NHS Digital Data Release Register - reformatted

National Centre For Social Research projects

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


Establishing trends and understanding inequalities and context in people’s mental health, 1993-2014 — DARS-NIC-175989-M9T7B

Type of data: information not disclosed for TRE projects

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

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

Purposes: No (Research)

Sensitive: Non-Sensitive

When:DSA runs 2018-04-01 — 2021-04-01

Access method: One-Off

Data-controller type: NATIONAL CENTRE FOR SOCIAL RESEARCH

Sublicensing allowed: No

Datasets:

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

Objectives:

NatCen Social Research requires APMS survey data for use in analyses investigating trends and inequalities in mental health in England.

NatCen is a research institute specialising in health and social survey data analysis. NatCen applied for and secured funding via the Public Health Research Consortium to undertake this work.

The primary objective for processing APMS data is to use the best available information on general population patterning in mental disorder and self-harm behaviours, to examine trends and identify risk factors for mental illness in women (and men). This is to ensure government have an accurate understanding about population trends in need, and can prioritise and plan services accordingly.

Four key sub-objectives are to:

Establish the trends: What are the trends across a range of different mental disorders and related behaviours in the population; how does this break down between women and men and by age group? Are the patterns emerging in different datasets and sources consistent?

Identify the drivers: There is a need to understand what is underpinning current trends. Do men and women have different levels of exposure to risk factors – such as violence, abuse, environmental stress, poverty and adversity – or do they respond differently to such adversities?

Establish trends in potential drivers: Known social and economic factors shaping mental health include violence and abuse, experience of childhood adversity, debt, housing conditions, psychosocial working environment, flooding and natural disaster, caring responsibilities, and other sources of stress and support. New contextual factors include social media and technologies. What are the trends in each of these factors?

Profile multiple adversities: Examine how different aspects of mental health and life circumstances cluster together in the population.

The context to why this objective is necessary to address now is outlined below:

Women are more likely to have a common mental disorder (CMD) than men (Castle and Abel, 2015). Women’s higher rates of depression and anxiety disorders have been evident consistently over time, across countries, and in different social and economic groups (Freeman and Freeman, 2013). In recent years, evidence has emerged suggesting that the gap in mental illness between young women and men may be growing (Campbell-Jack D et al 2016; McManus et al 2016). The gap in levels of low wellbeing also appears to be increasing between girls and boys (Children’s Society 2016; Lessof 2016).

APMS found a steep rise in self-reported self-harm, and identified young women as a high-risk group for several mental disorders. And while externalising responses to stress remained higher in men, this gap may be shrinking. For example, while hazardous use of alcohol declined in young men, it was stable in young women (McManus et al 2016). Recent suicide statistics showed an increase among women in the past year, and a decline in men (ONS 2016).
Hospital Episode Statistics support there being a greater rise in self-harm related hospital admissions in girls and young women (NHS Digital 2016). The number of girls treated as inpatients after cutting themselves almost quadrupled between 2005 and 2015.

Consistent with this, increases in antidepressant prescribing have been greatest in girls and young women (Sarginson et al 2017). It is likely that a complex mix of factors explain the differences in mental health between men and women, including physiological and biological factors (Castle and Abel 2015). However, if differences in rates are changing over time, it likely that changes in social and economic context must also be key.

In 2015, the CMO published a report on women’s health (Davies 2015). This was produced before the latest APMS data were available and was not able to take account of the emerging trends in the Scottish Health Survey or child datasets. It included chapters on eating disorders and perinatal mental health, but otherwise did not specifically focus on women’s mental health. There is a need, therefore, for a consolidation of recent evidence on trends and patterns.

In terms of background to this work: NatCen produced the initial survey report for APMS 2014. They also have used the survey data to produce a brief report for DH examining inequalities in mental health and treatment use among people with intellectual impairment, for which DARS approval was obtained. The objectives of those pieces of work are distinct from this study.

Expected Benefits:

Mental illness and mental wellbeing have risen up the policy agenda in recent years. The main political parties all featured mental health and mental health services in their election manifestos. Despite this attention, women’s mental health has not until recently been the focus of policy attention, specific study, or specialist provision. An initial benefit of this work will be to highlight and quantify the scale of the issue, and demonstrate whether it should be considered a priority for policy.

The government’s primary source of data on prevalence and trends in mental disorders in England – the Adult Psychiatric Morbidity Survey (APMS) – published initial results back in 2016 (McManus et al 2016). Not only did the report show that the longstanding higher rates of anxiety and depression in women remain, but also that this gap may have grown. That data is the best source for examining trends in detail, but has not yet been used to do so.

In response to the APMS results, and in recognition of the lack of policy attention that women’s mental health has generally received, the then Parliamentary Under Secretary for State for public health, Nicola Blackwood MP, set up a new Taskforce. It is now being carried for under Jackie Doyle-Price MP. It's remit is to make recommendations for what the country should do to address gender inequalities in mental illness. This policy development would benefit from a major consolidation of recent research evidence from different sources on what is known about women’s mental health. This is especially needed, given that the prevalence and patterning of mental illness in the population appears to be changing, with the highest risk age-group in women shifting to those in their teens and early twenties.

Groups like Agenda and Mind have called for the establishment of gender-specific mental health service provision. A recent Freedom of Information request from Agenda found just one NHS mental health trust with a strategy for providing gender-specific services to women, and most trusts provided no relevant policies or strategies in relation to gender specific services (Agenda 2016). Furthermore, most responding trusts had no policy on ‘routine enquiry’ about abuse, contrary to NICE guidelines. The results from this study could be used to help inform both the work of the Women's Mental Health Taskforce and in the development of gender-aware mental health service provision.

The project also seeks to bring benefits through enabling public engagement with the findings:

Third sector and policy involvement: Agenda – an alliance of 70 voluntary sector organisations with an interest in redesigning systems and services to better address the needs of women girls – will help in the development of both public-facing and policy-facing summary outputs.

Academic collaboration: an academic advisory group will provide insight from a range of disciplines and current thinking in those areas.

Public engagement: alongside conventional seminar/conference presentations, sessions at events aimed at the general public and schools will also be sought out, including sessions at the Cheltenham Science Festival, local TEDx events, and Wellcome Trust talks.

Outputs:

Comprehensive reports written clearly and making extensive use of graphs and charts. Methodological details will be contained in separate appendices or technical reports. The reports will address the main objectives, including presenting trends in women and men’s mental health over time; risk factors for poor mental health in men and women; trends in risk factors over time in men and women; and lived context for subgroups of men and women.

The following outputs will be produced:

Journal papers for publication in an open access peer-review journal.

Summary public and policy-facing briefings co-produced with third sector and other organisations (who will not have any access to the underlying data, only to the aggregated results), highlighting the most relevant and accessible findings. These will tie in with blogs and other social media promoted communications.

Seminars and presentations, including to the All Party Parliamentary Group on Sex Equality, at academic conferences (preferably, the European Public Health Association annual conference).

Events at existing festival events and talks with general public and school participation.

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

Processing:

The 2014 APMS dataset is held on behalf of NHS Digital by the UK Data Service (UKDS) (www.ukdataservice.ac.uk ) and UKDS are responsible for dissemination under direction by NHS Digital. NatCen will receive the whole dataset; as there is no facility to select individual variables. They will be able to download the dataset from UKDS for the period specific within the DSA and they must securely destroy all local copies of the dataset when the DSA expires and notify DARS in line with standard procedures.

As NatCen download the data securely from the UK Data Service archive, there are no flows of data from NHS Digital or to NHS Digital. NatCen will store this data securely for the agreed period and not share it with any third parties. There are no further data flows. The UKDS securely transfers the APMS dataset to NatCen. The data file contains no identifiers and the dataset is not linked and will not be further linked.

Data will only be accessed by individuals within NatCen who have authorisation to access the data for the purpose(s) described, all of whom are substantive employees of NatCen.

What will be done with the data:
The data will not be linked with any other data.
There will be no requirement nor attempt to reidentify individuals from the data.
The data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

APMS is only available as a full dataset, though minimisation has taken place centrally prior to the dataset being made available. In addition, the full dataset is required in order to cover all England (national analyses) and to be able to control for a range of factors in the analyses. A range of conditions will be examined in the analyses.

Data management and variable derivation will be done in SPSS. Analyses will be conducted in SPSS, Stata and Latent Gold, using survey weights and controlling for complex survey design. All patterns reported on will be tested for statistical significance.

Simple descriptive analyses will be used to generate prevalence rates and produce cross-tabulations. These will be presented in tabulations (using percentages and means, and always showing base sizes), bar charts, and line graphs. Trend data will be based on consistently defined age-ranges for comparability across years. Multiple regression analyses will be carried out to examine association between risk factors and mental health outcomes when other factors are controlled for, and to identify the role of different potential mediators. Population attributable fractions will be calculated to compare risk factors with variable extent and variable strength of association. NatCen will examine different options for testing what risk factors may be driving any changes in rate or distribution of mental illness. Latent class analyses will be conducted to profile how different and multiple adversities across mental health and other life circumstances cluster together.

While the focus of the study is women’s mental health, these patterns are most meaningful when examined in relation to men’s mental health. Where available, analyses will be conducted on both men and women and on the population as a whole, so that comparisons can be made.

Data
An objective of the study is to pull together information on trends in mental health from a range of different sources, and so while APMS will be the primary resource, a number of other datasets will also be analysed. Another objective is to look at trends in the risk factors for the relevant subgroups (e.g. is the proportion of 16-24 year olds in debt increasing? What are the trends in partner violence?). For this other datasets may need to be utilised, depending on what risk factors emerge from the initial analyses.

APMS: The national Adult Psychiatric Morbidity Survey (APMS) is England’s primary tool for monitoring the extent of mental illness in the population. Headline figures from the study indicate pronounced increases in self-harming behaviour among young women, but initial reporting only looked at associations with a few risk. The survey included screening for the presence of a range of different types of mental disorder, and information about many aspects of lived experience. As a general population survey, the sample is representative of people living in a household irrespective of whether they have a diagnosis or get treatment or services. While the survey doesn’t cover the prison population or people or are homeless, it does ask about previous experience of these things. Trends in symptoms will be examined, as well as trends in CMD as a whole. This will enable trends to be isolated for symptoms such as anxiety.

This 2014 version of the dataset available via DARS has been redacted on Disclosure Control Procedure advice to minimise the likelihood of individuals being able to identify anyone taking part in the survey.

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

No data will be shared with third parties.

The data will only be used for the purposes described in this agreement.

The data will not be linked to any other dataset.

The scope of the analyses have been conceived by and will be entirely conducted by NatCen Social Research. NatCen are supported by a steering group of experts from a range of organisations, including central government (DH, PHE), NHS England, third sector (Agenda), and other research institutions (such as UCL and Kings). The group will provide advice however, all final decisions about scope will be the preserve of NatCen. All analyses and processing of the data will carried out by NatCen.


Understanding mental health comorbidity, treatment and service use in adults with intellectual impairment — DARS-NIC-159399-K2M6H

Type of data: information not disclosed for TRE projects

Opt outs honoured: Anonymised - ICO Code Compliant (, )

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); Other-HSCA 261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii); Other-HSCA 261(2)(b)(ii)

Purposes: No (Research)

Sensitive: Non-Sensitive

When:DSA runs 2017-12-04 — 2020-12-03

Access method: One-Off

Data-controller type: DEPARTMENT OF HEALTH AND SOCIAL CARE

Sublicensing allowed: No

Datasets:

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

Objectives:

Adult Psychiatric Morbidity Survey (APMS 2014) is a key dataset as it is the only source to combine data on intellectual impairment and mental illness and treatment use in a probability sample of the whole adult population. The initial APMS 2014 report included one table showing that predicted verbal IQ is strongly linked with mental illness, with people with learning impairment being twice as likely as those with high verbal IQ to have an anxiety disorder or depression (25% compared with 13%). The disparity was even more pronounced for rates of probable psychotic disorder. This analysis is powerful because the experiences of those with intellectual impairment are compared with the rest of the population. The dataset can be used to classify the population in a categorical way (cases and non-cases) as well as in a dimensional way (looking at the population as a spectrum).

The Department of Health would now like further analysis of APMS data to describe the circumstances of people with learning impairment and the wider inequalities they face (such as whether or not are more likely to have particular physical health conditions or unmet needs for treatment and services) compared with others.

Expected Benefits:

Adults with a learning impairment, especially those whose learning impairment may not be severe enough to qualify them for additional public support, may be a particularly vulnerable group facing pronounced adversities and yet remaining largely invisible in much research and monitoring. This report aims to highlight the circumstances they face, and whether or not they access treatment and services to the extent that their needs warrant. Where it highlights that needs are not being met, this information will be key to informing decisions about resource allocation and the design and targeting of interventions and services. The report will address a major gap in the current evidence base, and provide much needed insight into circumstances. This information will be useful both to third sector organisations lobbying for change, and to government policy development officials designing programmes of support.

Outputs:

The planned output is a short descriptive report to the Department of Health. The report will only include figures based on aggregated data, and figures will not be presented where the base size for the cell is less than 30. The aim of the report would be to document the inequalities and life circumstances of people with a learning impairment compared with the rest of the population. It will be designed to provide an evidence base to inform the design and targeting of interventions and services. Because APMS is the primary source of data to provide current information on psychiatric morbidity and treatment and service use among people with intellectual impairment across the ability spectrum, the output will address an evidence gap. And because the dataset covers both people with and without impairment, it enables comparisons to be made and inequalities to be documented.

All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide

Processing:

The analysis would involve data management to derive analysis variables and the production of descriptive tables. These will be written up as a simple report for DH, with data presented in aggregate form only (min cell size 30 cases).

For the assessment of intellectual impairment – Natcen intend to use the National Adult Reading Test (NART) as the primary measure. The NART generates a reliable prediction of verbal IQ, identifying people with likely borderline intelligence of a level that would require assistance and support to manage everyday functioning. The dimensional nature of the measure means that both a sufficient number of people with greatest need can be identified and that comparisons can be made with other across the population.

For the measure of self-reported learning disability – in the most recent APMS a new module of questions was added. All participants were asked ‘Do you have a difficulty learning or an intellectual disability?’ Those responding positively were followed up with questions about whether the condition has a name, what the condition is, how severe the difficulty is, and how often it limits the amount or kind of activities that they can do. This information was not included in the main survey report, and analysis of it could form part of this study, providing an additional and alternative indication of learning impairment.

The 2014 APMS dataset is held on behalf of NHS Digital by the UK Data Service (UKDS) (www.ukdataservice.ac.uk ) and UKDS are responsible for dissemination under direction by NHS Digital. The customer will get the whole dataset; there is no facility to select individual variables. They will be able to download the dataset from UKDS for the period specific within the DSA and they must securely destroy all local copies of the dataset when the DSA expires and notify DARS in line with standard procedures.

This 2014 version of the dataset available via DARS has been redacted on Disclosure Control Procedure advice to minimise the likelihood of individuals being able to identify anyone taking part in the survey.

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

No data will be shared with 3rd parties.

The data will only be used for the purposes described in this agreement.

The data will not be linked to any other dataset.


MR579a - Research on Health and Ageing using English Longitudinal Study of Ageing (ELSA) data linked to NHS Digital data — DARS-NIC-311182-N0L1Y

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable (Consent (Reasonable Expectation))

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), Informed Patient consent to permit the receipt, processing and release of data by NHS Digital, Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(2)(c); Informed Patient consent to permit the receipt, processing and release of data by NHS Digital

Purposes: No (Research)

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

When:DSA runs 2019-02-22 — 2022-02-21 2017.09 — 2017.05.

Access method: Ongoing, One-Off

Data-controller type: NATIONAL CENTRE FOR SOCIAL RESEARCH

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Outpatients
  5. Hospital Episode Statistics Accident and Emergency
  6. MRIS - Flagging Current Status Report
  7. MRIS - Members and Postings Report
  8. MRIS - Cohort Event Notification Report
  9. Hospital Episode Statistics Accident and Emergency (HES A and E)
  10. Hospital Episode Statistics Admitted Patient Care (HES APC)
  11. Hospital Episode Statistics Critical Care (HES Critical Care)
  12. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

NatCen collects permission from respondents for data linkage to Hospital Episode Statistics (HES), Personal Demographics Service (PDS), Cancer Registrations and ONS Mortality data on the English Longitudinal Study of Ageing (ELSA). They also request the same data for the Health Survey of England (HSE), but an application to link data to the HSE has been submitted separately.

Being able to link the survey data to the administrative data is an important feature of the survey, because it allows association between the health and lifestyle information and measurements collected in the survey at a particular time with mortality, cancer and HES data over a longer time period.

This application is to keep data already supplied by the HSCIC and to request further data in order to maintain and further develop the ELSA databases. A request to be able to share the linked data with 3rd parties via sub licensing agreements will made via an amendment application to HSCIC via DAAG in the near future. There would be no standard outputs, but all would be for the purposes of research. Requests might relate to whether people have died, age at death, cause of death, frequency of hospital episodes in total or for particular diagnoses, etc – see below. The linked data required, and the purpose for which it is required, would be set out in the Data Release Form submitted to NatCen.

NatCen collects the survey data and acts as the data controller for providing access to the survey data (and in the future any associated linked data) to researchers who will use the data for specific research projects.

The purpose of requesting linked data is to provide important information relating to the health of respondents. This includes details which would be too burdensome to ask them to provide in an interview. It also allows NatCen to understand the health of ELSA respondents who have not been able to continue with the study owing to ill-health. Also to create a rich dataset by linking HES, PDS, Cancer Registrations and ONS Mortality to the survey data for planned onward sharing with researchers.

Linkage with HSCIC data is needed for many analyses to investigate the relationship between health conditions and behavioural and social characteristics reported in the survey and subsequent mortality. For instance, the linked data has been used to explore relationships between survey data on social class and raised blood pressure and mortality rates from coronary heart disease; the analyses also took into account demographic characteristics and other risk factors, such as smoking, family history of heart disease and cholesterol levels.

ELSA is a continuation of HSE, not only being based on the HSE sample of older people, but also in having considerable overlap in the type of health information collected such that, for some analyses, the HSE interview is being treated as the baseline (wave 0). The two datasets, however, are never linked.

There are 5 forms of usage that the researchers wish to address.
1. The maintenance and development of the ELSA database to be of the most benefit for health and social care
2. Administrative use when issuing the sample to interviewers, to avoid upsetting bereaved individuals by being prepared in advance
3. For calculation of response rates - NatCen need to know who has become ineligible because of death; the type of attrition is also important in understanding the study sample and the interpretations they can draw
4. For future 3rd party users more generally to have access to mortality information. Many longitudinal analyses will be completely misleading unless they can take account of censorship through death. For this the minimum needed is the month and year of death. This will be applied for separately
5. The interview data are deposited with the UK Data Service, a condition of ELSA funding. Linked data will be applied for separately

Yielded Benefits:

ELSA is currently in its 9th wave of data collection. The primary objectives remain as stated i.e. to collect longitudinal data on health, disability, economics, and social participation and networks, from a broad-based sample of the English population aged 50 and older. This includes a unique coverage of biomedical, genetic, performance and psychosocial measures. Wave 8 ELSA survey data has been added to the UK data archive and all ELSA datasets continue to be widely used by registered researchers.

Expected Benefits:

The primary objective of ELSA is to collect longitudinal data on health, disability, economics, and social participation and networks, from a broad-based sample of the English population aged 50 and older. This includes a unique coverage of biomedical, genetic, performance and psychosocial measures.

ELSA is not only a study of health but it produces a great number of analyses and outputs relevant to or aimed at improving the provision of health or adult social care, or the promotion of health. ELSA plays an important role in providing high quality data from a multidisciplinary perspective that integrates information about the economic, social, psychological, community and health experience of older people in England.

The ELSA research team publish a comprehensive report analysing each new wave of data that is collected. These are available to download from the study website - http://www.elsa-project.ac.uk/ - which itself hosts a wide range of information about the study and its findings.

Of particular note is ELSA’s publication list, maintained on the website: http://www.elsa-project.ac.uk/publications . This provides a comprehensive listing of 250 outputs which have included findings based on analysis of ELSA. These include journal articles, working papers, book chapters and conference papers/presentations.

Some examples relevant to the provision of health or adult social care or the promotion of health are included below:

• Sexual health and well-being among older men and women in England: findings from the English Longitudinal Study of Ageing, David Lee , James Nazroo , Daryl O'Connor , Margaret Blake and Neil Pendleton , Archives of Sexual Behavior , Epub ahead of print] , January 2015

• Taking up physical activity in later life and healthy ageing: the English longitudinal study of ageing., Mark Hamer , Kim Lavoie and Simon Bacon , British Journal Of Sports Medicine , Vol: 48 (3), pp:239-43 , February 2014

• Limited health literacy is a barrier to colorectal cancer screening in England: Evidence from the English Longitudinal Study of Ageing., Lindsay C. Kobayashi , Jane Wardle and Christian von Wagner , Preventative medicine , November 2013 Journal Articles

• The SES health gradient on both sides of the Atlantic, ELSA Working Paper, James Banks , Michael Marmot , Zoë Oldfield and James Smith , January 2007

• Association between low functional health literacy and mortality in older adults: longitudinal cohort study, Sophie Bostock and Andrew Steptoe , British Medical Journal , doi:10.1136/bmj.e.1602 , March 2012 Journal Articles

The ELSA survey dataset is also a key output. Similar to HSE, ELSA survey data can be accessed by bonafide researchers who register to use with the UK data archive and download data sets to do their own analysis. Selected summary tables are also made available via the study website in Excel format - http://www.elsa-project.ac.uk/data_elsa. There is considerable use of the data in these ways.
Like HSE, ELSA findings are used widely by those involved in the development of health and social care policy. As a result, questionnaire and related study content (such as the particular objective measurements used) are adjusted at each wave in response to policy concerns and to emerging social and health issues. For example, wave 7 included questions on hearing, oral health, extended questions on cognitive function and new questions on expectations and perceptions of the costs of social care.
Selected examples of the benefits to improving the provision of health or adult social care, or the promotion of health which ELSA data can or has provided are included below:
• Consequences of improved survival with serious illness and the rise in chronic disease. The longitudinal nature of ELSA makes it possible to monitor the experience of people as they acquire chronic illnesses, and evaluate the consequences of ill-health from economic, social and well-being perspectives. New measures of cognitive function included at waves 7 and 8 will allow us to estimate the prevalence of mild cognitive impairment and dementia throughout the country, as well as investigate their determinants and consequences for individuals and their families. We can use the detailed data on health, disability and functioning to monitor progress towards achievement of extended healthy life years. This information will also be relevant for government to inform the debate on key issues include the importance of improving care provision for the elderly, managing dementia more effectively through better treatment and research, and deciding how to pay for social care.
• Social care. ELSA provides data permitting an understanding of the impact of changes in the range of social services available to older people on their well-being, health and social integration. Developing this understanding requires the collection of robust data that not only covers all relevant features of the care received, but also covers in detail the characteristics of those receiving, and not receiving, care, and that measures short, medium and long-term outcomes. The multidisciplinary and longitudinal nature of ELSA means that it has great potential in this regard. Data from the survey’s questionnaire module on social care can be used to address questions such as: older people’s receipt of and payment for care; the pattern of take-up of Direct Payments and Personal Budgets by older people; the provision of informal care; and the relationships between receipt of formal care, informal care and care needs among older people.
• Public health and transport policy. ELSA data was used to explore the link between the introduction of a bus pass allowing free local travel during off-peak hours for those aged 60 or over and public health. The findings showed that the policy was associated with increased use of public transport and also that older people who used public transport were less likely to be obese, as were those eligible for free local bus travel. The results were noted by members of the Parliamentary Select Committees for Health and Transport, as well as policymakers at the Department for Transport, for whom they are valuable in future planning of concessionary bus travel policy.
• Organisational changes in the NHS. The health data collected in ELSA, particularly when coupled with linked administrative data on healthcare, will provide important information about the use of services by the elderly before and after the transition from PCT to GP commissioning of care, the quality of health care, the interface between primary and secondary case, and the identification of trends in health that will impact on future demand.
• Social isolation and loneliness. ELSA has provided valuable information about the relationship of social isolation and loneliness to well-being, health and cognition. The survey is an important resource for monitoring these experiences as people age.
• Subjective well-being and public policy. ELSA includes measures of the different elements of subjective well-being since its inception, and these have been supplemented by the core questions from the ONS experimental module. It provides unique information about the trajectories of well-being among older people in England, and relationships with economic position, social factors, health, and cognition.

The creation of these linked databases will provides further benefits in the near future when NATCEN requests that they be allowed to offer psuedonymised data via sub licence to 3rd party researchers. Each researcher would bring different benefits to health and social care, but by way of example the following are wishing to use linked ELSA /HSCIC (they are currently applying for the linked data separately with the HSCIC)

Imperial - have been funded by DH to do a 2 year project on the potential health and economic benefits of the free bus pass, and the ELSA-linked data is the central part of this. The linked data will allow the researcher to examine whether there are any differences in hospitalisation/mortality associated with the bus pass, and this information will be the main driver of the economic modelling (and a lot of the policy discussion focuses on costs to benefits). DH are expecting the report in about a year’s time.

University of East Anglia - The ELSA analysis is part of a larger programme of research which aims to develop an intervention to improve access to primary care for older people and test it within a feasibility study. The ELSA linked with HES analysis is vital because it will map risk factors along the patient pathway from recognising problems, accessing primary care and use of secondary care. The analysis will also be used to test concepts which come out of the interviews and literature review. Results from the ELSA linked with HES analysis will be triangulated with the other components to develop a new intervention to improve access to primary care for deprived older people in rural areas which will in turn should improve community healthcare

Outputs:

The primary objective of ELSA is to collect longitudinal data on health, disability, economics, and social participation and networks, from a broad-based sample of the English population aged 50 and older. This includes a unique coverage of biomedical, genetic, performance and psychosocial measures. Participants are approached biennially for the main interview. All waves included an interviewer-administered questionnaire and self-completion form; in waves 2, 4 and 6 there was also a nurse visit for biomedical measures.

ELSA is not only a study of health but encompasses many facets of ageing. The focus of the study is to provide data necessary for an exploration of the unfolding dynamic relationships between health and functioning, social networks and economic position, as people plan for, move into and progress beyond retirement. It therefore has extensive components measuring financial status and social participation as well as health and cognition.

Some of the questions that ELSA can address are: the nature and timing of retirement and post retirement labour market activity; the determinants of economic well-being at older ages; cognitive functioning and its impact on decision making among older people; disability and the compression of morbidity; the evolution of economic, social and health inequalities in an ageing population; social participation and social productivity at older ages; the impact of good quality health care on future health and well-being.

The main output from this data request would be the creation of a rich and extremely useful ELSA database to be used as a resource for research analysis. Future outputs would include those described as collaborators (who are applying to HSCIC separately) and the planned sub licencing agreements to bona fide 3rd party researchers.

Processing:

The requested data from the HSCIC would be linked to the survey data from the English Longitudinal Survey of Ageing (ELSA) . The linking process consists of matching the variables provided by HSCIC to the survey data via unique identifiers, termed ‘serial numbers’, for each participant who has consented to linkage.

NatCen creates a new set of serial numbers for the linked data, and maintains a look up file which allows this to be linked to the survey data.

The ELSA dataset and HSE dataset are not linked together and are linked to HSCIC separately and via separate agreements.

Simple data flow:

• NatCen provide NHS number, DOB, sex, postcode and ELSA member number to HSCIC
• HSCIC provides NHS number, latest demographic data, Exits/re-entries to NHS, ONS mortality data and cancer registration data and pseudo HES data
• No onward sharing is being requested in this application.


Project 4 — DARS-NIC-148332-V6CY3

Type of data: information not disclosed for TRE projects

Opt outs honoured: N ()

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

Purposes: ()

Sensitive: Sensitive

When:2016.04 — 2016.11.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cohort Event Notification Report
  2. MRIS - Scottish NHS / Registration
  3. MRIS - Cause of Death Report
  4. MRIS - Personal Demographics Service

Objectives:

HSE; The Health Survey for England is a major monitoring tool looking at the nation's health. It is used by the Government to plan health services and make important policy decisions that have an impact on us all.

It is an annual survey and has been running since 1991.
To investigate the relationship between social class and raised blood pressure at the time of the survey and mortality rates from coronary heart disease. The study will take into account other demographic characteristics and other risk factors, such as smoking, family history of heart disease and cholesterol levels

ELSA; is a continuation of HSE, not only in being the source of our sample, but in having considerable overlap in the type of health information collected such that, for some analyses, the HSE interview is being treated as the baseline. Three of the investigators for HSE are also investigators for ELSA (Bob Erens, Natcen; Michael Marmot, UCL; James Nazroo, UCL). MREC approval is sought for each phase of fieldwork but always make it clear that the sample derives from HSE . We specified to the MREC that linkage to the NHSCR had been sought during the HSE interview.

Mortality is a major –and common – outcome in its own right in this sample, and the way in which other outcomes are interpreted may be affected by the extent to which losses to the study are through death. There are 7 forms of usage that the researchers wish to address.

1. Administrative use when issuing the sample to interviewers, to avoid upsetting bereaved individuals by being prepared in advance (already done)
2. For calculation of response rates – we need to know who has become ineligible because of death; the type of attrition is also important in understanding the study sample and the interpretations we can draw (already done or in process)
3. Usage by the core ELSA team at UCL/IFS/NatCen and our named collaborators at Exeter, Cambridge, UCL and UEA – two immediate uses we have planned for our Wave 2 report, being written now, are:
a. For modelling mortality by broad cause according to socioeconomic status, gender, and age.
b. For analysing household change and reasons for it
4. Enabling users more generally to have access to mortality information. Many longitudinal analyses will be completely misleading unless they can take account of censorship through death. For this the minimum needed is the month and year of death. The interview data are deposited at the Economic and Social Data Service Archive, a condition of our funding. The funders will be expecting users to be able to undertake survival analyses.
5. Arrangements for users to work with cause of death – Cause at ICD chapter-level might not be considered sensitive but at finer detail it may be necessary to use an enclave. If applications had to be made to ONS by every such user, how long would the process take?
We consider item 3 to refer to ongoing analysis of the cohort flagged by NHSCR, and would hope, therefore, that this can be sanctioned immediately.