LSE) and the University of York will be responsible for directly analysing pseudonymous, individual-level survey datasets generated by the study and conducting follow-up qualitative research with purposive samples of people with dementia and their carers from the cohort.

Mortality data from NHS Digital will be used, where participants agree, to include fact of death, place of death and cause of death as variables in survey datasets. Before being shared with LSE and the University of York, these datasets will be pseudonymised; that is, they will not include personally identifiable data (i.e. name, full address or postcode, date of birth or NHS number) and each participant will only be directly identified in the dataset by their study ID number.

However, LSE and the University of York will be provided with non-pseudonymised data for individuals (including fact of death, place of death and cause of death) to support qualitative recruitment, fieldwork and analyses. The Study Coordinator at the University of Sussex will provide the researchers with contact details for a small number (approx 20-30 per qualitative study) of participants from the cohort of people with a recent diagnosis of dementia and their carers where they meet specified sampling criteria and have given consent to be approached for involvement in follow-up qualitative research.

Cambridge University and Newcastle University
Cambridge University’s and Newcastle University are neither Data Controller or Data Processor. Neither organisation is involved in deciding how data is used, or in gathering or directly analysing data from the cohort of 900 people with a recent diagnosis of dementia and their carers (total of 1800) or consequently, any mortality data obtained from NHS Digital for members of the cohort.

University of Cambridge will provide expert advice and Newcastle University will support consultation and dissemination of findings.

The role of research partners at the University of Cambridge is to provide expert advice on analysis plans for DETERMIND data, based on previous experiences of analysing similar large-scale data sets. Cambridge will not carry out any analyses of the data directly and University of Sussex can choose to either accept or reject any advice provided. They will also advise statisticians on secondary analysis of publicly archived data from the Cognitive Function and Ageing Study (CFAS II), which is a study that was designed to investigate dementia and cognitive decline in a representative sample of more than 18,000 people aged over 65 years, and a further study - the English Longitudinal Study of Aging (ELSA). This secondary analysis is undertaken early on in the DETERMIND project to help generate hypotheses to inform the DETERMIND survey and theory of change framework.

The Economic and Social Research Council (ESRC) are funding the project. The ESRC is the national research council for economic and social research. It has funded the research but plays no role in directing or undertaking the research, and is neither a data controller or processor.

Expected Benefits:

The DETERMIND research study has been funded by the Economic and Social Research Council (ESRC), following peer review and was consequently assessed against a range of quality criteria including the likely benefits it would provide to health and social care research and provision. In particular, the project will build an evidence base, of new data and theories of change, to inform policy and practice. The overall aims of the study, and of all of the constituent study outputs, are to ensure that findings about costs and about unequal access and outcomes, and the reasons for these inequities, as well as potential solutions and responses are used to inform policy-making and service-planning, as well as the agendas of national organisations seeking to influence policy and practice in dementia care. As detailed in Objectives for Processing, above, these focus on inequities in access to services and what drives these, inequities based on whether self-funding or not, inequities due to psychological factors (e.g. self-esteem), and inequities based on the timing of diagnosis, as well as the costs associated with these different pathways.

The programme will also generate new data, research findings and theory about inequalities in access, outcomes and costs for people with dementia and for their families and carers that will be of key academic interest. This will support and prompt new and innovative research that, in turn, is capable of improving health and social care.

Multiple opportunities for career development and capacity-building have also been built into DETERMIND, with post-doctoral and early career researchers embedded into the research team and two linked studentships. Dementia research and research into social care provision, in particular, are areas where the need for more high-quality research and researcher capacity is widely recognised in order to ensure ongoing research benefit to these sectors is realised.

Outputs:

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

In the DETERMIND study, survey and qualitative data will be gathered from members of a cohort of 900 people with a recent diagnosis of dementia and their carers (total 1800) with a view to better understanding a range of inequalities in dementia care, costs and outcomes. A number of published outputs will be produced and shared using an active public engagement/ communication strategy. The specific publications to be produced will be determined as the project progresses, reflecting the evolving and theory-led nature of the study. The project team are committed to producing multiple peer-reviewed journal papers in high-quality, internationally recognised dementia and social policy journals and various related outputs to reach a range of policy, practice and lay audiences, (including blogs, policy briefings, conference presentations and media interviews and articles). Target audiences are discussed in more detail below. There will be a number of over-arching publications and outputs, covering findings from the project as a whole, as well as specific outputs for each of the seven constituent workstreams. These will be promoted through a dedicated DETERMIND website (https://determind.org.uk), with links from the institutional websites of organisations involved in this study. The project team will work with dissemination partners. In particular, there are funds available to work with the Alzheimer’s Society to promote findings across the Alzheimer's Society networks and website. The project team will also work closely with Making Research Count, a national network of social work and social care departments in 10 English universities with partnerships with local agencies. A summary of planned outputs are as follows:

• An early peer-reviewed journal paper has already been accepted for publication. This is a protocol paper, which summarises for academic audiences the reasons for undertaking the research and describes the research design and methods.

• The project team will prepare descriptive reports of findings after analysis of data from each of the three waves of the longitudinal survey (baseline, wave 2 and wave 3, conducted across 3 consecutive years). These will be written in an accessible format and contain a clear summary of descriptive findings, with use of suitable infographics. These will be posted online and distributed to stakeholders, including to research participants. The project is partnered with the Oxford charity, DIPEx, who are responsible for the highly successful healthtalk.org website that has disseminated text and audio-visual outputs covering more than 100 research studies. DIPEx will develop and manage a dedicated DETERMIND website and produce video and audio summaries for professional and lay audiences. These will be linked to the project team's organisational websites and by the project's dissemination partners, including the Alzheimer’s Society.

• Research partners leading each of the workstreams will produce further high-quality, international peer-reviewed journal papers for academic audiences and other publications for professional and lay audiences in their specific topic areas, with integrated and cross-cutting outputs agreed during the course of the research study as the research evolves.

• At the end of the project, the project team will produce a policy‐based report summarising the findings and associated implications for policy, practice and future research. This will be disseminated as described above, and the project team will hold a major final event, inviting all the stakeholders engaged with throughout including politicians, policy‐makers, academics, carers, people with dementia and the voluntary sector.

• The above policy-based report will be accompanied by fact sheets providing accessible information about the study's overall findings.

• The project team will also produce a guide for the wider public connecting research findings to the everyday life and challenges of people with dementia, families and carers. This guide will include infographics, pictures and real‐life testimonials, thereby contextualising and humanising the academic evidence.

• Findings will be used to update projections of future expenditure on dementia care and update the ESRC-funded MODEM (Modelling the Costs and Outcomes of Dementia) study legacy model (this was previously produced, under a separate grant, by a project team that included team members from the current study, from University of Sussex and LSE) to assist policymakers, commissioners and others to plan services

In all publications, the data will be reported thematically and aggregated with small numbers suppressed. No individual participants taking part in the study will be identified. It is unlikely that any data presented could lead to an individual being identified, however specific efforts will be made to manage this risk by, for example, changing details in examples and quotes presented in qualitative research findings and by suppressing small numbers in reported statistical findings.

The project team will hold seminars and workshops throughout the study to obtain stakeholder views on our plans and emerging findings. They will invite people with dementia, carers, professional staff and stakeholder organisations, including DHSC, NHS England, the Alzheimer’s Society, and the Association of Directors of Adult Social Care. The team members have strong existing links with all these groups. The team also plans to present findings from the research at relevant academic and policy conferences, provide briefings for relevant parliamentary committees, request a special session on the project to be presented at the All Party Parliamentary Group on Dementia and feed into relevant government consultations.

Target audiences for findings from the research include the public, people with dementia and family carers, clinicians and care staff, the third sector, health and social care commissioners, policy-makers, policy networks, groups representing older people and their families, regulatory and other sector-specific bodies, and various knowledge brokers and change agents. Supported with a small dedicated budget from the study, the Alzheimer’s Society will work closely with the DETERMIND team to assist in reaching these audiences effectively and to support the project’s knowledge exchange and communication strategy. The Alzheimer’s Society will also help to facilitate wide stakeholder engagement throughout, including in the annual stakeholder workshops that will be held to make sense of evolving findings (using a theory of change model) to inform development of the study and the specific research questions that will be addressed in successive rounds of analyses. Key organisations that the DETERMIND project team will engage with include the following:

• Association of Directors of Adult Social Services (ADASS),
• Age UK,
• Alzheimer’s UK,
• Carers UK,
• Care and Support Alliance,
• UK Home Care Association,
• Care England,
• Care Quality Commission (CQC),
• Skills for Care,
• Social Care Institute for Excellence (SCIE),
• The National Institute of Health and Care Excellence (NICE) and
• Think Local Act Personal, with whom the research team already, collectively, have strong links.

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).

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 those organisations who are data processors and will not be accessed or processed by any other third parties not mentioned in this agreement.

There will be no attempts made by The University of Sussex to re-identify individuals involved in this project as there is no requirement to do so.

DETERMIND is a multi-centre research project, funded by the Economic and Social Research Council (ESRC) and led by University of Sussex. Co-applicants on the research grant are University of York, the London School of Economics and Political Science, the University of Cambridge, Newcastle University and clinical consultants (with academic affiliations) in three NHS Trusts (Gateshead Health NHS Foundation Trust, South London and Maudsley (SLAM) NHS Foundation Trust and Sussex Partnership NHS Foundation Trust). The study protocol clarifies, in detail, the roles of these different research partners. In summary:

• The University of Sussex is the sole data controller and is the lead partner within the research team. It is solely responsible for deciding how research data gathered from the cohort of 900 people with a recent diagnosis of dementia and their carers (total 1800) is used. It is also solely responsible for decisions about how mortality data obtained from NHS Digital for members of this cohort is used.
• Gateshead Health NHS Foundation Trust, South London and Maudsley (SLAM) NHS Foundation Trust and Sussex Partnership NHS Foundation Trust (SPFT) will recruit the cohort of 900 people with a recent diagnosis of dementia and their carers (total of 1800) and collect and process participants’ data, on behalf of the University of Sussex and the project as a whole. This will include obtaining and processing mortality data for members of the cohort.
• The University of Sussex, LSE and the University of York will analyse pseudonymised individual-level research (survey) datasets generated by the research project and undertake recruitment, fieldwork and analyses for qualitative research using identifiable data for eligible individuals (meeting sampling criteria and having provided consent to be approached for qualitative research).

• Cambridge University and Newcastle University are co-applicants on the research grant but are not data processors; they play no role in collecting, managing or analysing data gathered from the cohort of 900 people with a recent diagnosis of dementia and their carers, nor the associated mortality data obtained from NHS Digital.

Data management arrangements in DETERMIND in support of these roles
The process for managing the flow of data gathered from and about the 900 people with a recent diagnosis of dementia and their carers (total 1800) is designed to minimise the extent to which research data can be linked to personally identifiable data. This is achieved by ensuring that a clear distinction is made between research data and personally identifiable data, that these are stored separately and by controlling who is able to access each of these two different datasets.
• Pseudonymised research data, identified only by Study ID, will be stored with the data controller (University of Sussex), using REDCap, a secure web application for building and managing online surveys and databases - https://www.project-redcap.org. Research Assistants located at each of the NHS sites will enter pseudonymised research data into REDCap. Once research data has been entered into REDCap, the Research Assistants will no longer be able to access it without contacting the Study Co-ordinator at the University of Sussex. No personally identifiable data will be stored in the REDCap system.
• Direct access to personally identifiable data (participant name, address, date of birth and NHS number) will be limited to authorised staff (Local Principal Investigators and Research Assistants) at Gateshead Health NHS Foundation Trust, South London and Maudsley (SLAM) NHS Foundation Trust and Sussex Partnership NHS Foundation Trust (SPFT. Authorised staff will also have access to participants’ Study ID numbers and thereby comprise the only route through which research data is capable of being re-identified (i.e. de-pseudonymised).
Personally identifiable information will be entered into a locally-held Participant Contact log, which will exist independently at each site. This will also contain additional information concerning contact preferences, consents for NHS Digital data linkage, consents to be contacted about further studies and other meta-data associated with research visits. No research data or information about participants’ health will be stored in the Participant Contact log. This log will be updated as needed (e.g. following survey visits, following any communication or following receipt of notification of death from NHS Digital).

Data management arrangements for processing data from NHS Digital.

Flows out of data into NHS Digital: Research Assistants at the three local NHS sites will send personally identifiable data about members of the cohort of 1800 comprising people with a recent diagnosis of dementia and their carers to NHS Digital quarterly (until the full cohort is achieved), using agreed and authorised methods of data transfer. The legal basis for processing data throughout DETERMIND is GDPR Article 6.1(e) and Article 9.2(j). Consent will be sought from participants to provide their data to NHS Digital for the purposes of data linkage to mortality records and data linkage will not be sought for any participants that do not provide this consent. This is the only time that personally identifiable data will be shared outside of the research team.

Study ID, NHS Number, Date of Birth, Gender and Postcode will be shared with NHS Digital to facilitate linkage.

Flows of data out of NHS Digital: NHS Digital will return mortality data identified by Study ID number to the Study Coordinator at the University of Sussex.

Flows of NHS Digital data within DETERMIND: The mortality data from NHS Digital will remain with the local NHS sites and will not be further circulated. However, it will prompt a number of actions.
• Upon receipt of information concerning someone's death, the locally-held Participant Contact log will be updated
• A REDCap form will also be completed. This will inform the Study Co-ordinator at University of Sussex that a participant has died. The participant will be identified as deceased in the research (survey) dataset, and variables about place and cause of death will also be included in the research (survey) dataset. Participants in the research (survey) dataset are identified by Study ID number only and no personally identifiable information is included. This is the dataset that will be analysed by researchers at University of Sussex, the London School of Economics and Political Science, and the University of York.

• The deceased participant and their carer will be formally withdrawn from the main study (annual face-to-face survey) and from consideration for involvement in associated qualitative research.

There are two reasons for DETERMIND to have the data linkage:

1) To withdraw people from the study so they are not approached inappropriately
2) To invite bereaved carers to participate in relevant linked studies, where they have consented and expressed interest in being informed of further studies (and that they are contacted appropriately in doing so)


All data processing for the study is only carried out by substantive employees of the data processor(s) and or data controller(s) and these employees have been appropriately trained in data protection and confidentiality.

Evaluating the Heterogeneous Impacts of the Improving Access to Psychological Therapies (IAPT) Programme — DARS-NIC-403870-H8L5B

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2022-01-06 — 2025-01-05 2022.07 — 2022.07.

Access method: One-Off

Data-controller type: LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)

Sublicensing allowed: No

Datasets:

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

Objectives:

Mental health conditions account for 30% of non-fatal diseases and 10% of the overall disease burden, including death and disability worldwide (World Bank & World Health Organization, 2016). In the UK, mental health problems are the largest single cause of disability, with one in four adults experiencing at least one diagnosable mental health problem in any given year. Poor mental health has significant adverse effects on individuals and their immediate community (physical health, longevity, employment, social relations, and overall life satisfaction) and on the economy as a whole. The Organisation for Economic Co-operation and Development (OECD) estimates the total costs of mental ill health in the UK at approximately 3.5% to 4% of gross domestic product (GDP) (OECD, 2017). Apart from putting additional pressure on the healthcare system, mental ill health reduces employment, productivity, and accounts for almost half of disability benefits. The COVID-19 pandemic and associated lockdowns have worsened the mental health of countless individuals in the UK. We expect mental health issues to be high on the policy agenda in the recovery from COVID-19 and in a post-COVID-19 world.

In 2008, the National Health Service (NHS) began with the nationwide implementation of the Improving Access to Psychological Therapies (IAPT). One of the ambitions of IAPT is to achieve at least a 50% recovery rate. While this goal was achieved in 2017, treatment outcomes differ substantially between patients of different socio-economic and ethnic groups as well as different geographical locations. For instance, in 2017, there was a 17% difference in the recovery of patients between the most and the least deprived areas (58.1% for the least and 41% for the most deprived area, see Moller et al. (2019)). Recovery rates were also found to differ significantly across patients’ religion, disability, ethnicity, and sexual orientation.

The goal of this project is to help policy-makers understand the sources of heterogeneity (dissimilarities and diversity) in the effectiveness of IAPT treatments for different patients and in the performance of different service providers. The London School of Economics and Political Science (LSE) seek to answer the following research questions:
1. What is the treatment effect of the IAPT programme on patient outcomes?
2. Which types of patients benefit most and least from which type of treatment, and why? What are the characteristics of the service provider (e.g. waiting times, average number of sessions, share of referrals who completed the treatment) and local area (e.g. urban or rural location, local infrastructure, or local deprivation and unemployment) that are associated with a higher or lower probability of individual recovery?
3. Building on identified sources of heterogeneity on patient level, what makes a successful IAPT service provider? How important are the characteristics that service provides can control (e.g. waiting times) and cannot control (e.g. local area characteristics). Should the expected average recovery rates of services vary in line with the socio-economic characteristics of an area?

In order to address these research questions, the LSE are requesting access to the IAPT dataset from NHS Digital. To understand the sources of variation in the recovery rates of IAPT patients, we must first distinguish the causal effect of a treatment programme from confounding factors such as natural recovery (or deterioration). Our plan is to begin with the estimation of individual treatment effects using pseudonymised patient-level data. The treatment effect is a causal quantity that measures the difference between the patient outcome after treatment and the hypothetical outcome if the same patient had not obtained treatment. This is a challenging task that has not been done previously for IAPT treatments. Prior work provides only descriptive evidence that is useful for analysing treatment outcomes, i.e. the change in the intensity of symptoms from before and after treatment. The challenge in building a causal treatment model to control for confounders with the IAPT data is that the IAPT programme is not a randomised controlled experiment. We propose to construct a quasi-control group using variation in waitlists, i.e. waiting times between the initial assessment and the treatment sessions.

Another intention of ours is to combine different data sources and use them as efficiently as possible. In particular, we will combine the IAPT data with publicly available geographical information that will enable us to construct measures of wealth, education, ethnicity, and other characteristics of the area, where each service is located. Statistically, such data will help control for otherwise unobserved socio-economic effects. Our combined dataset will include patient characteristics (i.e. demographic and socio-economic characteristics, diagnoses, treatments, treatment outcomes, service characteristics (i.e. number of new referrals, the number of patients who started/finished treatment, waiting times, and other indicators), and session details during the course of treatment), and local area characteristics (i.e. incomes, employment, education, crime, quality of housing and living environment) of the geographical areas where services are located (which we will collect separately from publicly available data sources). Importantly, we plan to use intermediate patient-level data, i.e. data on the sessions attended during the course of the treatment, not only because we believe the dynamics of the treatment process are informative for explaining treatment effects, but also to enable our model to inform proposals for future treatments that would be conditional on treatment paths.

Record level data is crucial for the purpose of the analysis. The chosen timeframe, covering IAPT data between 2012 and 2020, would allow enough observations to perform the heterogeneity analysis, i.e. to have a sufficient amount of patients with different combinations of characteristics. It would also allow us to analyse the evolution in the performance of the programme over time. The project requires use of all IAPT packages, including Core package, Waiting Time package, Disability and Patient Experience packages. Since we intend to identify the inequality in the effects of the programmes for the patients with different characteristics, we need to be able to observe all the possible characteristics that are available in the dataset. Also, our identification strategy for treatment effect estimation relies on the ability to control for potential confounders. Omitting some patients’ characteristics might result in constructing a less reliable quasi-control group.

The NHS’s Mental Health Implementation Plan 2019/20 – 2023/24 states that one of IAPT’s current priorities is “[…] reducing geographic variation between services and reducing inequalities in […] outcomes for particular population groups”. The need to understand the source of these variations will be more pressing than ever with the ongoing COVID-19 pandemic. Indeed, the pandemic has been having a direct effect on increasing the number of patients in the short to mid-term and indirect effects as the NHS, strained by COVID-19, must allocate resources to identify and treat patients as efficiently as possible.

Our findings about the treatment effect of the programme and its heterogeneities will allow us to better understand the sources of inequality in treatment outcomes of the IAPT patients. They will reveal which patient groups are more responsive to particular treatments in particular services, allowing us to identify the best diagnosis-treatment match. We will also learn if different patient groups are systematically influenced by different service and local area characteristics and in which ways. We will use the sources of heterogeneity identified in this analysis and their relevant importance to understand what drives heterogeneity in the share of recovered patients among different service providers. The sources of the variation that can be controlled by the service, e.g. waiting times, will form the basis for relevant policy recommendations. The sources of variation that cannot be controlled by the service, e.g. local area characteristics in terms of health or deprivation levels, if found important for the recovery rates, can be used to construct different expected outcomes for services located in different areas.

Our team will be hosted by the Community Wellbeing Programme in the Centre for Health Economics (CEP) at the LSE. The CEP is one of the leading interdisciplinary economics and policy think tanks worldwide and has been awarded Economic and Social Research Council Research Institute status (being one of only two institutes that have received this recognition so far). The LSE is the sole data controller who also processes the data for this study. The LSE also provides funding for the project. The study team are all substantive employees of the LSE, and they will receive advice from experienced academic mentors and scientific advisors in the field based at the University of Surrey and the University of Oxford. These mentors and advisors have significant experience in the fields of mental health, the economics of wellbeing, cost-effectiveness as well as cognitive behavioural therapy. These organisations do not determine the purpose or the means of the data processing, and will not be accessing NHS Digital data under this agreement. The study team is also advised from a general policy perspective by the former Cabinet Secretary at the time the scheme was rolled out.

The LSE is a ‘public authority’, as defined in the Data Protection Act 2018, with a principal object of the organisation being research and its dissemination. The processing of pseudonymised personal data, including special category data, is necessary to carry out medical research that serves the public interest. The legal basis for processing personal data is: Article 6(1)e of the GDPR, ‘processing is necessary for the performance of a task carried out in the public interest’; and Article 9(2) j of the GDPR ‘processing is necessary for archiving purposes in the public interest, scientific or historical research purposes’. The processing of sensitive personal data is in the public interest as the results of this work will help to identify the ways in which services can be improved and patients and treatments better matched, informing evidence-based health policy on how to improve mental ill health in the UK and beyond.

Reference List
Moller, N. P., Ryan, G., Rollings, J., & Barkham, M. (2019). The 2018 UK NHS Digital annual report on the Improving Access to Psychological Therapies programme: A brief commentary. BMC Psychiatry, 19(1), 252. https://doi.org/10.1186/s12888-019-2235-z

OECD. (2017). OECD Mental Health Performance Framework.

World Bank Group, & World Health Organization. (2016). Out of the Shadows: Making Mental Health a Global Development Priority. https://www.who.int/mental_health/advocacy/wb_background_paper.pdf?ua=1

Yielded Benefits:

This is a new Data Sharing Agreement. No NHS Digital data has been received by the London School of Economics at the time of compiling the Data Sharing Agreement. There are no Yielded Benefits.

Expected Benefits:

This project caters to several groups of beneficiaries, including researchers in public health, mental health, and wellbeing; health economics, economics, and econometrics; public administration; and applied researchers in public and health policy; clinical research and therapy; and in the third sector such as mental health charities, both in the UK and worldwide. We expect to provide the benefits after 24 months.

We intend to investigate the sources of inequalities in the effectiveness of the IAPT programme with the goal to inform the policies on improving services and delivering the most relevant treatment to each patient. Of particular interest is expected to be the estimation of the treatment effects of the IAPT programme, a nationwide public mental health programme which is widely recognised as the largest and the most ambitious programme of applied cognitive behavioural therapy in the world. Apart from estimating the average treatment effect, we intend to estimate heterogeneous treatment effects by service and patient characteristics as well as characteristics of the area in which services and patients are located. This is anticipated to be a significant value added to the existing literature which has important implications not only to our beneficiaries involved in the further development of IAPT and its current priority of “[…] reducing inequalities in […] outcomes for particular population groups”; but also to our beneficiaries involved in the design, testing, and implementation of similar emerging mental health programmes around the world.

We believe that our project caters to several groups of beneficiaries, including researchers in public health, mental health, and wellbeing; health economics, economics, and econometrics; public administration; and applied researchers in public and health policy; clinical research and therapy; and in the third sector such as mental health charities.

At a more practical level, our findings are likely to have important implications for individuals in applied research positions in public and health policy, clinical research and therapy, and mental health practitioners. Understanding the sources of the variation in the effectiveness of treatment that can be controlled by a single therapist, a service provider, or the overall programme are hoped to inform evidence-based policy recommendations on how to improve treatment practices, service operations, or overall programme guidelines. Our analysis of the dynamics of the treatment process (i.e. the number and the characteristics of attended sessions) is expected to shed light on whether and how treatment effects can be improved during the course of the treatment. We also expect to be able to identify the kind of patients who respond better to treatment and to better understand current inequality in the treatment outcomes of the programme. The results of this work are expected to help to identify the ways in which services can be improved and patients and treatments better matched.

Finally, our analysis is anticipated to be of interest to administrators of the IAPT programme and similar initiatives due to our ability to identify the sources of the variation in the effectiveness of treatment that cannot be controlled by a single therapist, the service provider, or the overall programme, for example, local area characteristics such as local deprivation or infrastructure. This is likely to have important implications for local and central Government beyond public health, as they can inform the decisions on how to improve public mental health services and can guide more efficient resource allocation, which is particularly important given scarce resources, especially in the recovery from COVID-19.

Although it is difficult to conclusively say how many patients in England are likely to benefit from our work, we can make an estimate. England has a population of 56 million. At any point in time, approximately 5 percent of the population is living with depression or anxiety, yielding about 2.8 million individuals. IAPT currently treats about 15 percent of these and aims at raising this share to about 25 percent (Five Year Mental Health Forward Plan). We take the latter target figure. Hence, we expect that about 700,000 patients are likely to directly benefit from our work in the future, and in particular, the most vulnerable amongst these who are currently lagging behind the average in terms of treatment effectiveness.

The CEP are working closely with the ‘What Works Centre for Wellbeing’, and have a direct connection to a member of the House of Lords. Mentors to the project include the initiators and designers of the IAPT programme, and leading researchers on mental health and social care in the UK. These organisations and individuals are well placed to facilitate the CEP in translating the results of the analysis into policy.

We measure the policy impact of our project by citations in policy publications (for example, by the NHS itself or related UK Government Departments or Agencies, such as the Department of Health and Social Care, UK Health Security Agency or Office for Health Improvement and Disparities; or by charities working in the sector, like the Mental Health Foundation). Its academic impact will be measured by citations in academic publications (that is, how often our published paper is being cited in other papers).

Outputs:

We intend to release a LSE-CEP Discussion Paper: “Using Machine Learning to Evaluate Average and Heterogeneous Impacts of a Nationwide Public Health Service: The Improving Access to Psychological Therapies (IAPT) Programme”. The LSE-CEP Discussion Paper series is amongst the most widely quoted and read discussion and working paper series in the world. We anticipate submitting the discussion paper to a leading economics journal (Review of Economics and Statistics, Journal of Public Economics, or Journal of Health Economics). Of particular academic interest will be our ability to estimate the causal effect of a nationwide public mental health service on patient outcomes, including heterogeneous treatment effects, and our insights on using waiting times to construct a control group.

These academic deliverables are expected to be accompanied by non-technical, general-audience summaries in form of LSE-CEP CentrePiece articles (the quarterly magazine of the CEP, with 500k downloads a year), policy briefs targeted at public and health policy-makers as well as mental health practitioners, clinicians, therapists; and blog entries on the web sites of organisations we have published with over the years (What Works Centre for Wellbeing and VoxEU). These activities are flagged by press releases, to be led by the media and communications team at LSE-CEP and other departments (LSE Department of Psychological and Behavioural Science, LSE Department of Health Policy, University of Oxford Department of Experimental Psychology), all of which have slightly different target groups.

Finally, we intend to present our findings at general economics (American Economic Association Annual Meeting, Royal Economic Society Conference, and European Economic Association Congress), health economics (International Health Economics Association Congress and European Health Economics Association Conference), and econometrics conferences (Econometric Society Meetings and International Association of Applied Econometrics Conference) as well as interdisciplinary conferences in public and health policy, including related seminars (LSE-CEP Wellbeing Seminar and LSE-PBS Research Seminar) and workshops. Through our scientific advisors, we are in the unique position to disseminate our research and findings to the highest policy level (e.g. by being able to access the All Party Parliamentary Group on Wellbeing Economics, an officially recognised cross-party group of MPs and Lords in the UK Parliament).

In terms of key stakeholders, our research and findings are expected to be of interest to public and health policy-makers; mental health practitioners, clinicians, therapists; and researchers in the field of health economics and microeconomics broadly. We recognise the importance of involving these stakeholders from early on to inform specific aspects of our research. To this end, we are looking to organise a kick-off workshop at the beginning of our project (month 1), leveraging our scientific advisors’ and mentor’s networks to invite selected representatives from each stakeholder group. Midway through the project, we aim to share preliminary results in the form of draft discussion papers to obtain further comments and suggestions. A final conference is being planned for the end of the project (month 24), involving a larger group of representatives from each stakeholder group to present our findings formally as well as media and press effectively. Speakers will include our scientific advisors, mentor, and other leading specialists in the field.

The LSE-CEP Community Wellbeing Programme has strong networks across policy and practice, which have led to substantial prior policy impact. Importantly, the IAPT programme itself was conceptualised, designed, and preliminarily evaluated by our scientific advisors. The CEP work was also pivotal for the inclusion of wellbeing cost-effectiveness in the updated HM Treasury Green Book and the establishment of mental health support teams in schools.

Processing:

The London School of Economics and Political Science (LSE) are requesting access to pseudonymised patient-level information from the Improving Access to Psychological Therapies (IAPT) Data Set. The LSE request a one-off transfer of the patient-level IAPT data covering the period between 2012 and 2020.

Data will be processed by the LSE, which is the sole data controller and processor. Data processing will only be carried out by substantive employees of the LSE. LSE's Centre for Economic Performance (CEP) has rich experience in using sensitive data, including NHS data, for economic research and it provides all the necessary infrastructure to guarantee that all the security requirements of storing data are met. The data set will be uploaded in an extra secured area of LSE’s network where the storage architecture is compliant with NHS Digital’s Data Security and Protection Toolkit. There will be no subsequent flows of IAPT data from the LSE.

Data will only be stored offsite at Amazon Web Services London data centre. Amazon Web Services supply Cloud Services for the London School of Economics and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Researchers who will work with NHS Digital data need to request access to the environment where it is stored. When the team that manages the environment have approved the request, the researcher can connect to the environment through a Virtual Private Network (VPN) via Multi-Factor Authentication into a remote desktop environment. No data can be downloaded onto individual devices. All data processing and analysis occurs using networked LSE devices on campus, or remotely via the VPN.

High standards of data handling are further guaranteed by the fact that all researchers who access data have the relevant skills in working with sensitive data. All members of the team have been appropriately trained in data protection and confidentiality, they have also passed the Office for National Statistics (ONS) course on safe data handling and received Approved Researcher status from the ONS.

In order to protect patient confidentiality, when presenting results calculated from IAPT record level data, the following suppression rules are to be applied:
· any figures based on a count of between 0 and 4 referrals are to be suppressed by replacing the number with an asterisk (*);
· all sub-national counts are to be rounded to the nearest 5;
· sub-national rates (which are presented as percentages and are based on unrounded numbers) are to be rounded to the nearest whole percent
· national rates are to be rounded to one decimal place.

IAPT data will be grouped according to region. Groups of individuals within a region can then be linked to publicly available data containing local area characteristics in which patients and services are located, such as those found in the Office for National Statistics (ONS) online database. Since data will not be linked to other individual-level data, only data at the level of the Lower Layer Super Output Area (LSOA), we estimate the risk of reidentification to be negligible. There will be no requirement/attempt to re-identify individuals.

From the IAPT data, the LSE will match patients receiving treatment for mental health problems with patients diagnosed with the same problems who should have been treated but were not, yet the progress of their symptoms was recorded over time as though they were receiving treatment. These patients will be referred to as the ‘waitlist control group’.

The LSE will ensure a match between treated and waitlist control patients by controlling for observable regional and individual characteristics as well as centre fixed effects. Importantly, patients in treatment and waitlist control groups are matched based on type of initial diagnosis and self-reported duration of symptoms when they obtain their initial diagnosis and have their baseline measures taken. That is, only patients within the same centre who had the same initial diagnosis and the same self-reported duration of symptoms are compared; the only difference between them being that the centre can only treat a limited number of patients at the same time. Sample selection bias can be minimised by controlling for service types (i.e. look at within-service variation) and initial health status (by matching severity of symptoms and other observed characteristics).

We anticipate that the state of the patients in the waitlist group might change due to natural recovery and deterioration. We will ensure that these natural changes are similar between the treatment and control groups by controlling for the symptom’s duration of the patients in our model. This will guarantee that we compare the patients at a similar stage of the illness.

We will focus on average individual treatment effects and inequalities therein. For each patient, we observe individual characteristics such as age, gender, ethnicity, religion, sexual orientation, long-standing health conditions, employment status, and reported social scale scores, as well as the characteristics of treatment such as referral, diagnosis, treatment mode, and waiting times. Some of those are collected during each of the appointments (on average, patients have seven treatment sessions).

We will begin by estimating the treatment effect on the treated of the IAPT programme. We use three outcomes for this model: depression scale, anxiety scale, and an indicator for recovery, i.e. the scores on both scales are below the clinical cut-off at the end of treatment.

We then will explore heterogeneity of treatment effects. We will estimate five models, sequentially adding different sets of characteristics to each subsequent model (Diagnosis and prescribed treatment characteristics - Model 1, adding patient characteristics in Model 2, service characteristics in Model 3, local area characteristics in Model 4, treatment dynamics (data on the attended sessions) in Model 5). Sequentially adding potential sources of heterogeneity will allow us to better understand the characteristics which are associated with the inequality in treatment effects. Models 1 to 4 will use the data at the start of the treatment and will allow us to draw conclusions about expected treatment effects for different patients when they join the programme. Model 5 will also incorporate intermediate data, i.e. data on the number of attended sessions and their characteristics. This will be informative about how final treatment effects can be improved during treatment (e.g. whether some characteristics of early sessions are associated with different treatment effects), which can be of interest for clinicians and therapists.

Investigating the impact of the Health in Pregnancy Grant on birth outcomes in England, 2009-2011 — DARS-NIC-309029-P7H1D

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), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2020-05-01 — 2021-04-30 2020.12 — 2022.05.

Access method: One-Off

Data-controller type: LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)

Sublicensing allowed: No

Datasets:

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

Objectives:

This request to process HES data is made on the legal basis outlined in the GDPR, Article 6.1.(e). That is, this research establishes a task in the public interest as it is undertaken within the London School of Economics, and is necessary for the university to fulfil its function in the public interest (Article 6(e)). The LSE considers this project to be a task in the public interest due to the potential societal implications of the policy being evaluated - the Health in Pregnancy Grant (2009-2011). It is also impossible to analyse the impact of the grant on prematurity without this data, as gestational age is unavailable elsewhere. In terms of processing special category data (such as ethnicity), the legal basis is Article 9(2)(j) - that is, the research is conducted for archival, research or statistical purposes that are in the public interest. The research is proportionate to the aim pursued (see below). The researchers have agreed to respect the right to data protection at all times, and will carry out suitable and specific measures to safeguard the fundamental rights and the interests of the data subject, including ensuring non-disclosive output and the maintenance of data protection measures when using the data.

This research establishes a public interest due to the societal implications of the policy under evaluation - the Health in Pregnancy Grant, 2009-2011. This grant was a lump sum of £190.00 given to all pregnant women in the UK from the third trimester of pregnancy, regardless of income or work status. In order to receive the grant, pregnant women had to make an appointment with their GP or midwife, when they would be invited to fill out a form. Participants would receive the money via a bank transfer within seven days. When the grant was introduced, policymakers made it clear that it had two main aims: first, to reduce low birthweight; second, to reduce prematurity. Both low birthweight and prematurity are associated with low-income and lack of funds during pregnancy for healthy nutrition and lifestyle.

The rationale of the grant, therefore, was that boosting women's incomes during pregnancy, would facilitate the purchase and consumption of healthier food, invest in healthy lifestyle choices and reduce any financial stress caused by having a baby (for example, the grant could help to cover the large one-off cost of buying a buggy). This would, the government argued, improve birthweight and reduce prematurity. The grant cost over £130 million pounds a year and is estimated to have affected over 700,000 pregnant women a year. It is therefore a matter of public significance both in terms of its human impact and its cost to the public purse. The grant was also abolished in 2011 specifically on the grounds that it was ineffective. No follow-up study of the grant was made, and take-up was not recorded.

A robust analysis of whether the grant was effective, therefore, is needed to investigate how public money is used, and whether it is being used efficiently and effectively. There is very strong evidence that health outcomes during pregnancy - birthweight and prematurity have a significant impact on future health, through childhood and adulthood. The grant was therefore seen as an early intervention policy which aimed to alleviate pressure on NHS services by reducing health problems throughout the lifecycle. By accessing the HES data, it is possible to identify whether the grant achieved this aim.

To assess the impact of the Health in Pregnancy Grant on birthweight and prematurity in England, the London School of Economics will use a methodology called a 'Regression Discontinuity Design' which makes use of the arbitrary eligibility rule that was attached to the grant: all babies with due dates on or after 6 April 2009-16 April 2011 were eligible.

This meant that if the mother had a due date of 5 April 2009, you were not eligible for the grant, but if you had a due date of 6 April 2009 you were. Since it is random whether you have a due date on 5 or 6 April, the LSE can make use of this to isolate the causal impact of the grant on birth outcomes. The methodology essentially mimics the setup of a randomised control trial, which is considered the gold standard in estimating the causal impact of policies. Expected week of birth (calculated using week of birth and gestational age) will be used as a proxy for whether an individual was likely to receive the grant or not.

The LSE researchers require data from 2006-2014 as this includes a 3-year window before and after the grant was introduced. This will enable the researchers to check whether there were any notable changes in birthweight or prematurity over the period and check that the methodology is valid.

The London School of Economics require NHS Digital to create a 'week of birth' variable. The week of birth variable is required in order to calculate women’s due dates, since due dates were eligibility criteria for the Health in Pregnancy Grant (all women with due dates of 6 April 2009 - 16 April 2011 inclusive were eligible for the grant).

Month of birth would be too large a category as it would not enable the researchers to precisely identify how close a baby’s due date was to the eligibility cut-off, and it would therefore significantly reduce the effectiveness of their methodology. Date of birth would be very fine-grained, by contrast, but it would be identifiable. Based on existing research using birth registrations data accessed through the ONS, the researchers have concluded that week of birth is a practicable alternative to using date of birth.’

The data subjects are women who gave birth in England from 2006-2012 and their babies. Since the grant had a cut-off eligibility date, all births before 6 April 2009 act as the control group.

The research builds on previous work conducted by the London School of Economics (LSE) using ONS Birth Registrations microdata (provided through the ONS's Secure Research Service). This work was awarded a Distinction and an academic prize as part of a Masters programme undertaken by one of the researchers at the LSE. This proposed follow up to the research has secured funding from the Social Policy Department at the LSE. It will be a standalone research project, unconnected to any wider programmes of work.

This is a small standalone project, which is funded and will be disseminated on its own. However, it does run alongside a similarly-themed, wider programme of work within the Centre for Analysis of Social Exclusion (CASE) at LSE on the impact of recent social policy changes on outcomes. The Funder will not place any restriction or limitations on any publications and there is no time restriction on the funding given that the work is due to be completed in July 2020.

The dataset requested and the variables selected within it are essential for this research. The LSE researchers need access to anonymised, record-level data including crucially: week of birth of the baby, birthweight and gestational age. The ONS's birth registrations data do not include gestational age, so it is impossible to assess the impact of the grant on prematurity without the HES data.

'Week of birth' will be used to avoid having to use identifiable (full date of birth) in calculating due dates and to minimise the amount of data required. Due dates will be used as a proxy to separate the sample into a treatment and control group. Gestational age is needed for two reasons: first, to calculate due dates (in conjunction with week of birth); second, to construct an outcome variable for prematurity. Other important outcome variables are birthweight, first antenatal assessment date (to see whether the grant incentivised women to seek medical advice earlier during pregnancy), healthy neonate indicator, postnatal stay, neonatal level of care, and the well baby flag. Other useful variables to serve as controls are sex of the baby, birth status, number of babies, number of previous pregnancies, ethnic category, and mother's age at birth. To see whether the impact of the grant differed depending on levels of deprivation, the researchers also need access to the Index of Multiple Deprivation and Lower Super Output Area. This will enable them to see how the effect of the grant varied across different groups (by IMD, ethnicity, etc), given that it may have mattered more to more disadvantaged groups.

The purpose of the research cannot be achieved in a less intrusive way, as record-level data is necessary to implement this analysis of the impact of the grant on birthweight and prematurity. No identifiable or sensitive variables are being requested.

The data has not been narrowed by demographics (e.g. age), clinical factors (e.g. diagnosis/procedure) or live/singleton birth status, since the aims of the Health in Pregnancy Grant were broad-based and universal, and as such were intended to improve birth outcomes for all women and babies, regardless of these characteristics. The researchers would like to investigate and compare the impact of the grant on stillbirths, live births, and multiple births, as it may be that the grant affected these groups differently.

In terms of episodes, only maternity episodes are required - and specifically those variables that have been previously specified. The unborn child and neonatal records are fundamental to the purpose of this research as they enable the measurement of the health and status of the baby, which was the main target of the Health in Pregnancy Grant.

In selecting the necessary variables, the researchers have been careful to minimise the amount of fields necessary to achieve the purpose of the research. Indeed, the request does not include any identifiable/high risk field.

The data controller is the London School of Economics. The Data Processor will be the LSE specifically the LSE Secure Research Computing Governance Group, whose secure environment will be used for the data controllers to access the data. The VIRTUS datacentre provides physical storage of the data only - the processing is conducted by LSE only. The research is funded by the Social Policy Department at the LSE, and their role will be to help publish the findings of the research. This will include a public seminar, and the publication of a free working paper and/or copy of a peer-reviewed journal article via LSE Research Online.

Expected Benefits:

The dissemination of the data benefits the provision of health because the stated aim of the Health in Pregnancy Grant was to promote and improve healthy birthweight and to reduce prematurity. Given the long-term impact of low birthweight and prematurity on a range of child and adult health outcomes, this is an issue of crucial importance. Assessing whether the Health in Pregnancy Grant was, in fact, effective at boosting these health incomes is therefore important to health. If the researchers found, for example, that the grant was effective in promoting healthy birth outcomes, then one possible implication could be that the grant should be re-introduced in order to improve the health of the population. Indeed, the researchers' preliminary research using ONS Birth Registrations data indicates that the grant led to an approximate 20 gram increase in average birthweight in England and Wales.

The grant interacted with health services, since it was conditional on attending an ante-natal check-up from 25 weeks, and there is evidence to suggest that in Scotland the grant led pregnant women to seek health advice earlier in their pregnancy (Leyland et al., 2017). Using the 'first antenatal appointment' variable, the researchers will be able to examine the impact of the grant on how early women seek medical help during pregnancy. An evaluation of the effectiveness of the grant, and the political implications of that analysis, are therefore directly relevant to health and maternity services.

The dissemination is in the public interest because the Health in Pregnancy Grant came at a significant fiscal cost to the public purse and was justified on the grounds of improving health outcomes. The public therefore have an interest in ascertaining whether that public money was well spent, and if so, whether the grant should be re-introduced to the public benefit.

The outputs (peer reviewed journal submission and public seminar) will expand the evidence base on the impact of the Health in Pregnancy Grant on birth outcomes. It is hoped that it will be used to inform current policy debates about public health and early intervention. The researchers are in contact with a range of Members of Parliament who were vocal about the grant, and plan to keep them updated about the findings of the research.

The findings of the research will also be communicated beyond academia to the public sphere and to citizens through engagement with politicians and stakeholders, a CASE seminar, writing blogs and articles, and social media.

It is anticipated that a submission to a peer-reviewed journal will be made in 2021, when a CASE seminar can also be held.

Given that the researchers' preliminary research from ONS Birth Registrations data suggests the Health in Pregnancy Grant had a positive impact on birth weight in England and Wales, it is anticipated that this will be mirrored with the HES data and possibly also hold for prematurity as an outcome. Positive outcomes could encourage politicians to consider the re-introduction of the Health in Pregnancy Grant.

The Health in Pregnancy Grant cost £130 million a year and is estimated to have affected over 700,000 pregnant women and their babies a year. If this research found that the grant was effective and it were to be re-introduced, it could therefore have significant implications both fiscally and in terms of patient health. Conversely, if the research found that the grant was not effective and it confirmed that its abolition in 2011 was justified, this would strengthen the argument that it was a wasteful use of public funds and consolidate efficiency savings within the NHS.

In terms of expanding the evidence base on the impact of the Health in Pregnancy Grant, that benefit would be achieved with the publication of the research in a peer-reviewed journal



Outputs:

The main output from the data analysis will be a submission to peer-reviewed academic journals, specifically the Journal of Health Economics alternatively the Journal of Public Health

All figures produced will be aggregate figures (e.g. coefficients, means) and the researchers will ensure that each figure contains a sufficient cell count (e.g. the ONS's requirement of 10) to avoid disclosure of patient data.

In addition to the peer-reviewed journal submission, the researchers intend to hold a public seminar at the Centre for Analysis of Social Exclusion (CASE) at the LSE at which the findings of the research will be presented to a range of stakeholders, policymakers and members of the public. The peer-reviewed journal is likely to require a fee from readers for access, but the full content will be made available for free via LSE Research Online, as part of LSE’s commitment to the Open Access model.

The researchers may also write blogs or other short articles summarising the findings to ensure they communicate the findings of the research to wider audiences, including interested groups and civil society. The researchers' preliminary research from ONS Birth Registrations data suggests the Health in Pregnancy Grant had a positive impact on birth weight in England and Wales. Findings from this research will inform whether the Health in Pregnancy Grant was effective.

The target date for the submission of the peer-reviewed journal and the CASE Seminar is 2021.

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).

There will be no flow of data from the London School of Economics into NHS Digital.

Pseudonymised HES Admitted Patient Care data will flow from NHS Digital and will contain special category data including baby's week of birth and health status.

There will be no subsequent data flows after the initial transfer of data from NHS Digital to the London School of Economics.

The data will be processed by the LSE Secure Research Computing Governance Group. Please note that VIRTUS is the hosting environment (the physical data centre) of the LSE Secure Research Environment - it is LSE’s off-site data centre, managed by VIRTUS as a third-party organisation. As such, it is only the physical bricks and mortar location of the server - the actual processing is done by the end user (LSE), who works remotely and analyses data on the server after connecting.

Once the data has been received to the LSE Secure Research Computing Governance Group, the data will then be analysed in the LSE Secure Research Computing Governance Group Environment, exclusively by the LSE researchers . All figures produced will be aggregate figures (e.g. coefficients, means) each figure contains a sufficient cell count (e.g. the ONS's requirement of 10) to avoid disclosure of patient data.

The data will be cleaned, with duplicate records removed, and missing values and outliers dropped in an appropriate manner. To implement the regression discontinuity design, the researchers will use the baby's week of birth and gestational age to create a new variable for their due date. This variable will be used to separate the sample into a control and treatment group, such that observations are classed in the treatment group if they have an expected week of birth between 6 April 2009 and 16 April 2011 inclusive, and they are in the control group otherwise.

The researchers will then implement the agreed methodology using econometric techniques within statistical software. The outcomes of the treatment and control group will be compared, enabling estimates to be reached of the causal impact of the Health in Pregnancy Grant.

After the study has ended, the LSE would like to store the research data for a further 18 months and will apply for an extension to this Data Sharing Agreement. This will enable LSE to respond to any critiques or peer-review of the research and to make the necessary adaptations. The data will be stored in the LSE Secure Research Computing Governance Environment with the usual security arrangements in place at all times, and only the researchers will have access to the data.

Data is not being matched to publicly available data.

Both researchers accessing the data are ONS Accredited Researchers and have undergone training and assessment by the Office of National Statistics' Secure Research Service in data confidentiality and data protection. Both have experience using confidential sensitive microdata. Similarly, all employees of the LSE Secure Research Computing Governance Group who will be receiving and processing the data have undergone appropriate training.

Data will be accessed within the LSE Secure Research Computing Governance Environment at the LSE in London.

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 LSE and will not be accessed or processed by any other third parties not mentioned in this agreement.

There will be no attempts made by the LSE to re-identify individuals involved in this project as there is no requirement to do so.