NHS Digital Data Release Register - reformatted

University College London (ucl)

Project 1 — DARS-NIC-00656-V0Z4C

Opt outs honoured: Y

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2016/08. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Outpatients
  • Office for National Statistics Mortality Data

Benefits:

Improved transition-related health policy 2) This research will guide Department of Health policy on the need, as well as specific policy measures, for improving transitions from paediatric care. This research will inform policy recommendations regarding appropriate age of transition and measures for reducing upheaval to service provision during the transition to adult services including frequency of appointments in late paediatric care and improving retention in adult services. 3) The development of recommendations of quality indicators for health transition. 4) UCL will develop recommendations of indicators of high-quality transition (based on the outcomes studied within the research). With support from the Department of Health, these recommendations would help health services identify where improved transition is justified and may subsequently contribute to the development of standards for transition quality. Subsequent benefits resulting from this research would relate to improved health outcomes and healthcare (including, for example, retainment in adult services, fewer missed appointments and less usage of emergency services) stemming from improved transition services. Planned target date is December 2016.

Outputs:

Two key outputs will be produced based on the data analyses: Peer-reviewed publication(s) designed to disseminate the findings regarding the effect of transition from paediatric to adult care on subsequent service use including inpatient and A&E use, missed hospital appointments and changes in the frequency of healthcare appointments. Paedeatrics Journals, the Lancet and the Journal of Public Health planned to be targeted. They aim to publish this by the end 2016. These publications will target researchers and practitioners to stimulate debate and subsequent research regarding alternative transition models, evidence-based improvements to existing transition services and reducing the negative impacts of poor transition. The Department of Health, who are funding the research, will be sent briefing reports in mid-2016 (before they are published in peer review journals) summarising the key findings and suggesting policy measures for maximising health outcomes resulting from service transition. Policy guidance may include the development of measures for monitoring transition care and outcomes. Outputs will include aggregated data only and will be limited to: - Average age of transition overall and across sentinel health conditions - Frequency of inpatient appointments pre- and post-transition - Frequency of inpatient, A&E and critical care appointments pre- and post-transition - Frequency of missed appointments pre- and post-transition Due to the nature of the aggregated data in the outputs, there will be no cases of small numbers requiring suppression.

Processing:

Data will be stored in and accessed through UCL’s ‘Information Data Safe Haven’ (IDHS) which ensures the appropriate and safe handling of sensitive data (see: http://www.ucl.ac.uk/isd/itforslms/services/handling-sens-data/tech-soln). Only authorised UCL staff members will have access to the data and it will not be accessible by any third parties, nor will it be accessed outside the UK. Data analyses will be conducted in Stata to extract summary statistics. UCL will determine age of transition with reference to paediatric and adult codes within the data. UCL will also define two additional characteristics of transition for each patient: the delay in transition (the gap between last paediatric code and first adult code), and retention in adult services (including changes in regular, planned outpatient appointments). The analysis is limited to comparing outcomes pre and post transition, and examining the effect of age of outcome, delays in transition to adult services and retention in adult services on these outcomes. UCL will conduct this analyses across conditions, as well as in three sentinel conditions: renal pathologies, diabetes and gastroentological diseases. Outcomes are limited to use of inpatient care, A&E attendances, frequency of hospital admissions, critical care admissions, and mortality.

Objectives:

This agreement supersedes NIC-330769-C9Y8Y as a new DSA Improving the transition of young people from child-centred to adult-centred health care systems has been a health policy priority for successive governments. There is clear evidence that poor transitions result in poor health outcomes, particularly related to drop-out from healthcare at a key time in chronic disease management. Conversely, there is now reasonable quality evidence that improving transitions improves outcomes in diseases such as diabetes and chronic renal disease/transplantation. Such evidence has resulted in a decade of clear guidance to health services on the importance of providing good transitional care in England. Yet despite this, and subsequent DH initiatives, such as the Transitions Champions programme, there appears to have been little change in the majority of health services in England. It is anecdotally believed that the majority of young people with long-standing conditions in England do not receive appropriate transitional care, although routine data on transitional care are not collected. The objective for processing the data is to investigate, using a contemporary UK sample, the effect of transitioning from paediatric to adult care on indicators of illness management relating primarily to health service usage. UCL will also investigate how specific features of the transition to adult services are associated with these outcomes to contribute to the evidence-base regarding the features of successful transitions. This research is limited to the following three purposes: 1) Examine the health impact of transitions from paediatric to adult care. This will help determine the extent to which current transition arrangements are fit for purpose. This entails examining whether transition itself is associated with detrimental health-related outcomes such as changes in planned health service use, increases use of inpatient, A&E and critical care services and changes in the frequency of missed outpatient appointments. 2) Guide healthcare policy to improve health transitions. This research will identify factors associated with good transition outcomes to guide policy efforts to improve healthcare transitions. First, UCL will examine how age of transition is associated with transition-related outcomes. This may influence guidance regarding appropriate age of transition. Secondly, UCL will examine the impact of the frequency of outpatient appointments in paediatric care on adult outcomes. Finally, UCL will examine differences in transition outcomes across sentinel health conditions and specialties: diabetes, renal disease and gastroenterology. This will identify areas requiring improvement in transition approaches. 3) Contribute to the development of a measureable outcome metric for transition that could be included in the NHS outcomes framework, to drive improved attention to transition by providers. The research will constitute a trial of an approach to measuring transition outcomes using routine health data. Health outcomes which are found to be associated with health transitions could form the basis of quality measures for transition across regions, specialties or health authorities.


Project 2 — DARS-NIC-03422-Y7Y0Z

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/09 — 2016/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Flagging Current Status Report
  • MRIS - Cause of Death Report

Benefits:

The aim of GALA-5 was to establish whether the combined use of 5-ALA and Carmustine wafers during surgery in patients diagnosed with Glioblastoma (GBM) is safe and does not compromise delivery of standard chemo radiotherapy. The results of the GALA-5 trial are also expected to guide the protocol for a new phase III trial that will further explore the use of 5-ALA and Carmustine wafers during surgery in patients diagnosed with Glioblastoma (GBM). This trial will also be run by the Cancer Research UK and UCL Cancer Trials Centre. The data provided will inform the clinical impact of combination therapy incorporating Carmustine wafers with Temozolomide by providing additional information regarding the cause of death in patients given this combination. The data will also inform reappraisal of NICE TA121 (https://www.nice.org.uk/guidance/ta121). While the NICE TA121 guidelines comment on the use of carmustine wafers and temozolomide, they do not comment on the combined use of carmustine wafers and temozolomide, and are based on historical studies that antedate temozolomide and 5-ALA. By treating patients with both 5-ALA, carmustine and temozolomide, GALA-5 will therefore inform reappraisal of NICE TA121 (and hence patient treatment). Results will be disseminated to NICE by forwarding the final publication. No record level data will be disseminated to NICE.

Outputs:

The mortality data obtained from HSCIC will be used to calculate the overall survival of GALA-5 trial patients otherwise lost to follow-up. The GALA-5 trial dataset will be analysed as soon as data are received (ie early in 2016), and will submit the trial results for publication in a peer reviewed journal. The manuscript will most likely be submitted to the Journal of Neuro-Oncology. Once the manuscript has been accepted for publication, the results will also be entered on the clinicaltrials.gov database, the EU Clinical Trial Register, and potentially NICE (see below). No record level data will be disseminated. The publication will also be forwarded to Cancer Research UK, who will produce a lay summary that will be uploaded on to the CRUK website. End of trial reports for the GALA-5 trial have been submitted to the REC and MHRA, which summarised the main findings. Submitting these requirements within 12 months of end of trial being declared was a requirement. No further report will be required by the MHRA. Survival data of individual patients will not be published, no individuals will be identifiable from any publications, nor will record level data be shared with third parties.

Processing:

The Cancer Research UK and UCL Cancer Trials Centre will provide patient date of birth, NHS number and date of diagnosis of glioblastoma to the HSCIC in order to identify these patients. Data from the HSCIC will be integrated into the existing anonymised trial dataset in a MACRO database (internally maintained database) by the Trial Coordinator. This dataset is held at the Cancer Research UK and UCL Cancer Trials Centre at UCL. Data will be stored electronically on servers with restricted access. There will be no linkage of the received data to other datasets. No third parties have access to these data. This dataset will be analysed by the trial statistician at the Cancer Research UK and UCL Cancer Trials Centre using STATA (statistical programme software) to produce statistical outputs for publications.

Objectives:

Date and cause of death are being requested to enable the assessment of patient survival in the GALA-5 trial. As some patients have become lost to follow up over time, obtaining this information for these patients will ensure that the dataset is as complete as possible. Mortality information is being requested only for patients that have become lost to follow up. The primary objective of the GALA-5 trial is to establish whether the combined use of 5-ALA and Carmustine wafers is safe and does not compromise a patient from receiving or completing standard chemoRT. The secondary objective of this study is to gather preliminary evidence as to whether the combined use of 5-ALA and Carmustine wafers at surgery has the potential to improve clinical outcome.


Project 3 — DARS-NIC-137864-T1P9B

Opt outs honoured: Y

Sensitive: Sensitive

When: 2018/03 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report

Benefits:

Benefits of the list cleaning: Submitting the cohort for list cleaning will allow the researchers to recontact the participants who CLS have lost touch with and give them the opportunity to re-engage or clearly state that they wish to withdraw. It will also minimise the risk of literature going to the incorrect address. and contact being made with participants who have died. Benefits of the Study: The 1958 cohort, as it approaches age 60, has now entered a critical period for the understanding of the heterogeneous processes of ageing, and in particular how earlier life experiences impact on health and well-being later in life. The continued ageing of the population in the UK and elsewhere make the understanding of healthy ageing a top priority policy concern, across a wide range of health and social policy domains. The main outcome from the NCDS Age 61 survey will be fully documented, anonymised research dataset and this will be archived with the UK Data Service in early 2022 to provide a strategically important resource for UK Social Science, including researchers in health and social care. GENERAL BACKGROUND INFORMATION & CONTEXT inc. PUBLICATIONS The Age 61 Survey will be comprised of two major components: 1) A core interview which will cover the following topics: - Health, well-being and cognition: physical health, mental health, medical care, health behaviours (e.g. smoking, drinking, diet, exercise), cognitive function. - Finances and employment: work, income, wealth (savings and debts, pensions, & housing), retirement plans & education. - Family, relationships and identity: social networks, relationships with partners, parents, children, friends, neighbourhood, social capital, social and political participation, attitudes and values, and religion. 2) A detailed biomedical assessment including measures of anthropometry, physical functioning, cardiovascular risk factors and a full range of blood tests. The central aim of this proposed biomedical assessment is to enable new research that will inform key public health concerns. The cohort is now transitioning between midlife and early older age, a critical time when biological ageing in key systems (e.g., cardiovascular, metabolic, immunity) start to accelerate, and a series of health conditions that have a profound influence on well-being first become clinically manifest. The information collected during the Age 61 Survey will enable researchers to uncover life course and inter-generational factors which contribute to healthy ageing among this generation, and thus to inform the development of preventative health policies across the whole of life that will expand healthy life expectancy, and reduce the burden of ill-health and disease at older ages. Below are some examples of existing publications using NCDS data benefiting public health: • Power, C., & Matthews, S. (1997). Origins of health inequalities in a national population sample. The Lancet, 350(9091), 1584-1589. • Hyppönen E, Power C. Hypovitaminosis D in British adults at age 45 y: nationwide cohort study of dietary and lifestyle predictors. Am J Clin Nutr. 2007; 85 (3):860-8. • Strachan, D.P., 2000. Family size, infection and atopy: the first decade of the 'hygiene hypothesis'. Thorax, 55 (Suppl 1), p.S2. • Clark C, Rodgers B, Caldwell T, Power C, Stansfeld S. Childhood and adulthood psychological ill health as predictors of midlife affective and anxiety disorders: the 1958 British Birth Cohort. Arch Gen Psychiatry. 2007; 64 (6):668-78. • Orfei L, Strachan DP, Rudnicka AR, Wadsworth M. Early influences on adult lung function in two national British cohorts. Arch Dis Child. 2008; 93 (7):570-4. • Johnson W, Li L, Kuh D, Hardy R. How Has the Age-Related Process of Overweight or Obesity Development Changed over Time? Co-ordinated Analyses of Individual Participant Data from Five United Kingdom Birth Cohorts. PLoS Med. 2015; 12 (5):e1001828.

Outputs:

Any study members choosing not to take part in the study are flagged on this the secure confidential address database at the CLS with a code denoting whether their refusal is temporary (i.e. for a particular wave/survey) or permanent (i.e. they wish to have no further involvement in the study). Any previously deposited pseudo-anonymised survey data for a study member and confidential data from the address database are retained unless the study member specifically asks us not to, in which cases this data is securely deleted. These addresses obtained from NHS Digital will be used to maintain contact with study members e.g. to send them a special birthday mailing for their 60th birthday in March 2018 and then later to invite them to take part in the Age 61 survey. However this data received via this application is never sent or published to the UK Data Service. The main outcome from the NCDS Age 61 survey will be a fully documented, anonymised research dataset and this will be archived with the UK Data Service in early 2022 to provide a strategically important resource for UK Social Science, including researchers in health and social care.

Processing:

NHS address tracing. CLS wish to use the patient status and tracking products which uses NHS registration data to trace as many NCDS study members as possible, either by finding new address details or verifying existing address details for the cohort. CLS will supply NHS Digital with a file of around 3500 study members to match to the NHS data. The file supplied will only contain eligible study members who have participated in at least one wave of NCDS. It will not include study members known to have died or to have withdrawn from the study. The file will contain the following data items: - CLS identifier - First name - Last name - Middle name (where available), - Date of birth - Sex - Last known address, and postcode - NHS Number NHS Digital would supply the following details to CLS: - CLS identifier - Latest surname - Latest forename - Latest middle name (where available), - Date of birth - Gender - Latest address and postcode - Fact of Death (and embarkations) - Date of address registration or update - NHS Number. In addition to the receipt of any 'new' matched address information for the study members, NHS Digital will add an additional variable that describes the outcome of the matching process to the data that is returned to CLS – that is, this additional variable will allocate each study member to one of the following four categories: • new/different address found, • existing address confirmed, • no match found, . participant has died. The data file supplied from NHS Digital, will be processed within CLS only and entered into CLS’s secure database i.e. CLS will load more recent addresses into the database. All NCDS study members contact information is held in this secure confidential address database at the Centre for Longitudinal Studies and used to maintain contact with study members and to invite them to take part in the NCDS Age 61 survey. Study members newly traced would be written to and invited to re-­engage with the study. Any newly traced study members who on being contacted were to indicate that they no longer wish to participate in the study would be recorded as a 'permanent refusal' on the CLS database and not approached again. All those accessing the data supplied by NHS Digital are substantive employees of University College London. 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).

Objectives:

The National Child Development Study (NCDS) is the second of Britain’s world renowned national longitudinal birth cohort studies. It follows all those born in one week in 1958 through the course of their lives, charting the effects of experiences in early life on outcomes and achievements later on. The study has its origins in the Perinatal Mortality Survey. Sponsored by the National Birthday Trust Fund, this was designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the children born in Great Britain in that one week. Information was gathered from almost 17,500 babies. Since 1958 information has been gathered from the NCDS cohort on nine occasions. Over time, the scope of enquiry has broadened from a strictly medical focus at birth, to encompass physical and educational development at the age of seven, physical, educational and social development at the ages of eleven and sixteen, and then to include economic development and other wider factors at ages 23, 33, 42, 44, 46, 50 and 55. The next NCDS survey will take place in 2019 when study members will be aged 61. In 1958, when the birth survey was carried out, consent to participate in surveys was gained by respondents agreeing to be interviewed or respondents returning the completed questionnaire to the study team. Involvement in subsequent surveys adopted the same approach. Individuals could withdraw from the study at any time by simply expressing the wish to do so. In all recent follow-ups the approach to collecting consent has been very similar. During fieldwork, study members were sent an advance letter advising them about the survey. The letter was accompanied by an information leaflet explaining what is involved. Study members had the opportunity to request further information, or to opt out of the survey at this point. They could also seek further information, or refuse further involvement when the interviewer attempted to make an appointment to visit; when the interviewer visited and at any point during the administration of any elements of the surveys. Of the approximately 18,500 individuals that have ever participated in the study there are now approximately 3,500 for whom the Centre for Longitudinal Studies (CLS) at University College London do not currently have a confirmed address. These are not individuals which have informed CLS that they wish to withdraw from the study, CLS have simply lost touch with them. The ongoing success of the study depends on maintaining contact with as large a number of study members as possible. Therefore, CLS are seeking permission to be supplied with updated addresses for these 3,500 study members whose whereabouts are currently unknown. All of these individuals have made an informed decision to participate in the study over the years and have been made aware that the study is seeking to follow them throughout their lives. Objective: Each year CLS sends an annual birthday card postal mailing in March to all NCDS participants. CLS asks that participants complete a ‘reply slip’ which is returned to CLS which allows participants to provide CLS with any change in their details e.g. a new email address, phone number, etc. CLS also ask them to return the reply slip even if none of their details have changed i.e. seeking a positive confirmation that that is the address CLS hold for them. As a result CLS, can maintain the cohorts' latest details on the NCDS database. In the event of the birthday card not reaching the participant it is returned to CLS as a ‘return to sender’. CLS will attempt to trace all these returns – but if CLS cannot locate the participants then they are flagged on the database as a ‘gone-away’. It is these cases (3500) that are being sent to NHS Digital for list cleaning as the NHS may potentially hold a more recent address and provide CLS with an opportunity to invite the cohort to re-join the study. NHS Digital will supply new addresses for untraced study members who can be matched to the NHS Central Registry/Personal Demographics Service (PDS). CLS require to trace lost study members between now and the Age 61 survey in 2019 which is currently in the planning stage. Any study members successfully traced via this route would be written to and asked to provide updated contact details. They will then subsequently be invited to participate in the NCDS Age 61 survey (unless they withdraw from the study). All those the researchers would seek to trace have participated in at least one prior sweep of the study and none have ever informed CLS that they no longer wish to participate in the study. The researchers feel that a substantial number of these individuals would be willing to participate in the study if they could be contacted. Previous efforts to re-establish contact for other cohort studies have been very successful using this route. When the cohort are contacted they will be given the opportunity to withdraw. If the participant has died no contact will be made and the study will be updated to reflect this.


Project 4 — DARS-NIC-147793-R05H3

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2016/09 — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • MRIS - Scottish NHS / Registration

Objectives:

The objectives of the study are: 1. to determine whether intelligent decision support can improve interpretation of the intrapartum cardiotocograph (CTG) and therefore improve the management of labour for women who are judged to require continuous electronic heart rate monitoring. Specifically. will the system, compared with current clinical practice: I. identify more clinically significant heart rate abnormalities ii. result in more prompt and timely action on clinically significant heart rate abnormalities? iii. result in fewer "poor neonatal outcomes"? iv. change the incidence of operative interventions? 2. to determine whether use of the decision-support software has any effect on the longer term neurodevelopment of children born to women participating in the INFANT study.


Project 5 — DARS-NIC-147817-KPFRY

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/04 (or before) — 2016/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Scottish NHS / Registration
  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

To provide data required for an informed decisiion about the introduction of population screening for ovarian cancer. This involves establishing the impact of screening on ovarian cancer mortality, determining the best screening strategy and assessing the physical and psychological morbidity and health economic implications of screening


Project 6 — DARS-NIC-147922-T7W2F

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Objectives:

British Cohort Study 1970 (BCS70) is a national longitudinal study which provides the medical and social science community with a set of data comprising information about the lives of its cohort members and their parents. This data set can be used to investigate factors which influence physical, psychological, educational and social development and outcomes. Since 1970, there have been three subsequent attempts to gather information from the full cohort. With each successive attempt, the scope of enquiry has broadened from a strictly medical focus at birth, to encompass physical and educational development at the ages of ten and sixteen. Aims and Investigations 1) To monitor child development educational, physical and psychological in the 1970s, in comparison with those made by the two previous surveys during the 1950s and 1960s. There is a desirability of further study of early hospitalisation, e.g. maternal employment, immunisation and vaccination, housing conditions. 2) To analyse via in-depth studies, with comparison of special groups from the 1958 Study, involving 'deprived' children or children in anomalous family situations e.g. children in care, illegitimate, fostered, from one-parent families, or socially disadvantaged. 3) To examine associations between high-risk medical and social factors in the perinatal period and subsequent child development, e.g. smoking in pregnancy, X-rays etc. This type of analysis, as in any longitudinal investigation, involves gathering data on a large range of 'intermediate variables', both social and environmental, which might affect both the 'casual' factor and the outcome. 4) To identify special groups in childhood who fail to use the services provided by DHSS and DES e.g. schooling, health centres, dental care, speech therapy, etc. and the availability of these services to children in different areas, e.g. urban/rural districts. 5) To analyse regional variations in many factors on which data are available. The needs of pre-school children, for example, may vary from one region to the next on account of differences in the degree of urbanisation, level and type of industrialisation, family incomes, educational and housing policies, etc. This type of information would be of great value in the administrative and policy-making areas of local government.


Project 7 — DARS-NIC-147936-1L3FD

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2017/08. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Cause of Death Report

Objectives:

The study has two main aims i) to determine whether a low dose of radioactive iodine is as effective as the commonly used high (3.7GBq) in ablating any remaining thyroid tissue after surgery. ii) to determine whether taking recombinant human Thyroid Stimulating Hormone (rhTSH) has no adverse effect on radioactive ablation of the thyroid remnant compared to the current practice of withdrawing thyroid hormone


Project 8 — DARS-NIC-147948-6MSGP

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Outputs:

The Ethics committee has been informed that this trial will be registered with the NHS Information Centre. Patients’ names are required for this and the ethics committee have approved this on the Patient Information Sheet and Consent Form.

Processing:

Patients sign the consent form after reading the Patient Information Sheet. This states that information from the National Health Service Care Register, the NHS Information Centre and/or Cancer Registries will be used to follow the patients’ progress It also states these bodies need to be sent names identifiable information to be able to provide information. This means the patient is happy for their details (including name and date of birth) to be sent to the Cancer Research UK & UCL Cancer Trials Centre. With the patient’s permission, their GP will be notified of their patient’s participation in the trial.

Objectives:

The Lung-SEARCH trial is a national screening study of >1700 individuals who smoke and have COPD are randomised to yearly sputum (and those who are positive have an annual bronchoscopy and CT scan) or no screening. All patients are followed up for 5 years, and the aim of the trial is to determine whether this screening policy can identify cancers at a lower stage at diagnosis. Because patients are free from cancer when they enrol in the study, we need to flag all patients with the national register to ensure that we find out about cancers in both trial groups, and also deaths and cause of death. The target sample size is about 80 lung cancers, so it is important that we can identify as many as possible, particularly those in the unscreened group.


Project 9 — DARS-NIC-147953-SXCMS

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

It is proposed that the main focus of this service evaluation will be assessing the effectiveness of cardiopulmonary exercise testing to predict outcome (mortality & morbidity) following major elective surgery compared to existing risk assessment tools such as the Revised Cardiac Risk Index and the Duke Activity Status Index. In the context of preoperative assessment, CPET is used to provide information on the risks related to planned surgery (based on the fitness of the patient measured during CPET) and thereby guide perioperative care management. By conducting this service evaluation, we will be able to assess the risk stratification criteria currently used, which we apply to high and low risk patient groups in our specific sub-population of patients (both at the Whittington Hospital and University College London Hospital) and therefore improve/refine the prognostic ability of cardiopulmonary exercise testing to predict surgical outcome.


Project 10 — DARS-NIC-148100-6RFK9

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/04 (or before) — 2018/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing, One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data, Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration
  • MRIS - Members and Postings Report
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Accident and Emergency
  • MRIS - Flagging Current Status Report

Benefits:

In NHSD, 15% have already died by age 65: cancers (41%), circulatory disorders (25%), external (12%). Evidence is growing from this cohort study (see above for references) and others, that factors from early life (such as growth, neurodevelopment, nutrition and family socioeconomic circumstances) as well as later life (such as adult smoking, diet, exercise and socioeconomic circumstances) affect the opportunity to age well. This is of interest to policymakers, practitioners, and older people themselves, given the increased numbers over the age of 65 hears (from 10.8 million now to 16 million in 2035), and the eightfold increased expected in the number reaching 100 years.

Outputs:

The data will be used on an ongoing basis to update study member records. The database will be updated after each data release. This MRC Unit is committed to research on ageing – outputs arising from ONS data will be anonymised in the form of tables, graphs, peer reviewed journals, presentations and books. Some examples are provided below: Mortality papers 1. Davis D, Cooper R, Muniz Terrera G, Hardy R, Richards M, Kuh D. Verbal memory and search speed in early midlife are associated with mortality over 25 years follow-up, independently of health status and early life factors. A British birth cohort study (under review). 2. Hartaigh B, Gill TM, Shah I, Hughes AD, Deanfield JE, Kuh D, Hardy R. Association between resting heart rate across the life course and all-cause mortality: longitudinal findings from the Medical Research Council (MRC) National Survey of Health and Development (NSHD). J Epidemiol Community Health, 2014 Sep;68(9):883-9. 3. Cooper R, Strand BH, Hardy R, Patel KV, Kuh D. Physical capability in midlife and survival over 13 years follow-up in a British birth cohort study. British Medical Journal 2014;348:g2219. 4. Maughan B, Stafford M, Shah I, Kuh D. Adolescent conduct problems and premature mortality: follow-up to age 65 in a national birth cohort. Psychological Medicine 2013 Aug 21:1-10. 5. Ong K, Hardy R, Shah I, Kuh D on behalf of the NSHD scientific and data collection teams. Childhood stunting and mortality between 36 and 64 years: the British 1946 birth cohort study. Journal of Clinical Endocrinology and Metabolism. 2013 May;98(5):2070-7. 6. Strand BH, Kuh D, Shah I, Guralnik J, Hardy R. Childhood, adolescent, and early adult body mass index in relation to adult mortality: Results from the British 1946 birth cohort. Journal of Epidemiology and Community Health 2012 Mar;66(3):225-32. 7. Henderson M, Hotopf M, Shah I, Hayes RD, Kuh D. Psychiatric disorder in early adulthood and risk of premature mortality in the 1946 British Birth Cohort. BMC Psychiatry 2011 Mar 8;11:37 8. Kuh D, Shah I, Hardy R, Richards M, Mishra G, Wadsworth MEJ. Do childhood cognitive ability or smoking behaviour explain the influence of lifetime socioeconomic conditions on premature adult mortality in a British post war birth cohort? Social Science and Medicine 2009;68:1565-73. 9. Clennell S, Kuh D, Guralnik J, Patel K, Mishra G. Characterisation of smoking behaviour across the life course and its impact on decline in lung function and all-cause mortality: evidence from a British birth cohort. Journal of Epidemiology and Community Health 2008;59:304-14. 10. Kuh D, Richards M, Hardy R, Butterworth S, Wadsworth MEJ. Childhood cognitive ability and deaths up until middle age: a post war birth cohort study. International Journal of Epidemiology 2004;33:408-13. 11. Kuh D, Hardy R, Langenberg C, Richards M, Wadsworth MEJ. Mortality in adults aged 26-54 years related to socioeconomic conditions in childhood and adulthood: post war birth cohort study. British Medical Journal 2002;325:1076-80. 12. Barker DJP, Osmond C, Golding J, Kuh DJL, Wadsworth MEJ. Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. British Medical Journal 1989;298:564-7.

Processing:

Mortality and cancer registration data are held on consented study members from linked data received from ONS. There are also historic records on mortality for study members parents, siblings and their own children. The Unit for Lifelong Health and Ageing (LHA) has a responsibility, as custodian of the NSHD, to all study members and their families to ensure that data held are treated responsibly and that the risk of re-identification of study members, living or dead, is reduced to a minimum. A range of information security measures are employed, by the LHA, to control such risks. Although the absolute level of risk of re-identification may be very small, the concerns, anxieties, and sensitivities of study members and their families, about the use of these data, remains a priority for the LHA. In particular, the NSHD maintains contact with study members primarily through an annual newsletter and birthday card, notification to participate in data collections is managed through confidential invitation letters. Without the information, we risk causing distress to relatives by attempting to contact deceased study member. The LHA also has obligations under the terms of the data sharing agreement with HSCIC to ensure that all legal and contractual requirements are strictly adhered to. The procedures for handling and managing these data at the LHA are set out below. The LHA also holds hospital admissions data from consented study members and has implemented a three level security model for all data items held in the NSHD data repository. All personally identifiable information used to match ONS cancer or mortality data with NSHD records are classified as Confidential. Such items would include names, addresses, NHS numbers and other fields that may be used to identify an individual. These data can only be viewed by named LHA scientific and support staff for the purposes of data matching and cohort management. All non-identifiable data pertaining to the death of a study member or other family members, or cancer registrations are classified as Restricted and may only be analysed on-site at the offices of the LHA. Data sharing requests to the LHA by external scientists for cancer or mortality data will be confined to those aggregated and derived data items that are classified as Open by the LHA security model. The rest of this section will describe the data currently held in the NSHD data repository and the constraints on creating aggregate and derived data items from ONS mortality and cancer data so that they can be classified as Open. Type of mortality data held We currently hold data on: • The death registration details of a study member (from death certificate) • The exact date of death (from death certificate) • The cause of death (from death certificate) • The death status of the study member’s mother and father (SM reported) • The date of death of the study member’s mother and father (SM reported) • The cause of death of the study member’s mother and father (SM reported) • The date of death of a study member’s spouse Cancer Registry data sources These data are acquired from quarterly notifications from the Office of National Statistics (ONS). Mortality data sources The mortality data held are collected from a variety of sources. In addition to the annual birthday card returns and tracing exercises and study fieldwork we receive notification of death from the study members’ family and friends. Since 1971 we have received quarterly notifications of deaths from ONS. More recently these data are received from the NHS Information Centre (NHS-IC) and now through the Health & Social Care Information Centre (HSCIC). The data held from the HSCIC are subject to the conditions of the original application and Data Sharing Agreement. Data supplied through HSCIC are classified as ‘personal information’ according to the Statistics and Registration Service Act 2007. Conditions of the Data Sharing Agreement with ONS/HSCIC The agreement spells out key conditions which the LHA data sharing process must comply with regard to ONS/HSCIC supplied data. In summary these are: • All access to these data must be audited; • ONS derived data must be anonymised before release to any external scientists or organisations; • Data must not be released in a form where small cell counts might increase the risk of re- identification of individuals. The LHA data ingestion and data sharing process directly deals with these conditions through the aggregation of original ONS microdata into derived variables, e.g. quarter of year replacing day and month of death, and through the anonymisation of all datasets supplied through the NSHD data sharing system. Where possible, suitably broad categories for aggregation are chosen when deriving from Restricted variables. Furthermore, variables derived from Restricted data items are inspected for small cell counts before release. Where it is not possible, due to scientific requirements, to derive an aggregated variable that avoids small cell counts, then that derived variable will continue to be marked and treated as a Restricted variable. Cause of Death Cause of death is recorded as the immediate cause of death and underlying cause. These are recorded as ICD codes (ICD 8 for deaths prior to 1979, ICD 9 for deaths between 1979 and 1999 and ICD10 for deaths from 2000 onwards). Whilst it may be permissible to release aggregated data on death through illness, releasing figures on suicide or unnatural causes (that could have been non-illness or accident related) or that have small frequencies will be subject to the conditions stated in the section above. Family mortality data The following data items have been obtained as part of the on-going NSHD data collections. Although these data are not derived from ONS/HSCIC they will be treated in the same manner as ONS/HSCIC derived mortality data. Parents In 1972 study members were first asked whether their parents were still alive and if not the date of their death. In 1972 cause of death was not recorded. The questions were repeated in 1977 but again cause of death was not asked. In 1982 study members were asked if their parents were still alive and if not when they died and the cause of death. It is not clear if those who had previously reported the death of a parent were asked these questions. This information was updated in 1989, 1999 and 2006-10, and confirmed through death certificates. Spouse Through marital status there is a record of whether a study member has lost their partner and the date of their death but not the cause. Rules for Data Sharing 1. The derived response variable indicating whether a study member was alive or deceased at each wave of data collection is classified as Open and made available for data sharing. 2. Original ONS/HSCIC cancer and mortality data will be classed as Restricted. LHA scientists, who wish to access these data, are required to obtain the approval of the Director before these data items can be released to them. This will allow the LHA to audit the use of the mortality data supplied as outlined in the Data Sharing Agreement signed in 2008. 3. Any data item that is present in the original mortality microdata supplied by ONS and any potentially disclosive variable in an NSHD anonymised dataset that has been trivially derived from these should be considered to be subject to the full set of restrictions set out as part of the LHA agreement with ONS and subject to the Statistics and Registration Service Act of 2007. In particular this stipulates that ONS/HSCIC data defined as personal information in the Act must not be disclosed except as permitted under s39(4). 4. Where the proposal and analysis warrants it, the year of death and the quarter of the year the study member died may be released for data sharing to bone fide scientists subject to successful approval of the NSHD Data Sharing Committee and a signed Data Sharing Agreement. 5. Data pertaining to cause (either immediate or underlying) of death will only be released in binary form for those conditions specified in the data sharing proposal, and only if the cell counts are not small. The data will be derived for each project and will take the form 1 ‘Died of specified cause’ 2 ‘Died of different cause’ 0 ‘Living’. 6. Data pertaining to the age at death, or the date of death, of a study member’s parent may be released for data sharing. These data will be released in the form specified in 4 above. 7. Data pertaining to the cause of death of a study member’s parent(s) may only be released in the form specified in 5 above. 8. All other requests to use more detailed mortality data must be conducted in house at LHA offices or by a member of LHA staff using an analysis script written by the researcher. The contribution of the relevant member of LHA staff should be acknowledged in the final piece(s) of research.

Objectives:

The MRC National Survey of Health and Development (NSHD), funded by the Medical Research Council since 1962, is the longest running longitudinal study with continuous follow-up in the world, consisting of a nationally representative sample of 5,362 men and women whose health and socioeconomic circumstances have been measured 24 times since their birth in March 1946. The NSHD is a prospective observational epidemiological study of lifetime risk factors on adult health, ageing and mortality. We need to have the regular data on death and cause of death, cancer registration and emigration reinstated so we can continue to carry out our MRC funded research.


Project 11 — DARS-NIC-148128-815J1

Opt outs honoured: Y

Sensitive: Sensitive

When: 2016/12 — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012

Categories: Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Objectives:

The data supplied by the NHS IC to UCL Institute of Child Health will be used only for the approved Medical Research Project, National Mother and Child Cohort.


Project 12 — DARS-NIC-148407-LRP3M

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing, One-Off

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012, Other-Data originally supplied on the basis of National Health Service Act 2001 – s60, and subsequently National Health Service Act 2006 - s251., Other-Data originally supplied on the basis of National Health Service Act 2001 – s60. Subsequent data releases under Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007., Other-Data originally supplied on the basis of National Health Service Act 2001 – s60, and subsequently National Health Service Act 2006 - s251 - 'Control of patient information'. | New data to be disseminated on the basis of Informed Patient consent to, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Flagging Current Status Report
  • MRIS - Scottish NHS / Registration
  • Hospital Episode Statistics Admitted Patient Care
  • MRIS - Members and Postings Report

Benefits:

The rich phenotypic and genotypic dataset gathered over a 25 year period will enable analyses assessing mid-life predictors of health and ill-health in older age and will enable unique analyses of how these associations may be related to ethnicity and migration. Good physical and cognitive functions are vital to healthy ageing and factors which influence these across the life course are poorly understood, particularly in non-European populations. As the cohort is reaching older age, an increase in risk of heart failure, which can be severely debilitating, is expected. Ethnic differentials in heart failure rates are not well studied to date. Increasing length of follow-up and novel analytic techniques, both statistical and relating to stored images and samples bring opportunities for more sophisticated analyses and the addition of hospital admission data to key outcome variables enhances the study’s power to identify events and to further elucidate mechanisms underlying the very marked ethnic differences in cardiometabolic disorders which were observed at visit 2. Understanding of mechanisms in people of different ethnicities will ultimately lead to appropriate preventive strategies and treatments at different stages of life. As noted with some detailed examples under ‘specific outputs’, previous use of HES data (1989-2011) enabled improved ascertainment of incident coronary heart disease and stroke events and resulted in 10 publications in high impact journals relating these outcomes to risk factors measured in mid-life (ages 40-70 at baseline). A brief summary of some of these findings in the cohort to 2011 follows: Diabetes incidence in older British South Asians and African Caribbeans remains at least 2-fold even at age 80 years compared with British Europeans. The ethnic differentials in women were largely explained by midlife truncal obesity and insulin resistance, but the study was unable to explain the ethnic difference in men. The study showed that obesity cut-points of 24 and 27 kg/m2 in South Asians and African Caribbeans respectively were equivalent to a body mass index of 30kg/m2 in Europeans in terms of diabetes risk; these latter analyses contributed to recent NICE guidelines for prevention of diabetes. Diabetes was also found to be more ‘toxic’ in terms of stroke risk in the ethnic minorities. Widely used tools (Framingham and QRISK2) for estimation of cardiovascular disease risk were found to be less precise in South Asians and African Caribbeans (particularly women), while a selection of 3 metabolic markers measured by NMR spectroscopy was found to be strongly predictive of cardiovascular risk regardless of ethnicity. Lack of adherence to four combined health behaviours was associated with 2 to 3-fold increased risk of incident CVD in Europeans and South Asians. A substantial population impact in the South Asian group indicates important potential for disease prevention in this high-risk group by adherence to healthy behaviours. The study also found marked ethnic differences in associations between blood pressure parameters and stroke and concluded that undue focus on systolic blood pressure for risk prediction, and current age and treatment thresholds may be inappropriate for individuals of South Asian ancestry. This is not an exhaustive list of study findings in relation to incident cardiometabolic disease but indicates that the study is building a steady accumulation of understanding of ethnic differentials. There is clear need for further study, which this cohort is uniquely able to address. The addition of HES data to 2016 is key to maximising event ascertainment in old age.

Outputs:

Study findings will continue to be published in peer-reviewed scientific journals, predominantly related to epidemiology, cardiovascular and metabolic disorders, cognitive, physical and psychological function, but also including more generic journals such as the BMJ, reflecting the increasing focus on overall health in older age. Publications will contain only aggregate level data without local identifiers and with suppression of small numbers in line with HES analysis guide. Publications to date are listed on the study website: www.sabrestudy.org. All publications since 2008 are open-access. The audience is expected to consist mainly of academic researchers and clinicians. Two examples of previous SABRE study related publications are listed below, both sets of analyses were importantly informed by data from a previous HES extract (no longer retained), and were published in high-impact factor peer-reviewed journals. These generated considerable media interest and are widely cited. Tillin T, Hughes AD, Mayet J, Whincup P, Sattar N, Forouhi NG, McKeigue PM, Chaturvedi N. The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians and African Caribbeans. SABRE (Southall and Brent revisited) – a prospective population based study. J Am Coll Cardiol. 2013 Apr 30;61(17):1777-86. http://dx.doi.org/10.1016/j.jacc.2012.12.046. This paper published in JACC, the world no 1 cardiology journal (impact factor 16.5) confirmed ongoing excess coronary heart disease incidence in South Asians, with lower incidence in African Caribbeans compared with Europeans and confirmed elevated risk of stroke in both ethnic minority groups. Measured baseline metabolic risk factors could not explain the ethnic group differences. Future work in the cohort will examine whether these ethnic differentials continue into older age and whether newer genetic, epigenetic and metabolomic analyses will add to understanding of the underlying mechanisms. Of particular concern was a much stronger association between diabetes and stroke risk in both ethnic minority groups compared with Europeans with diabetes- an association which is the subject of ongoing study in the SABRE cohort. HES data, although not directly reported in the manuscript, contributed importantly to the identification of incident coronary and stroke events reported in these analyses. Tillin T, Hughes AD, Godsland IF, Whincup P, Forouhi NG, Welsh P, Sattar N, McKeigue PM, Chaturvedi N. Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared to Europeans? The Southall And Brent REvisited (SABRE) cohort. Diabetes Care 2013;36(2)(383-393). http://care.diabetesjournals.org/content/36/2/383.long. This paper demonstrated the extraordinarily high risk of incident diabetes continuing into old age in South Asians and African Caribbeans in comparison with Europeans. Metabolic pathways leading to diabetes remain poorly understood. The study found that baseline insulin resistance and truncal obesity could explain the ethnic differences in women but not in men. Further work continues to determine the reasons for the excess risk in men and to understand what underlies insulin resistance and truncal obesity. HES data, although not directly reported in this manuscript, supported these analyses by enabling sensitivity analyses to assess the effects of bias due to loss to follow-up. A further 8 journal publications have examined associations between baseline risk factors and incident coronary heart disease or stroke where the outcomes were a composite of first events identified through participant reported events, primary care record review identified events and HES identified hospital admissions. One of these was published in Circulation (Wurtz et al), impact factor 14.3 and demonstrated in 3 separate population based studies (including SABRE) that metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk, substantiating the value of high-throughput metabolomics for biomarker discovery and improved risk assessment. Another publication in Heart (Tillin et al) identified that 2 widely used cardiovascular risk prediction tools (QRISK2 and Framingham) did not perform consistently well in all ethnic groups and suggested that further validation of QRISK2 in other multi-ethnic datasets, and better methods for identifying high risk African Caribbeans and South Asian women, are required. In addition to journal publications, UCL will continue to submit abstracts for presentation at national and international conferences, such as Diabetes UK, the European Association for the Study of Diabetes, the European Society of Cardiology, Artery, and the British Hypertension Society. All data for abstracts/presentations will be at aggregate level with suppression of small numbers in line with HES analysis guide. The study team will further disseminate findings via participant and GP feedback sessions; newsletters, and the study website. All data for these occasions will be at aggregate level with suppression of small numbers in line with HES analysis guide. At the end of the current funding period (2018) a report will be submitted to the funders (the British Heart Foundation) summarising findings. This may be published on their website. It will only contain at most aggregate level data, with small numbers suppressed in line with HES Analysis Guide.

Processing:

The identifiers of SABRE participants have previously been shared with NHS Digital’s predecessor organisation(s) and NHS Digital has provided regular event notifications including notifications of mortality and cancer registrations. The cohort was previously split into two groups: cancer notifiable participants and non-cancer notifiable participants. The cohort will be reorganised into three groups: participants who gave informed consent (cancer notifiable); cancer notifiable participants covered by section 251 support, and non-cancer notifiable participants covered by section 251 support. To ensure that participants are correctly reorganised into the appropriate groups, UCL will send NHS Digital 3 separate files (one for each respective group) containing participant identifiers. NHS Digital will then provide reports on a monthly basis while the study is in active follow-up. Notifications will contain no participant identifiers other than unique study Pseudo-IDs. Month and Year of Death will also be included. NHS Digital will link the respective cohort groups to HES data and will supply to UCL encrypted files containing hospital admissions data identified only by study Pseudo-ID and encrypted HESID and containing no other identifiers. The dataset will be placed immediately into UCL’s Data Safe Haven. Using the Pseudo-ID, the data is linked at record level to the existing dataset of mortality and cancer records, clinical measures, primary care record review and participant responses to health and lifestyle questionnaires across the course of the study. The data is stored in an encrypted file within the Data Safe Haven at the Gower Street location. The data can be remotely accessed at the Institute of Cardiovascular Science by accredited SABRE study researchers only – all of whom are substantive employees of UCL. Access must be approved by the Data Manager. The data supplied by NHS Digital will not be downloaded or otherwise transferred from the Data Safe Haven. Data including variables derived from the NHS Digital data may be downloaded from the Data Safe Haven and stored on a UCL server at the Institute of Cardiovascular Science to be used solely for the purposes of statistical analyses in accordance with the study objectives. Such variables include, for example, date of first admission related to a diagnosis of coronary heart disease but will not include any part of the dataset supplied by NHS Digital. Using this pseudonymised dataset, study analysts will examine associations between risk factors measured during the course of the study and cardiometabolic events. The rich phenotypic and genotypic dataset will enable identification of ethnic differences in cardiometabolic disease risk and physical, mental and cognitive function into older age and it will be possible to identify which measured risk factors may explain ethnic differentials and at which period of life they may act most strongly. To meet study objectives UCL require information on admissions where diagnostic code lists include coronary heart disease, stroke, heart failure, diabetes, renal failure, dementia, retinopathy, hypertension, other cardiovascular disease. Respiratory diseases will also be studied and mental health disorders and other common disorders may be added which are considered to exert important influences on function and well-being in older age. As an example, from the HES extract, and within the UCL Data Safe Haven, it is expected that a variable will be generated which identifies a first or subsequent admission with coronary heart disease (ICD-9 codes 410 through 415 or ICD-10 codes I200 through I259, or any of the following operation codes from the Office of Populations and Surveys classification of interventions and procedures: K401 through K469, K491 through K504, K751 through K759, or U541 (coronary revascularization interventions or rehabilitation for ischemic heart disease)). Date of first or subsequent event would be summarised as year of event. The data is stored separately to participant identifiers. The two datasets will not be re-linked and the data will remain pseudonymised as described above. Month and Year of Death are stored in the dataset and used for statistical analyses but the dataset does not include full Date of Death. Participant identifiers are retained separately solely for study administration purposes.

Objectives:

The data supplied by the NHS IC to University College London will be used only for the approved Medical Research Project MR472.


Project 13 — DARS-NIC-148411-Q64H8

Opt outs honoured: Y

Sensitive: Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

The data supplied by the NHS IC to UCL Medical School will be used only for the approved Medical Research project


Project 14 — DARS-NIC-152228-DL5MK

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

Clinical Cohorts in Coronary disease Collaboration (4C) Aim; To evaluate important new opportunities for improving quality of care and outcomes for patients with angina and acute coronary syndromes, integrated across the patient journey, and at different levels of care. Objectives; (i) To determine the cumulative impact on patient outcome of missed opportunities for improving patient outcome, from the beginning to the end of the patient journey, across five of the most common symptomatic coronary presentations, assessing inequalities in care and outcome. (ii) To determine at the level of the individual hospital the extent to which the organisation and processes of care have an impact on the patient journey. (iii) To establish the effectiveness and cost-effectiveness of a multi-faceted intervention targeting initial specialist management at hospital chest pain clinics of patients early in the symptomatic phase of the patient journey. (iv) To determine whether novel biomarkers are a cost-effective addition to existing clinical information in predicting the progression of chronic stable angina to acute fatal and non-fatal events.


Project 15 — DARS-NIC-15226-X7Z9R

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2018/03 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report

Benefits:

The expected measurable benefits from the original agreement: The study produces rich, longitudinal, policy-relevant data, currently unavailable elsewhere, for a large, representative sample of young adults. LSYPE data is widely used by policy makers to evaluate and develop policy and improve services for young people and also by academic researchers to chart and understand social change. The information provided by cohort members provides valuable evidence for the research and policy community about the cohort’s transitions out of education and into early adult life. To enhance the research resource for secondary users, a fully documented, anonymised dataset has been archived with the UK Data Service in May 2017. Next Steps Age 25 survey data will enrich the already deposited data for the cohort (waves 1 to 7) and is expected to be particularly valuable for the research community, including researchers in health and social care, providing rich survey data on a range of different domains of young people’s lives. Particularly beneficial is the opportunity for a life course approach and to follow young people’s experiences over time to analyse later life outcomes. Next Steps data is a resource with great potential for the research and policy community, and the information collected on health and its social determinants widens its potential value for health research and policy interventions. Through the set up at the UK Data Service, researchers are able to apply and carry out research utilising the established link to benefit health and social care. Next Steps Age 25 Survey data has been deposited with the UKDS and the cohort members’ health is an important aspect in the Age 25 Sweep. Cohort members were asked a range of questions about their physical and emotional health and wellbeing and CLS is currently looking at initial findings on probable mental ill health at age 25 and its association with a number of potential risk factors. There is, however, a great deal more information about potential underlying determinants, in this and the earlier sweeps of Next Steps, available for researchers via the UKDS. This request is to extend the Data Sharing Agreement. Retaining contact details of non-responders (to an annual mail-out) will enable the researcher to try and re-establish contact before the next wave of the longitudinal study (date to be confirmed) to be able to continue with the research.

Outputs:

Previous outputs from original agreement: On receipt of the data, CLS processed the files and loaded more recent addresses to the database. Contact was made with the cohort members via the contracted fieldwork agency, NatCen, inviting them to take part in the survey. CLS have posted a participant survey information pack to all cohort members announcing the imminent launch of the survey. It will be made explicitly clear to study members that they can withdraw from the study if they no longer wish to participate. CLS: Participant contact information is held in a secure address database at the Centre for Longitudinal Studies. Any participants choosing not to take part in the study are flagged on this database with a code denoting whether their refusal is temporary (i.e. to this particular wave of data collection) or permanent (i.e. they wish to have no further involvement in the study). Anonymised survey data and confidential data from the address database are retained unless the participant specifically asks us not to, in which case this data is deleted. As mentioned earlier in this section, CLS have contracted an external supplier (& Data Processor) NatCen Social Research to carry out the survey fieldwork and associated mailings for the Next Steps (LSYPE) Age 25 survey – specifically to carry out: (1) Email and postal mailings to LSYPE cohort members about the study; (2) Interviews with Next Steps (LSYPE) cohort members. These activities have been completed and the data files will be securely deleted from NatCen Social Research systems - this is in the process of being completed and a special condition has been added stating that NATCEN will delete the data within 3 month of the signing of this new agreement. The fully documented, anonymised research dataset was archived with the UK Data Service in early-2017 to provide a strategically important resource for UK Social Science, including researchers in health and social care. Extension request 2018: There will be no further outputs at this stage, this request is to extend the Data Sharing Agreement to enable the researcher to retain the cohort contact details so that they can be contacted at the next wave of the longitudinal study (date to be confirmed) to be able to continue with the research.

Processing:

Previous processing activities from the previous agreement: ACTIVITY 1. NHS address tracing. CLS wished to use NHS Digital patient status and tracking products which uses NHS registration data to trace as many study members as possible, either by finding new address details or verifying existing address details for the cohort. 1.1 CLS supplied NHS Digital with a file of around 15,600 cohort members to match to the NHS data. The file supplied only contained eligible study members who had participated in at least one wave of Next Steps. It did not include study members known to have died or to have withdrawn from the study. The file contained the following data items: - CLS identifier, - First name, - Last name, - Middle name (where available), - Date of birth, - Sex, - Last known address, and postcode, NHS numbers were not available for any study members. 1.2 CLS required all 15,600 cases to be sent for auto-matching. 1.3 Once the auto-matching process was complete, CLS reviewed the results and took a decision about which cases should be put forward for operator matching. CLS thought that any cases classified as status: 'gone away' or ‘unconfirmed address’ (1,026 and 3,367 cases respectively) were likely sub-groups for operator matching. 1.4. NHS Digital supplied the following details to CLS. - CLS identifier, - Latest surname, - Latest forename, - Latest middle name (where available), - Date of birth, - Gender, - Latest address and postcode, - Fact of Death - Date of address registration or update. In addition to the receipt of any 'new' matched address information for the cohort members, CLS required NHS Digital to add an additional variable that described the outcome of the matching process to the data that is returned to them. This additional variable allocated each cohort member to one of the following three categories: • new/different address found, • existing address confirmed, • no match found. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement. Extension request 2018: There will be no further processing at this stage, this request is to extend the Data Sharing Agreement to enable the researcher to retain the cohort contact details so that they can be contacted at the next wave of the longitudinal study (date to be confirmed) to be able to continue with the research.

Objectives:

Next Steps Longitudinal Study of Young People in England (LSYPE) is an established longitudinal study which has followed the lives of 15,620 people born in 1989/90, since year 9 of secondary school. Study members were interviewed annually between 2004 and 2010 to map their transitions through education and into adulthood and the labour market. Therefore, LSYPE is the largest and most detailed research study of its kind. The most recent round of data collection - Next Steps Age 25 survey - took place between August 2015 and September 2016. Information was collected from 7,707 cohort members on many aspects of cohort members’ lives such as education, employment, health and well-being, relationships and family life, housing and finances. Additionally, during the Age 25 survey, a wide range of data linkage consents were collected, including consent to health records linkage held by NHS. The study was previously managed by the Department of Education (DfE). In 2013 the Economic and Social Research Council took over the funding and the study management legally transferred to the Centre for Longitudinal Studies (CLS) at the University College London Institute of Education. CLS have already received data under the original data agreement (NIC-316681-W7P2R) and, under the subsequent amendment (NIC-349413-F1J1N), were able to pass data to their contracted fieldwork agency NATCEN to allow them to initiate/conduct the survey. Next Steps was conducted annually between 2004 and 2010. Data collection focused on young people’s transitions into further/higher education and the labour market or to other outcomes, such as parenthood. The next wave took place in 2015 when cohort members were aged 24/25 years. The Age 25 survey gathered information about the lives of the cohort including education, employment, economic circumstances, family life, physical and emotional health and well-being, social participation and attitudes. The ongoing success of the study depends on re-establishing and maintaining contact with as many study members as possible. The aim of being provided with the address details and other up to date identifiers for the cohort was to trace as many study members as possible in advance of the next wave of fieldwork. CLS supplied NHS Digital with a file of study members and their last known address, extracted from the CLS address database. CLS asked that these details are matched with NHS Registration Data and registered addresses supplied, where available. The data supplied was entered into the secure address database and is used to maintain contact with study members and to invite them to take part in each fieldwork wave. When study members were contacted to invite them to participate in the Next Steps Age 25 study, it was made explicitly clear that they can inform CLS that they no longer wish to participate in the study and they will not be contacted again. To conduct the age 25 survey, CLS contracted an external supplier NatCen Social Research (the trading name of the National Centre for Social Research - www.natcen.ac.uk) to carry out the individual cohort study members’ interviews’. The survey is now completed and the aggregated outputs from the survey have now been deposited with the UK Data Archive, located at the University of Essex, no patient identifiable data is deposited. Please note that the data files supplied from NHS Digital, as part of this application, have been processed within CLS and entered into CLS’s secure address database. They have been used to maintain contact with study members and to invite them to take part in each fieldwork wave – this data is not sent to the UK Data Service. NatCen Social Research, were an external Data Processor and carried out the survey fieldwork and associated mailings for the Next Steps Longitudinal Study of Young People in England (LSYPE) Age 25 survey – specifically they were contracted to carry out: (1) Email and postal mailings to LSYPE cohort members about the study; (2) Interviews with Next Steps (LSYPE) cohort members. As the survey is now completed, the contract with NatCen is also ended and therefore NatCen are no longer acting as a data processor for CLS, and any data files have been securely deleted from NatCen’s systems. CLS also require access re-instated to NHS Numbers for the cohort participants so that future matching and linkage exercises including those related to a Next Steps linked data application (NIC-51342), a separate data sharing agreement which provides HES data linked to the cohort).


Project 16 — DARS-NIC-161339-RC2NB

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/12 — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Other-Health and Social Care Act 2012 - section 261(1)

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • Office for National Statistics Mortality Data (linkable to HES)
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care

Benefits:

The continuation of research with HES/ONS data will provide vital evidence regarding organisation and provision of acute stroke services, building on Morris et al [2014]. In particular, it will provide further evidence on the impact of centralised acute stroke services on stroke patient mortality and length of stay and the sustainability of such effects over time. Importantly, it will also provide evidence on the cost-effectiveness of such changes. At present, many parts of the English NHS are exploring ways how to reorganise care, including acute stroke services. Current developments, such as Sustainability and Transformation Plans, are anticipated to have a strong focus on major system change and centralisation. The Stroke Research Team at UCL anticipate that by providing evidence on two issues that are central to such decision-making - i.e. impact on outcomes and cost-effectiveness - it is anticipated to have a significant and ongoing influence on the organisation and provision of stroke care and other services across the English NHS, with significant increases in likelihood of patients receiving evidence-based care and improvements in outcomes. IMPACT OF PREVIOUS PUBLICATIONS BASED ON THIS DATA SHARE Key findings have already been published based on the HES/ONS data shared to date (Morris et al, British Medical Journal, 2014 - uploaded with this submission), with significant impacts on national policy and local stroke service organisation. 1. NATIONAL: findings were referred to in the Five Year Forward View (NHS England, 2014) as evidence of the benefits of greater concentration of care in terms of patient outcomes [http://www.england.nhs.uk/wp-content/uploads/2014/ 10/5yfv-web.pdf]. 2. REGIONAL: findings were cited as evidence in support of the decision to further centralise stroke care services across Greater Manchester (a region covering approximately 3 million people) [see http://www.hsj.co.uk/5083372.article for summary]. Since the further centralisation, the Greater Manchester Operational Delivery Network reports that 84% of stroke patients are now transported to a HASU; this is an increase from 39% in 2010-12 [Ramsay et al, 2015]. Given evidence suggesting that HASUs are more likely to provide evidence-based care, this change should lead to increased likelihood of stroke patients in Greater Manchester receiving evidence-based care, and improved clinical outcomes.

Outputs:

The Stroke Research Team at UCL have employed an active dissemination strategy throughout this study. In line with the strategy, this data share will contribute to a range of academic and other outputs, including the final report to the funder (NIHR Health Services & Delivery Research); high impact, open access academic papers; accessible 1-page summaries of the findings; presentations to academic conferences; and presentations and workshops for other key stakeholders (including stroke patients and their carers, stroke clinicians, hospitals and commissioning organisations, national policy makers, and the wider public) at national and regional levels; The Stroke Research Team at UCL also make the findings available through the study webpage and promote outputs on social media. All outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. Further, the Stroke Research Team at UCL will not explicitly identify the provider organisations providing services in different localities, but rather the impacts of centralisation at regional level (e.g. ‘London’, ‘Greater Manchester’). FINAL REPORT The final report will be submitted to the funder (NIHR Health Services and Delivery Research programme) in July 2017. This will cover all findings of the study, covering factors influencing planning, implementation, impact, and sustainability of major system change in acute stroke services. Once finalised, this will be published in the open access, peer-reviewed NIHR journal, Health Services and Delivery Research, with an estimated publication date of January 2018. ACADEMIC PAPERS All academic papers are published open access in high impact, peer-reviewed academic journals. Below, is a summary of the papers the Stroke Research Team at UCL anticipate publishing over the coming months. PUBLISHED PAPER: IMPACT OF CENTRALISATION ON PATIENT MORTALITY AND LENGTH OF STAY Morris et al. Impact of centralising acute stroke services in English metropolitan areas on mortality and length of hospital stay: difference-in-differences analysis. BMJ 2014 This paper presented a controlled difference-in-differences analysis of HES/ONS data (2008-2012) to ascertain the effect of the centralisations in London and Greater Manchester on length of hospital stay and mortality at 3, 30, and 90 days for stroke patients. The key findings were: - length of hospital stay reduced significantly more in London and Greater Manchester than in the rest of England; - mortality in London reduced significantly more than that in the rest of England, while a similar reduction was not observed in Greater Manchester. These findings suggest that fully centralised models are associated with better outcomes for stroke patients. Data on the significantly greater effect of the London changes on mortality and length of stay were reported in the final decision to further centralise stroke services in Greater Manchester, and presented as evidence of the potential benefits of centralising specialised healthcare services in the Five Year Forward View. FUTURE PAPERS 1. COSTS AND COST-EFFECTIVENESS OF CENTRALISATION Hunter et al have analysed costs and cost-effectiveness of the changes in London and Greater Manchester in 2010, using the current HES/ONS data share. The analysis has been written and submission is anticipated in December 2016. 2. FURTHER ANALYSIS OF IMPACT ON PATIENT MORTALITY AND LENGTH OF STAY Morris et al will lead a further analysis of how centralisations in London and Greater Manchester influence patient mortality and length of stay, using the rest of England as a control. In this case, the focus will be the sustainability of the changes in London, and the impact of further centralisation in Greater Manchester in 2015. Analysis should be complete for submission in June 2017, and the article will be submitted to the BMJ. It is anticipated that this article will generate similar interest and impact to that of Morris et al (2014), discussed above. 3. FURTHER ANALYSIS OF COSTS AND COST-EFFECTIVENESS OF CENTRALISATION Hunter et al will run a follow-up analysis of costs and cost-effectiveness of services, in terms of sustainability of the London centralisation and further changes implemented in Greater Manchester in 2015, using the rest of England as a control. Again, it is anticipated that this analysis will be completed for submission in June 2017. ACCESSIBLE SUMMARIES For all the academic papers published, The Stroke Research Team at UCL produce an accessible 1-page summary of the findings. These summaries present a clear outline of 1) what the Team knew; 2) what the Team found; and 3) what the findings mean. The Stroke Research Team at UCL share the summaries with stakeholders (>200 members) and encourage them to share with their wider networks. All summaries are uploaded to the study webpage, from which they may be freely downloaded. CONFERENCE PRESENTATIONS The research team has presented findings from this study at a wide range of national and international conferences. The aim is to present findings from the proposed share at the following academic conferences: 1. Health Services Research UK Symposium (July 2017) 2. UK Stroke Forum (November 2017) 3. Health Economists’ Study Group (HESG) conference (June 2017) PRESENTATIONS AND WORKSHOPS FOR KEY STAKEHOLDERS For each paper published, a short presentation is developed to summarise the findings for a range of stakeholders, including A) healthcare professionals and B) stroke patients and their carers. To share the lessons from the analyses described above, it is planned to develop similar presentations, which the Stroke Research Team at UCL aim to share at the following meetings: 1. UK Stroke Assembly 2017 (national conference for patients and carers, organised by the Stroke Association) 2. London Clinical Network stroke leaders’ meeting 3. Greater Manchester Stroke Network meeting 4. Kings’ Stroke Research Patients and Family Group 5. A number of local stroke patient and carer groups in Greater Manchester and London. ENGAGING WITH THE STEERING COMMITTEE The research team includes nationally and internationally respected clinical leaders in stroke. Effective sharing of lessons from this research is a key priority for the team. The Stroke Research Team engage actively at national and local levels, to ensure that findings are communicated accessibly to a wide range of stakeholders, including stroke patients, their carers and the wider public, stroke clinicians, hospitals and commissioning organisations, and national policy makers. The Study Steering Committee (SSC) includes people in leadership roles in NHS England Strategic Clinical Networks and Clinical Commissioning Groups covering London, Greater Manchester, and the Midlands and East of England; it also features representatives of charities, the Stroke Association and Different Strokes, and a number of service user representatives. The team present developing findings to the SSC regularly, and explore how best to ensure these findings are communicated effectively and meaningfully to key stakeholders. SSC members have been highly supportive in sharing findings across local networks. Initial findings will be presented from the new analyses with our SSC in March 2017, where the team will discuss methods, interpretation, and how best to disseminate these findings. WEBSITE AND SOCIAL MEDIA The study website provides links to our open access papers and offers free downloads of accessible summaries of findings. All publications and conference presentations are promoted on twitter, via the UCL Department of Applied Health Research account (>700 followers) and the NIHR Collaboration for Leadership in Applied Health Research and Care West North Thames account (>1000 followers).

Processing:

The aim is to use the data requested to update analysis of the impact of centralising acute stroke services in Greater Manchester and London on stroke patient mortality, length of hospital stay, and cost-effectiveness of services. CLINICAL OUTCOMES - LENGTH OF HOSPITAL STAY AND PATIENT MORTALITY The analysis will use a between-region difference-in-differences regression analysis: this will allow comparison of clinical outcomes in each studied region (Greater Manchester and London), before and after centralisation, using the rest of England as a control. This approach was used in a previously-published paper (Morris et al, BMJ 2014), which analysed the impact of the changes implemented in London and Greater Manchester in 2010 and established significantly different outcomes of the two centralisations. This approach will now be used to study A) the impact of further centralisation in Greater Manchester in March 2015, and B) the sustainability of the impact of the London centralisation. This requires patient-level data for the whole of England over several years as this allows control for differences in patient characteristics between areas and over time. This analysis will have two stages: Stage 1: To calculate expected risks of death at 3, 30 and 90 days after admission, and length of hospital stay, using patient level regressions, controlling for gender and age interactions (age measured in five year bands), stroke diagnosis using the first four digits of the primary ICD-10 diagnostic code (19 categories), Charlson index derived from secondary ICD-10 diagnostic codes, presence of 16 comorbidities included in the Charlson index, ethnic group, and deprivation quintile and urban/rural classification of the Lower Layer Super Output Area in which the patient lived. The regression coefficients will be used to predict the probability of mortality and the length of hospital stay for every patient. Stage 2: These expected values will be aggregated to create a dataset of the actual percentage of patients who died and the expected percentage, and also the actual and expected length of hospital stay, by admitting hospital and quarter. This will test whether the reconfigurations have an impact on mortality and length of hospital stay using least squares regression of the actual minus expected mortality percentage and actual minus expected length of hospital stay against interaction terms between Greater Manchester and the post-reconfiguration period and London and the post-reconfiguration period. This exactly replicates the approach used in previous analysis. To analyse the impact of the further changes in Greater Manchester implemented in 2015, replication of previous approaches will be used as described above, comparing outcomes before and after the latest reorganisation was implemented. To analyse the sustainability of the London changes, estimation of separate effects of the centralisation over time by including terms for the interaction between the intervention group and annual post-implementation periods will be analysed . The requested data covers the time period 2003/04 – 2015/16 so that a comparison of performance before and after changes took place can be conducted (permitting a year of post-centralisation data for Greater Manchester B, and over 5 years of post-implementation data for London). The research team has requested data covering the whole of England, including areas outside London and Greater Manchester. Using the rest of England as a control allows the team to replicate its previous analysis of HES/ONS data, published in the BMJ (Morris et al, 2014), and build on the influential lessons presented in that article. In addition, it means that any changes observed in Greater Manchester and London can be understood within the context of changes occurring at the national level and help ensure that any changes observed are attributed appropriately. To enable the research team to carry out the analysis objectively it is essential that data for the whole nation is used. This will enable the team to publish results based on several different variables rather than focus on a selection of geographical areas. To pick out certain areas would not be representative of the nation and therefore not comparable. Further, if a reduced number of areas were selected rather than using national data, the team would be unable to guarantee that the data selected are truly representative. This would A) weaken the analysis, and B) reduce the credibility of the findings (e.g. by prompting suggestions of ‘cherry-picking’). As noted, patient-level data has been requested in order to be able to control for patient level factors affecting outcomes. COST-EFFECTIVENESS The analysis will combine the outputs from the analyses of length of stay and patient mortality described above with procedure data to update a cost-effectiveness model built as part of an evaluation of the London Stroke Strategy. The analysis will update research previously undertaken to evaluate the cost-effectiveness of the changes in London and Greater Manchester , which will be submitted to a journal soon. The update will account for the sustainability of the changes in London and the introduction of the Manchester B model, as described above. STORAGE/SECURITY The HES/ONS data will be stored on UCL's Data Safe Haven (https://www.ucl.ac.uk/isd/itforslms/services/handling-sens-data/tech-soln). It is built using a walled garden approach, where the data are stored, processed and managed within the security of the system, avoiding the complexity of assured end point encryption. A file transfer mechanism enables information to be transferred into the walled garden simply and securely. The data will be accessed and analysed on secure, password-protected computers at two locations within UCL: 1-19 Torrington Place, and the UCL Medical School, based at the Royal Free Campus. This reflects the locations of the two health economists who will work on the econometric analysis and cost effectiveness analysis respectively. Both sites are compliant with all statements regarding data security within our application. All of the 4 approved users of the data are substantively employed by University College London and the data available will only be used for the purposes of this project.

Objectives:

This request forms part of an NIHR HS&DR funded evaluation that aims to study reconfiguration of acute stroke services in London and Greater Manchester. In doing so, it aims to identify lessons that will guide future reconfiguration work in stroke and other services. BACKGROUND Considerable changes in the provision of clinical care within the English National Health Service (NHS) have been discussed in recent years, with proposals to concentrate specialist services, such as major trauma, cardiac surgery, and specialist paediatrics, in fewer centres serving larger populations. The case for reconfiguring acute stroke services was strong, with clear evidence of unacceptable variations in quality of care and clinical outcomes, with many patients not receiving timely, evidence-based care, which may in turn influence patient outcomes. Major system change in acute stroke care was prompted by the National Stroke Strategy (2007), which noted the importance of stroke services offering rapid access to evidence-based care, and the potential benefits of reorganising acute stroke services. London and Greater Manchester led the way in this process, radically re-organising their stroke services in 2010. Before reconfiguration, in both areas, patients with suspected stroke were taken to the nearest hospital with an Accident and Emergency (A&E) service, then admitted to a specialist stroke unit or general medical ward. LONDON After reconfiguration in 2010, the following system was implemented, with 8 hyper-acute stroke units (HASUs) set up to provide rapid access to brain imaging, assessment by stroke specialists, and interventions including thrombolysis, 24 hours per day, seven days per week (24/7); 24 stroke units established to provide acute rehabilitation services; in addition, 5 NHS Trusts had all stroke services withdrawn. In London, all patients with suspected stroke are eligible for treatment in a HASU; once stable, they are transferred to a stroke unit, nursing home or their own home. GREATER MANCHESTER ‘A’ After reconfiguration in 2010, the following system was implemented: 3 HASUs (one operating 24/7, the other two operating from 7am to 7pm, Monday to Friday); and 10 NHS Trusts providing district stroke centre (DSC) services. Patients with suspected stroke arriving at hospital within four hours of developing symptoms were eligible for treatment in a HASU; once stable, were transferred to a DSC, nursing home or their own home. Patients with suspected stroke presenting outside the four-hour ‘window’ were taken to the nearest DSC, similar to the care pathway before reconfiguration. GREATER MANCHESTER ‘B’ In March 2015, a revised service model was implemented in Greater Manchester: all suspected stroke patients are now eligible for HASU treatment; the in-hours HASUs have been extended to cover 7am-11pm, 7 days per week; and DSCs are no longer designated to treat patients with suspected acute stroke. With these changes, the Greater Manchester acute stroke system is now similar to the care pathway in London. AIMS This request is in support of an analysis of the impact of major system change in acute stroke services on stroke patient mortality (at 3, 30, and 90 days after admission), patient length of stay and cost-effectiveness of services, focusing on centralisation of services in London and Greater Manchester, and using the rest of England as a control. The Stroke re-configuration team has already published an analysis of the impact of the changes implemented in London and Manchester in 2010. In this paper it was established that while both London and Greater Manchester centralisations were associated with significantly greater reductions in length of hospital stay than the rest of England, only the London centralisation was associated with significantly greater reductions in patient mortality than the rest of England (Morris et al, BMJ 2014). The research team now wish to conduct a follow up analysis, studying A) the sustainability of the impact of the London centralisation, and B) the impact of further centralisation in Greater Manchester. Since this is a follow-up analysis the team is requesting exactly the same variables requested previously, but now through to 2016.


Project 17 — DARS-NIC-17218-B0W9X

Opt outs honoured: Y

Sensitive: Sensitive

When: 2018/03 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report

Benefits:

The British Cohort Study 1970 (BCS70) is one of Britain’s world renowned national longitudinal birth cohort studies. It follows all those born in one week in 1970 through the course of their lives, charting the effects of experiences in early life on outcomes and achievements later on. They show how histories of health, wealth, education, family and employment are interwoven for individuals and vary between them. The study has its origins in the British Births Survey in which information was gathered about almost 17,500 babies. The original study focused on the circumstances and outcomes of birth but since then the study has broadened in scope to map all aspects of health, education, social and economic development. Forthcoming sweeps will provide an updated picture of the circumstances and experiences of those born in the early seventies in England, Scotland and Wales and will help develop an understanding of their progress into the latter period of their lives. The study is run by the Centre for Longitudinal Studies (CLS), at the UCL Institute of Education and funded by the Economic and Social Research Council (ESRC). Since 1970 information has been gathered from the cohort on nine previous occasions with the scope of enquiry broadened from a strictly medical focus at birth, to encompass physical and educational development at the age of seven, physical, educational and social development at the ages of eleven and sixteen, and then to include economic development and other wider factors at ages 23, 33, 42, 44, 46, 50 and 55. The age 46 study is currently in the field and is expected to end in the summer of 2018. Future sweeps of the study are planned to take place every five years. The data collected by the study is used extensively by researchers in the UK and elsewhere and has had much impact on policy over the years, for example the Welsh Government policy on early years planning - http://www.closer.ac.uk/news-opinion/2013/welsh-governments-early-years-childcare-plan-draws-evidence/ The continuing success of the study will be underpinned by the successful matching of untraced cases. The information collected during the Age 46 Survey will enable researchers to uncover life course and inter-generational factors which contribute to healthy ageing among this generation, and thus to inform the development of preventative health policies across the whole of life that will expand healthy life expectancy, and reduce the burden of ill-health and disease at older ages. Benefits of the list cleaning: Submitting the cohort for list cleaning will allow the researchers to recontact the participants who CLS have lost touch with and give them the opportunity to re-engage or clearly state that they wish to withdraw. It will also ensure that literature goes to the correct name and address. It will also ensure that no contact will be made with participants who have died.

Outputs:

The data file supplied from NHS Digital, as part of this list clean application, will be processed within CLS and entered into CLS’s secure confidential address database i.e. CLS will load more recent addresses into the database. Furthermore, any updated addresses will be used by NatCen to invite study members to take part in the current survey. All BCS70 study members contact information is held in this secure confidential address database at CLS. Any study members choosing not to take part in the study are flagged on this database with a code denoting whether their refusal is temporary (i.e. for a particular wave/survey) or permanent (i.e. they wish to have no further involvement in the study). Any previously deposited anonymised survey data for a study member and confidential data from the address database are retained unless the study member specifically asks us not to, in which case this data is securely deleted. These addresses obtained from NHS Digital will be used to invite study members (whom we have lost contact with are class as ‘UNTRACED’) to take part in the BCS70 Age 46 survey. However, this data received via this application is never sent or published to the UK Data Service. With regard to a request for 'withdrawal' from a participant CLS classifies them as a 'withdrawal from the current survey' or a 'withdrawal from the study' and these are handled slightly differently: • Withdrawal from the current survey: CLS will flag this on its computer system to indicate that the participant will not be taking part in the current survey and the reason for not wanting to take part is also recorded. For example, they may just not have the time to take part. Therefore there will be no further contact with the participant for the duration of the current survey but they will be invited to take part in the next survey. • Withdrawal from the study: CLS will flag this on its computer system as a permanent refusal to indicate that the participant will not be taking any further part in the study itself and the reason for this type of withdrawal is also recorded for analysis purposes. Therefore there will be no further contact with the participant for the remainder of the longitudinal study. If this request is received in writing then CLS will acknowledge the request and notify the participant that they have been flagged and will no longer be contacted or receive any further communications. This request may sometimes be accompanied by a request for the destruction of their data. Outputs for the List Clean: The main outcome from the BCS70 Age 46 survey will be a fully documented, anonymised research dataset and this will be archived with the UK Data Service in late 2019 to provide a strategically important resource for UK Social Science, inc. researchers in health and social care.

Processing:

ACTIVITY 1. NHS address tracing. CLS wish to use the patient status and tracking products which uses NHS registration data to trace as many of the 1370 supplied BCS70 study members as possible, either by finding new address details or verifying existing address details for the cohort. 1.1 CLS will supply NHS Digital with a file of 1370 study members to match to NHS data. The file supplied will only contain eligible study members who have participated in at least one wave of BCS70. It will not include study members known to have died or to have withdrawn from the study. The file will contain the following data items: - CLS identifier - First name - Last name - Middle name (where available), - Date of birth - Sex - Last known address, and postcode - NHS Number 1.2 CLS want all 1370 cases to be sent for auto-matching. 1.3 Once the auto-matching process is complete, CLS want any unsuccessful cases to be put through for operator matching. 1.4. NHS Digital would supply the following details to CLS: - CLS identifier - Latest surname - Latest forename - Latest middle name (where available), - Date of birth - Gender - Latest address and postcode - Fact of Death - Date of address registration or update - NHS Number. In addition to the receipt of any 'new' matched address information for the study members, CLS would like NHS Digital to add an additional variable that describes the outcome of the matching process to the data that is returned to CLS – that is, this additional variable will allocate each study member to one of the following three categories: • new/different address found, • existing address confirmed, • no match found.

Objectives:

The British Cohort Study 1970 (BCS) is one of Britain’s world renowned national longitudinal birth cohort studies. It follows a large sample of individuals born over a limited period of time (all those born in one week in 1970) through the course of their lives, charting the effects of events and circumstances in early life on outcomes and achievements later on. They show how histories of health, wealth, education, family and employment are interwoven for individuals and vary between them. The study is run by the Centre for Longitudinal Studies (CLS), at the Institute of Education, University of London and funded by the Economic and Social Research Council. Since 1970 there have been nine attempts to gather information from the whole cohort. Over time, the scope of enquiry has broadened from a medical focus at birth, to encompass physical and educational development at the age of five, physical, educational and social development at the ages of ten and sixteen, and then to include economic development and other wider factors at ages 26, 30, 34, 38 and 42. The current survey is at Age 46 and future sweeps surveys will take place roughly every 5 years. The ongoing success of the study depends on maintaining contact with as many study members as possible. The study has its origins in the British Births Survey in which information was gathered about almost 17,500 babies. The original study focused on the circumstances and outcomes of birth but since then the study has broadened in scope to map all aspects of health, education, social and economic development. The current survey will provide an updated picture of the circumstances and experiences of those born in the early seventies in England, Scotland and Wales and will help develop an understanding of their progress into the latter period of their lives. The current Age 46 survey began in July 2016 and is scheduled to run until July 2018. As of the beginning of February 2018, data has been collected from just over 6,000 participants and by completion it is projected that approximately 8,500 will have taken part. The Age 46 Survey has a particular focus on health and is being conducted by interviewers and registered nurses. The survey involves an interview, anthropometric measurements, blood pressure assessment, measures of physical functioning (grip strength and balance assessments) and the collection of blood samples (for immediate analysis of cholesterol and glycated haemoglobin, storage for future analysis and future DNA extraction). In addition, participants are asked to wear a device which measures physical activity levels for 7 days and to complete an online questionnaire about their diet. The objective measures of health have been funded by the Medical Research Council and the British Heart Foundation. For the BCS70 Age 46 survey, CLS have contracted an external supplier NatCen Social Research (the trading name of the National Centre for Social Research) to carry out the individual study members’ interviews’. The pseudonymised research data will be deposited at the UK Data Service. Aggregated data with small numbers suppressed will be made available to the research community in late 2019, forming an invaluable resource for health research. Researchers will be able to use the rich life-history data collected over the duration of the study’s life in conjunction with the data collected in the BCS70 Age 46 survey to examine of the longitudinal predictors of health in mid-life. It is then planned that the measures conducted will be repeated in future sweeps of the study, which will allow for research which deepens the understanding of changes in health which occur with ageing. No patient identifiable data is made available for research. Of the roughly 12,500 individuals invited to participate in the BCS70 Age 46 Survey there are approximately 1,370 where the address held by CLS has been found to be out of date. These are not individuals which have informed CLS that they wish to withdraw from the study, the researchers have simply lost touch with them as they have moved home and not informed CLS. This application for the MRIS list cleaning report is for the members of the cohort lost to follow up only, namely 1,370 of the cohort. The ongoing success of the study depends on maintaining contact with as large a number of study members as possible. Therefore, CLS are seeking permission to be supplied with updated addresses for these 1370 study members whose whereabouts are currently unknown. CLS feel that a substantial number of these individuals would be willing to participate in the Age 46 survey if they could be contacted. Previous efforts to re-establish contact for our BCS70 cohort study have been very successful using this route. CLS will supply NHS Digital with a data file containing the personal contact details currently held for these study members - CLS are specifically interested in receiving the 'address' that NHS Digital holds for them. The data returned by NHS Digital will be entered into the secure address database for BCS70 and used to invite these newly traced study members to take part in the BCS70 Age 46 survey. The on-going success of the study depends on maximising participation. The successful list clean exercise which took place in August/September 2015, prior to the launch of fieldwork, allowed CLS to invite around 600 previously untraced study members to take part. Therefore, a 2nd matching list clean exercise was planned i.e. this application (for 1370 cases), to try and find updated addresses for those who are found during fieldwork to have moved from the address held. This will further boost the number who can be contacted and invited to participate.


Project 18 — DARS-NIC-180665-GJMW5

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/06 — 2017/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off, Ongoing

Legal basis: Health and Social Care Act 2012, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Section 251 approval is in place for the flow of identifiable data

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • MRIS - Bespoke

Benefits:

With 2% of babies now born through ART every year, this research is important for those babies, their mothers and those parents considering ART, the clinicians that treat these patients and the healthcare system as a whole. There are still many questions over the best ART methods and the long term health outcomes for these children. This study will benefit patients and the healthcare system by providing robust analysis to help remove some of these uncertainties. This research has clear public benefits and its outcomes will significantly add to the currently very limited body of research in this area. The results will be used to provide information to ART stakeholder groups, including fertility experts, patients wishing to undergo ART, children born after ART and their families and public health workers. Benefits from this study will include robust risk estimates for children born after assisted conception in comparison to both control groups (spontaneously conceived siblings and spontaneously conceived unrelated children). This information is crucial for; i. counselling of families of children born after assisted conception ii. couples who wish to have assisted conception iii. informing practitioners of any increased health risks enabling early diagnosis iv. future health service planning for this population

Outputs:

Outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guidance. The expected outputs from this project include a number of scientific papers, detailing robust risk estimates for the outcomes under investigation (including hospitalization incidence, and incidence of specific diagnoses). It is expected these papers will be submitted by the end of 2018 beginning of 2019 and published shortly afterwards. It is expected that these papers will be submitted to broad medical peer-reviewed journals which may or may not be subscription only, however abstracts of this work will be open access. The main audience for these papers will be a scientific/ clinical audience, in order that clinicians disseminate results to their service users. Additionally the study will produce a report for the HFEA to publish open access on their website and disseminate via their networks (the fertility clinics, clinicians and directly to patients via these clinics and their website) aiming for June 2019 or 2 years after receiving data from NHS- Digital. It is aimed to also submit abstracts to the Royal College of Paediatrics and Child Health to further publicise results. It is not possible to say exactly which journal will be the appropriate one for submission of these reports as this depends to some extent to the results the study will find. However, it is expected that these to be high quality journals. For example, work previously done linking this dataset to national cancer registries was published by the New England Journal of Medicine which has the highest impact factor of any medical journal (impact factor 59.6). (http://www.nejm.org/doi/full/10.1056/NEJMoa1301675#t=article) Findings will also be presented at the European Society of Human Reproduction and Embryology conference.

Processing:

Data processing for this project has been designed to ensure that identifiable data are seen by the fewest number of people at secure locations in secure methods as possible. A dataflow diagram has been supplied, but below is a short summary of the data flow and processes; 1. NHSD produces an extract of women who were treated with ART (produced from MR1208) 2. NHSD sends extract containing mothers details to ONS to match to births 3. ONS matches mothers to all births (ART children and non-ART siblings) and returns matched births to NHSD 4. HFEA sends NHSD HFEA births (containing unique ID number) 5. NHSD matches HFEA births (to ONS births and creates ART Cohort) 6. NHSD sends the ART Cohort to ONS who return two controls for each member, creating the Control Cohort. 7. NHSD links all remaining ONS births that match to HFEA mothers and creates Sibling Cohort. 8. NHSD sends member numbers of all unmatched HFEA births to UCL. 9. NHD links ART, Sibling and Control Cohorts to ONS mortality and HES data and removes identifiers 10. NHSD supplies de-identified data to UCL along with a deprivation score and unique study ID (this study ID cannot be used by UCL to re-identify) Numbers 1 to five have been completed under the previous agreement. NHS Digital will produce de-identified outcomes for all the cohorts and securely send to UCL. UCL will then match this data to de-identified fertility treatment data (provided to UCL by HFEA) using the HFEA unique ID. The final de-identified data-set will then be held encrypted and securely at UCL using UCL's data safe haven. This storage has IG approval from NHS-Digital via IG toolkit Only individuals, working under appropriate supervision on behalf of data controller / processor within this agreement, who are subject to the same policies, procedures and sanctions as substantive employees will have access to the data and only for the purposes described in this document. ONS data will be processed in accordance to the standard Office for National Statistics terms and conditions. UCL have no requirement and will not attempt to re-identify the data. UCL will not share the data with any third parties.

Objectives:

UCL (Great Ormond Street Institute of Child Health) wish to establish if children born after assisted conception (including IVF and related techniques) are at an increased risk of specific diagnoses compared to spontaneously conceived siblings and unrelated spontaneously conceived controls. The diagnoses which are to be investigated include: a) Complications of Prematurity (such as respiratory distress syndrome, necrotizing enterocolitis, retinopathy of prematurity, intra-ventricular haemorrahges, per-ventricular leucomalacia) b) Cerebral palsy c) Congenital malformations d) Asthma and allergic disease e) Developmental delay f) Death g) Cancer h) Hospitalization rates and length of stay These comparisons will help to provide robust risk estimates for this ever growing population. This is important as all of these potential risks have been suggested by previous research but have never been confirmed, as previous studies lacked the necessary power and design to do so. UCL is not requesting identifiable data from NHS Digital and will not pass any data on to third parties, including the HFEA. Fertility clinics have been engaged in a campaign to inform patients of how their data may be used for research and how they could opt out. Families connected to ART have been consulted on what they would like research to achieve. These service user views are incorporated in UCL's study design.


Project 19 — DARS-NIC-18646-P0R3M

Opt outs honoured: N

Sensitive: Sensitive, and Non Sensitive

When: 2017/03 — 2017/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off, Ongoing

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

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Outpatients
  • MRIS - Cohort Event Notification Report
  • MRIS - Cause of Death Report

Benefits:

Both CROMIS-2 non vitamin k oral anticoagulant (NOAC) ICH and non NOAC ICH should expect to add a great deal to the understanding, prevention and management of ICH. Published papers: 1) This paper outlined how NOAC intracerebral haemorrhage volumes were smaller and clinical outcomes better when compared with warfarin intracerebral haemorrhage in the CROMIS-2 data set. This may lead clinicians to have more confidence in prescribing non vitamin K oral anticoagulants as there has been previous cncern that ICH on these medications would be large and devastating. 2) This paper outlines current vitamin K reversal stratergies in ICH and their correlation with clinical outcome. We were able to show the combination of fresh frozen plasma and prothrombin complex concentrate might be associated with the lowest case fatality in reversal of vitamin k antagonist associated intracerebral haemorrhage (VKA-ICH) (VKA=warfarin in most cases), and fresh frozen plasma (FFP) (used to reverse anticoagulation) may be equivalent to prothrombin complex concentrate (PCC) (Used to reverse vitamin k antagonists (i.e. warfarin)). This helps clinicians make treatment decisions in patients with intracerebral haemorrhage. Conference talks and published abstracts: 1) Missed opportunities to prevent stroke in patients with AF. European stroke organisation conference 2016. European stroke journal This talk highlighted that only 1/3 of patients with AF and fulfilling guidelines for anticoagulation were anticoagulated. This should highlight to clinicians and GPs that patients with AF should be on anticoagulation to prevent ischaemic strokes. Publications pending: 1) Results of CROMIS 2. Yet to be written/submitted It is difficult to hypothesis what the benefits will be of this paper as we do not know the results. Either way it will be the largest dataset of patients with ischaemic stroke, AF and anticoagulatants with rating of cerebral microbleeds. It should inform clinicians what the risk of cerebral microbleeds are with these patients. This has not been written or submitted at this stage. Through the papers listed above UCL would hope to be able to improve risk prediction for patients with ischaemic stroke and CMBs as well as for patients with ICH who have concurrent AF. Dissemination will be primarily through peer reviewed journals and presentations at major stroke conferences (International stroke conference, European stroke organisation conference and UKSF). This study will help guide clinicians an policy as to when it is safe to provide anticoagulants to patients who have had ischaemic stroked with atrial fibrillation. The study results are also being pooled with an international collaboration, involving 15 other studies around the world that have been collecting data on the same sample of patients. This will further inform policy and benefit clinicians. The stroke association have a conference every year which UCL are part of, with a stand and also with talks and presentations. The stroke association are supportive of UCL’s research.

Outputs:

All outputs will be aggregated with small numbers suppressed in line with the HES analysis guidance. Outcome measures are reoccurring stroke (by type), cardiac events, bleeding events and death. The study is funded until July 2017 and findings will be available early 2018. The data from the study will be anonymised fully, and be published in research journals, and the outputs will be accessible to clinicians, academics and the public, as well as charities such as the Stroke Association and the British Heart Foundation. The data will be beneficial to the health and social care system by enabling clinicians and academics to make informed decisions about anticoagulation for patients who have had an ischaemic stroke due to Atrial Fibrillation. The study findings from CROMIS 2 study I (AF) will be available at the end of the study in 2017 (primary output). The study findings will be disseminated via a research article which will be submitted to a high impact peer reviewed journal (e.g. Lancet Neurology) and aims to help guide clinicians with anticoagulant decisions in patients with cerebral microbleeds. In the interim, baseline data and other sub studies will be published. UCL are presenting two papers at the UK Stroke Forum (2016), highlighting findings from the baseline data. Both presentations are published as abstracts in the International Journal of Stroke. These abstracts will be made into papers in due course and submitted to high impact peer review journals who will review and decide whether to disseminate based on merit. The first talk outlines how common microbleeds are in this cohort and the second talk highlights the missed opportunities in preventing stroke secondary to atrial fibrillation in the UK. CROMIS study II (ICH) has produced a paper currently in press on a sub study, showing the clinical and radiological characteristics as well as outcomes of warfarin intracerebral haemorrhage vs. direct thrombin inhibitor intracerebral haemorrhage. This paper has been accepted by Neurology, a high impact clinical journal. It will help guide clinicians in their choice of anticoagulant for stroke prevention in AF. This sub study has lead to an international collaboration of 12 centres worldwide looking at the same question. CROMIS-2 has the most patients within this collaboration. CROMIS- 2 (ICH) also contributed to another international collaboration looking at outcomes for different treatment regimens in ICH. Again this was published in a high quality peer reviewed journal and helps clinicians in their management of ICH (Annals of Neurology. Ann Neurol. 2015 Jul;78(1):54-62. doi: 10.1002/ana.24416. Epub 2015 May 14. PMID:2585722). Published papers: 1) Volume and functional outcome of intracerebral hemorrhage according to oral anticoagulant type Wilson D, Charidimou A, Shakeshaft C, Ambler G, White M, Cohen H, Yousry T, Al-Shahi Salman R, Lip GY, Brown MM, Jäger HR, Werring DJ; CROMIS-2 collaborators. Neurology. 2016 Jan 26;86(4):360-6. doi: 10.1212/WNL.0000000000002310. Epub 2015 Dec 30.PMID: 26718576. 2) Reversal strategies for vitamin K antagonists in acute intracerebral hemorrhage. Parry-Jones AR, Di Napoli M, Goldstein JN, Schreuder FH, Tetri S, Tatlisumak T, Yan B, van Nieuwenhuizen KM, Dequatre-Ponchelle N, Lee-Archer M, Horstmann S, Wilson D, Pomero F, Masotti L, Lerpiniere C, Godoy DA, Cohen AS, Houben R, Al-Shahi Salman R, Pennati P, Fenoglio L, Werring D, Veltkamp R, Wood E, Dewey HM, Cordonnier C, Klijn CJ, Meligeni F, Davis SM, Huhtakangas J, Staals J, Rosand J, Meretoja A.Ann Neurol. 2015 Jul;78(1):54-62. doi: 10.1002/ana.24416. Epub 2015 May 14.PMID:25857223 In addition, UCL are investigating genotypes of haptoglobin in ICH among CROMIS-2 patients and risk prediction scores in ICH patients from CROMIS-2. UCL hope to publish these papers in 2016. The HES data is vital for the primary outcome of the study, which is reoccurring stroke/TIA; bleeding events; cardiac events and death. Without this information UCL cannot be sure if they may have missed many primary outcomes which are absolutely essential to the reliability of the data and its outputs.

Processing:

UCL originally supplied the flowing identifiable details of the cohort to NHS Digital to flag on its IT system; Member number, NHS number, surname, forename, date of birth, sex, postcode, address, date of address. NHS Digital provides quarterly updates on participants’ deaths or changes in NHS registration status. The cohort identifiers NHS Digital holds will be used to link to HES data and the linked data will be supplied back to UCL. The supplied data files will be downloaded by the Study Co-ordinator only. This will allow the patient database (accessed only by the Study Co-ordinator) to be updated with any primary and secondary outcomes that have occurred whilst the patient is being follow up and that UCL have not already been informed about via our follow up methods (GP and patient questionnaires). The identifiable data will be removed from the clinical data. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guidance. The data will only be stored on a secure database at UCL at one location only. Standard ONS Terms and conditions will be adhered to in regards to the data being processed. The record level data will be accessed only by the study coordinator at UCL they are a substantive employee of UCL.

Objectives:

The Clinical Relevance of Microbleeds in Stroke (CROMIS-2) study is an observational inception cohort study of patients throughout the UK (80 hospitals) started on best practice oral anticoagulant (without prior use) for presumed cardioembolic ischaemic stroke due to non-valvular AF with follow up for the occurrence of ICH, ischaemic stroke and cognitive function for two years. Over the last decade, increasing use of oral anticoagulants to prevent cardioembolic ischaemic stroke due to atrial fibrillation (AF) in an ageing population has led to a five-fold increase in the incidence of anticoagulant-related intracranial haemorrhage (ICH) - a rare but unpredictable and catastrophic complication. Cerebral microbleeds (CMBs) on magnetic resonance imaging (MRI) may predict ICH risk, as may genetic polymorphisms influencing brain small-vessel integrity or anticoagulation stability. The CROMIS-2 study aims to establish the value of CMBs and genetic factors in predicting symptomatic ICH following best practice oral anticoagulation to prevent recurrent ischaemic stroke due to AF. The data provided by NHS Digital about patients recruited to the CROMIS-2 study will inform UCL when an outcome event occurs during their 24 month follow up period. This supports the objectives of the study and will allow reporting on the whole study population, and helps ensure families are not approached after a relative’s death. The study has also recruited patients admitted to participating centres with intracerebral haemorrhage -ICH and DNA is collected to increase the power of the genetic studies. Clinical and imaging data is collected from these ICH cases to investigate risk factors associated with anticoagulant-related ICH compared to non anticoagulant-related ICH. The CROMIS-2 primary research question asks: (1) whether the presence of CMBs helps predict the risk of symptomatic oral anticoagulant-related ICH in patients who are anticoagulated following cardioembolic stroke due to non-valvular AF? (2) Do the burden (number) and distribution of CMBs at baseline influence the risk of ICH in this cohort? Secondary Questions (3) In patients anticoagulated after ischaemic stroke due to non-valvular AF, are CMBs associated with an increased risk of recurrent TIA, ischaemic stroke or death? (4) Are genetic polymorphisms related to the integrity of brain small vessels or anticoagulant metabolism associated with an increased risk of ICH? (5) Are CMBs a better predictor of oral anticoagulant-related ICH than clinical risk factors and/or leukoaraiosis on MRI scans? (6) Can a useful risk prediction model incorporating clinical, imaging and genetic factors be developed to assess the risk of best practice oral anticoagulant-related ICH? (7) Can UCL identify new genetic, clinical or radiological risk factors associated with anticoagulant-related ICH? The data will not be used for commercial purposes, and will not be provided in record level to any third party, or used for direct marketing in any way. The study tracks patients using NHS Digital's patient tracking service, and also by contacting the patients and their GPs at 6, 12 and 24 months to obtain follow up for our outcome measures. The purpose for receiving HES data is to ensure all hospital episodes of the cohort are captured if missed via the other methods of follow up, to ensure a high completion of data and success to the study, which aims to benefit directly the health and social care system by allowing better management of patients following stroke. Patients were recruited from August 2011 until July 2015, as an extension was granted by the funders to increase our sample size. UCL are now in follow up phase. Follow up will end July 2017.


Project 20 — DARS-NIC-27803-W8G1B

Opt outs honoured: Y

Sensitive: Non Sensitive

When: 2017/12 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care

Benefits:

Very little is known about the long term outcomes of patients who have been through critical care. Following their recovery, do they have on-going specialist health needs? Despite surviving a critical illness, are they more likely to die earlier than before those who have not? Current data is conflicting and there is no national strategy to follow these patients up. The nature of the combining databases would allow examining the impact that individual, or combinations, of organ failures and severity of illness would have on chronic health. The benefits of this exemplar include: · Allowing clinicians to make accurate predictions on the course of an illness and the long term outcome · Allow patients and next of kin to be informed of the condition, empowering them to make choices regarding their treatment with a level of confidence currently unobtainable · To predict future health care needs and resources · Education of both patients and healthcare professionals on the sequelae of critical care · Identify longer term research end points that may be more informative. Cancer treatments often look at 1 and 5 year survivals to measure success or failure of treatment. Critical Care research often looks at day 28 mortality (an FDA requirement for drug trials) or other short term markers, rarely are patients followed up for more than a year that may be relevant for researchers such as 5 year survival. · The ability to look at regional differences in care and outcome and endpoints etc. may impact on running the study. Other benefits would include: · Studying the effect of time on the control group. Clinical trials often run over many years, during this time the introduction of other interventions or processes of care may impact on the trials endpoints, this database could be used to examine this. Providing this data is accurate and up to date, it could conceivably act as a ‘virtual’ control group and allow trials to adapt to secular changes · Better information would allow more accurate recruitment targets, whilst the database can examine the impact of how recruitment rates may change by: o altering entry criteria o altering the time permitted to enrol patients · Using this database, researchers could look accurately at a variety of different primary or secondary outcomes and endpoints for the trial, enabling each point to be accurately powered. · Health Service Researchers can use this data to monitor impacts on longer term health care utilisation and long term survival of research interventions · The potential for an alerting system, such that a recently admitted patient meeting specified criteria could be flagged to the research team. This could give patients the opportunity to take part in clinical trials despite being in a hospital remote to where the research team are based. · Mapping morbidity relative to disease intervention strategies, thus improving our understanding of disease projection As an IT resource, rather than a specific research project, UCL expect the outputs and benefits to be broad and ongoing. However, it is envisaged that the benefits will come from several angles. Little is known about the long term survival or outcome of patients who have survived a critical illness. Most previous research follows their acute admission but rarely look beyond hospital discharge. The linking of the proposed databases will bring benefit to several areas that are directly relevant to UK patients. 1) The patient. Long term outcomes and healthcare utilisation is vital if patients, or their proxy, are to make informed decisions over their care and have the appropriate expectations. The only available data is obtained from clinical trials which are not reflective of the general critical care population, often US based and so not very relevant to the UK. As this is about tracking longer term outcomes its value will grow over years. Examples would include: a. Are those who have survived an episode of severe respiratory failure more likely to be admitted back to hospital with respiratory failure in the future? b. Are those who require haemodialysis for an episode of acute kidney injury more likely to develop chronic kidney disease? c. There is an argument that the intense and prolonged inflammation may predispose to cancer and coronary artery disease in the future. Linkage to HES (and disease registries) will help address this argument. It is recognised that this work will take many years to complete. With all outcome research it is important to try and identify and potentially modify any risk factors? 2) Clinicians and researchers will be able to objectively evaluate the impact of interventions or process change on the longer term health of the critical care survivors. They will also be able to look at potential predictors of outcome enabling them to keep the patient or next of kin fully informed. This will also allow the beginnings of health economic studies to also evaluate these interventions, be it a new drug or a new process 3) Commissioning groups. Knowing these outcomes will enable appropriate planning and anticipation of future healthcare needs. 4) Clinical trialists. All drug trials have failed to produce any reproducible benefit in critical care. The reasons behind this are multi-factorial, but a poor understanding of the patient population is certainly a component. A highly comprehensive database would enable researchers to examine the impact of altering their trial entry criteria and look at different longer term endpoints. This would enable the trial to be appropriately powered and recruitment rates more accurately predicted. This database will enable: a) Appropriate powering of studies by examining the properties of the control subjects and how this may change as inclusion/exclusion criteria are altered. This is important, as the nature of the control group appears highly variable between different trials (mortality rate ranging from 15 to 40% within the same therapeutic area). This database will enable trialists to investigate how altering inclusion/exclusion criteria may alter the outcome of their control group and allow appropriate powering of their study. b) Predict more accurate recruitment rates. The majority of large NIHR sponsored critical care trials have failed to recruit to target; many believe the targets to be unrealistically set. This database will enable accurate modelling of trial recruitment. c) Critical Care research, unlike cancer research, usually focuses on short term goals such as 28 day mortality or hospital length of stay. This database would enable researchers to examine the impact of interventions on long term outcomes (mortality and health care utilisation). It could be envisaged that this data will be of value (but not exclusively) to: • Allow clinicians to make accurate predictions on the course of an illness and the long term outcome. • Allow patients and next of kin to be informed of the condition, empowering them to make choices regarding their treatment with a level of confidence currently unobtainable. • Predict future health care needs and resources following critical care discharge. • Educate both patients and healthcare professionals on the sequelae of critical care. • Identify longer term research end points that may be more informative. Cancer treatments often look at 1 and 5 year survivals to measure success or failure of treatment. Critical Care research often looks at day 28 mortality (an FDA requirement for drug trials) or other short term markers, rarely are patients followed up for more than a year that may be relevant for researchers such as 5 year survival. • The ability to look at regional differences in care and outcome. • Act as a ‘virtual’ control group and allow trials to adapt to secular changes, examine different end-points and inclusion criteria and more accurate recruitment targets. • Health Service Researchers can use this data to monitor the impact of new changes or interventions longer term health care utilization and long term survival of research interventions. The nature of the combining databases would allow examining the impact that individual, or combinations, of organ failures and severity of illness would have on chronic health. Data extracted from HES will be linked with clinical data obtained from their critical care admission to track long term mortality and hospital re-admissions.

Outputs:

As the CCHIC database and the HES linkage should be seen as a core resource for researchers around the UK. The outputs are expected to be broad. However there are specific projects that would use the HES linked data and these include: Research A well designed, adaptable and accessible database could help researchers, including the pharmaceutical and medical device industry, to design more accurate, predictable and efficient trials that are relevant to the NHS. This would continue to make the UK an attractive place to run these vital pieces of research. This database will include the clinical, pathological, outcome and demographic data that would allow researchers to construct ‘virtual trials’ examining how altering inclusion/exclusion criteria and endpoints etc. may impact on running the study. The linkage to longer term outcome databases (e.g. HES) will enable more relevant (and patient centred) endpoints to be examined as well as the impact on healthcare resources and the wider health economics. We are using this approach to help identify a patient cohort that may respond to a new treatment for pneumonia. Initial output expected end of 2017, however, longer term outcomes following HES linkage will also be examined and initial output expected end of 2018. A novel project is also underway to map the new sepsis criteria onto the CCHIC cohort. These results will be submitted for publication. Longer term outcomes of this cohort are unknown, HES linkage will allow us to examine this highly relevant aspect. However, as data is collected prospectively it will take a minimum of a year before any publication becomes relevant. Audit and Quality Improvement The data base can be used to audit the performance of individual units in complying with national and international guidelines. For example, we will use the data to examine the ability of ICUs to ventilate their patients within the recognized safe limits. Ventilating above these limits is associated with a poor outcome. There is a complexity in this data that means achieving this locally without specialist data analysts will be difficult, it also enables variation between ICUs to be studied. It is envisaged that these reports could be automated for participating units. Currently data quality reports are now automated (and returned to the Trusts) as the first step. An initial review of this data (excluding HES) will aim to be submitted for publication by the end of 2017. Linkage to HES will track these outcomes into the future this is important to again analyse whether any differences are sustained Patient safety: Novel research modelling to identify potential complex signals preceding a clinical deterioration, this could potentially warn clinical staff of impending problems before they become clinically apparent. This approach will then be used to model and predict the longer term outcomes and problems that the HES data will be used for. CCHIC is being created as a resource for researchers to use. Although the CCHIC team will produce some technical papers around the utility of the database, it is hoped that the majority of the outputs will come for researchers who can use the data. As with all research UCL would expect the output of the research to be disseminated in the appropriate academic journals and meetings. CCHIC aims to be completely open and transparent. All data releases will be logged on a public facing website. Any coding associated with the database development is freely available on a GitHub repository (no data) and the associated NIHR website is being updated (http://www.hic.nihr.ac.uk/nihr-hic-themes). Any publications stemming from CCHIC will be required to acknowledge the database. UCL are already presenting the concepts and utility in Critical Care Conferences such as the Intensive Care Society State of the Art meeting and the UK Critical Care Forum. UCL have held ‘datathons’ where jittered and anonymised data can be examined by interested researchers to examine the utility. UCL aim to submit the first paper to a peer reviewed speciality journal such as Critical Care.

Processing:

This project aims to collect a rich clinical data set during a patient’s admission to a participating critical care unit. This data set (containing identifiable data for linkage) would be encrypted and transferred to the University College London Identifiable Data Handling Solution where it will be pseudonymised on landing and the identifiers split from the clinical data. At regular intervals a data extract from HES will be requested so as to link with the clinical data, thus providing the long term outcomes. The dataset will be created from merging routine electronic data extracted from several existing databases. These databases are: 1. Critical Care patient information system, ICIP (IntelliVue Clinical Information Portfolio, Phillips). This database integrates data from the hospital Patient Administration System, pathology database, patient monitors and point of care devices. It is also capable of extracting 2. The ICNARC (Intensive Care National Audit and Research Council) pre submission database: Data is routinely collected by NHS Trusts and submitted to ICNARC in order to bench mark and compare performances of critical care units around the country. This database is partly populated by ICIP and through manual entry by a data manager 3. Hospital Episode Statistics. Held by the NHS Digital, this database contains information of hospital admissions Data flow: - • Identifiable patient data extracted from NHS Trust clinical systems • Encrypted and transferred to UCL Safe Haven (legal basis covered by s251) • Pseudonymised on landing • Identifiers split from clinical data • Identifiers sent to NHS Digital to extract requested HES data (Study ID, NHS number, date of birth, sex and postcode) • HES data linked in UCL Identifiable Data Handling Solution to the clinical data – identifiers removed • Researcher requests access to data (with appropriate approvals, including information governance concerns addressed, signed data sharing agreement) • Anonymous, crippled, example dataset of fields requested released to researcher. All data has been randomly adjusted, to give an appearance of what the data will look like • Researcher works in their own time, with their own tools using a cloned analysis engine (R package or virtual machine) • Analysis script posted back to safe haven to run on live database • Analysis results (summary data) returned to researcher • Primary (patient level) and identifiable data never leaves the Identifiable Data Handling Solution Data will automatically be extracted to a SQL database within each Trust’s firewall. Integration engine ensure the data is compatible (defined by the dataset) between trusts. Data will then be moved to a SQL database within the University College London IDHS (Identifiable Data Handling Solution) Safe Haven using a secure, encrypted point to point protocol (AS256). The Safe Haven meets the Information Governance standards required to hold sensitive and identifiable NHS data. It is envisaged that data will be transferred to the UCL Safe Haven every 24 hours. At the point of entry the Research Data Indexing Service within the Safe Haven pseudonymises the data and separates the clinical from the identifier demographics. All personnel involved with the handling of sensitive and identifiable data will have been trained in information governance. For those involved with handling the data within the participating trusts the training will be provided by the Trusts internal programme. All personnel will comply with local Trust guidelines. Those involved with handling data within the UCL safe haven will have undergone information governance training through the Information Services Division of UCL, it will be a requirement that they comply with all the UCL regulations. The research team currently consist of investigators from each site involved. Each investigator has a track record in high quality, academic critical care research. The IT support at University College London also has a highly successful research track record and the infrastructure required to support this project. All individuals with access to the record level HES data are substantive employees of UCL. The Critical Care HIC management team will review all requests to access data. The management team will consist of a representative from each of the five participating NHS Trusts and a member (independent of the investigators) who is skilled in Information Governance and a lay member. This review will look at the both the validity of the research and can the database provide the data required to answer the question and the Information Governance to ensure the requested data subset cannot lead to any patients being identified. The roll of the Management Committee is to evaluate applications from researchers to access data. The application will be assessed for research validity (by BRC members), information governance and chance of re-identification risk (IG expert) and in the public interest (lay member). Applications will need to show: 1. Submission from bona fide research institutions 2. The data is being acquired solely for valid research, that is non-commercial and intended for patient benefit. It is understood that some of these applications may be from the Bioscience Industry or researchers outside science field. 3. Researchers demonstrate understanding confidentiality and information governance and to abide by the UCL terms and conditions 4. Sign a data sharing agreement that states they will not to try to re-identify the data, will not share the data and will destroy the data within a specified period of time. This data sharing agreement will align with all the data sharing requirements from the HSCIC 5. Have appropriate Research Ethics approval Any request to the data will need to be from a researcher from a research institute. The research question will need the appropriate HRA ethical approval. The request will need to demonstrate that it is scientifically robust, for healthcare purposes and for public benefit. Providing this is satisfied a ‘dummy’ data set will be released to the researcher. This data set will look very similar to the real data but will not contain any identifiers and the data will be ‘jittered’ (randomly altered). This ‘dummy’ data set will allow the researcher to develop their analysis script, once developed this script will be returned to the UCL Identifiable Data Handling Solution (IDHS) where the IDHS staff will run the script on the live data base. Only aggregate data with small numbers suppressed in line with the HES analysis guide will be released to the researcher. All researchers will be required to sign an end user license agreement that is modelled on that used by the UK Data service (see https://www.ukdataservice.ac.uk). The Critical Care HIC Management Team will examine all data access requests. Requests that have the potential for identifying individuals (e.g. narrow date ranges, extremes of age) will be rejected. All data requests will be analysed by the Critical Care HIC management team so as to ensure that small data subsets cannot identify individuals. UCL will examine data requests classifying data into 4 categories: Direct Identifiers (always removed), Key Variables, Sensitive Fields, Non-identifying variables. The Key Variables and Sensitive Fields could potentially become identifiers in small numbers, UCL employ the concepts of k-anonymity and l-diversity to address this issue. This methodology ensures that data does not contain numbers lower than ten thus minimising the risk of re-identification. Researchers will only have access to non-identifiable data as described above. Researchers will come from a range of research institutions: • Universities • NHS Institutions • Charitable Organisations • Bioscience industries It is likely interest in data will be from the whole population rather than a specific Trust. However it could be expected that an analysis of geographic variation in practice and outcome would be performed. Currently all Trusts who submit data get an automated data quality report. It is an aim that this data is automatically audited against recognised practice and the results fed back directly (this component of work is underway) The pharmaceutical, device and diagnostics industry may apply for access to the data as outlined above. UCL believe, that the industry are absolutely vital to progress in this field. The IP developed from the use of the data will remain with the researchers. The access cannot be provided for free as there are significant ongoing costs to hosting and maintaining CCHIC, we would expect such companies to cover these costs. Importantly CCHIC is under the control of the 5 BRCs and is not for profit. All revenue will be re-invested into the project. UCL are only permitted to approve projects to make use of the data for research where there is a clear benefit to the provision of healthcare or the promotion of health.

Objectives:

The Critical Care Health Informatics Collaborative (CCHIC) database is an informatics resource for health researchers. There is currently very little knowledge about the long term outcomes of patients who have been through critical care. Current data is conflicting and there is no national strategy to follow these patients up. Up to this point most outcomes research looks at a single cohort of patients over a defined (and relatively short) period of time. This project aims to create an information technology capability, enabling researchers to investigate the course and outcome of the critically ill. The data, which will be rich both longitudinally and in depth, will enable researchers to answer questions that have been impossible. The study aims to automatically collect and store routine clinical, demographic and long term outcome data of all patients admitted to participating critical care units. The database will be of interest to Health Services Researchers and Clinical Trial Researchers amongst others. Linkage of HES and critical care data will enable longer term outcomes for critical care survivors to be tracked. The broad objectives for this linkage are set out below: 1) Examining long term clinical outcomes. Do survivors of critical illness have significantly reduced life span or increased resource utilisation, compared to the healthy or general hospital population? Surprisingly this is not currently known but this knowledge would allow clinicians and patients alike to make informed care decisions. Equally, ongoing healthcare needs could be predicted and resources appropriately allocated. 2) Looking at predictors of long term outcome. Researchers have previously found a number of variables that may predict patient outcome however, the datasets are frequently limited by the inability to continually re-analyse or examine trends over time. This constantly updating database will enable researchers to analyse how multiple predictive variables interact with each other and over time. The numbers of patients within the database will grow rapidly (The 5 participating NHS trusts admit approximately 10,000 critically ill patients a year) allowing researchers to investigate these predictors in a large population of UK patients over a pro-longed period. 3) The impact of secular or process changes could be measured. The outcome of many Critical Care interventions, whether they are part of research, process change or just over time, are measured in the short term. Updating databases, such as CCHIC, that track longitudinally have the ability to either look for an immediate stepwise change, following implementation (e.g. High Impact Interventions in the Saving Lives campaign) and/or measure that impact over a longer period of time (following HES linkage). The latter is important as the perceived impact is often in reality attributable to secular change, as general levels of care improve rather than a specific intervention This approach ultimately allows for the use of registry trials of interventions, be it a new process of care, mode of ventilation or the introduction of a new drug. These trials are very efficient (can be run quickly with minimal cost) but until now this approach has been problematic in Critical Care. 4) Clinical Trials: Over the years, approximately 70 drug trials have been performed within critical care, not one has demonstrated a reproducible benefit to the patient. Our understanding of disease has taken huge leaps forward but we seem unable to translate that into patient benefit. The reason for this is multifactorial; however trial design has often been called into question. Outcomes and patient selection is based on small population research and may well not be relevant to the population being studied. Large, all inclusive datasets will allow trialists to examine the course and outcome of their target population and question whether they have chosen the appropriate end points. These end points are often short term survival (usually 28 days, an FDA requirement) which are not highly relevant to the patient. Longer term outcomes could be tracked with HES linkage which would give a far more meaningful outcome thus CCHIC will help design more efficient trials.


Project 21 — DARS-NIC-28051-Q3K7L

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/03 — 2017/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Benefits:

The outcome of this research is envisioned to support local Clinical Commissioning Groups (CCGs), local authorities and other public partners including third sector organisations in determining their budgeting and commissioning priorities, in which a long-term view and strategic need orientation is crucial. CCGs are expected to lead Joint Strategic Health Needs Assessments (JSNAs) and Joint Health and Well-being Strategies (JHWBs), and there is a need to find appropriate methods and data sources to do this. By investigating and contextualising specific health needs by different group of patients and areas, UCL intend to support these strategic players in assessing health needs as part of JSNAs and developing JHWBs by taking a novel long-term, geographic and temporal view of local health needs and preventive health care. At the strategic level, these will be the primary beneficiaries through the provision of a detailed, transparent and robust small area profiling. At an operational level, care providers (such as GP practices or hospital trusts) can use the classification in delivering personalised patient care, on which there is currently an important emphasis (see e.g. latest report of London Health Commission, 2014. ‘Better health for London’), including treatments and targeted health screening initiatives. The classification including metadata and policy recommendations will be available for download at the aforementioned website and is thus likely to reach a wide audience. In the long-run, a common evidence base will enable various players of the health care system to deliver a higher level of joint-up care. So far, analysis of the data has demonstrated the relevance of geographical variations in health needs and is being prepared for publication and sharing with CCG Ealing to link the findings to practice in North West London. It is intended to further and refine the investigation (with the inclusion of ethnicity) to deliver benefits using the existing formal connection to CCG of Ealing (which is their representation on the advisory board of the UCL research group of which the applicants are part). The data will be made available for download when classifications can be refined, i.e. by the end of the project at the latest, if not earlier for sufficiently robust, intermediate findings. The target date for this research is December 2017 In summary, there are three main areas in which this study is intended to contribute to improving patient care. 1, At the strategic level, UCL seek to support Clinical Commissioning Groups (CCGs), local authorities and other public sector partners in defining budgeting and commissioning priorities as part of the compulsory Joint Strategic Health Assessments (JSNAs) and Joint Health and Well-Being Strategies (JHWBs) (as per section 116, The Local Government and Public Involvement in Health Act 2007). 2. At an more operational level, the study is also intended to improve personalised patient care by providing an evidence base (the profiling) for care providers, such as GP practices with a detailed contextualisation of local challenges including conditions by a refined measure of ethnicity. In addition, the classification is intended to facilitate targeted health screening in local areas, in which ethnicity has emerged to be a crucial factor in both screening uptake as well as condition onset. 3. The output of this research is intended to support preventive care by all strategic and operational organisations and agencies, by suggesting potential causes of observed health challenges through contextualisation, including links to a refined measure of ethnicity.

Outputs:

The output of the research will be presented at seminars and conferences (such as AAG - American Association of Geographers – Annual Conference: AAG Health and Medical Geography Specialty Group; International Population Data Linkage Conference – Farr Institute; REVES International Network on Health Expectancy Annual conferences), and included in published research papers in peer-reviewed journals (such as Social Science & Medicine; Health & Place; Int Journal of Epidemiology and Community Health). Intermediate findings are currently being prepared for publication in Health & Place and presentation at the above-mentioned AAG conference as part of the International Geography, GIScience, and Urban Health featured theme. Once the data has been disseminated from NHS Digital to UCL , other conferences and papers may also be included but cannot be confirmed at this time. The final classification of LSOAs along with recommendations for policy will also be published and made available for download on a website that is linked to the ESRC-funded Consumer Data Research Centre (CDRC – data.cdrc.ac.uk). The CDRC websites attracts an increasing user base from a range of NHS organisations (Public Health England, CCGs, NHS trusts), local government, third sector organisations. New data products are announced and linked in email notifications, quarterly newsletters and special features, such as the CDRC Map of the Month, and Twitter feeds. On average, there are about 150,000 page views and 50,000 data downloads per year. Outside the CDRC community, UCL are keen to engage with Public Health England and are in contact with them to help identify suitable methods of dissemination, possibilities of promoting the CDRC website and products to the health care community, notably CCGs. In summary, the outputs of the research comprises • a novel geodemographic classification of health needs at LSOA level • a detailed, contextualised characterisation of health needs and challenges associated with each area profile • recommendations for policy and health and social care The results will be disseminated in the following ways: • presentation and publications within the academic community • provision on data.cdrc.ac.uk, the CDRC data catalogue • visualisation on maps.cdrc.ac.uk, the widely accessed map service of CDRC • targeted engagement with Public Health England using existing links with this research group • presentation of outputs to CCG Ealing using existing formal links between the CCG and CDRC All outputs will use aggregate data with small numbers suppressed in line with the HES analysis guide.

Processing:

Data minimisation The following data minimisation strategies have been considered: (1) Temporal censoring is deemed impractical for the following two reasons. First, health care benefit of the output (geodemographic profiles of health needs) is strongly limited, if the question of temporal stability remains unaddressed. Are the health needs only short-lived phenomena or a long-term challenge that require strategic attention? Second, the research needs to capture at least two census periods in the analysis so as to derive age-and sex standardisation. Currently, the existing work with data pertaining to years 2003-2009 is compromised due to potentially inaccurate age- and sex standardisation. (2) Demographic censoring (restricting the analysis to certain age, sex or ethnic groups) would limit the validity of the work, since the work aims at developing profiles for the entire population that comprises all ethnicities, age and sex groups. Only with the full population, will the research be able to display the full range of challenges and priorities facing local health care. (3) Geographic censoring would equally reduce the possibility to identify specifically local challenges and hence limit the utility of the geodemographic profiles for the health care sector. Specific regional and local challenges can only be identified by viewing local patterns against country-wide patterns (e.g. averages). For example, in previous analysis, it was found that London faces greater incidence of sense organs and nerves-related conditions than other city regions in England. This specific challenge would have remained masked in an analysis that focussed on London only. (4) A sample would undermine the validity, granularity and robustness of the work. In addition, sampling would be extremely difficult to implement within the context of this study. The estimation of geographically varying challenges need to be performed at a sufficient level of geographical granularity. Geodemographic indicators are typically developed at postcode or Census Output area level. The most granular level available in HES is LSOA (Lower Layer Super Output Area). There are approx. 27,000 LSOAs in England, and in previous work with HES data the research has confirmed there is a need for a sufficient number of cases to develop robust small area estimates and be capture temporal trends. A sample would need to cover each LSOA, be proportionate to local admissions within each LSOA, be demographically representative of all patients in each LSOA and be repeated in this way each year. As such, this would be extremely difficult to implement and significantly compromise the objective of the study to provide robust, full-fledged and sufficiently granular health profiles. This data are obtained for research purposes in medical geography and will be processed fairly according to regulations and standards of the Data Protection Act and corresponding UCL policies. The processing of the information is carried out for non-commercial research and educational purposes at a higher educational institution (UCL) in exercise of its legitimate functions of training and research. The data will be accessed only by substantive employees of UCL and only for the purposes described in this document. All relevant individuals (Data Protection Officer, Departmental IT Representative, Computer Security Team, Data Protection Coordinator) are informed about the research proposed and are able to monitor proper conduct in all procedures. The data are used as direct input in the analysis and some data will be processed for the purpose of names classification and geographical classification. Items that are not used as input in the way set out in this application are not of interest and therefore not requested. In order to reduce the risk of identification to an acceptable minimum, a special procedure to names extraction has been elaborated jointly with NHS Digital. At the end of the study, the data will be destroyed in accordance with UCL’s retention policy. The UCL Computer Security Team has developed guidelines of safe removal, and a retention schedule that is developed with the UCL Records Manager will ensure that timely removal can be monitored. No data will be transferred to third parties, EU countries or countries outside the EU at any point of time in the research. The project has successfully undergone Ethical Review and review by HRA's Confidentiality Advisory Group. The DH Information Governance toolkit has been completed and is reviewed regularly. The project has achieved level 2 of requirements for Hosted Secondary Use Team/Project (IG toolkit version 13). The data will be stored in a database at UCL's School of Life Science and Medical Studies’ safe haven environment and undergo statistical, multivariate analysis. The data will be processed from an authorised PC client located within UCL and queries will be performed through secure data access. Secure output files (e.g. statistical results) may be transferred through secure file transfer subject to standards of disclosure control. The data will also be related to census datasets (UK Census 2001 and 2011) using information on LSOA (Lower Layer Super Output Areas) level. The LSOA code held in HES extract will be used to match HES records to residential context. In terms of patient classification, UCL will use demographic data (age, sex), primary diagnosis and admission and discharge information. UCL will derive ethnicity from a patient by linking HES records to the PDS and classify patients’ names. This linkage will be performed by NHS Digital. In previous communication with NHS Digital (ref NIC-216528-N0N5Q), UCL established the technical feasibility of a procedure to link HES extracts and use names stored in PDS to create a bespoke patient classification. CAG has reviewed this procedure and confirmed that section 251 support is not required as no confidential data will be disclosed to the researcher. The data processing is a five-point process which is as follows; 1. NHS Digital extracts the patient identifiers of all patients in the HES index for the years 1998/99 to 2012/14 2. NHS Digital links the identifiers to the PDS (Personal Demographics Services) data which is held on the MIDAS system 3. NHS Digital applies the names classification algorithm provided by UCL. No names are disclosed to the researcher. 4. NHS Digital adds to each row of HESID to the name class 5. NHS Digital supply a file of HES records with requested fields including pseudonymised HESIDs and linked classes UCL expect to develop such a method by grouping and aggregating health diagnosis for each small area by different patient categories over a study period from 1999/00 to 2013/14, which covers 2001 and 2011 Census periods. This allows area linkage to contextual variables, including the prevalence of long-term limiting illness and aggregate demographic characteristics of Lower-Layer Super Output Areas (LSOAs), which is also required for age-and-sex standardisation. The Census neighbourhood statistics, which the data will be linked to, are Office for National Statistics-cleared aggregate statistics; they do not contain information on individuals. The work will be carried out by specified users at UCL at the Department of Geography at University College London, who are substantive employees of UCL and only for the purposes described in this document. The project objectives and plan have been reviewed by academic staff from UCL Epidemiology and Public Health, and the interaction will continue throughout the project to ensure scientific rigour and maximum impact to relevant interest or user groups, notably CCGs.

Objectives:

The objective of this research project is to create geodemographic, small area profiling of Health Needs, which takes into account a range of patient and contextual characteristics. Comprehensive and precise assessment of health needs, as required by The Local Government and Public Involvement in Health Act 2007 (see section 116 on JHWBs and JSNAs) remains a significant challenge due to the existence of complex pathways and multi-level processes. Because different groups of people have been shown to have different vulnerabilities to ill-health in different circumstances, this study seeks to identify; (1) general health needs/vulnerability at the small area level and (2) the geographical circumstances in which certain types of patients (classified by ethnicity) appear to be vulnerable/express different health needs. This is novel research that will account for health needs at multiple levels (patient group, small areas) by taking a distinct geomedical angle that draws on latest advances in spatio-epidemiological modelling, geodemographics, analysis of surname geographies and population genetics. The work that has been completed under the current Data Sharing Agreement has found marked differences in health needs between geographical areas. However, the work was limited by the available years of HES extracts (2003-2009). This time period does not allow for appropriate and robust age-and-sex standardisation based on reliable data sources (i.e. Census 2001 and 2011). In addition, the short time period does not permit investigation of temporal trends and thereby the stability of area health profiles. Stability of small areas health profiles is an essential dimension of health needs assessment and crucial for informed policy decisions, such as resource allocation. UCL therefore wish to refine these objectives in two ways: a) to break down small area health profiles by patient category; and b) to assess the stability of health profiles by using more years of HES extracts covering two Census periods. As soon as 2001 and 2011 HES extracts become available, the work completed so far can be updated and put forward to peer review and academic publication as well as dissemination to the health care community (see section 5c below). In short, the follow-up research will develop a dynamic model to predict long term health needs refined for groups of patients and small areas. Reasons for this study: Geo-temporal small-area profiling is useful in identifying need for intervention, assessing causes of health challenges (specifically different profiles of disease burdens) by characterising their nature and spatial extent as well as the geographic and demographic context in which they are manifest. Small area profiling thus supports the definition of policy priorities at the strategic level as well as more tactical decisions by care providers. For example, a temporally persistent disadvantaged profile in a number of neighbourhoods in a city can support evidence-based policy making by defining local strategies to address these challenges. On the provider level, awareness about locally specific health challenges and their contexts can support operational decisions, such as treatment or screening choices. Small-area profiling are suitable methods to support strategies and decisions, because of their capacity to estimate health challenges in robust ways (or at least with a number of confidence measures about the certainty of the estimate) and in accounting for geographical and demographic context. Similar approaches have been applied on ONS mortality data, and geographically and demographically varying challenges could be identified (see e.g. Green et al 2014 Health & Place 30C, Shelton et al 2006 Health & Place 12.4). Yet, mortality data are limited in a sense that they are retrospective and less useful for defining care priorities (since a death has already occurred) and only considers the cause of death, leaving out non-fatal and temporary conditions. There is no study that uses HES data in this way, but work that has been carried out under the previous agreement (NIC-33864-6226N) suggests that resulting classifications can provide significant benefit in summarising and contextualising health needs with direct implications for care. A similar product that already exists is healthACORN, but this product is limited in terms of health conditions it focusses on and its transparency: as a commercial product, the methods are not revealed, only the resulting classifications of fairly broad categories. More generic classifications have been employed in health studies, particularly focussing on health screening (e.g. Sheringham et al 2009 Sexual Health 6.1, Noaham et al 2010 Journal of Public Health 32.4). But these studies, too, had to rely on generic and partly commercial products, and it is expected that the utility in assessing need and health screening can be improved if classifications are devised more scientifically and focussed on observed morbidity. Therefore, a transparent, robust, dynamic and contextualised small area classification would be a valuable resource of policy makers and care providers alike who seek to define priorities and take decisions that are informed by a detailed and local understanding of health needs in the spirit of joined-up health care (see NHS Institute for Innovation and Improvement 2010 ‘Joined-up Care’).


Project 22 — DARS-NIC-294605-F1P7F

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2016/08. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - Scottish NHS / Registration

Objectives:

To develop an optimised screening procedure for ovarian cancer in women who are at high-risk due to a strong family history or genetic predisposition to cancer. To determine the physical morbidity, resource implications, feasibility and acceptability of screening this high-risk population. To establish a serum bank for the future assessment of novel tumour markers which may help in the prevention, diagnosis and/or treatment of ovarian cancer.


Project 23 — DARS-NIC-300282-G9Q0Q

Opt outs honoured: Y

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2017/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • MRIS - Members and Postings Report
  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Benefits:

Results from the study are expected to confirm the importance of early identification and treatment of heterozygous FH patients and to inform guidelines for patient treatment with the aim of preventing coronary mortality. Criteria from the study are used in the diagnosis of FH and the study results helped substantively in the development of the 2008 NICE guidelines. Previous results showed that the high risk of CHD in FH can be reduced to that of the general population by early treatment, particularly with high potency statins. These results were published in the European Heart Journal along with an editorial emphasising the importance of these results for diagnosis and management of FH in adults.

Outputs:

Results will be published in medical journals. A publication list for this study has been included with our application.Our 2008 results were published in the European Heart Journal along with an editorial emphasising the importance of these results for diagnosis and management of FH in adults ( http://dx.doi.org/10.1093/eurheartj/ehn448). No other such registry data are available elsewhere and the register has provided uniquely valuable data which helped substantively in the development of the 2008 NICE guidelines. The published data will be summary data showing mortality rates for the study participants. No patient level data will be shown. We would aim to publish the main findings within 6 months of receiving the ONS data.

Processing:

Recruitment to the study is not ongoing, with the last patients recruited in 2012. However the study committee would like to recruit further patients to the study at a later date. The identifiable data will be stored and processed within the UCL Data Safe Haven (IDHS) which is a safe haven environment designed to meet the requirements of the NHS Information Governance Toolkit (certified to ISO 27001:2013 – certificate number: IS612909). Data from HSCIC will be collected via secure electronic data transfer by the study statistician, encrypted and immediately transferred to the IDHS. All other study data is pseudonymised. Only the study statistician and study PI have access to the identifiable data which will be used to link and update mortality data for the study. Causes of mortality will be coded and added along with dates to the pseudonymised data ready for analysis. Patients will be censored on reaching the age of 80 years and on emigration on date of embarkation. All deaths will be coded to ICD 9th revision. Person-years at risk will be accumulated within 5-year age groups and 5-year calendar periods for men and women in the cohort. The age and calendar-specific death rates for men and women in the general population of England and Wales will be applied to the person-years accumulated by men and women in the cohort to estimate the expected numbers of deaths from specified causes. The ratio of the number of deaths observed to the number expected will be expressed as the Standardised Mortality Ratio (SMR for reference population = 100) and the exact 95% confidence intervals will be calculated for men and women separately and aggregated for both sexes. The test of significance used will be a two-sided Poisson probability of observing the number of deaths that occurred given the expected number of deaths. The absolute rates of mortality from coronary heart disease and all causes will be calculated per 100,000 person-years per five-year intervals of attained age and also aggregated by 20-year periods of attained age. Previous publications from the Simon Broome Register have used these methods. Patients recruited after 1996-1998 have consented to the use of their data in this way. For patients recruited earlier we have obtained section 251 exemption. Approved researcher forms have been submitted for those processing the mortality data. Published data will be at a summary level and no record level data will be shared with third parties.

Objectives:

The Register was designed as a dynamic prospective cohort study with notification of embarkation and death being provided by the NHS Central Register. 3,623 patients with FH (and a separate cohort of 340 patients with severe hypertriglyceridemia were registered from 1980 through to 2012 by up to 22 UK hospital lipid clinics. Results from the Simon Broome Register published in 2008 showed that the high risk of CHD in FH can be reduced to that of the general population by early treatment, particularly with high potency statins as recommended in England by the National Institute of Health and Clinical Excellence. However, although the 3,382 patients were followed up for 46,560 years from 1980 to 2006, a number of important questions remain unanswered which may be addressed by continued follow-up until the end of 2014 by when it is estimated that the person-years follow-up will have increased by at least half. UCL therefore wish to update mortality data for this study in order to increase power for this analysis examining the changes in coronary, all-cause, and cancer mortality in men and women with Definite and Possible heterozygous familial hypercholesterolaemia (FH) before and after lipid-lowering therapy with statins.


Project 24 — DARS-NIC-334952-R5M7K

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off, Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration
  • MRIS - Members and Postings Report

Benefits:

Primary Objective: Ovarian cancer is the fourth commonest cause of death from cancer amongst women in the UK. The majority of women unfortunate enough to develop this cancer have few symptoms until it has spread outside the ovaries. By this time it is difficult to treat and approximately 70% of these women will die. In contrast, the outlook for the small proportion of women diagnosed before ovarian cancer has spread is good. This research trial is based on the premise that screening detects ovarian cancer at an early stage may reduce the number of deaths. At the end of the study UKCTOCS will have information about how many lives ovarian cancer screening can save, how much this will cost; how women feel about being screened and the complications of screening. The Director of NHS Cancer Screening Programme, sits on the UKCTOCS steering committee and is already aware of the project and its detailed publication plan. The NHS Cancer Screening Programme has contacted the UKCTOCS team to plan independent analysis (which does not include HES data) of the cost effectiveness of any screening within the NHS to make a timely decision as to whether an NHS national screening programme for ovarian cancer should be introduced. Supporting Secondary Research Studies: UKCTOCS will also have information on the potential of endometrial cancer screening and the cost benefits if any. It is expected that the serum bank and associated data will lead to biomarkers that will be used for the early detection of disease in asymptomatic subjects as well as predictive and prognostic tests of clinical use. In the short term some of these may be incorporated into new clinical trials.

Outputs:

Primary Objective: Products are limited to publications in peer-reviewed Medical and Scientific Journals, oral and written presentations at national and international conferences. Personal data is not disclosed. The data will form the basis of the cost effectiveness analysis, supplemented by additional treatment data extracted from patient notes. The final output will be a publication which will only contain aggregate results with small number suppression, in line with the HES Analysis Guidelines. The aim is to publish the paper by June 2016 although timelines are dependent on approval and release of HES data. The final mortality results were published in December 2015. With regard to other outputs, in most cases, the data received from HSCIC is/will be used only to identify/trace women diagnosed with ovarian cancer so that the study team can retrieve the patient notes on which analysis is based. The data has contributed to the identification and classification of some of these cancers. To date, there have been 15 published UKCTOCS papers and significantly more oral presentations. Multiple data sources have been used. Further planned outputs in 2016 include publications on performance characteristics of the ultrasound screening strategy and the primary analysis results (Impact of screening on ovarian cancer mortality). In addition, there will be analysis of data source contribution towards a confirmed diagnosis. E.g. In common cancers/diseases what proportion had data available through HSCIC cancer registration, death registration, HES, follow-up questionnaire etc.). This will be a useful indicator for future researchers working in the disease area as a comparison will be made between cases identified through the listed sources and the confirmed diagnosis classification. Progress reports are also submitted to the NIHR (Evaluation, Trials and Study Coordinating Centre) who oversee the governance of the MRC funded clinical trials. These do not contain any patient data. Supporting Secondary Research Studies: The output of using the data is typically the identification of other cancers/diseases (in the same way as for ovarian cancer) to select cases for inclusion in nested case control studies. The data is then no longer used in such secondary studies. One exception is the CRUK-funded study of the cost effectiveness and possible impact of endometrial cancer currently being undertaken by UCL’s Gynaecological Cancer Research Centre in collaboration with the London School of Hygiene and Tropical Medicine. This study used HES data in analysis and will produce outputs containing aggregate results (complying with the HES Analysis Guidelines). For this study, no data was transferred outside the department and only UCL employees had access to the HES data. A publication with the working title ‘Cost of Survival Over 5 Years Following Endometrial Cancer Diagnosis’ was submitted to the British Journal of Obstetrics and Gynaecology in December 2015.

Processing:

UCL supplied the identifying details of a cohort to the HSCIC including name, date of birth, NHS Number and address. This cohort also included a Volunteer Reference Number in order to link to UCL’s participants and only pseudonymised data is transferred back to UCL where it is linked back to the original study database containing patient identifiable data. The data supplied by HSCIC is physically located at the Gynaecological Cancer Research Centre, Department of Women’s Cancer, Institute of Women’s Health, University College London. The data is stored and processed within the Gynaecological Cancer Research Centre (GCRC) System. The data is held within a Microsoft SQL 2008 database with access limited to staff specifically granted access. Primary Purpose: As part of the primary UKCTOCS analysis, diagnosis codes associated with an ovarian cancer and other gynaecological malignancies and corresponding operation codes are analysed. Where such codes are identified, UKCTOCS will request medical records from the GP or treating consultant for review and confirmation of diagnosis. As part of the cost-effectiveness analysis relating to the UKCTOCS study, the project will need to consider hospital in-patient and out-patient resource use and costs relating to standard therapy and any follow-on costs associated with an ovarian cancer diagnosis as well as with false positive surgery/investigations in the screened population. A cost-effectiveness analysis compares the cost to the NHS of screening and treatment for ovarian cancer in the screen arms of the trial to costs of diagnosis and treatment in the control (no screening) arm of the trial. In- and out-patient HES data is critical in identifying the procedures and treatments that both groups received. In addition HES A&E and HES Critical Care data are required for the routes to diagnosis analysis as many ovarian cancer come through A&E. Some ovarian cancer patients are also likely to be admitted in critical care post-surgery or following a complication. All of this would be need to be included in the cost-effectiveness analysis of treatment. Supporting Secondary Research Studies: In the context of ethically approved secondary studies, the data will contribute to identification of cases for nested case control studies mostly for biomarker discovery and validation projects. The data contributes to disease identification and helps to establish the interval between sample collection and diagnosis date. In many but not all studies, once potential cases are identified, the treating clinician is contacted for the details and confirmation of diagnosis followed by selection of the appropriate serum samples sets from the UKCTOCS biobank. The serum samples are stored at a commercial facility the costs of which are covered by the UCL contract with Abcodia which allows Abcodia access to serum samples. Many of the secondary studies involve collaborations with data analysis limited to academic collaborations and biomarker studies involving both academic and industry collaborations. Third parties will only be provided with anonymised serum samples for nested case control studies. Data will be provided that identifies whether a sample originated from a case (as defined by whether the individual was diagnosed with the cancer/disease being investigated) or control. The source of the diagnosis will not be revealed to the third party. HES data is only used as described above to supplement data from the patient follow up questionnaires, HSCIC cancer registrations and deaths, Myocardial Ischaemia National Audit Project (MINAP) data and GP data in identifying the appropriate samples that UKCTOCS make available for any studies – commercial or academic. The UCL’s Gynaecological Cancer Research Centre (GCRC) is undertaking a secondary CRUK-funded project in collaboration with the London School of Hygiene and Tropical Medicine to assess the ‘cost effectiveness of endometrial cancer screening’. For this, the GCRC performed data analysis of management trends and costs of specific diseases e.g. breast and endometrial cancer. HES data previously provided was used to calculate the costs of endometrial cancer treatment costs in the ultrasound and control arms of the study. For this study, no data was transferred outside the department and only UCL employees had access to the HES data.

Objectives:

There are two distinct purposes for which the data are required. Primary Purpose: The United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) is a multicentre randomised control trial which aims to assess the impact of screening on ovarian cancer mortality while comprehensively evaluating physical and psychological morbidity, compliance and resource implications of screening and performance characteristics of a serum CA125 versus ultrasound based screening strategy. The data will be processed only for the purposes of: • identification of episodes of ovarian and related cancers; • identification of the treating primary care physician for the purposes of requesting medical documentation required for the review process; • identification of treatment – surgery which may have resulted in removal of ovaries; • identification of resource costs involved in the diagnosis/treatment of ovarian cancer. The UKCTOCS is a 14-year clinical trial considering screening for ovarian cancer, which as part of the analysis plan will undertake a cost-effectiveness analysis. A major part of this analysis will consider the potential savings to the NHS through the early identification and treatment of ovarian cancer, arising from reducing the subsequent sequalae of treatment events that would have occurred if the cancer had not been identified. The data allows the long-term follow-up of all patients to appropriately and correctly identify these treatment events, particularly in the control group, as well as capturing any related events during the follow-up period (which is on average 11 years) across the full patient population. The events will then have costs attached to them to estimate the full long-term treatment costs borne by the control arm and the two active screening arms. In association with the up-front screening costs and the recorded mortality, the HES data will therefore allow calculation of the cost-effectiveness associated with screening. University College London is using the data to ensure that UKCTOCS have tracked individual patients for the cost-effectiveness analysis which will be conducted as part of UKCTOCS’s clinical trial analysis plan. The data will ensure that UKCTOCS capture ALL hospital events over the follow-up period (which is 11 years for each individual on average). The analysis will be undertaken on an Intent to Treat basis from the perspective of the NHS provider and therefore, as there will be judgment used over which hospital events are directly related to ovarian cancer, particularly for the control arm patients, it will be valuable to include all hospital events for the individual patients enrolled in the trial. Supporting Secondary Research Studies: At the point of recruitment, which took place between 2001 and 2005, participants gave written consent to allow access to their medical notes and to permit use of their data and stored serum samples in future ethically approved secondary studies. All secondary studies are focused on the early detection and treatment of specific diseases. To support this objective, the UKCTOCS team will use the data to identify participants whose serum samples are appropriate for use in the specific secondary studies (i.e. by identifying participants with episodes related to the disease being investigated). The data supplied by the HSCIC is used solely for the purpose of identifying serum samples and data from the UKCTOCS database (limited to: the specific disease of the participant; the participants’ age on the date the sample was collected and age on the date of diagnosis of the disease; and, potentially, information about the timings of the collection of the serum) to be released to the third party. No data supplied by the HSCIC is released to third parties.The data will only be used by the UKCTOCS team at the University College London (UCL) and will not be shared with any third party. Only serum and information on the participants’ age on the date of its collection; age on the date of diagnosis of the specific disease and, potentially, information about the timings of the collection of the serum, not originating from data supplied by HSCIC, will be shared with collaborators. The team is committed to using the data collected during the long-term follow up of the cohort together with donated serum samples for identification and validation of novel screening/early detection/predictive and prognostic biomarkers, insights into natural history of diseases affecting older women and ability to explore the impact of lifestyle and screening on disease outcome. Proposals for secondary studies have to be ethically approved and reviewed by a steering committee at UCL. Secondary studies in the field of cancer, cardio-vascular disease and increasingly other common diseases in older women are continuously being proposed, submitted for grant applications and being funded, with CRUK and European Union the largest funders so far. As it is not possible to predict exactly which cancer/disease will be investigated UKCTOCS requires HES data on a wide range of diagnoses rather than just the ovarian cancer episodes needed for the primary purpose. The secondary studies involve both academic partners and industrial partners including Abcodia (a UCL spin-off) which has an exclusive commercial license to work on biomarker discovery and validation using serum samples from the UKCTOCS biobank. Neither the storage facility nor any third party (including Abcodia) will have access to the data. Only the Gynaecological Cancer Research Centre of the UCL will have access to the patient level data and data will only be accessed at the approved locations.


Project 25 — DARS-NIC-346693-F2X1G

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing, One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration
  • Mental Health and Learning Disabilities Data Set
  • Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset
  • Diagnostic Imaging Dataset
  • Hospital Episode Statistics Admitted Patient Care

Benefits:

The Whitehall II researchers will use peer-review journals to report the contribution of midlife inflammatory, vascular, and metabolic factors to chronic disease, depression, cognitive impairment and functional health in later life. They will also assess whether the adoption of healthy lifestyle even at older ages modifies functional trajectories, and aim to develop multi-factorial predictive algorithms, like those developed for cardiovascular diseases, to facilitate early identification of adverse ageing outcomes. UCL’s analyses will continue to generate evidence to improve public health policies, clinical guidelines, health care professionals, workplaces and promote healthier lifestyles in the general public. The study dissemination plan, which has been very successful up to now (please see examples below), involves publications in high impact scientific journals, scientific meetings, briefing papers for policy makers, regular and ad hoc meetings with interested parties such as the UK Health Forum. Dissemination activities are encouraged at every level of seniority from doctoral student to principal investigator. Evidence from previous benefits: Whitehall II have contributed evidence to current clinical guidelines, such as the “European Guidelines on Cardiovascular Prevention in Clinical Practice” (see Eur Heart J 2012;33:1635-1701) and the “Guidelines for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association” (see Stroke. 2014;45:2160-2236). UCL have used Whitehall II data in their state-of-the-art reviews on prediabetes (Tabak A,…Kivimaki M. Lancet 2013; 379:2279–2290) and stress (Steptoe A, Kivimäki M. Nature Reviews Cardiology 2012;9(6):360-70 and Steptoe A, Kivimäki M. Annu Rev Public Health. 2013;34:337-54.) to inform health professionals and policy makers in the UK and elsewhere. UCL have contributed evidence to the World Health Organization (WHO) policy documents for reducing social inequalities in heath globally (Commission of Social Determinants in Health 2008) and the European and the UK reviews of inequalities and working conditions (Review of Social Determinants and the Health Divide in the WHO European Region, updated in 2014 and Fair Society, Healthy Lives, 2010) and developed a guide for evidence-based public health in a project led by the UK National Institute of Clinical Excellence (NICE, Killoran 2009). In addition, UCL have provided evidence to European Union Occupational Safety and Health recommendations and contributed to priority settings in occupational health research at a European level (https://osha.europa.eu/en/tools-and-publications/publications/e-facts/efact18/view; https://osha.europa.eu/en/tools-and-publications/publications/reports/management-psychosocial-risks-esener; https://osha.europa.eu/en/tools-and-publications/publications/reports/summary-priorities-for-osh-research-in-eu-for-2013-20). In addition, UCL have contributed evidence to the American Heart Association prevention policy, which in turn influences UK policy (American Heart Association Behavior Change Committee of the Council on Epidemiology and Prevention, Council on Lifestyle and Cardiometabolic Health, Council for High Blood Pressure Research, and Council on Cardiovascular and Stroke Nursing. “Better population Health through behaviour change in adults: a call to action”. Circulation. 2013 Nov5;128(19):2169-76) Whitehall II findings on modifiable protective factors and risk factors have generated wide interest in media in the UK and worldwide. For example, our paper on alcohol consumption and cognitive ageing was ranked Altmetric Top 100 in the world in 2014 (http://www.altmetric.com/top100/2014/).

Outputs:

A research dataset will be created for the UCL study researchers named in the Data Sharing Agreement. It will have all records identified by a non-identifiable study ID and will not contain any personal variables. Any sensitive variables that might identify a participant (such as hospital dates or ICD-10 codes) will never be published, reported or provided to third parties. The scientific conclusions of the Whitehall II study will be published in international peer-reviewed journals starting from a few months after the data are available. UCL aim to continue publishing the analyses in journals with high coverage and high impact factor and UCL’s preference is journals with an open access option (web version of the paper available free of charge). Some examples of journals where the Whitehall II researchers have published their results in 2015 are PLoS One, American Journal of Medicine, Stroke, European Heart Journal, Neurology, Nature, Lancet, Epidemiology, British Medical Journal, etc. A full list of the project publications to date is published on the UCL website. Results will continue to be published at a group level not allowing identification of individual participants, i.e. the outputs will always be aggregated with small numbers suppressed in line with the HES analysis guide.

Processing:

UCL holds sensitive and identifiable data from the Personal Demographics Service (PDS), Cancer Registrations, ONS Mortality, HES admitted patient care and HES outpatients datasets, linked to the cohort. In addition to an update to the datasets held, UCL has requested the MHLDDS and HES Accident and Emergency data for the cohort. UCL will supply NHS number, date of birth, and gender to the HSCIC for linkage. Linking with electronic health records is at the core of the project, as they provide the objective health outcomes needed for our project. These data will be used by the researchers using a variety of statistical methods to fulfil the study aims described above. All personal information about the study participants is treated in the strictest confidence in accordance with the Data Protection Act (1998) and the NHS Information Governance requirements. As described in the study NHS IG Toolkit, the study safeguards and security policies ensure appropriate use of all personal information collected. Personal data about study participants (e.g. name, NHS number, contact details, date of birth, GP details, etc) are stored securely on the UCL secure computer network managed by the UCL School of Life and Medical Sciences (SLMS). These data are handled by the Whitehall II administrative and data management personnel and are used only to contact participants. Clinical information about participants provided by external sources such as from HSCIC and ONS are also stored on this secure UCL SLMS area. Whitehall II researchers do not have direct access to these identifiable records in neither paper nor electronic form. No identifiable personal data will ever be published. The clinical, questionnaire and medical data collected by the study (including HES and ONS data) are used for research purposes only. These data are pseudonymised before being moved from the secure area to the research area on the UCL SMLS network. Pseudonymisation is achieved by assigning each participant a unique identifier and by removing all personal information (e.g. name, NHS number, contact details, date of birth, GP details, etc) before the data are added to the database used by the researchers. A data sharing policy is in place to make the pseudonymised research data available to the scientific community. However, collaborators must be bona-fide scientists with an established record, who will conduct high quality, ethical research. The research files provided to these external collaborators are tailored to their project and are securely transferred for their use only. Any data supplied to third parties, will comprise of: • Self-reported data provided voluntarily by the participants; and/or • Variables derived from the ONS mortality data. Specifically ‘yes’ or ‘no’ indicators to indicate if the participant is deceased and, if so, if specific causes of death were applicable or not; and/or • Verified self-reported clinical events in the form of ‘yes’ or ‘no’ variables to indicate if the participant is has had a specific clinical event such as a stroke, cancer or CHD episode. Regarding the last item, HES, Mental Health and Cancer registration data are used solely for the purpose of verifying self-reported medical events and are not included in any datasets shared with third parties. As an example, if a participant self-reported a stroke, the applicant would cross-check the data with the HES data to verify the diagnosis. If verified, the research data that could potentially be made available to third parties would include an indicator confirming the self-reported stroke.

Objectives:

The Whitehall II study was setup in 1985 as a prospective cohort project to explore the relationship between socio-economic status, stress and cardiovascular disease. The study, based at University College London (UCL), recruited 10,308 civil servants working in London. The participants were sent a self-completion questionnaire covering a wide range of topics, and underwent a comprehensive clinical examination. Since 1985 there have been eleven phases of data collection of similar nature. These data have always been collected on the original cohort of 10308 recruited in 1985, and no additional recruitment of participants has taken place since then. In addition to cardiovascular measures, the Whitehall II study have over the years added further measures to test physical functioning, cognitive functioning, mental health, measures of cortisol levels and new cardiovascular tests such as HRV and PWV. There are three distinct aspects to the study: 1) The compilation of research data, which consists of the collection of self-completion questionnaires and medical examination data from the Whitehall II cohort participants. Medical data and mortality data from this cohort are also obtained through data linkage with external data sources such as HSCIC and ONS. The totality of these data are compiled into the Whitehall II research database for use as a research resource; 2) Public health research studies undertaken within the scope of the Whitehall II study, which aim to answer specific questions and are primarily funded by grants from the Medical Research Council and the British Heart Foundation. Further studies have been funded by the European Commission Horizon 2020 and the Economic and Social Research Council, as specified in section 4.Period and Funding; 3) Making pseudonymised data available to the scientific community for use in UCL-approved research studies beyond the scope of the Whitehall II study. Any data supplied to third parties, whether as part of the EU-funded LIFEPATH project or for any other purpose will comprise of: a. Self-reported data provided voluntarily by the participants; and/or b. Variables derived from the ONS mortality data. Specifically ‘yes’ or ‘no’ indicators to indicate if the participant is deceased and, if so, if specific causes of death were applicable or not; and/or c. Verified self-reported clinical events in the form of ‘yes’ or ‘no’ variables to indicate if the participant is has had a specific clinical event such as a stroke, cancer or CHD episode. Regarding the last item, HES, Mental Health and Cancer registration data are used solely for the purpose of verifying self-reported data and are not included in any datasets shared with third parties. As an example, if a participant self-reported a stroke, the applicant would cross-check the data with the HES data to verify the diagnosis. If verified, the research data that could potentially be made available to third parties would include a ‘yes’/’no’ n indicator confirming the self-reported stroke. The data will be used for public health research purposes. Based on 30 years of follow-up, the aim is to examine the interrelationships between biological, psychosocial and behavioural factors in the ageing process, and identify key determinants of late life depression, cognitive decline, chronic disease, and physical functioning. The study’s healthy ageing cohort is an ideal platform for studying primary prevention of vascular disease (CHD, stroke) and diabetes. The cohort is now aged 62-84 years and is measured for age-related physical and cognitive functioning and mental health. The study contributes to the evidence on the potential for preventing functional decline through therapeutic risk factor reduction and behavioural interventions. Self-reported clinical events data are open to major limitations of bias, including missing responses and attrition. Therefore, since 1997, UCL have supplemented the self-reported events with information extracted from GP and paper hospital notes, and also with data provided initially by the NHS-Wide Clearing Service and subsequently by HES up to 2009. UCL now need to link again its research data to HES data to identify the clinical outcomes of our participants since 2009. The Whitehall II MRC grant (K013351, Adult Determinants of Late Life Depression, Cognitive Decline and Physical Functioning - The Whitehall II Study of Ageing) has dementia, disability and depression as the outcome variables. In order to be able to study these outcomes in older individuals it is crucial to have complete data from all possible sources. Data on psychiatric conditions are important outcomes in their own right, but it is also needed to study other conditions. For example, the diagnosis of dementia involves ruling out major psychiatric disorder as an underlying condition for the observed clinical phenotype. In order to do this one would need external data on psychiatric conditions. This cannot possibly be achieved without access to the Mental Health and Learning Disabilities Data Set (MHLDDS).


Project 26 — DARS-NIC-366913-C2V5F

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2016/04 (or before) — 2018/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report

Benefits:

The benefits are that ‘Join Dementia Research’ will be able to process data fairly without unintentionally breaching the undertaking given to volunteers that their identifiable information will be removed from the register after their deaths and a reduction in the risk of causing distress by attempting to contact members of the cohort that have deceased. The following information provides background on ‘Join Dementia Research’: The service has been running since July 2014, and was nationally launched in February 2015. The benefits described are already being recognized, but they will increase over the next 2-3 years and the register grows. Benefits of the register - ‘Join dementia research’ has been funded by the Department of Health and is delivered in partnership with the National Institute for Health Research, Alzheimer Scotland, Alzheimer’s Research UK and the Alzheimer’s Society. Its development was prompted by the Prime Ministers Challenge on Dementia, and it’s purpose is to support the PM Challenge target to ensure 10% of all people with dementia are involved in dementia research. The benefit of this being that: a. The system enables everyone in the country aged over 18 has an opportunity to express an interest in being involved in research. b. All dementia research studies taking place in the UK (funded by government, NIHR, charities and commercial organizations) with ethical approval can use the system. Providing a new and improved way of identifying and recruiting volunteers into vitally important dementia research studies. c. The traditional way of recruiting dementia research volunteers, in through NHS memory clinics. This method takes time, as researchers wait for suitable subjects to come through clinic. Join dementia research removes this barrier, by having volunteers ready and waiting to join studies. All dementia research studies will recruit more quickly, saving time and money. Currently over 70% of research studies exceed recruitment target times, this system will speed up those times. d. As a result of studies being concluded more quickly, we can ensure that the findings of those studies can be acted upon and implemented or considered for the benefit of patients and the public. The service will also help ensure that studies funded and delivered across the world could be attracted to take place in the UK. e. The studies look at prevention, diagnosis, treatment, care and potentially cures for people living with dementia. f. Over the next 12-18 months we expect the service to have attracted 100,000 volunteers and to become the main mechanism by which researchers find study volunteers. g. The system is already supporting recruitment to 29 studies (over half of all studies on the NIHR CRN portfolio) and has recruited 219 (over 10%) of all participants into research studies which as the PROTECT study at Kings College, an important study which gathers data to support innovative research to improve our understanding of the ageing brain and why people develop dementia, and EXPEDITION 3 and Eli Lilly study which is testing a new medication for people with mild early dementia symptoms. h. The service was nationally launched in February, it was announced in the media and here is a link to the press release with comments from Secretary of State for Health and Chief Medical Officer http://news.joindementiaresearch.nihr.ac.uk/press-pack-toolkit/ i. It will contribute to delivery of the Prime Minister Challenge on Dementia target of having 10% of all people with dementia involved in research. www.joindementiaresearch.nihr.ac.uk

Outputs:

The link to HSCIC will lead to the removal of records of deceased patients from the register which enables ‘Join Dementia Research’ to comply with the following undertaking from the consent forms used when recruiting patients: “I understand that if I withdraw, or after my death, then all identifiable information will be removed from ‘Join dementia research’.” The timely removal of deceased patients records will reduce the chances of contacting people who are deceased. The register will be updated at least quarterly and possibly more frequently.

Processing:

University College London Hospitals NHS Foundation Trust (UCLH) will periodically provide HSCIC with lists of identifying details of patients from the register. The lists will include name, date of birth, NHS number, postcode and gender Using its List Cleaning service, the HSCIC will confirm which patients are deceased. The information is then used to remove deceased patients from the register. Once the deceased patients have been removed from the register, the data supplied by the HSCIC will be permanently deleted. The data provided by HSCIC will not be shared, or processed by any third party and no third party can access records of patients deleted from the register to identify which were reported as deceased by the HSCIC.

Objectives:

The ‘Join dementia research’ register is a national service funded by the Department of Health; it enables members of the public to register to be contacted about potential research studies. In registering they consent for their information to be available to the dementia research community The link requested to HSCIC information will ensure people who are deceased are removed from the Department of Health (DOH) letter states that the ‘delegation will run up until September 2015’the ‘register’ of potential research volunteers to ensure that no harm or distress is caused by contacting people who have died. The intention is to send HSCIC information on all volunteers from the register on a monthly or quarterly basis (depending on cost). The HSCIC will simply confirm if any of the volunteers have died by supplying fact of death. No updated demographics will be provided to University College London (UCL).


Project 27 — DARS-NIC-393510-D6H1D

Opt outs honoured: Y

Sensitive: Non Sensitive, and Sensitive

When: 2017/06 — 2017/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Accident and Emergency
  • Office for National Statistics Mortality Data (linkable to HES)
  • Office for National Statistics Mortality Data

Benefits:

The research carried out by UCL directly influences DoH policy makers, service providers, healthcare professionals and the general public. This directly benefits the health of children and the healthcare provided to children in the here and now and this is key to reducing future burden on the NHS. Benefits from the data already received include: For example, UCLs research recently published in The Lancet, showed similarly increased risks of death over the 10 years after hospital discharge for adolescents hospitalised for self-harm as for those hospitalised for drug or alcohol misuse or violence. The results have led to recommendations for similar psychosocial interventions to be considered for both groups, not just those admitted for self-harm and to include preventive strategies for drug and alcohol misuse, which accounts for just as many deaths in the 10 years after hospital discharge as does suicide. UCLs research will extend this type of preventive thinking to a range of population subgroups within the child and young adult age range and allow follow up to determine long-term, and potentially preventable outcomes. Additionally, UCLs research on readmissions has shown that for children and young people these occur predominantly in patients with underlying long-term conditions. When looking specifically at 30-day readmissions (emergency readmissions within 30 days of a previous discharge, which are subject to the readmission rule) UCL found that about half of readmissions were for a problem different from the reason for the first emergency admission. This further suggests that readmissions in children and young people are due to complexity of cases rather than hospital failings, and urge a review of the current policy of not reimbursing hospitals for care provided for 30-day readmissions. UCL have also shown that chronic conditions underlie the sharp increase in admissions across the transition from paediatric to adult services which has important implications for specialist care. The measurable benefits to the health service will be in improving the understanding of longitudinal patterns of emergency health care use overall and which groups (e.g. with chronic conditions) are most at risk. The study will provide new knowledge about long-term outcomes across the child life course and into adulthood. Specifically, 1) assessing the use of hospital service and relevant outcomes, including mortality before and after transition from paediatric to adult health care for young people with chronic conditions, 2) assessing variation in readmission rates by hospital and determine to what extend this variation is due to case mix (based on the full longitudinal hospitalisation record), organisational factors or changes over time. 3) comparing outcomes for vulnerable mothers (e.g. those with a past history of adversity-related injury admissions) and children. The research (using the new data) will extend this type of preventive thinking to a range of population subgroups within the child and young adult age range. The benefits to the service will be in improving the understanding of longitudinal patterns of emergency health care use overall and which groups (e.g. with chronic conditions) are most at risk. The study will provide new knowledge about long-term outcomes across the child life course and into adulthood. The results may be used to inform NHS services through, for example, targeting of preventive care strategies, evaluation of the quality of care, and development of services and policy to support follow up of risk groups. UCL will also engage with CLARHC about implementation of the research into practical services within UCL Partners. Finally, UCL have a focus on vulnerable children and families, and use admission data, combined with their indicators for chronic conditions and birth characteristics to explore use of health services for vulnerable mothers and children. All papers are reviewed and commented on by DoH and findings fed back to DoH policy makers as well as more widely, for example, through presentations to young people groups (through the National Children’s Bureau), to the NHS (e.g. through the Child and YoCLARHC), and through trusts to clinicians (e.g. through seminars and CPRU symposia involving patient groups, policy makers and clinicians). The results may be used to inform NHS services through, for example, targeting of preventive care strategies during pregnancy to support vulnerable mothers and children, evaluation of the quality of care, and development of services and policy to support follow up of risk groups who can be recognised by hospital services (eg those with underlying chronic conditions, or indicators of adversity). All of the CPRU current and past research can be found on the CPRU website (https://www.ucl.ac.uk/cpru).

Outputs:

The programme of research in this application informs policy and practice and all proposals and outputs are seen and approved by the Department of Health (DoH). Through UCLs Children’s Policy Research Unit (CPRU) there will be regular engagement with the DoH about the projects during development and outputs, and DoH will review outputs to give feedback. All analyses undertaken as part of this programme of research for the policy research unit aim to provide evidence to inform health care professionals, service providers, policy makers, and service users about children’s health and how services meet their needs. Other outputs include presentations to service providers through meetings with the RCPCH (Royal College of Paediatricians), the North London Collaborations for Leadership in Applied Research and Care (CLARHC) and through the academic health sciences network (AHSN). The findings will also be presented to clinicians at clinical practice meetings including but not limited to the Royal College of Paediatrics and Child Health in 2017 and 2018. This engagement is occurring with the direct goal of changing practice in the health care field. The findings will also be published in peer reviewed journals and policy briefings for the DoH. The projects in this application are expected to finish by December 2018. Specifically, the research will inform DoH policy makers, service providers and practitioners about patient and service factors associated with emergency use of secondary care and long-term adverse outcomes through the child life course and into adulthood. The research programme will engage with DoH policy makers, practitioners and public during the research, to refine questions and applications of findings, and during the dissemination phase. In this way, UCL will ensure that the study is relevant to NHS systems and UCLwill endeavour to feedback results to the NHS. The mechanisms for engagement and dissemination with NHS systems and the public are as follows: a) UCL has well-established mechanisms for patient and public involvement through CPRU. This is facilitated by the National Children’s Bureau (NCB) Research Centre. b) The study is conducted as part of a programme of research for the Policy Research Unit (CPRU) for Children, Young People and Families, funded by the Department of Health Policy Research Programme. CPRU aims to improve the health of children, young people and families by undertaking research to provide evidence for health policy and practice. The CPRU program requires regular engagement with policy makers at the Department of Health. c) The project team at the Great Ormond Street Institute of Child Health contribute to the Academic Health Science Network at UCL Partners AHSN theme on Integrated children and young people’s programme which aims to implement research findings into practice. Engagement is also through the CLARHC, hosted by UCL Partners. The papers resulting from these studies will be published in peer-reviewed journals (such as the Lancet, Archives of Disease in Childhood, PLoS Medicine, BMJ Open) and presented at scientific conferences (such as the, International Population Data Linkage Conference, International Society for the Prevention of Child Abuse and Neglect, Royal College of Paediatrics and Child Health annual conference, and Informatics for Health conference). UCL aim to present the work at scientific conferences during 2017 and use feedback provided at these meetings to write up papers to be submitted for publication in late 2017 and 2018.

Processing:

Only individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and sanctions as substantive employees will have access to the data and only for the purposes described in this agreement. ONS mortality data will be processed according to the standard Office for National Statistics terms and conditions. The data will not be shared with third parties or linked to any other datasets. UCL have no requirement nor will attempt to re-identify the supplied data. The data requested will be kept in UCLs Data Safe Haven (IDHS). It has been certified to the ISO27001 information security standard and conforms to the NHS Information Governance Toolkit. A file transfer mechanism enables information to be transferred into the Safe Haven simply and securely. IDHS uses Dual Factor Authentication to access and handle data transferred into the IDHS service. This ensures that only the named applicants will have access to the data from IDHS. Removing data from the Data Safe Haven is only allowed for the PI. Data flows When the data extract is available from NHS Digital, a nominated researcher will download the data and immediately transfer it into the UCL data safe haven. Once in the data safe haven, researchers based at the Institute of Child Health and Farr Institute of Health Informatics London (the researchers are all substantive employees of UCL apart from one PHD student) will be able to access the data in the safe haven. The IDHS safe haven operates as a walled space and researchers are not able to connect to the internet or export data from it. All outputs will be in aggregate form only with small numbers suppressed in line with the HES analysis guide.

Objectives:

The data is requested for a programme of research within the healthcare provision theme of the Policy Research Unit for Children, Young People and Families, within University College London (UCL) funded by the Department of Health (DoH). The objectives of the research are: a) To determine variation in use of secondary care services by children and young people over time and their transition to adult services. UCL will analyse variation by patient characteristics (e.g. age, gender, GP registration), and by area/unit level area characteristics such as trust, practice characteristics such as QOF scores, and area indicators for deprivation. b) To determine risk factors for emergency use of secondary care and risk factors for recurrent use (e.g. according to individual patient characteristics such as age, chronic conditions, deprivation, sex), past use (e.g. frequency and type of past contact such as A&E or admissions). UCL will also examine NHS trust and area factors associated with secondary care use. Where possible, UCL will use birth cohort analyses, based on postnatal admissions of children linked to maternity to maternal risk factors (e.g.: maternal age) and birth factors (e.g.: birth weight, prolonged stay in neonatal intensive care), to investigate associations with risk of emergency use of secondary care and other outcomes, including mortality. c) UCL will conduct prognostic analyses for children and young people based on diagnosis and procedure codes to identify risk factors for emergency hospital care and for subsequent long-term adverse outcomes into adulthood (e.g.: further emergency admissions or death). d) UCL would also like to request all ONS death records for deaths registered in England from 1st January 1998 until as late as possible, for all persons who died aged 0-55; in other words, both records that link to HES as well as those that do not link to HES. UCL need all deaths in order to assess the degree of misclassification of outcome (alive/dead) due to linkage errors between ONS and HES datasets. It is crucial that UCL get the age at death on the ONS records in order to do this. Full dates of death is required to be able to estimate age of death in days for the work on infant mortality (for instance, to be able to distinguish between first week from later neonatal deaths and from postneonatal deaths), as well as for the work on cause specific mortality where UCL classify deaths based on admissions within a certain number of days from death. Additionally, having date of death available enables UCL to determine delay in death registration for data validation. What will be done with the data? UCL will use longitudinal HES data, linked to ONS death records, to construct cohorts for a number of patient subgroups defined by age, sex, and clinical characteristics, to address the questions above. All analyses will be done within the safe haven. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.


Project 28 — DARS-NIC-49297-Q7G1Q

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/12 — 2018/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Critical Care

Benefits:

NCDS surveys include questions relating to health outcomes and hospitalisations. CLS will use these responses to compare with their data available on HES to obtain a better understanding of relationship between self-reporting and administrative data. This will be shared via methodological information which will assess the data quality and comparability of two important data sources. This will be of benefit to research investigating health and social care. This data linkage will facilitate research that CLS anticipate will be carried out on the effects of familial socioeconomic circumstances, lifestyle and environmental factors on the evolution of the wellbeing, health and development of family members. This could be of direct benefit to the NHS, patients and to community services interfacing with schools through informing policy to improve healthy lifestyles. Below are examples of existing publications using NCDS data benefiting public health in the areas of pregnancy health, birth, breastfeeding, vitamin D, obesity, diabetes, respiratory disease. Delpierre, C., Fantin, R., Barboza-Solis, C., Lepage, B., Darnaudéry, and M., Kelly-Irving, M. (2016) The early life nutritional environment and early life stress as potential pathways towards the metabolic syndrome in mid-life? A lifecourse analysis using the 1958 British Birth cohort. BMC Public Health. 2016 Aug 18; 16(1):815. Epub 2016 Aug 18. LLEWELLYN, A, SIMMONDS, M, OWEN, C.G and WOOLACOTT, N. (2016) Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obesity Reviews, 17(1), 56-67. BARBOSA-SOLÍS, C, KELLY-IRVING, M, FANTIN, R, DARNAUDÉRY, M, TORRISANI, J, LANG, T and DELPIERRE, C. (2015) Adverse childhood experiences and physiological wear-and-tear in midlife: Findings from the 1958 British birth cohort. Proceedings of the National Academy of Sciences of the United States of America, 112(7), E738–E746. BERRY, D.J, HESKETH, K, POWER, C and HYPPONEN, E. (2011) Vitamin D status has a linear association with seasonal infections and lung function in British adults. British Journal of Nutrition, 106(9), 1433-14440. MONTGOMERY, S.M and EKBOM, A. (2002) Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. British Medical Journal, 324, 26-27. In it’s nearly sixty years research the NCDS cohort has been responsible for proving beyond doubt that mothers who smoked heavily during pregnancy harmed the health and reduced the weight and height of their children, continuing on to damage English and maths scores at 16 years old. The study also informed the debate about the best place to deliver babies, indicating that mothers should only opt for home births when very early transfer to hospital is possible at the first sign of need and where highly experienced midwives and doctors are available. The study repeatedly demonstrated the need for steps to promote the health of pregnant mothers and facilities for safe childbirth. This led to the modernisation of maternity services with ready availability of high quality obstetrics on the one hand and better and more personal care for all. The case was made for adequate numbers of hospital beds and abolition of the lottery of where to give birth. Research has also made use of the longitudinal nature of the NCDS to examine the long-term effects of breastfeeding. For example, Rudnicka et al (2007) demonstrate that, compared with those who were bottle-fed with formula milk, children who were breastfed for more than a month had a reduced waist circumference and waist/hip ratio, and lower odds of obesity as adults in their mid-forties. RUDNICKA, A. R, OWEN, C. G and STRACHAN, D. P. (2007) The effect of breast feeding on cardio-respiratory risk factors in adulthood. Pediatrics, 119(5), E1107-15. Delpierre, Fantin, Barboza-Solis, Lepage, Darnaudéry, and M. Kelly-Irving (2016) examined the influence of both the early nutritional environment, and the psychosocial environment, on the subsequent risk of metabolic syndrome (MetS) in midlife. Early nutritional environment, represented by mother’s pre-pregnancy BMI, was associated with the risk of MetS in midlife. An important mechanism involves a mother-to-child BMI transmission, independent of birth or perinatal conditions, socioeconomic characteristics and health behaviors over the lifecourse. However this mechanism was not sufficient for explaining the influence of mother’s pre-pregnancy BMI which implies the need to further explore other mechanisms in particular the role of genetics and early nutritional environment. Adverse Childhood Experiences (ACEs) (identified through categories such as child in care, physical neglect, offenders, parental separation, mental illness, alcohol abuse) was not independently associated with MetS. However, the authors suggest that other early life stressful events such as emergency caesarean deliveries and poor socioeconomic status during childhood may contribute as determinants of MetS (Delpierre, C., Fantin, R., Barboza-Solis, C., Lepage, B., Darnaudéry, and M., Kelly-Irving, M. (2016) The early life nutritional environment and early life stress as potential pathways towards the metabolic syndrome in mid-life? A lifecourse analysis using the 1958 British Birth cohort. BMC Public Health. 2016 Aug 18; 16(1):815. Epub 2016 Aug 18). Early negative circumstances during childhood, collected prospectively in the British birth cohort 1958, could be associated with physiological wear-and-tear in midlife as measured by allostatic load. This relationship was largely explained by health behaviors, body mass index, and socioeconomic status in adulthood, but not entirely. The results suggested that a biological link between adverse childhood exposures and adult health may be plausible. The authors’ findings contribute to the development of more adapted public health interventions, both at a societal and individual level (BARBOSA-SOLÍS, C, KELLY-IRVING, M, FANTIN, R, DARNAUDÉRY, M, TORRISANI, J, LANG, T and DELPIERRE, C. (2015) Adverse childhood experiences and physiological wear-and-tear in midlife: Findings from the 1958 British birth cohort. Proceedings of the National Academy of Sciences of the United States of America, 112(7), E738–E746). In meta-analysis, including the NCDS, Llewellyn, Simmonds, Owen, and Woolacott (2016) investigated the ability of childhood body mass index (BMI) to predict obesity-related morbidities in adulthood. The authors found that high childhood BMI was associated with an increased incidence of adult diabetes, coronary heart disease (CHD) and a range of cancers, but not stroke or breast cancer. The accuracy of childhood BMI to predict any adult morbidity was low. Only 31% of future diabetes and 22% of future hypertension and CHD occurred in children aged 12 or over classified as being overweight or obese. Only 20% of all adult cancers occurred in children classified as being overweight or obese. Childhood obesity was associated with moderately increased risks of adult obesity-related morbidity, but the increase in risk was not large enough for childhood BMI to be a good predictor of the incidence of adult morbidities as the majority of adult obesity-related morbidity occurred in adults who were of healthy weight in childhood. Therefore, the authors suggest, targeting obesity reduction solely at obese or overweight children may not substantially reduce the overall burden of obesity-related disease in adulthood (LLEWELLYN, A, SIMMONDS, M, OWEN, C.G and WOOLACOTT, N. (2016) Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obesity Reviews, 17(1), 56-67. Using cross-sectional data from the NCDS biomedical survey, Berry, Hesketh, Power and Hypponen (2011) found that vitamin D status had a linear relationship with respiratory infections and lung function, but randomised controlled trials are warranted to investigate the role of vitamin D supplementation on respiratory health and to establish the underlying mechanisms (BERRY, D.J, HESKETH, K, POWER, C and HYPPONEN, E. (2011) Vitamin D status has a linear association with seasonal infections and lung function in British adults. British Journal of Nutrition, 106(9), 1433-14440). Montgomery and Ekbom (2002) tested the hypothesis that maternal smoking during pregnancy increases both the risk of early onset type 2 diabetes and non­diabetic obesity in offspring. The association of diabetes with maternal smoking during pregnancy (independent of finer-grain measures of mothers' smoking in 1974, own smoking at age 16, and other potential confounding factors) suggested that it is a true risk factor for early adult onset diabetes. Cigarette smoking as a young adult was also independently associated with an increased risk of subsequent diabetes. In utero exposures due to smoking during pregnancy may increase the risk of both diabetes and obesity through programming, resulting in lifelong metabolic dysregulation, possibly due to fetal malnutrition or toxicity. The odds ratios for obesity without type 2 diabetes are more modest than those for diabetes and the scope for confounding may be greater. Smoking during pregnancy may represent another important determinant of metabolic dysregulation and type 2 diabetes in offspring. The authors stress that smoking during pregnancy should always be strongly discouraged (MONTGOMERY, S.M and EKBOM, A. (2002) Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. British Medical Journal, 324, 26-27). Research using this cohort has also shed light on cancer and leukaemia in childhood, behavioural disorder, educational delay and disability. Linking Hospital Episodes Statistics (HES) to the NCDS survey data will greatly increase the potential of this unique dataset which has already been benefiting health outcomes for nearly 60 years. Our society is changing fast. This cohort study will be used to chart and understand how society has changed over the years, and how life experiences are different for each generation. They help understand the impact of societal trends such as the ageing population and the growth in lone-parent and step-families, and changes such as growing employment insecurity. This study helps understand that change. Evidence from this cohort study have contributed to many policy decisions in diverse areas – such as increasing the duration of maternity leave, raising the school leaving age, updating breast feeding advice given to parents. Further examples of benefits to health can be found on the study website athttps://ncds.info/home/what-have-we-learned/

Outputs:

Following the data quality and validation work, the first output will be the creation of the linked NCDS/HES dataset. The HES data will add an important layer to this already rich data as well as providing the means for data quality checking. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. The creation of this HES/NCDS Aged 50 database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. The onward sharing to researchers via an agreed mechanism will be subject to a further application to NHS Digital. The outputs in the long term from this dataset are difficult to quantify, but the CLS currently has a searchable bibliography on it's website with over 3,600 publications based on data from the 1958, 1970, Next steps and millennium cohort studies. CLS actively promotes the use of their data among the research community through publications and events, as well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care.

Processing:

Only individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and sanctions as substantive employees will have access to the data and only for the purposes described in this document. Identifiers will be held separately from attribute characteristics. HES data will not be re-linked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study ID which is used to destroy the data. 1. CLS team will supply NHS Digital with identifiers of cohort members who have consented to this data linkage, including full name, sex, postcode, date of birth and unique ID (study-specific pseudonymised identifier). 2. NHS Digital will link the identifiable study data to HEs data. NHS Digital will then remove identifiers from the linked dataset and return to the CLS team at UCL with the study ID. 3. CLS will carry out validation of the administrative data received (linked HES data) and will combine the supplied administrative data with the information collected from the participant as part of the NCDS study using the study ID. Once the linked survey-administrative data files have been created, CLS may perform other activities to prepare the data for use by other researchers, such as coding and cleaning, derivation of summary variables and compilation of data documentation but as above. 4. CLS researchers will use these data to create an analysis file that will not contain any identifiable data. 5. CLS will create derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.), and will compare NCDS survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys. UCL are prohibited from linking the identifiable data they hold with data disseminated from NHS Digital. The only exception to this condition would be where a participant wishes to withdraw from the study, the identifiable data would be used to locate the study id and then in turn to destroy the data. UCL will not share the linked HES/NCDS Aged 50 data with third parties.

Objectives:

The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council (ESRC) Centre, based at the Department of Quantitative Social Science, UCL Institute of Education. It is responsible for three of Britain's internationally renowned birth cohort studies, the 1958 National Child Development Study, the 1970 British Cohort Study and the Millennium Cohort Study (MCS). All these studies are 'birth' studies, following the groups of participants from cradle to grave. As such, this group of studies is unique and has, and still is, providing a wealth of information used in the policy decisions affecting society's health and well-being. This application is for data to be linked to a subset of the 1958 National Child Development Study (NCDS). In 1958 doctors and scientists were concerned at the high rate of infant death and ill health in Britain. There were an alarming number of stillbirths and children dying in the first few weeks of life. And so the National Child Development Study (NCDS) began as the Perinatal Mortality Survey. Nearly 17,500 babies were studied. Information was collected on the family background of the mother, her pregnancy and labour, and about her baby at birth and during its first week of life. Seven years later it was decided that it would be worthwhile to find the families included in the original birth survey and see what had happened to the babies since they were born – how healthy they were, how they were getting on at school, and so on. This second survey was carried out in 1965. Since then there have been eight other major surveys, attempting to trace all those born in the week of the original 1958 survey – in 1969, 1974, 1981, 1991, 1999/2000, 2004/5, 2008/9 and most recently in 2013. In addition, a major ‘bio-medical’ survey took place in 2002/3. During the 2008 (Aged 50) survey, CLS obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from 6529 cohort members who at the time were in England. Linking health data from Hospital Episodes Statistics (HES) to the NCDS survey data will greatly increase the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyles choices such as drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data will enrich these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which could help us better understand how health conditions could be better treated or supported. Data about health behaviours may be more accurate if obtained from administrative records as a result of misreporting of complex health conditions, under-reporting of particular health problems or due to perceived sensitivities around certain behaviours and lifestyle choices. So this also offers a methodological opportunity to validate the data collected in the survey and vice versa. At this stage the aim of the research is to; 1. validate and improve the quality of the cohort data 2. produce methodological papers describing the quality of the data and its benefit to health and social care 3. develop and create a useful and rich HES linked NCDS Aged 50 dataset


Project 29 — DARS-NIC-49826-T0J7C

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/06 — 2017/08. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

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

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Outpatients

Benefits:

The BCS70 surveys include questions relating to health outcomes and hospitalisations. CLS will use these responses to compare with their data available on HES to obtain a better understanding of relationship between self-reporting and administrative data. This will be shared via methodological information which will assess the data quality and comparability of two important data sources. This will be of benefit to research looking at health and social care issues which in turn, through time and cost savings will be of benefit to patients. This data linkage will facilitate research that CLS anticipate will be carried out on the effects of familial socioeconomic circumstances, lifestyle and environmental factors on the evolution of the wellbeing, health and development of family members. This will be of direct benefit to the NHS and to community services such as those interfacing with schools through informing policy to improve healthy lifestyles. It is difficult to predict in advance the type of research question that might be put forward. Below are four examples of existing publications using BCS70 data benefiting public health. GREENE, G, GREGORY, A.M, FONE, D and WHITE, J. (2015) Childhood sleeping difficulties and depression in adulthood: the 1970 British Cohort Study. Journal of Sleep Research, 24(1), 19-23. VINER, R.M. and TAYLOR, B. (2007) Adult outcomes of binge drinking in adolescence: findings from a UK national birth cohort. Journal of Epidemiology and Community Health, 61(10), 902-907. CABLE, N, KELLY, Y, BARTLEY, M, SATO, Y and SACKER, A. (2014) Critical role of smoking and household dampness during childhood for adult phlegm and cough: a research example from a prospective cohort study in Great Britain. BMJ Open, 4(4), e004807. SMITH, L, GARDNER, B, AGGIO, D and HAMER, M. (2015) Association between participation in outdoor play and sport at 10 years old with physical activity in adulthood. Preventive Medicine, 74(May 2015), 31–35. Below expands further on the benefits of these examples of existing publications using BCS70 data drawing attention on how early life course experiences/exposures shape health outcomes into adulthood. Greene, Gregory, Fone and White (2015), for example, investigated the relationship between childhood sleeping difficulties (at age 5) and depression in adulthood (age 34), to conclude that severe sleeping problems in childhood may be associated with increased susceptibility to depression in adult life. Adjusting for the potential confounding influences of maternal depression and sleeping difficulties, parental reports of severe sleeping difficulties at 5 years were associated with an increased risk of depression at age 34 years [odds ratio (OR) = 1.9, 95% confidence interval (CI) = 1.2, 3.2] whereas moderate sleeping difficulties were not (OR = 1.1, 95% CI = 0.9, 1.3). Further research, however, is needed to explore whether screening and the treatment of children for poor sleeping patterns might impact upon their mental health in adulthood. Persistent sleep problems are an increasing health concern. In addition, poor sleep in adulthood has been linked with hypertension, diabetes, depression and obesity, as well as from cancer and increased mortality (Colten and Altevogt, 2006). Therefore, successful identification and treatment for children with sleeping difficulties could, if the association identified by the authors is causal, have large dividends across many aspects of health in the future (GREENE, G, GREGORY, A.M, FONE, D and WHITE, J. (2015) Childhood sleeping difficulties and depression in adulthood: the 1970 British Cohort Study. Journal of Sleep Research, 24(1), 19-23.). Viner and Taylor (2007) studied outcomes in adult life (at age 30) of binge drinking in adolescence (at age 16). Adolescent binge drinking predicted an increased risk of adult alcohol dependence (OR 1.6, 95% CI 1.3 to 2.0), excessive regular consumption (OR 1.7, 95% CI 1.4 to 2.1), illicit drug use (OR 1.4, 95% CI 1.1 to 1.8), psychiatric morbidity (OR 1.4, 95% CI 1.1 to 1.9), homelessness (OR 1.6, 95% CI 1.1 to 2.4), convictions (1.9, 95% CI 1.4 to 2.5), school exclusion (OR 3.9, 95% CI 1.9 to 8.2), lack of qualifications (OR 1.3, 95% CI 1.1 to 1.6), accidents (OR 1.4, 95% CI 1.1 to 1.6) and lower adult social class, after adjustment for adolescent socioeconomic status and adolescent baseline status of the outcome under study. The authors draw attention that these associations appear to be distinct from those associated with habitual frequent alcohol use, and binge drinking may contribute to the development of health and social inequalities during the transition from adolescence to adulthood (VINER, R.M. and TAYLOR, B. (2007) Adult outcomes of binge drinking in adolescence: findings from a UK national birth cohort. Journal of Epidemiology and Community Health, 61(10), 902-907.). Cable, Kelly, Bartley, Sato, and Sacker (2014) findings from BCS70 data give support to current public health interventions for adult smoking and raise concerns about the long-term effects of a damp home environment on the respiratory health of children. The authors examined the associations between childhood exposures to smoking and household dampness (at age 10), and phlegm and cough in adulthood (29 years of age), and found that childhood smoking and exposure to marked household dampness at age 10 were associated with phlegm (childhood smoking: relative risk ratio (RRR) =1.45, 95% CI 1.02 to 2.05; dampness: RRR=2.05, 95% CI 1.07 to 3.91) and co-occurring cough and phlegm (childhood smoking: RRR=1.35. 95% CI 1.08 to 1.67; dampness: RRR=2.73, 95% CI 1.88 to 3.99), while exposure to two or more adult smokers in the household was associated with cough-related symptoms (cough only: RRR=1.28, 95% CI 1.04 to 1.58; phlegm and cough: RRR=1.32, 95% CI 1.06 to 1.64). These associations were independent from adult smoking, childhood phlegm and cough, early social background and sex. Smoking at age 29 contributed to all symptom patterns, however, a substantial association between household dampness and co-occurring phlegm and cough suggest long-term detrimental effects of childhood environmental exposures. The authors findings support current public health interventions to reduce adult smoking, but also indicate that the management of childhood risk factors such as exposure to smoke (active or second-hand) and household dampness can be a way to prevent adults experiencing poor respiratory health (CABLE, N, KELLY, Y, BARTLEY, M, SATO, Y and SACKER, A. (2014) Critical role of smoking and household dampness during childhood for adult phlegm and cough: a research example from a prospective cohort study in Great Britain. BMJ Open, 4(4), e004807). Smith, Gardner, Aggio and Hamer (2015) investigated whether active outdoor play and/or sports at age 10 is associated with sport/physical activity at age 42. Final adjusted Cox regression models showed that participants (n=6458) who often participated in sports at age 10 were significantly more likely to participate in sport/physical activity at age 42 (RR 1.10; 95% CI 1.01 to 1.19). Active outdoor play at age 10 was not associated with participation in sport/physical activity at age 42 (RR 0.99; 95% CI 0.91 to 1.07). The finding authors suggest that childhood activity interventions might best achieve lasting change by promoting engagement in sport rather than active outdoor play (Tammelin et al., 2003a, 2003b) (SMITH, L, GARDNER, B, AGGIO, D and HAMER, M. (2015) Association between participation in outdoor play and sport at 10 years old with physical activity in adulthood. Preventive Medicine, 74(May 2015), 31–35.) To provide an example of the sorts of benefits to health that this linkage and use of this data may provide, it may be useful to be aware of the impact and benefit to health the 1958 National Child Development Study (NCDS) cohort has made. This is a similar birth tracking cohort still following it's members today. In it’s nearly sixty years research from this cohort has been responsible for proving beyond doubt that mothers who smoked heavily during pregnancy harmed the health and reduced the weight and height of their children, continuing on to damage English and maths scores at 16 years old. The study also informed the debate about the best place to deliver babies, indicating that mothers should only opt for home births when very early transfer to hospital is possible at the first sign of need and where highly experienced midwives and doctors are available. The study repeatedly demonstrated the need for steps to promote the health of pregnant mothers and facilities for safe childbirth. This led to the modernisation of maternity services with ready availability of high quality obstetrics on the one hand and better and more personal care for all. The case was made for adequate numbers of hospital beds and abolition of the lottery of where to give birth. Research has also made use of the longitudinal nature of the NCDS to examine the long-term effects of breastfeeding. For example, Rudnicka et al (2007) demonstrate that, compared with those who were bottle-fed with formula milk, children who were breastfed for more than a month had a reduced waist circumference and waist/hip ratio, and lower odds of obesity as adults in their mid-forties. Research using this cohort has also shed light on cancer and leukaemia in childhood, behavioural disorder, educational delay and disability. RUDNICKA, A. R, OWEN, C. G and STRACHAN, D. P. (2007) The effect of breast feeding on cardio-respiratory risk factors in adulthood. Pediatrics, 119(5), E1107-15. BCS70 data is a rich and unique resource for the research and policy community, and the information collected on health and its social determinants widens its potential value for health research and policy interventions. Linking health data from Hospital Episodes Statistics (HES) to the BCS70 survey data will greatly increase the potential of the data and future research in the area of health. The validation of self reported and hospital reported outcomes will benefit health research in terms of being able to offer research methodologies that are quicker and more cost effective. This will be of benefit to patients who are the recipients of research such as in public health and medical interventions etc. For example, using health administration data such as HES could make the delivery of research more efficient and potentially more accurate, it may increase the volume of research and it may ensure that research takes place into diseases which are currently difficult to fund.

Outputs:

Following the data quality and validation work, the first output will be the creation of the linked BCS70 (Age42)/HES dataset. The HES data will add an important layer to this already rich data as well as providing the means for data quality checking. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. The creation of this database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. No onward sharing to researchers will take place. Any onward sharing will be subject to a further application to NHS Digital. The outputs in the long term from this dataset are difficult to quantify, but the CLS currently has a searchable bibliography on it's website with over 3,600 publications based on data from the 1958, 1970 and millennium cohort studies.

Processing:

Data disseminated from NHSD to UCL will only be accessed by substantive employees of UCL and only for the purposes described in this document. HES data will not be relinked to the identifiable data which is held separately from the survey response data. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study id, and then in turn destroy their data. 1. CLS team will supply NHS Digital with the following identifiers of cohort members who have consented to this data sharing; sex, postcode, date of birth, NHS number (if known) and unique ID (study-specific pseudonymised identifier). 2. NHS Digital will link the identifiable study data to HES data. NHS Digital will then remove identifiers from linked dataset and return the dataset to the CLS team at UCL with the study ID. 3. CLS will carry out validation of the linked HES data and will combine the supplied HES data with the information collected from the participant as part of the BCS70 study. Once the linked survey-HES data files have been created, CLS may perform other activities to prepare the data for use in research, such as coding and cleaning, derivation of summary variables and compilation of data documentation. 4. CLS researchers will use these data to create an analysis file that will not contain any identifiable data. 5. CLS will create derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.), and will compare BCS70 survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys.

Objectives:

The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council (ESRC) Centre, based at the Department of Quantitative Social Science, UCL Institute of Education. It is responsible for three of Britain's internationally renowned birth cohort studies, the 1958 National Child Development Study, the 1970 British Cohort Study and the Millennium Cohort Study (MCS). All these studies are 'birth' studies, following the groups of participants from cradle to grave. As such, this group of studies is unique and has, and still is, providing a wealth of information used in the policy decisions affecting society's health and well-being. The 1970 British Cohort Study (BCS70) has its origins in the late 1960s, when there was a great deal of concern amongst doctors and others about the number of babies born with abnormalities, or dying very early in life. They decided to compare those mothers and babies who had problems, with those who did not in order to see what could be done about this issue. The simplest way to do this was to study all the babies born in one week. With the help of doctors, midwives, and health authorities throughout England, Wales and Scotland, this study was carried out in 1970. Information was collected on the family background of the mother, her pregnancy and labour, and about her baby at birth and in the first week of life. Almost 17,500 babies were studied. It was not for another 5 years that it was decided that it would be worthwhile trying to find the families from the original birth survey to see what had happened to the babies since 1970 – how healthy they were, how they were getting on at school, and so on. This second survey was carried out in 1975. Since then there have been seven other major surveys, attempting to trace all those born in the week of the original 1970 survey – in 1980, 1986, 1996, 1999/2000, 2004/5, 2008 and in 2012 when study members were aged 42. During the 2012 survey, CLS obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from 6181 cohort members who at the time were in England. Linking health data from Hospital Episodes Statistics (HES) to the BCS70 survey data will greatly increase the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyles choices such as drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data will enrich these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which could help improve understanding of how health conditions could be better treated or supported. Data about health behaviours may be more accurate if obtained from administrative records as a result of misreporting of complex health conditions, under-reporting of particular health problems or due to perceived sensitivities around certain behaviours and lifestyle choices. So this also offers a valuable methodological opportunity to validate the data collected in the survey and vice versa. At this stage the aim of the researchers is to; 1. Validate and improve the quality of the cohort data 2. Produce methodological papers describing the quality of the data and its benefit to health and social care 3. Develop and create a useful and rich HES linked (Age 42) BCS70 dataset UCL will not link the identifiable data they hold with data disseminated from NHS Digital. The only exception would be where a participant wishes to withdraw from the study. UCL will not share the linked HES/Age 42 BCS70 dataset with third parties.


Project 30 — DARS-NIC-51342-V1M5W

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/09 — 2017/11. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Accident and Emergency

Benefits:

Next Steps surveys include questions relating to health outcomes and hospitalisations. CLS will use these responses to compare with their data available on HES to obtain a better understanding of relationship between self-reporting and administrative data. This will be shared via methodological information which will assess the data quality and comparability of two important data sources. This will benefit research looking at Health and Social Care This data linkage will facilitate research that CLS anticipate will be carried out on the effects of familial socioeconomic circumstances, lifestyle and environmental factors on the evolution of the wellbeing, health and development of family members. This could be of direct benefit to the NHS and to community services interfacing with schools through informing policy to improve healthy lifestyles. It is difficult to predict in advance the type of research question that might be put forward. Below are examples of existing publications using Longitudinal Study of Young People in England (LSYPE) data benefiting public health. Calderwood, L., and Sanchez, C. (2016). Next Steps (formerly known as the Longitudinal Study of Young People in England). Open Health Data, 4(1), e2 Hale, D., and Viner, R. (2016). The correlates and course of multiple health risk behaviour in adolescence. BMC Public Health. 2016 May 31; 16: 458. doi: 10.1186/s12889-016-3120-z. Semlyen, J., King, M., Varney, J., and Hagger-Johnson, G. (2016). Sexual orientation and symptoms of common mental disorder or low wellbeing: combined meta-analysis of 12 UK population health surveys. BMC Psychiatry. 2016 Mar 24;16: 67. doi: 10.1186/s12888-016-0767-z. Symonds, J., Dietrich, J., Chow, A., and Salmela-Aro, K. (2016). Mental health improves after transition from comprehensive school to vocational education or employment in England: A national cohort study. Developmental Psychology, 52(4), 652-665 Chatzitheochari, S., Parsons, S., and Platt, l. (2015). Doubly Disadvantaged? Bullying Experiences among Disabled Children and Young People in England. Sociology, advance online access, 28 April 2015 Debell, D. (2015). Public Health for Children, Second Edition. London: CRC Press. Hale, D., and Viner, R. (2015). Health in adolescence influences educational attainments and life chances: longitudinal associations in the Longitudinal Study of Young People in England (LSYPE). Archives of Disease in Childhood, 100 (Suppl.3), A210-A211. Hatton, C., and Emerson, E. (2015). International Review of Research into Developmental Disabilities: Health Disparities and Intellectual Disabilities. London: Academic Press. Department for Education, TNS BMRB. (2015). Second Longitudinal Study of Young People in England: Wave 1, 2013: Secure Access. [Data collection]. UK Data Service. SN: 7838, http://dx.doi.org/10.5255/UKDA-SN-7838-1. Department for Education, NatCen Social Research (2013). First Longitudinal Study of Young People in England: Waves One to Seven, 2004-2010: Secure Access. [Data collection]. 2nd Edition. UK Data Service. SN: 7104, http://dx.doi.org/10.5255/UKDA-SN-7104-2. Next Steps (formally known as the Longitudinal Study of Young People in England - LSYPE) data is a resource with great potential for research and policy community, and the information collected on health and its social determinants widens its potential value for health research and policy interventions. Researchers currently have access to the LSYPE data and are able to apply and carry out research utilising the established link to benefit health and social care. Below are some examples of existing publications using LSYPE data (waves 1 to 7) benefiting public health. Hale and Viner (2015), for example, examine longitudinally the causal pathways from poor adolescent health to low academic attainment and unemployment in young adulthood, and make recommendations for policy interventions to focus on improving outcomes for unhealthy adolescents. Having a chronic condition, poor mental health and poor self-reported general health were assessed between ages 13 and 15. Outcome variables included poor academic performance (non-attainment of expected academic proficiency based on mandated school examinations) at age 16 and NEET status (not in education, employment or training) at age 19. The authors examined associations between health and subsequent outcomes, and conducted mediator analyses to assess the proportion of the association attributable to hypothesised mediators including school absences, classroom behaviour, truancy, social exclusion, health behaviours and psychological distress. The study revealed that poor mental and general health and long-term conditions predicted low educational attainment at age 16. Poor mental health and poor general health (but not long-term conditions) predicted unemployment. Social exclusion was a consistent mediating variable. Long-term absences mediated associations between general health and mental health and later outcomes whereas school behaviour, truancy and substance use were significant mediators for general health and mental health. Poor adolescent health disrupts educational and employment pathways. Due to the economic and social costs of educational underachievement and unemployment, policy interventions should focus on improving outcomes for unhealthy adolescents (Hale, D., and Viner, R. (2015). Health in adolescence influences educational attainments and life chances: longitudinal associations in the Longitudinal Study of Young People in England (LSYPE). Archives of Disease in Childhood, 100 (Suppl.3), A210-A211.). The same authors - Hale and Viner (2016) - examined the association between health risk behaviours (such as smoking, alcohol use, illicit drug use, delinquency and unsafe sexual behaviour) throughout adolescence (and at ages 14, 16, and 19) and identified common risk factors for multiple risk behaviour (involvement in two or more risk behaviours) in late adolescence (at age 19), drawing attention to policy focus on prevention of adolescence health risk behaviours. All early risk behaviours were found to be associated with other risk behaviours at age 19. A number of sociodemographic, interpersonal, school and family factors at age 14 predicted risk behaviour and multiple risk behaviour at age 19. Past risk behaviour being a strong predictor of age 19 risk behaviour with those involved in multiple risk behaviour in early adolescence being far more likely to be multiple risk-takers at age 19, while many involved in only one form of risk behaviour in mid-adolescence do no progress to multiple risk behaviour (Hale, D., and Viner, R. (2016). The correlates and course of multiple health risk behaviour in adolescence. BMC Public Health. 2016 May 31; 16: 458. doi: 10.1186/s12889-016-3120-z.). LSYPE data has also contributed to evidence on childhood disability. Chatzitheochari, Parsons and Platt (2015) enhanced the evidence on school bullying experience among disabled children, likely to have a strong negative impact on social and psychological later life outcomes. The authors studied the relationship between bullying victimisation and childhood disability, and revealed an independent association of disability with bullying victimisation, suggesting potential pathway to cumulative disability-related disadvantage, drawing attention to the school as a site of reproduction of social inequalities (Chatzitheochari, S., Parsons, S., and Platt, l. (2015). Doubly Disadvantaged? Bullying Experiences among Disabled Children and Young People in England. Sociology, advance online access, 28 April 2015). Semlyen, King, Varney, and Hagger-Johnson (2016) studied the association between sexual orientation identity and poor mental health and drew attention on LGB adults in UK and their higher prevalence of poor mental health and low well-being when compared to heterosexuals. They addressed the need of routine measurement of sexual orientation in health studies and administrative data in order to influence national and local policy development and service delivery. Their findings reiterate for local government, NHS providers and public health policy makers to consider how to address inequalities in mental health among these minority groups (Semlyen, J., King, M., Varney, J., and Hagger-Johnson, G. (2016). Sexual orientation and symptoms of common mental disorder or low wellbeing: combined meta-analysis of 12 UK population health surveys. BMC Psychiatry. 2016 Mar 24;16: 67. doi: 10.1186/s12888-016-0767-z.). Symonds et al. (2016) analysed mental health at the school to work transition. The authors examined how adolescents’ anxiety, depressive symptoms, and positive functioning developed as they transferred from comprehensive school to further education, employment or training, or became NEET (not in education, employment or training), at age 16 years. Controlling for childhood achievement, socioeconomic status, ethnicity, and gender, the authors found that NEET adolescents had the largest losses in mental health. This pattern was similar to adolescents staying on at school who had increased anxiety and depression, and decreased positive functioning, after transition. In comparison, adolescents transferring to full time work, apprenticeships or vocational college experienced gains in mental health (Symonds, J., Dietrich, J., Chow, A., and Salmela-Aro, K. (2016). Mental health improves after transition from comprehensive school to vocational education or employment in England: A national cohort study. Developmental Psychology, 52(4), 652-665). Next Steps Age 25 survey broadens the information collected on health and well-being, including family relationships, employment, education and income, which will add to the potential of the data and future research in the area of health.

Outputs:

Following the data quality and validation work, the first output will be the creation of the linked Next Steps/HES dataset. The HES data will add an important layer to this already rich data as well as providing the means for data quality checking. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. The creation of this HES/Next Steps database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. The onward sharing to researchers via an agreed mechanism will be subject to a further application to NHS Digital. The outputs in the long term from this dataset are difficult to quantify, but the CLS currently has a searchable bibliography on its website with over 3,600 publications based on data from the 1958, 1970, Millennium cohort and Next Steps studies. CLS actively promotes the use of their data among the research community through publications and events, as well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care.

Processing:

Data disseminated from NHSD to UCL will only be accessed by substantive employees of UCL and only for the purposes described in this document. Identifiers will be held separately from attribute characteristics. HES data will not be relinked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study id, and then in turn destroy their data. 1. CLS team will supply NHS Digital with identifiers of cohort members who have consented to this data linkage, including full name, sex, postcode, date of birth and study ID (study-specific pseudonymised identifier). 2. NHS Digital will link the identifiable study data to HES data. NHS Digital will then remove identifiers from linked dataset and return the dataset to the CLS team at UCL with the study ID. 3. CLS will carry out validation of the administrative data received (linked HES data) and will combine the supplied administrative data with the information collected from the participant as part of the Next Steps study using the study ID. Once the linked survey-administrative data files have been created, CLS may perform other activities to prepare the data for use , such as coding and cleaning, derivation of summary variables and compilation of data documentation. 4. CLS researchers will use these data to create an analysis file, which to confirm will not contain any identifiable data. 5. CLS will create derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.), and will compare Next Steps survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys.

Objectives:

The Centre for Longitudinal Studies (CLS) is an academic resource centre responsible for producing and disseminating data resources for the scientific community. CLS manages three world-renowned birth cohort studies; the National Child Development Study 1958, the British Cohort Study 1970, and the Millennium Cohort Study 2000 and now have the Next Steps cohort in their portfolio. Next Steps is a longitudinal study following the lives of 16,000 people born in 1989/90, originally sampled from schools in England at age 13/14 years and initially managed by the Department for Education. Next Steps participants were interviewed annually between 2004 and 2010 and again in 2015/16 to map their journeys through education and transitions into adulthood and the labour market. Next Steps is the largest and most detailed research study of its kind trying to understand the changing experiences of this generation. As such, Next Steps has already been highly valuable in informing policy decisions and in enhancing understanding of how specific Government policies can influence and shape the lives of young people. Next Steps data has also been widely used by academic researchers in the UK and elsewhere. During the 2015/16 survey, CLS obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from approximately 4941 cohort members. Linking health data from Hospital Episodes Statistics (HES) to the Next Steps survey data will greatly increase the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyles choices such as drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data will enrich these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which could help us better understand how health conditions could be better treated or supported. Data about health behaviours may be more accurate if obtained from administrative records as a result of misreporting of complex health conditions, under-reporting of particular health problems or due to perceived sensitivities around certain behaviours and lifestyle choices. So this also offers an interesting methodological opportunity to validate the data collected in the survey and vice versa. At this stage the aim of the researchers is to: 1. Validate and improve the quality of the cohort data 2. Produce methodological papers describing the quality of the data and its benefit to health and social care 3. Develop and create a useful and rich HES linked Next Steps dataset


Project 31 — DARS-NIC-86666-V7Z1L

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2018/03 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Office for National Statistics Mortality Data
  • Bridge file: Hospital Episode Statistics to Mortality Data from the Office of National Statistics

Benefits:

The findings of this study will feed into policy development for care pathways for homeless people accessing hospital and housing services. Policy responses are expected within 5-10 years, i.e. 2023-2028. In addition the outputs from this study will enable Commissioners to make better informed decisions in relation to the specialist services within this research. The findings from this study will enable Commissioners to ensure specialist services offered within their area are better targeted to suit the needs of the local population. Specific Outputs Expected, Including Target Date include: - Report for the funders The National Institute for Health Research (February 2018), including a Health Technology Assessment. This document will outline the results from the objectives and analyses outlined in the study protocol and include a "roadmap" for commissioners (see Benefits section). - Manuscript of findings for peer-reviewed publication (Spring 2018). As above this document will outline the results from the objectives and analyses outlined in our study protocol. UCL's target journals are BMJ, The Lancet Public Health and BMC public health. - Aggregated, with small numbers suppressed in line with the HES analyse guide, site-specific summary of readmissions and mortality for each of the sites participating in the study (February 2018). This information will help sites better understand the care pathways of patients accessing their services. As a direct result of this research, subsequent policy development and better commissioning, UCL expect that this study will directly benefit patients the aim is to identify and improve: - How hospital readmissions can be prevented through the provision of specialist services for people with experience of homelessness - How UCL can help prevent people with experience of homelessness having to visit emergency departments after a hospital admission - How UCL can provide more appropriate hospital treatment to people with experience of homelessness - How UCL can avoid deaths in people with experience of homelessness through the provision of better services and treatment whilst in hospital

Outputs:

A report for the funders The National Institute for Health Research (February 2018), including a Health Technology Assessment. This document will outline the results from the objectives and analyses outlined in the study protocol and include a "roadmap" for commissioners (see Benefits section). - Manuscript of findings for peer-reviewed publication (Spring 2018). As above this document will outline the results from the objectives and analyses outlined in our study protocol. UCL's target journals are BMJ, The Lancet Public Health and BMC public health. - Aggregated, with small numbers suppressed in line with the HES analyse guide, site-specific summary of readmissions and mortality for each of the sites participating in the study (February 2018). This information will help sites better understand the care pathways of patients accessing their services. To assist with development of policy from the findings a series of planned feedback events across the country. These events will be attended by local stakeholders - including the homeless health charity Pathway, homeless service users, health and social care commissioners and service providers - who are well placed to help us disseminate the findings in an effective way. These events will aid translation of the results into a "roadmap" against which commissioners can explore the strengths and weaknesses of their local provision. A final version of the roapmap will be published as part of a Health Technology Assessment paper, for use by policy developers such as NICE.

Processing:

Three groups of individuals admitted to hospital will be included in the study. The first group are homeless individuals admitted to hospital at any one of the 16 sites which offer a specialist discharge scheme. The second group are individuals seen by a community homeless service in London, Find and Treat. Find and Treat is a specialist outreach team that work alongside over 200 NHS and third sector front-line services to tackle TB among homeless people. The third group are a random sample of individuals equal in size to the Find and Treat group and are living in lowest quintile of deprivation areas. Three main datasets will be used for analysis: • Unconsented data collected at the 16 sites that are part of this study • Unconsented data from Find and Treat • Linked HES and ONS mortality data Data flow from specialist hospital discharge services for homeless people, and from Find and Treat services for homeless people: 1) At each site the research team will compile identifiers (NHS number, forename, surname, aliases, date of birth, sex) for each patient accessing the service between November 2013 to November 2016 and create a unique study identifier for each record for the service provider. 2) The data requested for the study will then be securely uploaded and processed at University College London (UCL). The data will be stored and cleaned. 3) Identifiable information (NHS number, forename, surname, aliases, date of birth, sex,) required by NHS Digital for the linkage to Hospital Episode Statistics/Office for National Statistics (HES/ONS) will at this point be transferred to NHS Digital with the unique study identifier. 4) NHS Digital will use the Personal Demographics Service (PDS) to add NHS number (where missing) to the identifiable data from homeless healthcare users. 5) When NHS Digital have confirmed that the list is clean, and linkage to HES has been completed, the researchers will de-identify all data held by the researchers at UCL (i.e. all identifiers except the unique study identifier will be destroyed). 6) The data requested for the study - including the unique study identifier - will then be securely uploaded and processed on the data safe haven at UCL in line with the published study protocol. Dataset 1 will consist of data from homeless healthcare users and include forename, surname, aliases, date of birth, sex, address, contact numbers, hospital of admission, date of hospital admission, nationality, ethnicity, and NHS number. This data will be collected from study fieldwork sites. Dataset 3 will consist of data from homeless healthcare users and include forename, surname, aliases, sex, address, land contact number from Find and Treat Service. Dataset 4 Personal Demographics Service data from homeless healthcare users, including date of hospital admission, date of hospital discharge, date of hospital appointment and date of death. The data within PDS will be used to provide missing NHS numbers for Dataset 1 and Dataset 3. The research team will not at any point have access to these NHS Numbers, which will be used to improve the linkage of data to HES data. Dataset 5: HES ONS mortality data from homeless healthcare users and a geographically comparable and representative sample of lowest quintile of deprivation population in HES equal in size to the Find and Treat dataset. This data will be de-identified by NHS Digital. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide.

Objectives:

The aim of this work is to evaluate the impact of specialist hospital discharge services developed as part of the Department of Health’s Section 64 (voluntary sector-led) ‘ten million pound cash boost’ to improve hospital discharge for homeless people compared to "usual care" and are funded to do this work. Specifically University College London (UCL) will evaluate evidence of differences in time-to-subsequent hospital admissions (re-admissions) and mortality between i) homeless people accessing care at discharge services delivered by participating study sites, ii) homeless people admitted to hospitals without specialist services and iii) hospitalised non-homeless people in the most deprived quintile. In addition, the study aims to quantify differences in the characteristics of people in each of the three groups that could drive differences in rates of readmission or mortality irrespective of the hospital care available. UCL will also present stratified data for the two dominant types of specialist hospital discharge schemes (housing-link and clinically-led services). The principal research objectives are outlined as follows: 1. What are the rates of hospitalisation (overall admission rates, unscheduled admission rates and 28-day emergency readmission rates) for homeless people? Do these rates vary by type of specialist homeless hospital discharge service? 2. What are the mortality rates (including avoidable mortality) in homeless people and do these rates vary by type of specialist homeless hospital discharge service? How do mortality rates compare to people in the most deprived quintile who are not homeless? 3. What is the duration of hospital admission in homeless people accessing and not accessing specialist homeless hospital discharge services? In addition UCL will present summary data for the characteristics of hospitalised individuals within each group (homeless accessing specialist services, homeless not accessing specialist services, deprived non-homeless). The first work package seeks to gain an informed understanding of the ways in which specialist integrated homeless health and care services (SIHHC services) are being developed and implemented to facilitate hospital discharge in England and the impact this is having on quality of care and organisational outcomes such as the prevention of readmission to hospital. For this work package, local service providers will be asked to identify and nominate potential participants. The second work package (WP2, for which support is requested for datasets 1, 3, 4, and 5) is a data linkage and health economic analysis work package that will work with twenty sites across England where homeless patients have been admitted to hospital. A cohort of homeless people who have used specialist discharge scheme will be compared to a cohort of homeless people who have not used such provision. The study will also compare patient’s hospitalisation history before and after engagement with specialist services. Analysis will also be undertaken to understand whether the outcomes are a factor of homelessness specifically or are tied to deprivation. Data provided under this agreement is not being used for work package one. Data provided under this agreement will not be shared with Kings College London.


Project 32 — DARS-NIC-86954-Y0R2N

Opt outs honoured: Y

Sensitive: Sensitive

When: 2018/03 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Admitted Patient Care
  • MRIS - Flagging Current Status Report
  • MRIS - List Cleaning Report
  • MRIS - Cause of Death Report

Benefits:

The NSHD has informed UK health care, education and social policy for 70 years and is the oldest and longest running of the British birth cohort studies. Today, with study members in their early seventies, the NSHD offers a unique opportunity to explore the long-term biological and social processes of ageing and how ageing is affected by factors acting across the whole of life. Evidence is growing from this cohort study and others, that factors from early life (such as growth, neurodevelopment, nutrition and family socioeconomic circumstances) as well as later life (such as adult smoking, diet, exercise and socioeconomic circumstances) affect the opportunity to age well. This is of interest to policymakers, practitioners, and older people themselves. The research using NSHD life course information will provide insights into when in the life course interventions to prevent disease (in particular CVD). This information will inform the design of future interventions which can then be tested in controlled trials. In particular, through knowledge transfer, public engagement, publications, presentations and invited commentaries (http://www.nshd.mrc.ac.uk/findings/) the MRC LHA has contributed to a body of evidence to influence policies and support evidence based medicine. For example, recent paper in PLOS Medicine comparing lifetime trajectories of overweight and obesity across NSHD and the later born cohorts has been cited in the recent Government’s Child Obesity Strategy. Other examples highlighting the depth and breadth of this lifelong study include: • NSHD is a member of the Dementias Platform UK, a £53 million collaboration between universities and industry established by the MRC in 2014, to transform the best dementia research into the best treatments as quickly as possible. It combines the power of multiple population studies to compare healthy people with people at all stages of dementia. • The NSHD finding, in 2014, that more rapid rises in systolic blood pressure during midlife (even if not crossing into hypertension) were related to poorer cardiac structure (published in the European Heart Journal in 2014) has implications for treatment guidelines as it suggests that identification and treatment of people with rapidly increasing SBP, even if they are not reaching the criteria for hypertension, may be beneficial in preventing subsequent cardiovascular disease. • The NSHD findings (published in The Lancet Diabetes & Endocrinology in 2014) suggesting that those who lost weight at any age during adulthood, even if weight was regained later, had better cardiovascular risk profiles than those who remained overweight or obese supports public health strategies that help individuals to lose weight at all ages. • In 2014, the NSHD finding that better performance in tests of physical capability (i.e. grip strength, chair rising and standing balance) in midlife was linked to higher survival rates over 13 years of follow-up was published in the British Medical Journal. This highlighted the value of these simple objective physical tests in helping to identify those people who from at least as early as midlife onwards may require more support than others to achieve a long and healthy life. • Subsequent work examining changes in objective measures of physical capability between ages 53 and 60-64 has highlighted that age-related decline may not be entirely inevitable and is potentially modifiable. This work has also suggested that there may be a need to monitor physical capability from at least as early as midlife onwards as opportunities to help some high risk groups may already have been missed if no action is taken until later in life. • A 2009 report on adult life chances in relation to childhood mental health using NSHD was cited by the government in support of a case for early intervention to build mental capacity and resilience. • The study’s findings of the continuing effect of early life growth and development on health outcomes in adulthood add to the arguments for early intervention of the kind provided by the national SureStart programme. • The 1999 paper comparing children’s diet in 1950 with that in the 1990s (‘Food and nutrient intake of a national sample of four-year-old children in 1950: comparison with the 1990s’, Public Health Nutrition) had an impact because of its evidence that the quality and nutrient value of infant and childhood diet had declined between 1950 and 1990. • The study’s finding (published in All our Future in 1968) of the extent and inequity of the ‘waste of talent’ – in terms of high ability children who did not continue into further or higher education – added to arguments for improving opportunities for, and expectations of, children from poorer families. • The Home and the School (1964) had a great impact, probably because it provided the first hard evidence that parents and preschool circumstances had a significant impact on ability and attainment at age eight, and so showed that preschool development and experience formed the bedrock on which primary schooling was built. • Press reports that followed the publication of Maternity in Great Britain (1948), which were concerned with the ‘Need for Better Care and Lower Costs’ (The Times), are likely to have influenced the arguments for improvements in the care of mothers and babies. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Outputs:

The data will be used on an ongoing basis to update study member records. The database will be updated after each data release. The primary output of the linkages with HES, ONS mortality and Cancer Registration data are the maintenance and enhancement of the NSHD-DR. This is in turn used to achieve multiple research outputs that benefit health and social care. The programme ‘Enhancing NSHD’ examines many of the genomic and other epigenomic (genetic material of a cell) and metabolomics (systematic study of the unique chemical fingerprints that specific cellular processes leave behind) factors that influence the risk of many age-related diseases and quantitative traits, often in collaboration with external researchers. The programme ‘Functional Trajectories and Cardiovascular Ageing’ examines which factors from across the life course promote good adult cardiovascular function and prevent disease onset, and which increase vulnerability to accelerated cardiovascular ageing. The programme ‘Physical Capability and Musculoskeletal Ageing’ examines which factors from across the life course promote good adult physical capability and musculoskeletal health, and which increase vulnerability to accelerated decline in capability. The programme ‘Mental Ageing’ examines which factors from across the life course promote cognitive capability and protect against depression and which factors increase vulnerability to cognitive decline. The programme ‘Wellbeing in older age’ examines what social contexts and experiences in childhood and early adulthood promote wellbeing in later life and whether wellbeing protects against functional ageing. Each of these programmes generate multiple publications in peer review journals annually and findings are further disseminated via conference presentations. A full list of publications produced to date plus details of the current priorities for each programme are published on the MRC LHA website at: http://www.nshd.mrc.ac.uk/. Publications and presentations only use data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. This MRC Unit is committed to research on ageing – outputs arising from ONS data will be anonymised in the form of tables, graphs, peer reviewed journals, presentations and books. These data have been used in a number of publications. A full list of publications can be found at http://www.nshd.mrc.ac.uk/findings/ Examples of NSHD publications using mortality data are below: 1. Davis D, Cooper R, Terrera GM, Hardy R, Richards M, Kuh D.Verbal memory and search speed in early midlife are associated with mortality over 25 years' follow-up, independently of health status and early life factors: a British birth cohort study.Int J Epidemiol. 2016 Aug 6. pii: dyw100. 2. Zhou CK, Sutcliffe S, Welsh J, Mackinnon K, Kuh D, Hardy R, Cook MB.Is birthweight associated with total and aggressive/lethal prostate cancer risks? A systematic review and meta-analysis.Br J Cancer. 2016 Mar 29;114(7):839-48. 3. Teschendorff AE, Yang Z, Wong A, Pipinikas CP, Jiao Y, Jones A, Anjum S, Hardy R, Salvesen HB, Thirlwell C, Janes SM, Kuh D, Widschwendter M. Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer. JAMA Oncol. (2015 Jul 1); 1(4):476-85 4. Hartaigh B, Gill TM, Shah I, Hughes AD, Deanfield JE, Kuh D, Hardy R. Association between resting heart rate across the life course and allcause mortality: longitudinal findings from the Medical Research Council (MRC) National Survey of Health and Development (NSHD). J Epidemiol Community Health, 2014 Sep;68(9):8839. 5. Albanese E, Strand BH, Guralnik JM, Patel KV, Kuh D, et al. (2014) Weight Loss and Premature Death: The 1946 British Birth Cohort Study. PLoS ONE 9(1): e86282. 6. Maughan B, Stafford M, Shah I, Kuh D. Adolescent conduct problems and premature mortality: follow up to age 65 in a national birth cohort. Psychological Medicine 2013 Aug 21:110. 7. Ong K, Hardy R, Shah I, Kuh D on behalf of the NSHD scientific and data collection teams. Childhood stunting and mortality between 36 and 64 years: the British 1946 birth cohort study. Journal of Clinical Endocrinology and Metabolism. 2013 May;98(5):20707. 8. Strand BH, Kuh D, Shah I, Guralnik J, Hardy R Childhood, adolescent and early adult body mass index in relation to adult mortality: results from the British 1946 birth cohort. J Epidemiol Community Health. 2012 Mar; 66(3): 225–232. 9. Henderson M, Hotopf M, Shah I, Hayes RD, Kuh D. Psychiatric disorder in early adulthood and risk of premature mortality in the 1946 British Birth Cohort. BMC Psychiatry 2011 Mar 8;11:37. 10. Kuh D, Shah I, Richards M, Mishra G, Wadsworth M, Hardy R. Do childhood cognitive ability or smoking behaviour explain the influence of lifetime socioeconomic conditions on premature adult mortality in a British post war birth cohort? Soc Sci Med. 2009 May; 68(9): 1565–1573. 11. Clennell S, Kuh D, Guralnik J, Patel K, Mishra G. Characterisation of smoking behaviour across the life course and its impact on decline in lung function and allcause mortality: evidence from a British birth cohort. Journal of Epidemiology and Community Health 2008;59:30414. 12. Kuh D, Richards M, Hardy R, Butterworth S, Wadsworth MEJ. Childhood cognitive ability and deaths up until middle age: a post war birth cohort study. International Journal of Epidemiology 2004;33:40813. 13. Kuh D, Hardy R, Langenberg C, Richards M, Wadsworth MEJ. Mortality in adults aged 26-54 years related to socioeconomic conditions in childhood and adulthood: post war birth cohort study. British Medical Journal 2002;325:107680.

Processing:

NSHD receives data from two main sources i) collected from the study members themselves over the past 70 years and ii) from NHS Digital; these data are held in the NSHD-Data Repository (NSHD-DR). Study participants are flagged with NHS Digital. NHS Digital provides notifications of deaths and cancer registrations on a quarterly frequency. These data are incorporated into the NSHD-DR to enhance that dataset for research purposes. The mortality data (fact of death) are also used for administrative purposes. As well as being used to identify specific health events, linkage to HES data will allow the derivation of useful aggregate variables such as number of hospital admissions and length of time in hospital. The derived aggregate variables are then used for other research analyses by LHA scientists and may be shared with external researchers. In scientific studies in the period that pre-dated the MREC/LREC structure, consent was assumed by participation. In this study, the period of assumed consent covers the years from birth to age 35 years (from 1946 to 1981). Ethical permission for the 1982 and 1989 studies was obtained from the local ethical committees that preceded the LRECs and were run by the teaching hospital to which the NSHD research team were then affiliated (Bristol in 1982 and UCL in 1989. In 1999, MREC approval was obtained for the data collection and its use for research purposes by the team and their collaborations (MREC98/1/121). Ethical approval for the feasibility study (MREC06/Q1407/26) and extension study (07/H1008/245) was obtained from the Central Manchester Research Ethics Committee, and additional Scottish approval (08/MRE00/12) was granted through the Scotland A Research Ethics Committee. Most recently, a favourable opinion was obtained from the London Queen Square REC (14/LO/1073) and Scotland A REC (14/SS/1009). The legal basis for access to NHS Digital data for DARS-NIC-148100 (MR1a) is through consent. In this parallel agreement for members who are lost to follow up, DARS-NIC-86954 (MR1b), the legal basis is through Section 251 of the NHS Act 2006 (CAG approval ref: 15/CAG/0139). ONS Terms and Conditions will be adhered to. Derived NHS Digital data will be linked to the NSHD-DR which stores all study member data in pseudonymised form going back to 1946. NHS Digital identifiable data can only be viewed by named NSHD staff and is stored separately from pseudonymised derived data. The NSHD-DR additionally holds hospital admissions data that was previously obtained directly from the hospitals or General Practitioners. 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). All those with access to the data are substantive employees of University College London. All processing of ONS data will be in line with ONS standard conditions. The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement. There will be no onward sharing of record level data as part of this application.

Objectives:

The Medical Research Council National Survey of Health and Development (NSHD) is the oldest and longest running of the British birth cohort studies. From an initial maternity survey of 13,687 (82%) of all births recorded in England, Scotland and Wales during one week of March, 1946, a socially stratified sample of 5,362 singleton babies born to married parents was selected for follow-up. The NSHD study team is housed within the MRC Unit for Lifelong Health and Ageing (LHA) at University College London (UCL). Linkages for Scotland and Wales will be performed separately to the NHS Digital linkage. The linkages to central NHS held data will only involve the transfer of data for patients recruited in those nations, so for example there will be no data transferred to NHS Digital for patients recruited in Welsh institution. The NSHD study team has collected unique lifetime data on body size and maturation, cognitive and physical function, socioeconomic status and diet; and has repeat adult data on diet, smoking, physical activity, blood pressure and lung function. The most intensive data collection in 2006-2010, when study members were aged 60-64 years, included measurement of cardiac structure and function, body composition and bone density. The 24th and most recent data collection to the whole sample included a postal questionnaire in 2014 and a home visit by a trained research nurse for interview and assessment in 2015/2016. At the 24th follow-up, the target sample was 2816 study members still living in mainland Britain; this is the maximum sample used in the analyses. Of the remaining 2546 (47%) study members: 957 (18%) had already died, 620 (12%) had previously withdrawn permanently, 574 (11%) lived abroad, and 395 (7%) had remained untraceable for more than 5 years. Where study members have become lost to follow up, the MRIS data being provided under this application will enable NSHD to seek to re-contact those study members and invite them to continue participating in the study, i.e. to re-consent these participants. The NSHD was the first study (in 1971) to have participants flagged on the NHS Central Register for mortality (ICD codes are used to code cause of death) and cancer registrations. The LHA receives notifications on an ongoing quarterly frequency. The LHA wishes to link NSHD study members to HES data in order to improve the quality of information on hospital admissions and health outcomes for research purposes. Currently, the study obtains self-reported hospital admission data at each follow-up which are then confirmed through contact with each hospital. The Unit has a 5-year Medical Research Council core funded programme of research based on the NSHD with the objective to investigate risk and protective factors from across the life course that influence the ageing process. This core funding has been in place since 1962 and is renewed every five years after scientific review. The data from HES will be used to improve the identification of acute events such as those caused by cardiovascular disease (CVD). For example, the unit will assess how life course risk factor trajectories of body size, resting heart rate, blood pressure, socio-economic position (SEP) and health related behaviours, accumulate and interact to influence incidence of CVD, thus potentially identifying possibilities for earlier prevention. As the cohort is entering older age, hospital care becomes increasingly frequent and study members are thus less likely to report hospital admissions over a number of years accurately. It is therefore important to capture this information in other ways. New research within LHA on health service use is being developed which will utilise these data and investigate life course predictors of health care utilisation. The data collected on the NSHD cohort, including that provided by NHS Digital, is used across five research integrated programmes with the overarching aim of identifying social and biological factors that affect lifelong health, ageing and the development of chronic disease risk. The five programmes are: 1) Enhancing NSHD 2) Functional Trajectories and Cardiovascular Ageing 3) Physical Capability and Musculoskeletal Ageing 4) Mental Ageing 5) Wellbeing in older age


Project 33 — DARS-NIC-91374-Z5V6Y

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2017/09 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing, One-Off

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • Hospital Episode Statistics Admitted Patient Care
  • MRIS - Members and Postings Report

Benefits:

The rich phenotypic and genotypic dataset gathered over a 25 year period will enable analyses assessing mid-life predictors of health and ill-health in older age and will enable unique analyses of how these associations may be related to ethnicity and migration. Good physical and cognitive functions are vital to healthy ageing and factors which influence these across the life course are poorly understood, particularly in non-European populations. As the cohort is reaching older age, an increase in risk of heart failure, which can be severely debilitating, is expected. Ethnic differentials in heart failure rates are not well studied to date. Increasing length of follow-up and novel analytic techniques, both statistical and relating to stored images and samples bring opportunities for more sophisticated analyses and the addition of hospital admission data to key outcome variables enhances the study’s power to identify events and to further elucidate mechanisms underlying the very marked ethnic differences in cardiometabolic disorders which were observed at visit 2. Understanding of mechanisms in people of different ethnicities will ultimately lead to appropriate preventive strategies and treatments at different stages of life. As noted with some detailed examples under ‘specific outputs’, previous use of HES data (1989-2011) enabled improved ascertainment of incident coronary heart disease and stroke events and resulted in 10 publications in high impact journals relating these outcomes to risk factors measured in mid-life (ages 40-70 at baseline). A brief summary of some of these findings in the cohort to 2011 follows: Diabetes incidence in older British South Asians and African Caribbeans remains at least 2-fold even at age 80 years compared with British Europeans. The ethnic differentials in women were largely explained by midlife truncal obesity and insulin resistance, but the study was unable to explain the ethnic difference in men. The study showed that obesity cut-points of 24 and 27 kg/m2 in South Asians and African Caribbeans respectively were equivalent to a body mass index of 30kg/m2 in Europeans in terms of diabetes risk; these latter analyses contributed to recent NICE guidelines for prevention of diabetes. Diabetes was also found to be more ‘toxic’ in terms of stroke risk in the ethnic minorities. Widely used tools (Framingham and QRISK2) for estimation of cardiovascular disease risk were found to be less precise in South Asians and African Caribbeans (particularly women), while a selection of 3 metabolic markers measured by NMR spectroscopy was found to be strongly predictive of cardiovascular risk regardless of ethnicity. Lack of adherence to four combined health behaviours was associated with 2 to 3-fold increased risk of incident CVD in Europeans and South Asians. A substantial population impact in the South Asian group indicates important potential for disease prevention in this high-risk group by adherence to healthy behaviours. The study also found marked ethnic differences in associations between blood pressure parameters and stroke and concluded that undue focus on systolic blood pressure for risk prediction, and current age and treatment thresholds may be inappropriate for individuals of South Asian ancestry. This is not an exhaustive list of study findings in relation to incident cardiometabolic disease but indicates that the study is building a steady accumulation of understanding of ethnic differentials. There is clear need for further study, which this cohort is uniquely able to address. The addition of HES data to 2016 is key to maximising event ascertainment in old age.

Outputs:

Study findings will continue to be published in peer-reviewed scientific journals, predominantly related to epidemiology, cardiovascular and metabolic disorders, cognitive, physical and psychological function, but also including more generic journals such as the BMJ, reflecting the increasing focus on overall health in older age. Publications will contain only aggregate level data without local identifiers and with suppression of small numbers in line with HES analysis guide. Publications to date are listed on the study website: www.sabrestudy.org. All publications since 2008 are open-access. The audience is expected to consist mainly of academic researchers and clinicians. Two examples of previous SABRE study related publications are listed below, both sets of analyses were importantly informed by data from a previous HES extract (no longer retained), and were published in high-impact factor peer-reviewed journals. These generated considerable media interest and are widely cited. Tillin T, Hughes AD, Mayet J, Whincup P, Sattar N, Forouhi NG, McKeigue PM, Chaturvedi N. The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians and African Caribbeans. SABRE (Southall and Brent revisited) – a prospective population based study. J Am Coll Cardiol. 2013 Apr 30;61(17):1777-86. http://dx.doi.org/10.1016/j.jacc.2012.12.046. This paper published in JACC, the world no 1 cardiology journal (impact factor 16.5) confirmed ongoing excess coronary heart disease incidence in South Asians, with lower incidence in African Caribbeans compared with Europeans and confirmed elevated risk of stroke in both ethnic minority groups. Measured baseline metabolic risk factors could not explain the ethnic group differences. Future work in the cohort will examine whether these ethnic differentials continue into older age and whether newer genetic, epigenetic and metabolomic analyses will add to understanding of the underlying mechanisms. Of particular concern was a much stronger association between diabetes and stroke risk in both ethnic minority groups compared with Europeans with diabetes- an association which is the subject of ongoing study in the SABRE cohort. HES data, although not directly reported in the manuscript, contributed importantly to the identification of incident coronary and stroke events reported in these analyses. Tillin T, Hughes AD, Godsland IF, Whincup P, Forouhi NG, Welsh P, Sattar N, McKeigue PM, Chaturvedi N. Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared to Europeans? The Southall And Brent REvisited (SABRE) cohort. Diabetes Care 2013;36(2)(383-393). http://care.diabetesjournals.org/content/36/2/383.long. This paper demonstrated the extraordinarily high risk of incident diabetes continuing into old age in South Asians and African Caribbeans in comparison with Europeans. Metabolic pathways leading to diabetes remain poorly understood. The study found that baseline insulin resistance and truncal obesity could explain the ethnic differences in women but not in men. Further work continues to determine the reasons for the excess risk in men and to understand what underlies insulin resistance and truncal obesity. HES data, although not directly reported in this manuscript, supported these analyses by enabling sensitivity analyses to assess the effects of bias due to loss to follow-up. A further 8 journal publications have examined associations between baseline risk factors and incident coronary heart disease or stroke where the outcomes were a composite of first events identified through participant reported events, primary care record review identified events and HES identified hospital admissions. One of these was published in Circulation (Wurtz et al), impact factor 14.3 and demonstrated in 3 separate population based studies (including SABRE) that metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk, substantiating the value of high-throughput metabolomics for biomarker discovery and improved risk assessment. Another publication in Heart (Tillin et al) identified that 2 widely used cardiovascular risk prediction tools (QRISK2 and Framingham) did not perform consistently well in all ethnic groups and suggested that further validation of QRISK2 in other multi-ethnic datasets, and better methods for identifying high risk African Caribbeans and South Asian women, are required. In addition to journal publications, UCL will continue to submit abstracts for presentation at national and international conferences, such as Diabetes UK, the European Association for the Study of Diabetes, the European Society of Cardiology, Artery, and the British Hypertension Society. All data for abstracts/presentations will be at aggregate level with suppression of small numbers in line with HES analysis guide. The study team will further disseminate findings via participant and GP feedback sessions; newsletters, and the study website. All data for these occasions will be at aggregate level with suppression of small numbers in line with HES analysis guide. At the end of the current funding period (2018) a report will be submitted to the funders (the British Heart Foundation) summarising findings. This may be published on their website. It will only contain at most aggregate level data, with small numbers suppressed in line with HES Analysis Guide.

Processing:

The identifiers of SABRE participants have previously been shared with NHS Digital’s predecessor organisation(s) and NHS Digital has provided regular event notifications including notifications of mortality and cancer registrations. The cohort was previously split into two groups: cancer notifiable participants and non-cancer notifiable participants. The cohort will be reorganised into three groups: participants who gave informed consent (cancer notifiable); cancer notifiable participants covered by section 251 support, and non-cancer notifiable participants covered by section 251 support. To ensure that participants are correctly reorganised into the appropriate groups, UCL will send NHS Digital 3 separate files (one for each respective group) containing participant identifiers. NHS Digital will then provide reports on a monthly basis while the study is in active follow-up. Notifications will contain no participant identifiers other than unique study Pseudo-IDs. Month and Year of Death will also be included. NHS Digital will link the respective cohort groups to HES data and will supply to UCL encrypted files containing hospital admissions data identified only by study Pseudo-ID and encrypted HESID and containing no other identifiers. The dataset will be placed immediately into UCL’s Data Safe Haven. Using the Pseudo-ID, the data is linked at record level to the existing dataset of mortality and cancer records, clinical measures, primary care record review and participant responses to health and lifestyle questionnaires across the course of the study. The data is stored in an encrypted file within the Data Safe Haven at the Gower Street location. The data can be remotely accessed at the Institute of Cardiovascular Science by accredited SABRE study researchers only – all of whom are substantive employees of UCL. Access must be approved by the Data Manager. The data supplied by NHS Digital will not be downloaded or otherwise transferred from the Data Safe Haven. Data including variables derived from the NHS Digital data may be downloaded from the Data Safe Haven and stored on a UCL server at the Institute of Cardiovascular Science to be used solely for the purposes of statistical analyses in accordance with the study objectives. Such variables include, for example, date of first admission related to a diagnosis of coronary heart disease but will not include any part of the dataset supplied by NHS Digital. Using this pseudonymised dataset, study analysts will examine associations between risk factors measured during the course of the study and cardiometabolic events. The rich phenotypic and genotypic dataset will enable identification of ethnic differences in cardiometabolic disease risk and physical, mental and cognitive function into older age and it will be possible to identify which measured risk factors may explain ethnic differentials and at which period of life they may act most strongly. To meet study objectives UCL require information on admissions where diagnostic code lists include coronary heart disease, stroke, heart failure, diabetes, renal failure, dementia, retinopathy, hypertension, other cardiovascular disease. Respiratory diseases will also be studied and mental health disorders and other common disorders may be added which are considered to exert important influences on function and well-being in older age. As an example, from the HES extract, and within the UCL Data Safe Haven, it is expected that a variable will be generated which identifies a first or subsequent admission with coronary heart disease (ICD-9 codes 410 through 415 or ICD-10 codes I200 through I259, or any of the following operation codes from the Office of Populations and Surveys classification of interventions and procedures: K401 through K469, K491 through K504, K751 through K759, or U541 (coronary revascularization interventions or rehabilitation for ischemic heart disease)). Date of first or subsequent event would be summarised as year of event. The data is stored separately to participant identifiers. The two datasets will not be re-linked and the data will remain pseudonymised as described above. Month and Year of Death are stored in the dataset and used for statistical analyses but the dataset does not include full Date of Death. Participant identifiers are retained separately solely for study administration purposes.

Objectives:

University College London (UCL) requires notifications of mortality and cancer registrations and linked HES data for its study cohort for use in the Medical Research Project: SABRE (Southall And Brent Revisited. This is a population-based cohort study, conducted at University College London, funded by the British Heart Foundation in its current 25 year follow-up phase. It is unique as a long-standing tri-ethnic cohort consisting of people of European descent and first generation migrants of South Asian or African Caribbean descent. This is an academic research study focusing on identifying and understanding the underlying reasons for ethnic group and sex differences in cardiometabolic disease and in physical, psychological and cognitive function in older age. Specific questions for the 25 year follow-up study are: 1. How large are ethnic /sex differences in cardiac function, cognitive function and hippocampal volumes in older age? 2. To what extent do cardiac function, cognitive function and hippocampal volumes change over a 5 year period in each ethnic group? 3. Which risk factors measured in mid-life and in early old age are most strongly associated with current cardiac and cognitive function and hippocampal volumes and with 5 year changes in these parameters? Can these risk factors explain ethnic differences in cardiac and cognitive function? 4. How large are gender differences in current disorders of cardiac and cognitive function and in their associations with current risk factors? 5. Do ethnic differences in incident cardiometabolic disorders persist into older age? 6. Which risk factors or risk factor profiles measured in mid-life and early old age are most strongly associated with incident cardiometabolic disorders and which best explain ethnic differences in incidence? The study receives ongoing notifications of mortality and cancer registrations from NHS Digital. Continuing supply of this data is required in order to meet study objectives. Death and cause of death are key outcomes for the research objectives and cancer registrations are also key to understanding ethnic disparities in development and survival from the most frequent types of cancer and how these impact upon function in older age. The study has previously utilised the List Cleaning service from time to time when in active follow-up in order to ensure that the correct participant addresses are used in order to contact participants. Use of this service has helped the study to avoid trying to contact deceased participants. The List Cleaning outputs were used to update the administration database (held separately from other data within the UCL data safe haven) so that UCL could write to as many participants as possible inviting them to complete questionnaires or come into the UCL clinic for a detailed investigation. Under this Data Sharing Agreement, UCL may retain List Cleaning outputs received previously but is not permitted to make further use of the List Cleaning service. Linked HES data is required to identify incident cardiometabolic events (in particular coronary heart disease, heart failure, stroke, dementia, diabetes), and other events which may affect physical and cognitive function, which have occurred during the follow-up period. Details of all hospital episodes involving the cohort (not limited to the previously stated conditions) are required to address key study objectives with regard to physical and cognitive function in older age in association with current and mid-life risk factors. Analysis needs to consider any and all potential contributing factors. These events will supplement information provided by participant self-report at 20 and 25 years, from primary care medical record review conducted during the 20 year follow-up and from cancer and mortality flagging, together with detailed clinical measurements made at the SABRE clinics at baseline, 20 and 25 year follow-up. The SABRE cohort is increasingly elderly (median age of survivors in 2016=77 years, range 65-98) and at visit 3, although many are willing and able to visit UCL’s clinic and/or to complete questionnaires, many who attended at the last follow-up 5 years ago are now too frail or unwell to attend the 25 year follow-up clinic or to complete the health and lifestyle questionnaires, and sadly many have died (approximately 1,500 (31%)). Diagnosis of disease events/states identified during admission to hospital is increasingly important in assessing health in this elderly cohort and will inform all key event outcomes. This is particularly important in assessing health in those otherwise lost to direct follow-up. The data will be used to analyse risk factors measured in mid- and later life in association with these incident events in order to build on current understanding of causal mechanisms. Data from 1989 to the present is required because participants underwent detailed examinations at baseline (1989-91) and the aim is to follow this cohort through their experiences since to understand what happened in later life and relate that to the baseline. This will enable UCL to gain as complete as possible a picture of hospital admissions, and hence incident events, over the entire cohort follow-up. Data from the entire study period are crucial for determining age of onset of events, as well as the extent and nature of ill-health from mid to later life, and for relating these to current and mid-life cardiometabolic and other risk factors and how these influence the key study outcomes of physical and cognitive function in older life in each of the three ethnic groups.


Project 34 — DARS-NIC-93084-W4B4L

Opt outs honoured: N

Sensitive: Sensitive

When: 2017/09 — 2018/02. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Accident and Emergency
  • Mental Health Minimum Data Set

Benefits:

This study aims to determine the effectiveness of Acute Day Units in reducing admission rates to acute psychiatric units, as well as rates of compulsory admissions in a one year period. This will make up a part of the delivery of a comprehensive report on the current value of ADUs, and recommendations about service models. The study is in a sense exploratory, as it aims to make up for a dearth of information regarding modern ADUs; measurable benefits are therefore highly dependent on its results. If, for example, the study finds that areas with ADUs have a 5% reduction in admissions, and can therefore recommend more widespread use of these units, then an expected measurable benefit would be a 5% reduction in admissions in areas which go on to introduce ADUs. The emphasis by NHS England on evidence-based practice suggests that if evidence was found of reduction of inpatient admissions in areas with ADUs, such units would be recommended by policy makers. The AD-CARE team have good working relationships with NHS England. Reduced admissions to inpatient units are of benefit in terms of reduced costs, waiting times, and future admissions, and improved outcomes for service users in terms of recovery and service experience. Given the low number of NHS trusts that currently have ADUs, a national roll-out of such services to every Trust would potentially impact a very large number of service users.

Outputs:

This is an NIHR funded study and all outputs of the trial will therefore be reported to the NIHR as part of the final report at the end of the study. AD-CARE is a 36-month study which began in 07/16. Write up of Work Package 3, for which this data will be used, is anticipated for months 30-36. The study final report submission is anticipated for month 36 (06/19). This will cover all findings of the study including: factors influencing planning and implementation; the key findings of the study; and the response to the research questions. The NIHR will then publish the report of the AD-CARE trial on its website. Dissemination will be carefully planned with The McPin Foundation and NHS England to ensure high quality peer review of outputs and stakeholder engagement and information sharing. The McPin Foundation is a UK charity which exists to transform mental health research, by placing the lived experience of people with mental health problems at the centre of research methods and the research agenda. NHS England mental health leads are keen to work with the study so that findings feed directly into developments regarding crisis care across the NHS. The usual full scientific reports, peer reviewed papers, powerpoint presentations, conference talks, and web output will be provided, as expected of all NIHR studies. PPI and NHS management colleagues will also be consulted to disseminate our findings across a range of NHS and health provider platforms. Summary documents will be provided in a range of formats suitable for different audiences. The AD-CARE study website is hosted at UCL and is updated with new information as appropriate (https://www.ucl.ac.uk/psychiatry/research/epidemiology/ad-care). All ADUs and crisis services have been sent links to the website. Twitter has been a useful vehicle for distributing publications to a variety of audiences and findings more widely once published in open access journals. In addition, an expert consensus meeting/conference will be held in the final two months of the project. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

No data will be provided to NHS Digital by UCL. NHS Digital will provide pseudonymised HES and Mental Health data to UCL. This will then be stored at the UCL SLMS Safe Haven, a technical environment for storing, handling and analysing identifiable data which has been certified to the ISO27001 information security standard and conforms to NHS Digital's Information Governance Toolkit. The data will only be accessible through the Data Safe Haven. NHS Digital will create a cohort of mental health patients (MHMDS) for the years 2013/14, 2014/15 and 2015/16. Data will then be extracted from the MHMDS for this cohort for the years 2011/12, 2012/13, 2013/14, 2014/15 and 2015/16. The cohort created in the first step of this process will then be linked to the requested HES datasets for the years 2011/12, 2012/13, 2013/14, 2014/15 and 2015/16. The core datasets will only be accessed by one statistician within UCL, who will be a substantive UCL employee. The full data set will be used to consider whether engagement with community services is a predictor or consequence of ADU treatment. Cases in the data set will then be identified that have used acute mental health care service. ‘Acute mental health care service’ will be defined by use of in-patient, Acute Day Service (ADU), Crisis Resolution Home Treatment (CRT), Crisis House, or other locally-defined acute services. Discussion and exploratory work with NHS Digital has resulted in the decision that UCL are best placed to identify these cases due to the lack of clarity over the definition of ‘Acute mental health care service’ users. UCL will identify the ‘Acute mental health care service’ users and the non-acute service users' records will be deleted from the dataset. UCL will also provide NHS Digital with the methodology on how to identify the non-acute service users’ records and remove them from the dataset. NHS trusts use a variety of nomenclature for the services they provide, and identifying all and only the acute services is anticipated to be a complex task. It is unlikely that NHS Digital would be able to filter the full data set to provide all cases with acute service use. The data set will then be restricted to service users with records in MHMDS for the study years who have used any acute (urgent) mental health care service during this time. Non-acute service users’ records will be deleted from the data set at this point. Access to and use of these services will be used to identify the start and end of episodes of acute care. Multiple episodes per service user will be included. No data about or from service users will be obtained from any other sources. Given that this study requires data from two separate databases (MHMDS and HES) across multiple years, a unique service user identifier (the MHMDS study ID) will be attached to each record by NHS Digital. This will make it possible to link care spells across reporting periods (years) and different providers of publically available data, as well as to HES data. MHMDS also includes geographic identifiers (Provider Trust or hospital, Commissioner, GP practice and Census Lower Super Output Area (LSOA) based on the postcode for the service user’s residence). MHMDS records will be linked to local Census and deprivation data at the area level: 2011 Census (A), which is publically available from ONS (www.ons.gov.uk/ons/datasets-and-tables/index.html), which will be linked by geocoding the postcode of residence for each service user to its corresponding lower super output area (LSOA), a suitable spatial scale at which Census data is made available. Index of Multiple Deprivation (A), created by the Department for Communities and Local Government, is publically available data and was last issued in 2015. Service users’ postcode at residence will be geocoded to their LSOA, as above, to obtain estimates of deprivation. There will be no requirement nor attempt to re-identify individuals from the data. The data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. 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). A secondary analysis will be undertaken of the MHMDS data using multilevel modelling (sometimes known as hierarchical modelling). Multilevel modelling is a statistical method designed to take account of clustered data, e.g. to take account of the fact that people using an NHS Trust in one area of the country may be more similar to each other than to people using an NHS Trust in a different area. Data will be analysed as a cohort. Two sets of analyses will be undertaken at the individual level : 1) Within those areas that have an ADU, outcomes for service users who use the ADU will be compared to outcomes for service users who do not; 2) Using all areas (whether or not they have an ADU), service users who access an ADU during an episode of acute care will be compared with those who do not. The overall association will be explored between services with and without ADUs in terms of overall admission rates to inpatient and crisis services, exploring how much variance is explained at the organisational/geographical level, accounting for social variables such as demographics (gender, age, ethnicity) and deprivation score. Time of cohort entry (the start of the first episode of acute care) will be defined by the first occurrence of any of (a) Crisis Resolution Home Treatment service contact; (b) admission to a mental illness bed; or (c) contact with an ADU. Associations will be investigated between ADU attendance and study outcomes. The hypothesis of the study will be tested: that service users who attend an ADU during an acute care episode spend less time as in-patients than matched controls, have fewer A&E attendances and a longer time to relapse (i.e. further use of acute care services). Two additional analyses will be undertaken, a survival analysis (which looks at how long it takes before someone uses acute services again), and a cohort analysis (which looks at the group of people who use a certain service for a period of time). The details of each analysis are as follows: (1) survival analyses (in which the outcome is time to subsequent acute care episode) using multilevel modelling to test for the main effects of ADU use versus no ADU use on study outcomes and to model variance in these associations between provider Trusts; and (2) cohort analysis in which the primary outcome will be the total time spent in acute care over the 6 months since entering the cohort. In the economic evaluation, source unit costs will also be identified for all service use within each person-level episode file. This will also use multilevel modelling, with cost as the dependent variable, identify predictors of costs incurred by service users, and estimate the impact of ADU attendance on subsequent costs associated with service use. As a summary:- • Individuals, working under appropriate supervision on behalf of data controller(s)/processor(s) within this agreement, are subject to the same policies, procedures and sanctions as substantive employees. • All outputs will be restricted to aggregated data with small numbers suppressed in line with the HES Analysis Guide • All outputs will follow the Mental Health (MHSDS, MHLDDS, MHMDS) disclosure control rules • The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement.

Objectives:

University College London requires the requested data for use in the Acute Day Care (AD-CARE) study. UCL applied for and was awarded funding in response to the NIHR’s commissioned call ‘15/24 Assessing service models of community mental health response to urgent care needs’. AD-CARE aims broadly to assess the real life effectiveness and user experience of Acute Day Units (ADUs) as a community response to mental health crises. The requested data will be used for the objective of answering the following research questions: a) Are acute readmission rates reduced in areas/Trusts with a more enhanced crisis care pathway, defined as having an Acute Day Unit (ADU) in the pathway? b) In Trusts with ADUs, do individuals who access NHS-funded ADUs have different outcomes compared to similar people who have had an acute episode but do not access ADUs? UCL is the only organisation that will have access to the record level data requested of NHS Digital, and the only people accessing the data will be substantive employees of UCL. AD-CARE is a new study for which data has not been supplied before. The data required from NHS digital are: 1) Mental Health data: 2013/14, 2014/15, 2015/16 (observation period), and data for 2011/12 and 2012/13 to provide information about cohort members prior to the start of the observation period. 2) HES data for the same period (2011-2016), to provide information about A&E attendance and admissions to acute care 3) Bridge file linking these two data sets.


Project 35 — DARS-NIC-99077-Q0K6Z

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2017/09 — 2018/05. SMLS reported a DPA serious incident; breached contract — audit report.

Repeats: Ongoing, One-Off

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • Hospital Episode Statistics Admitted Patient Care
  • MRIS - Members and Postings Report

Benefits:

The rich phenotypic and genotypic dataset gathered over a 25 year period will enable analyses assessing mid-life predictors of health and ill-health in older age and will enable unique analyses of how these associations may be related to ethnicity and migration. Good physical and cognitive functions are vital to healthy ageing and factors which influence these across the life course are poorly understood, particularly in non-European populations. As the cohort is reaching older age, an increase in risk of heart failure, which can be severely debilitating, is expected. Ethnic differentials in heart failure rates are not well studied to date. Increasing length of follow-up and novel analytic techniques, both statistical and relating to stored images and samples bring opportunities for more sophisticated analyses and the addition of hospital admission data to key outcome variables enhances the study’s power to identify events and to further elucidate mechanisms underlying the very marked ethnic differences in cardiometabolic disorders which were observed at visit 2. Understanding of mechanisms in people of different ethnicities will ultimately lead to appropriate preventive strategies and treatments at different stages of life. As noted with some detailed examples under ‘specific outputs’, previous use of HES data (1989-2011) enabled improved ascertainment of incident coronary heart disease and stroke events and resulted in 10 publications in high impact journals relating these outcomes to risk factors measured in mid-life (ages 40-70 at baseline). A brief summary of some of these findings in the cohort to 2011 follows: Diabetes incidence in older British South Asians and African Caribbeans remains at least 2-fold even at age 80 years compared with British Europeans. The ethnic differentials in women were largely explained by midlife truncal obesity and insulin resistance, but the study was unable to explain the ethnic difference in men. The study showed that obesity cut-points of 24 and 27 kg/m2 in South Asians and African Caribbeans respectively were equivalent to a body mass index of 30kg/m2 in Europeans in terms of diabetes risk; these latter analyses contributed to recent NICE guidelines for prevention of diabetes. Diabetes was also found to be more ‘toxic’ in terms of stroke risk in the ethnic minorities. Widely used tools (Framingham and QRISK2) for estimation of cardiovascular disease risk were found to be less precise in South Asians and African Caribbeans (particularly women), while a selection of 3 metabolic markers measured by NMR spectroscopy was found to be strongly predictive of cardiovascular risk regardless of ethnicity. Lack of adherence to four combined health behaviours was associated with 2 to 3-fold increased risk of incident CVD in Europeans and South Asians. A substantial population impact in the South Asian group indicates important potential for disease prevention in this high-risk group by adherence to healthy behaviours. The study also found marked ethnic differences in associations between blood pressure parameters and stroke and concluded that undue focus on systolic blood pressure for risk prediction, and current age and treatment thresholds may be inappropriate for individuals of South Asian ancestry. This is not an exhaustive list of study findings in relation to incident cardiometabolic disease but indicates that the study is building a steady accumulation of understanding of ethnic differentials. There is clear need for further study, which this cohort is uniquely able to address. The addition of HES data to 2016 is key to maximising event ascertainment in old age.

Outputs:

Study findings will continue to be published in peer-reviewed scientific journals, predominantly related to epidemiology, cardiovascular and metabolic disorders, cognitive, physical and psychological function, but also including more generic journals such as the BMJ, reflecting the increasing focus on overall health in older age. Publications will contain only aggregate level data without local identifiers and with suppression of small numbers in line with HES analysis guide. Publications to date are listed on the study website: www.sabrestudy.org. All publications since 2008 are open-access. The audience is expected to consist mainly of academic researchers and clinicians. Two examples of previous SABRE study related publications are listed below, both sets of analyses were importantly informed by data from a previous HES extract (no longer retained), and were published in high-impact factor peer-reviewed journals. These generated considerable media interest and are widely cited. Tillin T, Hughes AD, Mayet J, Whincup P, Sattar N, Forouhi NG, McKeigue PM, Chaturvedi N. The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians and African Caribbeans. SABRE (Southall and Brent revisited) – a prospective population based study. J Am Coll Cardiol. 2013 Apr 30;61(17):1777-86. http://dx.doi.org/10.1016/j.jacc.2012.12.046. This paper published in JACC, the world no 1 cardiology journal (impact factor 16.5) confirmed ongoing excess coronary heart disease incidence in South Asians, with lower incidence in African Caribbeans compared with Europeans and confirmed elevated risk of stroke in both ethnic minority groups. Measured baseline metabolic risk factors could not explain the ethnic group differences. Future work in the cohort will examine whether these ethnic differentials continue into older age and whether newer genetic, epigenetic and metabolomic analyses will add to understanding of the underlying mechanisms. Of particular concern was a much stronger association between diabetes and stroke risk in both ethnic minority groups compared with Europeans with diabetes- an association which is the subject of ongoing study in the SABRE cohort. HES data, although not directly reported in the manuscript, contributed importantly to the identification of incident coronary and stroke events reported in these analyses. Tillin T, Hughes AD, Godsland IF, Whincup P, Forouhi NG, Welsh P, Sattar N, McKeigue PM, Chaturvedi N. Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared to Europeans? The Southall And Brent REvisited (SABRE) cohort. Diabetes Care 2013;36(2)(383-393). http://care.diabetesjournals.org/content/36/2/383.long. This paper demonstrated the extraordinarily high risk of incident diabetes continuing into old age in South Asians and African Caribbeans in comparison with Europeans. Metabolic pathways leading to diabetes remain poorly understood. The study found that baseline insulin resistance and truncal obesity could explain the ethnic differences in women but not in men. Further work continues to determine the reasons for the excess risk in men and to understand what underlies insulin resistance and truncal obesity. HES data, although not directly reported in this manuscript, supported these analyses by enabling sensitivity analyses to assess the effects of bias due to loss to follow-up. A further 8 journal publications have examined associations between baseline risk factors and incident coronary heart disease or stroke where the outcomes were a composite of first events identified through participant reported events, primary care record review identified events and HES identified hospital admissions. One of these was published in Circulation (Wurtz et al), impact factor 14.3 and demonstrated in 3 separate population based studies (including SABRE) that metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk, substantiating the value of high-throughput metabolomics for biomarker discovery and improved risk assessment. Another publication in Heart (Tillin et al) identified that 2 widely used cardiovascular risk prediction tools (QRISK2 and Framingham) did not perform consistently well in all ethnic groups and suggested that further validation of QRISK2 in other multi-ethnic datasets, and better methods for identifying high risk African Caribbeans and South Asian women, are required. In addition to journal publications, UCL will continue to submit abstracts for presentation at national and international conferences, such as Diabetes UK, the European Association for the Study of Diabetes, the European Society of Cardiology, Artery, and the British Hypertension Society. All data for abstracts/presentations will be at aggregate level with suppression of small numbers in line with HES analysis guide. The study team will further disseminate findings via participant and GP feedback sessions; newsletters, and the study website. All data for these occasions will be at aggregate level with suppression of small numbers in line with HES analysis guide. At the end of the current funding period (2018) a report will be submitted to the funders (the British Heart Foundation) summarising findings. This may be published on their website. It will only contain at most aggregate level data, with small numbers suppressed in line with HES Analysis Guide.

Processing:

The identifiers of SABRE participants have previously been shared with NHS Digital’s predecessor organisation(s) and NHS Digital has provided regular event notifications including notifications of mortality and cancer registrations (for eligible participants only). The cohort was previously split into two groups: cancer notifiable participants and non-cancer notifiable participants. The cohort will be reorganised into three groups: participants who gave informed consent (cancer notifiable); cancer notifiable participants covered by section 251 support, and non-cancer notifiable participants covered by section 251 support. To ensure that participants are correctly reorganised into the appropriate groups, UCL will send NHS Digital 3 separate files (one for each respective group) containing participant identifiers. NHS Digital will then provide reports on a monthly basis while the study is in active follow-up. Notifications will contain no participant identifiers other than unique study Pseudo-IDs. Month and Year of Death will also be included. NHS Digital will link the respective cohort groups to HES data and will supply to UCL encrypted files containing hospital admissions data identified only by study Pseudo-ID and encrypted HESID and containing no other identifiers. The dataset will be placed immediately into UCL’s Data Safe Haven. Using the Pseudo-ID, the data is linked at record level to the existing dataset of mortality, clinical measures, primary care record review and participant responses to health and lifestyle questionnaires across the course of the study. The data is stored in an encrypted file within the Data Safe Haven at the Gower Street location. The data can be remotely accessed at the Institute of Cardiovascular Science by accredited SABRE study researchers only – all of whom are substantive employees of UCL. Access must be approved by the Data Manager. The data supplied by NHS Digital will not be downloaded or otherwise transferred from the Data Safe Haven. Data including variables derived from the NHS Digital data may be downloaded from the Data Safe Haven and stored on a UCL server at the Institute of Cardiovascular Science to be used solely for the purposes of statistical analyses in accordance with the study objectives. Such variables include, for example, date of first admission related to a diagnosis of coronary heart disease but will not include any part of the dataset supplied by NHS Digital. Using this pseudonymised dataset, study analysts will examine associations between risk factors measured during the course of the study and cardiometabolic events. The rich phenotypic and genotypic dataset will enable identification of ethnic differences in cardiometabolic disease risk and physical, mental and cognitive function into older age and it will be possible to identify which measured risk factors may explain ethnic differentials and at which period of life they may act most strongly. To meet study objectives UCL require information on admissions where diagnostic code lists include coronary heart disease, stroke, heart failure, diabetes, renal failure, dementia, retinopathy, hypertension, other cardiovascular disease. Respiratory diseases will also be studied and mental health disorders and other common disorders may be added which are considered to exert important influences on function and well-being in older age. As an example, from the HES extract, and within the UCL Data Safe Haven, it is expected that a variable will be generated which identifies a first or subsequent admission with coronary heart disease (ICD-9 codes 410 through 415 or ICD-10 codes I200 through I259, or any of the following operation codes from the Office of Populations and Surveys classification of interventions and procedures: K401 through K469, K491 through K504, K751 through K759, or U541 (coronary revascularization interventions or rehabilitation for ischemic heart disease)). Date of first or subsequent event would be summarised as year of event. The data is stored separately to participant identifiers. The two datasets will not be re-linked and the data will remain pseudonymised as described above. Month and Year of Death are stored in the dataset and used for statistical analyses but the dataset does not include full Date of Death. Participant identifiers are retained separately solely for study administration purposes.

Objectives:

University College London (UCL) requires notifications of mortality and linked HES data for its study cohort for use in the Medical Research Project: SABRE (Southall And Brent Revisited. This is a population-based cohort study, conducted at University College London, funded by the British Heart Foundation in its current 25 year follow-up phase. It is unique as a long-standing tri-ethnic cohort consisting of people of European descent and first generation migrants of South Asian or African Caribbean descent. This is an academic research study focusing on identifying and understanding the underlying reasons for ethnic group and sex differences in cardiometabolic disease and in physical, psychological and cognitive function in older age. Specific questions for the 25 year follow-up study are: 1. How large are ethnic /sex differences in cardiac function, cognitive function and hippocampal volumes in older age? 2. To what extent do cardiac function, cognitive function and hippocampal volumes change over a 5 year period in each ethnic group? 3. Which risk factors measured in mid-life and in early old age are most strongly associated with current cardiac and cognitive function and hippocampal volumes and with 5 year changes in these parameters? Can these risk factors explain ethnic differences in cardiac and cognitive function? 4. How large are gender differences in current disorders of cardiac and cognitive function and in their associations with current risk factors? 5. Do ethnic differences in incident cardiometabolic disorders persist into older age? 6. Which risk factors or risk factor profiles measured in mid-life and early old age are most strongly associated with incident cardiometabolic disorders and which best explain ethnic differences in incidence? The study receives ongoing notifications of mortality from NHS Digital. Continuing supply of this data is required in order to meet study objectives. Death and cause of death are key outcomes for the research objectives. The study has previously utilised the List Cleaning service from time to time when in active follow-up in order to ensure that the correct participant addresses are used in order to contact participants. Use of this service has helped the study to avoid trying to contact deceased participants. The List Cleaning outputs were used to update the administration database (held separately from other data within the UCL data safe haven) so that UCL could write to as many participants as possible inviting them to complete questionnaires or come into the UCL clinic for a detailed investigation. Under this Data Sharing Agreement, UCL may retain List Cleaning outputs received previously but is not permitted to make further use of the List Cleaning service. Linked HES data is required to identify incident cardiometabolic events (in particular coronary heart disease, heart failure, stroke, dementia, diabetes), and other events which may affect physical and cognitive function, which have occurred during the follow-up period. Details of all hospital episodes involving the cohort (not limited to the previously stated conditions) are required to address key study objectives with regard to physical and cognitive function in older age in association with current and mid-life risk factors. Analysis needs to consider any and all potential contributing factors. These events will supplement information provided by participant self-report at 20 and 25 years, from primary care medical record review conducted during the 20 year follow-up and from mortality flagging, together with detailed clinical measurements made at the SABRE clinics at baseline, 20 and 25 year follow-up. The SABRE cohort is increasingly elderly (median age of survivors in 2016=77 years, range 65-98) and at visit 3, although many are willing and able to visit UCL’s clinic and/or to complete questionnaires, many who attended at the last follow-up 5 years ago are now too frail or unwell to attend the 25 year follow-up clinic or to complete the health and lifestyle questionnaires, and sadly many have died (approximately 1,500 (31%)). Diagnosis of disease events/states identified during admission to hospital is increasingly important in assessing health in this elderly cohort and will inform all key event outcomes. This is particularly important in assessing health in those otherwise lost to direct follow-up. The data will be used to analyse risk factors measured in mid- and later life in association with these incident events in order to build on current understanding of causal mechanisms. Data from 1989 to the present is required because participants underwent detailed examinations at baseline (1989-91) and the aim is to follow this cohort through their experiences since to understand what happened in later life and relate that to the baseline. This will enable UCL to gain as complete as possible a picture of hospital admissions, and hence incident events, over the entire cohort follow-up. Data from the entire study period are crucial for determining age of onset of events, as well as the extent and nature of ill-health from mid to later life, and for relating these to current and mid-life cardiometabolic and other risk factors and how these influence the key study outcomes of physical and cognitive function in older life in each of the three ethnic groups.