NHS Digital Data Release Register - reformatted

University Of Leeds

Project 1 — DARS-NIC-04641-R3Y5V

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/12 — 2018/05.

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
  • MRIS - Cohort Event Notification Report

Benefits:

The results of the CEMARC 2 clinical research trial are expected to inform the next update of NICE guideline CG95 (Chest pain of recent onset: assessment and diagnosis) and therefore have enormous potential to change NHS practice, but also to inform future NHS capital expenditure. The research question is highly relevant to a large number of patients: what is the best initial test for patients coming to an outpatient clinic with chest pain that is suspected stable angina? It is well accepted that a variety of investigations may be used to diagnose Coronary Heart Disease and to determine the need for coronary revascularisation. Whilst the NICE guidelines (CG95) have provided a structured and evidence-based approach to the diagnosis of patients with chest pain, they are not without problems. Full adoption of the guidelines could lead to an increase in invasive angiography, when the University of Leeds know that the rate of unnecessary angiography is already high. From the involvement of expert patient at the design stage of this study the University of Leeds know that unnecessary angiography is something patients are particularly keen to avoid. The benefits to patients will be a reduction of unnecessary invasive angiography which would also constitute a cost saving to the NHS.

Outputs:

The primary analysis will be complete by April 2016 with a planned manuscript submission July 2016. The dates for manuscript submission and ESC 2016 presentation are correct, but the final analysis won’t be complete by April. It is being done in a staged approach. The University of Leeds will have enough analyses to submit abstract by end April 2016, enough for the manuscript by July 2016, and the rest by the time of presentation. The manuscript will include initial safety data (1 year follow up). Results will be disseminated through manuscript submissions to relevant peer reviewed journals. It is not possible to confirm the name of a journal until the manuscript has been accepted. As the study’s forerunner, CEMARC, was published in The Lancet, the University of Leeds expect to again publish in a 4 star journal with the New England Journal of Medicine being the target journal for submission for CE-MARC2. The planned follow-up period for the study is 3 years (will require on-going ONS data for the entire follow-up period) and an additional safety analysis will be completed at the end of this period. The data will also be presented at the European Society of Cardiology annual meeting (August 2016). ESC 2016 will be presentation of the primary and secondary endpoint analyses. Whatever follow-up data the University of Leeds have at that point will be considered for inclusion (should have 1 year’s data for most patients, at least). Trial results will be more widely disseminated to patient and public groups and to the lay community. Should the trial show that the intervention is effective, the results will ultimately inform NICE guidance and influence NHS practice in this area. No outputs will ever identify any individual, organisation, nor include any record level data.

Processing:

The University of Leeds receive data for an identified cohort for the CEMARC 2 trial, for whom the University of Leeds provide identifiers already collected from participants as part of the trial dataset (and with their consent). These are: name, address, NHS number and date of birth. This ensures the University of Leeds minimise the chance of receiving data for anyone other than consenting trial participants. The University of Leeds need to receive identifiable data to ensure that the University of Leeds have data for the correct individuals and match this with the existing trial data set. Data will be processed by the trial statisticians at the Clinical Trials Research Unit, Leeds Institute for Clinical Trials Research (LICTR) at the University of Leeds. All individuals with access to the data are employees of the University of Leeds. It will be securely stored on CTRU systems with access only granted to the statistical team. Data will not be accessed by any third parties, nor will it be accessible across multiple organisations. Data will be linked with existing CEMARC 2 Trial datasets (existing CEMARC2 Trial datasets consist of patient demographics and results from diagnostic tests, patients have given written informed consent to participate in the trial and the identifiers the University of Leeds are collected as part of the trial - data provided by participants and researchers in accordance with the REC-approved trial protocol and participant consent). The data will be used to determine safety endpoints in terms of cardiovascular event rates, which include cardiovascular cause of death as defined above. Data will not be used for any other purposes: it will not be used for commercial purposes, nor for direct marketing purposes.

Objectives:

The Clinical Trials Research Unit (CTRU) at the Leeds Institute of Clinical Trials Research (LICTR), University of Leeds, believe that the cardiology community requires an appropriately powered, randomised controlled trial of non-invasive ischaemia assessment (functional imaging) to determine diagnosis and patient management. LICTR know that invasive angiography rates are already too high, and that they will increase further if the NICE guidelines (CG95) are followed. LICTR know that from a previous small single centre trial (CECaT), that using functional testing (Cardiac Magnetic Resonance Imaging (CMR), Single Photon Emission Computed Tomography (SPECT), stress echo) invasive angiography could be avoided in 20-25% of patients. LICTR also know that patients rightly want to avoid unnecessary angiography if at all possible, but to date no clinical trial has tested the safety of this type of strategy in terms of clinical outcome. LICTR propose the CE-MARC 2 trial, which would be a major advance from the simple and usually small diagnostic accuracy studies that are all too prevalent in the imaging literature. Having benchmarked the diagnostic performance of CMR and shown superiority against SPECT in the CE-MARC study, CTSU now propose to evaluate 3T CMR prospectively in a three-arm trial to assess whether a CMR-guided management strategy is superior to current best clinical practice (based upon either the principles of NICE CG95 or AHA SPECT appropriateness criteria. This type of study would not previously have been acceptable to clinicians without the findings from CE-MARC defining its diagnostic performance. It is of note that the widely used modality of SPECT has never been prospectively tested as CTSU propose here for CMR. For the NHS, the objective of CE-MARC 2 is to provide robust evidence of: a) Potential improvement in patient care/Health-Related Quality of Life (HRQOL)/outcomes; b) A strategy by which to reduce unnecessary invasive angiography (by a true replacement test rather than an additional test in the diagnostic pathway); c) Cost effectiveness of Cardiac Magnetic Resonance Imaging (CMR) in order to inform future NHS capital investment (as general Magnetic Resonance (MR) systems are replaced this study will help inform key capital purchase decisions on whether a 3T system is justified for cardiovascular work). The purpose of CEMARC 2 is to understand the best method of managing patients with anginal chest pain. This will be quantified by measuring the number of unnecessary angiograms conducted in each arm of the trial. In line with secondary objectives of the CEMARC 2 Trial, a comparison of safety across the three arms of the trial will be made by measuring cardiovascular events, including cardiovascular cause of death defined as; • Fatal Myocardial Infarction • Heart failure • Acute Unexpected Death • Stroke • Pulmonary Embolism • Cardiovascular Procedure-Related • Other Cardiovascular • Unknown Comparison of safety relates to the long-term follow-up. The University of Leeds will use the events data to establish that MRI/SPECT strategies no not result in excess major cardiovascular events down the line due to missed coronary disease. To summarise, the study will consider whether patients in MRI/SPECT arms either have less events than NICE patients, or that said events happen much later.


Project 2 — DARS-NIC-11809-H1Y3W

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/11.

Repeats: One-Off, Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, 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
  • Hospital Episode Statistics Outpatients
  • Mental Health and Learning Disabilities Data Set
  • Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set

Benefits:

The linked HSCIC data will be used to derive key information which will be provided by the University research team to clinicians involved in the long-term care of young people identifying each individual’s risk stratification group (defined as being at ‘low’, ‘medium’, or ‘high’ risk of future complications or health effects, based upon their previous hospital activity patterns, treatment motality, dose, cancer type and stage). The risk stratification model will be devised by the YSRCCYP research team and disseminated to clinicians in the Yorkshire and Humber region via the Y&H Children’s and Young People’s Cancer Network (March 2017). Only those clinicians involved in the direct care of individuals with cancer will be provided with details of the risk stratification model. Health care commissioners will be provided with aggregated cancer intelligence data on the number of survivors currently being seen at each NHS Trust according to risk stratification group, so future services can be planned effectively (May 2017). The benefits to health and social care will include: 1. Improved patient care. This work will identify to clinicians, commissioners and patients themselves of those individuals who are at greatest risk of hospitalization; this will enable follow-up practices to be tailored to patient needs, help identify potential health problems early and intervene so that patient wellbeing is maximized and NHS burden minimized. For example, those individuals identified from the risk stratification model as being at greatest risk of mental health illness will be offered additional support from NHS services (e.g. psychiatry, social care) through their treating oncologist or GP. The risks of depression following cancer treatment will help to describe the NHS burden of mental health problems in this vulnerable population. This knowledge will be informative to pediatric oncologists and other allied health professionals caring for patients, as well as their GPs, by improving awareness of the timing when depression is likely to be diagnosed so that the quality of care can be improved. Patients will be informed of their risk group via their treating consultant or at their annual hospital clinic follow up appointment. Their GPs will also be informed of the results of the risk stratification via the hospital consultant team. Anticipated dates to complete these activities are by March 2017. 2. Evaluation of treatments to identify best practice and guidance. Work to understand the reasons for the hospitalisation so researchers can identify whether certain treatments are associated with an increased risk of hospitalisation and disseminate this information through scientific journal articles. This will mean that alternative treatment modalities and optimal care can be planned which minimize these complications. 3. Evaluation of service provision. Highlight any inequalities in access to specialist cancer care services, particularly in older teenagers and young adults, so that all patients have an equal chance of obtaining the best care irrespective of their personal circumstances and thereby having the best chance of cure. The work will be written up in the form of reports to commissioners and journal articles so that clinicians and commissioners can use this information in order to make any necessary changes to service delivery so that the entire Yorkshire and Humber cancer population is served equally well. 4. Financial planning. Information on hospital activity burden and NHS costs associated with the diagnosis and treatment of children and young adults with cancer will be calculated by the University research team in collaboration with health economists at the Leeds Institute of Health Sciences. Changes in costs over the last 20 years will be reported, adjusting for inflation, in order to provide cost projections over the next 10 years. This information will be collated in the form of a report to specialist commissioners of childhood and adolescent cancer services in the Yorkshire & Humber region so that, where required, service changes can be implemented in order to meet future NHS patient demand. At the moment, these data are lacking and once identified by the University research team, they will provide important information: to clinicians to help better manage their clinic populations, to specialist commissioners to monitor the effectiveness of cancer care and to patients in order to understand more about their own risks of complications associated with the treatment they have received and self-manage their own care and wellbeing. to identify gaps in access to specialist care by the research team for two distinct populations: i) teenagers and young adults, who do not benefit from the same level of centralised care as that in place for younger children, and ii) South Asians as they are more likely to present with cancer due to genetic risk factors. Improving care for teenagers and young adults and the south Asian population will ensure that their survival rates are optimal and equivalent to other age groups and ethnic groups, and any subsequent complications of treatment are minimized and if these do occur are then managed appropriately by specialist NHS professionals to ensure a full recovery. Outputs, such as the risk stratification model, will be integrated into clinical practice through established links between the YSRCCYP research team and pediatric and adolescent oncologists throughout the Yorkshire region. The research programme as a whole benefits enormously from the long-running, close collaboration with hematologists and oncologists in the Yorkshire and the Humber region who all help to ensure that our research findings are effectively translated into clinical practice and are involved in all outputs from the YSRCCYP database.

Outputs:

Work describing risks of health effects of treatment in relation to respiratory illnesses will be completed by the YSRCCYP research team and submitted for publication in the British Journal of Cancer (or similar) by June December 2016. Outcomes of the work will also be disseminated in open-access journals (e.g. BMC Cancer) and presented at conferences including the National Cancer Intelligence Network annual meeting, Teenage Cancer Trust and the International Society of Paediatric Oncology annual conferences. Further work will be submitted to the European Journal of Cancer (or similar) in relation to specific mental health outcomes by April 2017. Analyses describing the variation in clinical pathways including delays and time to diagnosis will be submitted for publication by June 2017 to Journal of Clinical Oncology (or similar). Additional work describing the rates of hospital activity and differences between ages at diagnosis (e.g. 0-14 vs 15-29) and ethnic group (e.g. south Asian vs non-south Asian) will be completed by October 2017 and submitted for publication to the British Journal of Cancer by December 2017. Details of risk stratification models and the methodology to derive these for individual patients will be disseminated by the research team to every clinician involved in the care of children and young people (CYP) in March 2017. This will be supported by the Yorkshire & Humber CYP cancer network that holds details of all practicing NHS CYP cancer teams and clinicians in the region. Summary reports of the work and research undertaken will be compiled and also made available on the Yorkshire Register University of Leeds website (http://medhealth.leeds.ac.uk/info/545/yorkshire_specialist_cancer_register), according to the timelines listed earlier in the document. Data will be held for as long as the research project is funded to undertake this piece of epidemiological and applied health research. Funding currently expires on 31st August 2017; therefore the planned data retention period runs initially until 31 December 2017 to make sufficient arrangements for data deletion. Individual level HSCIC data will not be onwardly disclosed in any form of output. Only aggregated data with small numbers suppressed may be disclosed in publications, e.g. peer review journals and reports.

Processing:

The linked HSCIC data will initially be cleaned to ensure no duplicate episodes remain, no multiple admissions less than 2 days apart with the same HSCIC_ID exist, and no admission entries occur after the date of death (if deceased). Length of hospital stay will then be calculated from the dates of admission and discharge and compared between diagnostic groups, age groups, gender, ethnic group, socioeconomic status, level of specialist care, and distance from residential address to hospital (another reason why the University needs OA code and grid reference). Rates of admission within the cancer survivors will be compared to aggregated hospital admission rates to work out standardised hospitalization admission ratios and assess whether these differ according to cancer diagnosis, treatment, ethnic group, gender, age group, period of diagnosis and socioeconomic status. Data will be processed and stored according to the data security policy in operation at the University. All linked HSCIC data are stored on an encrypted secure area network (SEED) and access is restricted to individuals working on the YSRCCYP register research project, all of whom are employees of the University of Leeds. Data will only be used for the purpose described in this statement. These HSCIC data will not be linked to any primary care data.

Objectives:

The University of Leeds (“the University”) requires approval for data processing to continue its epidemiology and health services research programme evaluating pathways and time to diagnosis for children and young adults diagnosed with cancer under the age of 30, and to calculate the risks and costs to the NHS of adverse health events requiring hospital admission for survivors of cancer in this age group. Work will exploit existing cancer registry data held by the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP) together with linked HSCIC data. HSCICS data are therefore vital in order to provide a comprehensive picture of NHS activity preceding, during and following diagnosis and treatment of children and young people’s their cancer. The purpose of the YSRCCYP research database is to facilitate population-based epidemiological research This type of research has the potential to benefit future patients by identifying important environmental risk factors, examining changes in incidence rates which may help to identify possible causes and understand survival patterns according to ethnic group and socio-economic status in order to ensure that there are no inequalities in outcomes or access to specialist cancer care. Identifiable data are necessary for a number of reasons: (1) So that the University can undertake accurate spatial analyses such as space-time clustering at a sufficiently high resolution (OA level) so that raised cancer risks can be precisely measured in relation to putative environmental exposures such as benzene levels from car exhausts and other specific sources of pollution such as petro-chemical factories, incinerators etc. (2) Consultant codes enable the University of Leeds to work out whether patients receive care at specialist cancer centers as opposed to general district hospitals, in order to address important health services research questions such as: ‘Does specialist care improve patient outcomes for children and young people including length of hospital stay and reduce subsequent morbidity and mortality?’. There are currently no databases in existence which link consultants to specialist cancer centers for childhood and young adult cancer, so this process needs to be done manually using linked HSCIC data and the YSRCCYP database. (3) The local patient identifier field is required to enable accurate validation of cancer registration data through linkage to records held on the Patient Pathway Manager (PPM) system at Leeds Teaching Hospitals NHS Trust (LTHT) . Validation is undertaken to ensure no eligible cancer patients are excluded from the YSRCCYP by looking at the number of un-linked records from PPM and to ensure that treatment information is complete and up-to-date. Some of these records pre-date the introduction of NHS number and therefore the University requires an alternative unique identifier to be used in place of NHS number. Note none of the LTHT (PPM) datasets will be linked to HSCIC data; these data are provided in order for the University to confirm and validate the correct cancer diagnosis, date of diagnosis, address/postcode and treatment is recorded i.e. core cancer registration data which the University and YSRCCYP are entitled to process under their current CAG approval (CAG 1-07(b)/2014). These and other identifiers are retained on the YSRCCYP database and their use will be restricted to this project to ensure that any retrospective data validation exercises, e.g. with the National Cancer Registration Service can be carried out effectively and in a timely manner, particularly as hospital provider codes, hospital unit numbers and NHS numbers etc. may themselves change over time. One previous specific example was a recent study in which the University was invited to collaborate with colleagues at Newcastle University on a retrospective observational study to determine factors associated with length of survival following neuroblastoma relapse for children diagnosed since 1990. This study devised and tested an information package for parents on the epidemiology of relapsed neuroblastoma and parental information needs, to better inform clinicians and parents of children with relapsed neuroblastoma when making treatment decisions in these difficult circumstances, in order to benefit future patients. This important research would not have been possible if the University had lost the right to retain identifiable information as the University was able to exploit the availability of detailed electronic information on treatment, diagnosis, and other molecular pathology over time available from the YSRCCYP. Patient benefit would therefore have been severely compromised if the University failed to retain this crucial information.


Project 3 — DARS-NIC-147908-CPCPG

Opt outs honoured: N

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2016/11.

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:

Primary aim: To assess the diagnostic accuracy of Cardiac Magnetic Resonance (CMR) in detecting coronary heart disease (CHD) compared to the current 'gold standard' X-Ray angiography. Secondary objectives: To assess the prognostic value of CMR in predicting long-term outcome. To compare the diagnostic accuracy of CMR with the current standard clinical investigations of exercise tolerance testing (ETT) and radionuclide perfusion imaging (SPECT) To evaluate the cost effectiveness of CMR in a diagnostic strategy for the systematic investigation of patients with suspected CHD. To assess patient preference of the different strategies for investigation of suspected CHD.


Project 4 — DARS-NIC-148057-2763T

Opt outs honoured: Y, N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/02.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

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

Objectives:

Background: In collaboration with the Paediatric Care Society, the Paediatric Intensive Care Audit Network (PICANet) was established in 2001 with funding from the Department of Health and Health commission Wales Specialised Services. This prospective clinical audit database of all admission to paediatric intensive care activity, casemix, structure and utilization which will facilitate the following:Identification of best practice;Monitoring of supply and demand;Monitoring and review of outcomes of treatment episodes;Strategic planning and resource requirements: and Study of the epidemiology of critical illness. Aims1) to determine the longer term outcome of children admitted to and discharged alive from paediatric intensive care 2) to examine the cause of death in children admitted to paediatric intensive care while on the unit and following discharge.3)to determine the overall burden of mortality due to critical illness in children admitted to paediatric intensive care.4) to analyse all of the above in relation to deprivation, ethnic group and geographical location as a means of addressing health inequalities


Project 5 — DARS-NIC-148098-9ZV2X

Opt outs honoured: N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/02.

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 - Flagging Current Status Report

Benefits:

To be completed by the applicant

Outputs:

To be completed by the applicant

Processing:

To be completed by applicant

Objectives:

The data supplied by the NHS IC to Institute of Cancer Research will be used only for the approved Medical Research Project identified above.


Project 6 — DARS-NIC-148160-G7YGJ

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2018/02.

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

Objectives:

The data supplied by NHS IC to LIGHT will be used only for the approved Medical Research Project identified above.


Project 7 — DARS-NIC-155843-0MQMK

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/06 — 2017/08.

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 benefits to health and social care will include: 1. Improved patient care. This work will identify to clinicians, commissioners and patients themselves of those individuals who are at greatest risk of hospitalization; this will enable follow-up practices to be tailored to patient needs, help identify potential health problems early and intervene so that patient wellbeing is maximized and NHS burden minimized. For example, those individuals identified from the risk stratification model as being at greatest risk of mental health illness will be offered additional support from NHS services (e.g. psychiatry, social care) through their treating oncologist or GP. The risks of depression following cancer treatment will help to describe the NHS burden of mental health problems in this vulnerable population. This knowledge will be informative to paediatric oncologists and other allied health professionals caring for patients, as well as their GPs, by improving awareness of the timing when depression is likely to be diagnosed so that the quality of care can be improved. Patients will be informed of their risk group via their treating consultant or at their annual hospital clinic follow up appointment. Their GPs will also be informed of the results of the risk stratification via the hospital consultant team. Anticipated dates to complete these activities are by December 2018. 2. Evaluation of treatments to identify best practice and guidance. Work to understand the reasons for the hospitalisation so researchers can identify whether certain treatments are associated with an increased risk of hospitalisation and disseminate this information through scientific journal articles. This will mean that alternative treatment modalities and optimal care can be planned which minimize these complications. Anticipated dates to complete these activities are by June 2019. 3. Evaluation of service provision. Highlight any inequalities in access to specialist cancer care services, particularly in older teenagers and young adults, so that all patients have an equal chance of obtaining the best care irrespective of their personal circumstances and thereby having the best chance of cure. The work will be written up in the form of reports to commissioners and journal articles so that clinicians and commissioners can use this information in order to make any necessary changes to service delivery so that the entire Yorkshire and Humber cancer population is served equally well. Anticipated dates to complete these activities are by December 2018. 4. Financial planning. Information on hospital activity burden and NHS costs associated with the diagnosis and treatment of children and young adults with cancer will be calculated by the University research team in collaboration with health economists at the Leeds Institute of Health Sciences. Changes in costs over the last 20 years will be reported, adjusting for inflation, in order to provide cost projections over the next 10 years. This information will be collated in the form of a report to specialist commissioners of childhood and adolescent cancer services in the Yorkshire & Humber region so that, where required, service changes can be implemented in order to meet future NHS patient demand. Anticipated dates to complete these activities are by December 2019. At the moment, these data are lacking and once identified by the YSRCCYP research team, they will provide important information: * to clinicians to help better manage their clinic populations, * to specialist commissioners to monitor the effectiveness of cancer care and * to patients in order to understand more about their own risks of complications associated with the treatment they have received and wherever possible self-manage their own care and wellbeing. * to identify gaps in access to specialist care by the research team for two distinct populations: i) teenagers and young adults, who do not benefit from the same level of centralised care as that in place for younger children, and ii) South Asians as they are more likely to present with cancer due to genetic risk factors. Improving care for teenagers and young adults and the south Asian population will ensure that their survival rates are optimal and equivalent to other age groups and ethnic groups, and any subsequent complications of treatment are minimized and if these do occur are then managed appropriately by specialist NHS professionals to ensure a full recovery. Outputs, such as the risk stratification model, will be integrated into clinical practice through established links between the YSRCCYP research team and paediatric and adolescent oncologists throughout the Yorkshire region. The research programme as a whole benefits enormously from the long-running, close collaboration with haematologists and oncologists in the Yorkshire and the Humber region who all help to ensure that the University's research findings are effectively translated into clinical practice and are involved in all outputs from the YSRCCYP database.

Outputs:

Work describing risks of health effects of treatment in relation to respiratory illnesses will be completed by the YSRCCYP research team and submitted for publication in the British Journal of Cancer (or similar) by June 2018. The June 2018 publication follows a June 2016 publication where descriptive statistics have been produced showing the respiratory conditions diagnosed within the linked cohort. The background admission rates in the general population are required over the same time period to enable further statistical analysis to be carried out. Outcomes of the work will also be disseminated in open-access journals (e.g. BMC Cancer) and presented at conferences including the National Cancer Registration and Analysis Service Cancer Outcomes annual meeting, Teenage Cancer Trust and the International Society of Paediatric Oncology annual conferences. Further work will be submitted to the European Journal of Cancer (or similar) in relation to specific mental health outcomes by December 2018. Analyses describing the variation in clinical pathways including delays and time to diagnosis will be submitted for publication by June 2018 to Journal of Clinical Oncology (or similar). Additional work describing the rates of hospital activity and differences between ages at diagnosis (e.g. 0-14 vs 15-29) and ethnic group (e.g. south Asian vs non-south Asian) will be completed by October 2017 and submitted for publication to the British Journal of Cancer by December 2017. Details of risk stratification models and the methodology to derive these for individual patients will be disseminated by the research team to every clinician involved in the care of children and young people (CYP) in December 2018. This will be supported by the Yorkshire & Humber CYP cancer network that holds details of all practicing NHS CYP cancer teams and clinicians in the region. Summary reports of the work and research undertaken will be compiled and also made available on the Yorkshire Register University of Leeds website (http://medhealth.leeds.ac.uk/info/545/yorkshire_specialist_cancer_register), according to the timelines listed earlier in the document. All outputs will be aggregated with small number suppression in line with the HES Analysis Guide. As the funder, the Candlelighters Trust may request information for use in its own information dissemination and publicity materials. For example, they may ask for the number of new cases diagnosed per year in Yorkshire and projected incidence rates. The University of Leeds would only share information that is available as a result of the processing activities described above – i.e. the YSRCCYP would not undertake further data processing in order to derive information requested by the Candlelighters Trust and any information shared would be put in the public domain. For clarity, the University of Leeds is not obliged to provide information on request to the Candlelighters Trust and would not share any data that are not aggregated with small numbers suppressed in line with the HES Analysis Guide. The linked NHS Digital data alongside the background hospitalisation rates will be used to derive key information which will be provided by the YSRCCYP research team to clinicians involved in the long-term care of young people identifying each individual’s risk stratification group (defined as being at ‘low’, ‘medium’, or ‘high’ risk of future complications or health effects, based upon their previous hospital activity patterns, treatment mortality, dose, cancer type and stage). The risk stratification model will be devised by the YSRCCYP research team and disseminated to clinicians in the Yorkshire and Humber region via the Y&H Children’s and Young People’s Cancer Network (December 2018). Only those clinicians involved in the direct care of individuals with cancer will be provided with details of the risk stratification model. Health care commissioners will be provided with aggregated cancer intelligence data on the number of survivors currently being seen at each NHS Trust according to risk stratification group, so future services can be planned effectively (June 2018). Data will be held for as long as the research project is funded to undertake this piece of epidemiological and applied health research. Though work is currently planned until December 2019, the current funding expires on 31st August 2017. Subject to securing ongoing funding, the data would be retained until December 2019 to allow sufficient time for completion of analyses, submission and final publication of papers.

Processing:

NHS Digital has previously supplied a pseudo-anonymised HES extract containing details of all inpatient episodes for patients in the Yorkshire and Humber SHA area only under the age of 46 at admission for the period from 1996/97 to 2010/11. A further extract for the period 2011/12 to 2016/17 (latest available) will be supplied and added to the previously supplied extract. These pseudo-anonymised HES extracts are specifically required for comparison of the cohort with the general population of Yorkshire. The University of Leeds stores the data on an encrypted secure area network (SEED) and access is restricted to individuals working on the YSRCCYP register research programme. Access to the record level data will only be by substantive employees of the University of Leeds and located within the Division of Epidemiology and Biostatistics. No NHS Digital data will be transferred outside of the University of Leeds or shared with any third party individual or organisation (apart from where stored at 2 disaster recovery sites at the University of York and Iron Mountain, where data will be stored only for the purpose of disaster recovery and not processed for any other purpose) Cohort linked data (HES and mental health), provided under a separate Data Sharing Agreement (reference: DARS-NIC-11809-H1Y3W), and the pseudo-anonymised HES extract are stored in separate files and are distinct from the YSRCCYP data itself. The pseudo-anonymised HES extract will not be linked to the cohort data supplied by NHS Digital or in the YSRCCYP database. Different pseudonymised HES IDs will ensure this is not possible. On receipt of pseudo-anonymised data (HES and mental health) the YSRCCYP research team undertake the following processing activities: The pseudo-anonymised HES extract is used to calculate the admission rates (per 100,000 per year) for the same HES diagnoses as the patient cohort. This work has been completed for cardiovascular diseases (van Laar et al, British Journal of Cancer 2014) and for a descriptive piece of health services research as part of a PhD doctoral thesis (Althumairi, University of Leeds, 2017). Work to examine respiratory outcomes according to patient characteristic groups, such as age group, sex, ethnic group, calendar period in the Yorkshire region will be carried out and these data will provide the background rate in the general population. Rates of admission within the cancer survivors have previously been compared to aggregated hospital admission rates to work out standardised hospitalization admission ratios and assess whether these differed according to cancer diagnosis, treatment, ethnic group, gender, age group, period of diagnosis and socioeconomic status, using statistical models adjusting for patient case-mix while also incorporating the general background hospital admission rates. (Althumairi, University of Leeds, 2017). This process will be repeated using the latest pseudo-anonymised HES extract with a focus on specific disease groups, including respiratory diseases and mental health data, using a similar methodology as the YSRCCYP research team’s previously published work on cardiovascular disease. The pseudo-anonymised HES extract will be used to compare (but not link) inpatient hospital admissions for the general population in Yorkshire under the age of 60 to data on a population of the same age range in the cohort who were diagnosed with childhood or young adult cancer (derived from data provided under a separate Data Sharing Agreement (reference: NIC-11809-H1Y3W)). The aim is to assess whether certain hospital admissions are more (or less) common amongst a population of survivors of childhood and young adult cancers following treatment compared to the general population. The risk of admissions of a certain diagnosis in the cancer population will be compared to that in the general population. The YSRCCYP research team aims to look at the whole admission pattern of patients, not simply those that occur in the primary diagnosis fields and therefore require an episode level extract as opposed to aggregated counts of admission. Summaries of the results will be presented orally at conferences and are intended to be published in academic or medical journals. All outputs will be aggregated with small numbers suppressed and in line with the HES Analysis Guide. Researchers who are not substantively employed by the University of Leeds may apply for access to data from the YSRCCYP but data supplied by NHS Digital will not be shared with any third parties. Data will only be used for the purposes described in this statement. The NHS Digital data will not be linked to any other data apart from YSRCCYP data. The YSRCCYP research team requires data from the full period from 1996/97 to 2016/17 (latest available) for several reasons. Firstly in order to address aim 1 investigating changes in levels of hospitalisation over time and within specific cancer types, age groups and sociodemographic groups. Secondly to maximise statistical power for analyses given the rarity of childhood and young adult cancer; also to assess changes in access over time to specialist cancer care services, as NHS policy regarding the recommendations for treatment of children and young people with cancer has changed with the opening of Principal Treatment Centres in hospitals across in England throughout this time frame. Evaluating clinical care pathways and time to diagnosis also require data of all admissions prior to cancer diagnosis. The late health effects for childhood and young adult cancer survivors may occur any time after treatment ends and the risk of late effects increases as the cohort ages. In order to fully evaluate the total burden of adverse health events in these survivors’, data are required for as long a time period as possible. This may also include any hospital admissions prior to the patient’s cancer diagnosis to identify any underlying health conditions. The YSRCCYP research team is also notified about any subsequent malignant neoplasms from the National Cancer Registration and Analysis Service prospectively following the original cancer diagnosis and therefore need to retain all historical HES and mental health data in order to scrutinise any such individual’s history of hospital admissions and understand potential reasons for those who experience multiple tumour diagnoses.

Objectives:

The University of Leeds requires HES and mental health data for a specific cohort to be used, alongside data collected in the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), to continue its epidemiology and health services research programme. For background, the YSRCCYP is a regional population based register containing detailed demographic and clinical information on children and young adults aged 0-29 years diagnosed with cancer since 1974. The YSRCCYP covers the Yorkshire and Humber Strategic Health Authority (SHA) which has a total population of 5 million people. Spanning an area of 15,000 square kilometres the Yorkshire and Humber SHA comprises a range of urban and rural communities with a significant ethnic minority population resident in parts of West Yorkshire. The YSRCCYP research team, within the University of Leeds, is notified of patients eligible for inclusion in the YSRCCYP either directly by the patient’s treatment centre or via electronic reports from the National Cancer Registration and Analysis Service. The YSRCCYP research team then obtains information on patients by manual data abstractions from hospital records. Detailed data on the patient and diagnosis, including treatment information for each of these cases is obtained by a sole data collection officer via the medical records at relevant hospitals in the area, and annual follow up of all cases takes place to ascertain data on any relapses or deaths through letters sent either to the patient’s treating consultant or general practitioner. Data on 9,500 patients have been collected since 1974, however linked HES and mental health data was required for only 8,500 as the cohort shared with NHS Digital excludes participants who passed away before 1996. The cohort submission under approval of this request will consist of approximately 7,000 patients. The YSRCCYP was originally set up in collaboration with local clinicians to provide research information. Since 1994, the YSRCCYP database and research programme has been managed by and at the University of Leeds’ Division of Epidemiology & Biostatistics. The University of Leeds is the Data Controller for the YSRCCYP with sole responsibility for determining the purposes for which and the manner in which any personal data are processed. The work is currently funded by the Candlelighters Trust. The purpose of the YSRCCYP is to facilitate population-based epidemiological and health services research. The use of HES and mental health data contributes to this by providing additional information that can be linked with and analysed with data from the YSRCCYP data. The HES and mental health data are not added into the YSRCCYP research database. The two datasets are stored separately but contain common unique study IDs enabling data to be linked at record level. Where required for specific research, relevant data are extracted from the respective databases, linked and analysed by the YSRCCYP research team. A current research focus is on hospital burden around the time of diagnosis and treatment and monitoring long term risks of hospitalisation associated with cancer treatment. One specific processing activity will relate to describing the risks and prevalence of mental health illness within the cancer cohort compared to the general population. This type of epidemiological and health services research has the potential to benefit future patients by identifying risk factors which can be used by health professionals to identify those at greatest risk of mental health illness so that interventions and appropriate support can be implemented. It may also reveal important environmental risk factors, examine changes in incidence rates which may help to identify possible causes and understand survival patterns according to ethnic group and socio-economic status in order to ensure that there are no inequalities in outcomes or access to specialist cancer care for certain sub-populations. The YSRCCYP research team’s research plans include the following objectives: 1) To describe the total burden of hospitalisation among the Yorkshire cancer population aged 0-29 years, to identify clinical and sociodemographic factors which influence the likelihood of hospitalisation and to investigate how hospitalisation rates have changed since 1997. 2) To understand patient care pathways through the NHS before, during and after cancer diagnosis. This includes assessment of time to diagnosis for children and young adults diagnosed with cancer under the age of 30 years to identify where improvements can be made to minimise delays in diagnosis leading to better prognosis and less stress and anxiety on patients and their families. 3) To calculate the risks and costs to the NHS of adverse health events requiring hospital admission for survivors of cancer in this age group so that clinicians can provide appropriate follow-up care. To address aim 1 above the YSRCCYP research team will utilise linked HES and mental health data to investigate long term risks of respiratory and mental health illness in the cohort and identify sociodemographic and clinical factors which may affect these risks. The linked HES and mental health data are covered under a separate Data Sharing Agreement (reference: NIC-11809-H1Y3W).The YSRCCYP research team also wish to determine the relative excess risk of these conditions within the cancer cohort compared to the general background population and, in order to make this comparison, requires a separate pseudo-anonymised extract of HES data containing all episodes for patients in the Yorkshire and Humber SHA area only under the age of 60 at admission (the oldest person currently registered). This separate extract is covered under this Data Sharing Agreement. The YSRCCYP research team require some data items classed as sensitive. These are the Referrer code, which indicates the manner in which the patient was referred to hospital by ascertaining the code of the referring organisation. This allows the YSRCCYP research team to identify particular patient pathways which are associated with an optimal time to diagnosis, a key indicator known to influence survival. Additionally the Consultant code data field is required because it enables the YSRCYYP research team to work out whether patients receive care at specialist cancer centres as opposed to general district hospitals, in order to address important health services research questions such as: ‘Does specialist care improve patient outcomes for children and young people including length of hospital stay and reduce subsequent morbidity and mortality?’. There are currently no databases which link consultant codes to specialist cancer centres for childhood and young adult cancer, so this process needs to be done manually using cohort linked NHS Digital data and the YSRCCYP database.


Project 8 — DARS-NIC-17649-G0X4B

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/03 — 2017/05.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Benefits:

This study will quantify the burden of hospitalisations and long term outcomes for patients who are admitted to NHS hospitals and surviving acute myocardial infarction (AMI) in England. The research outputs as described will be disseminated widely to University of Leeds' established informal networks including the academic community, clinicians, patients and the public as well as NHS commissioners via the formal networks discussed in section 5c. Dissemination of the factors which could lead to increased hospitalisations and mortality to clinicians (via academic publications, presentations at clinical conferences and dissemination via the British Cardiac Society and British Heart Foundation) is envisaged to be a driver for improved patient care and has far reaching clinical and social benefits as outlined below. Quantifying the burden of hospitalisations and long term outcomes for patients surviving AMI in England will for the first time, on a national scale, provide NHS commissioners with the necessary evidence to plan effectively for service provision and resource allocation for the large proportion of patients who now survive their AMI. Although improvements in treatment have resulted in improved survival rates for patients with AMI – the long term health burden of patients following their AMI is not yet known – and this is what the study team propose to determine. In addition, the findings from this study can be used to inform new endpoints for future clinical trials, to ensure that not only the mortality or short term cardiovascular outcomes of AMI patients are considered, but also longer term cardiovascular and non-cardiovascular outcomes in developing and testing new treatments in future. In addition, by quantifying the impact of lack of adherence to guideline recommended care on re-hospitalisation and mortality or the impact of delayed PCI treatment on re-hospitalisation and mortality, the Cardiovascular Epidemiology research group would provide the scientific supporting evidence to clinicians to strive for improved adherence to guidelines, which therefore has the potential to improve outcomes for patients. Increased patient awareness of the impact of AMI on future hospitalisation and mortality, through dissemination to the public as described, could not only lead to patients modifying their own health behaviours to minimise their own risks of future hospitalisation, but patient and public who are informed by this knowledge can also provide strong motivation for clinicians and commissioners to improve patient care and care pathways. Knowledge of geographical variation in hospitalisation and mortality from AMI will identify key areas of inequality in the NHS, dissemination of this knowledge to NHS commissioners will enable them to act upon such inequalities to ultimately drive up standards and provide direct and measurable benefit to patients and the NHS. This work forms part of the wider research conducted by the University of Leeds' research team (Cardiovascular Epidemiology), and will therefore contribute to a growing body of evidence regarding the quality of care and outcomes for patients surviving AMI, whilst adding important new insights into the long term health burden for the increasing number of AMI survivors which focusses not only on mortality, but importantly, also on re-hospitalisation for a range of cardiovascular and non-cardiovascular conditions. The Cardiovascular Epidemiology research group has expertise in health services research which directly impacts on patients, policies and healthcare professionals through research papers, the media and conferences. The research group has an excellent track record for translating research to clinical impact, which specific examples listed below: Research referenced in NICE guidelines: 1) Clinical risk scoring for acute myocardial infarction referenced in NICE Clinical Guideline 94 (Gale CP et al, Heart, 2008; 95(3):221-227) 2) Atrial fibrillation research referenced in Atrial fibrillation: management, NICE clinical guideline 180 (Cowan, Long and Gale et al. Heart (2013): heartjnl-2012) 3) Pre-hospital ECG research referenced in European Resuscitation Council Guidelines for Resuscitation 2015 (Quinn, and Gale, et al. Heart (2014): heartjnl-2013). Widespread media coverage and beyond: 1) Research by Dondo and Hall et al (Dondo T, Hall M, et al. Eur Heart J Acute Cardiovasc Care. 2016) has received widespread media coverage including radio and TV broadcasts as well as broadsheets and tabloids. This research also resulted in being invited to present at the Westminster health forum, and forms the critical evidence for the non-ST elevation myocardial infarction NICE Implementation Collaborative. 2) Research by Hall and Gale et al (JAMA 2016; Aug 30. doi: 10.1001/jama.2016.10766) as well as Wu, Hall and Gale et al (Eur Heart J Acute Cardiovasc Care 2016; Aug 29. pii: 2048872616661693) forms the evidence for the guidelines in practice NIC project (https://www.guidelinesinpractice.co.uk/nic-projects). 3) The geographic variation in AMI treatment by Dondo, Hall and Gale et al (BMJ Open 2016; 6 (7): e011600) has received widespread media coverage, and has led to a successful Department of Health / NHS England business case to develop the work further into a feedback quality improvement programme for patients, hospitals and commissioners (work currently ongoing). Several members of the Cardiovascular Epidemiology research group have experience with Public & Patient Involvement, meeting patients to discuss a range of different research proposal and allowing them to influence and be part of the research agenda as well as disseminate research findings back to them, whilst some members of the research group hold very close involvement with the British Heart Foundation, especially their press office and policy group who therefore act as a powerful conduit for change and knowledge dissemination. The research the Cardiovascular Epidemiology research group propose here is of direct and critical importance to the NHS and the Department of Health. Although heart attacks remain the biggest killer worldwide, survival rates are improving. As such, patients are living longer with their cardiovascular disease, and there are an estimated 7 million people living with cardiovascular disease in the UK. The Cardiovascular Epidemiology research group propose to look at the components of the disease process including the wide range of outcomes following acute myocardial infarctions so that the commissioners, hospitals and NHS England can make evidence informed policy decisions about the need for cardiovascular care, as well as where, when and in whom. The research group has an excellent track record to ensure this research outputs are far reaching with high impact.

Outputs:

Whilst the planned analyses will be disseminated to the academic and medical community in peer reviewed publications and presented at relevant conferences (see below), it is the clinical implications of the results for healthcare professionals, patients and regulators that are of greater virtue. It is clear that the results from the proposed study will help answer major gaps in the knowledge base of the health burden and ongoing hospitalisation for the increasing number of survivors following acute myocardial infarction which can therefore contribute to future healthcare policy. The Cardiovascular Epidemiology research team has established connections with numerous relevant groups through which findings will be disseminated to the NHS as well as patients. These groups include: The NICE Indicator Advisory Group, the European Society of Cardiology Acute Cardiovascular Care Association, the European Society of Cardiology Acute Cardiovascular Care Association Quality of Care Group, the British Cardiovascular Society Guidelines and Practice Committee and the National Institute for Cardiovascular Outcomes Research (NICOR) Research Executive. The Cardiovascular Epidemiology research group is led by an Associate Professor of Cardiovascular Health Sciences at the University of Leeds who is also a member of the above groups and is additionally an honorary consultant Cardiologist at York Teaching Hospitals NHS trust and secretary of the European Society of Cardiology Acute Cardiovascular Care Association – offering further dissemination routes which will be utilised. NICE identifies awareness and knowledge as well as lack of motivation by healthcare professionals to be some of the key barriers to change in the NHS. Patients are at the heart of providing motivation for healthcare professionals to improve care in the NHS, therefore the Cardiovascular Epidemiology research group will focus on dissemination to patients as well through charities listed in point 3 below. The Cardiovascular Epidemiology research group dissemination strategy will be as follows: 1). Peer-reviewed publication Paper 1: Hospitalisation and mortality after acute myocardial infarction. Anticipated submission date: March 2018. This paper will quantify the hospitalisations and long term outcomes for patients surviving acute myocardial infarction as well as determine the factors which lead to increased hospitalisation and morbidity. Paper 2: Geographical variation in hospitalisation and mortality for patients surviving acute myocardial infarction. Anticipated submission date: August 2018. This paper will quantify the potential geographical variation in hospitalisation for patients surviving AMI to identify potential healthcare inequalities for NHS Commissioners. Paper 3: Hospitalisation and mortality for patients surviving acute myocardial infarction according to receipt of timely percutaneous coronary intervention (PCI). Anticipated submission date: August 2020. This paper will specifically look at the association between timely receipt of PCI and the long term hospitalisations and outcomes for patients surviving AMI. 2). Wider academic dissemination of the research findings will also be made at major national and international conferences as appropriate, such as the British Society of Cardiology conference (June 2017/18/19) and the European Society of Cardiology Congress (August 2018/19/20). 3). Lay summaries of the research findings will be generated and disseminated to the following key stakeholders: the British Cardiovascular Society (BCS), British Heart Foundation (BHF), TakeHeart, NHS commissioners and clinicians/health professionals involved in managing heart attack. The Cardiovascular Epidemiology Research group have previously liaised with these organisations to ensure wide reaching research impact, beyond the academic community (see section 5d) In addition, the group website (Cardiovascular Epidemiology - https://medhealth.leeds.ac.uk/homepage/692/cardiovasucular_epidemiology-leeds_institute_of_cardiovascular_and_metabolic_medicine) as well as the research team's twitter account (@UoLCardioEpi) will be used to update the public, the network of stakeholders, charities, and health professionals throughout the project. All outputs will be aggregated with small number suppression in line with the HES Analysis Guide.

Processing:

Data will be stored on the University of Leeds' Secure Electronic Environment for Data (SEED) system. The data will only be accessible to authorized individuals in the Cardiovascular Epidemiology Research Group within the Leeds Institute of Cardiovascular and Metabolic Medicine at the University of Leeds. The data will only be accessed by substantive employees of the University of Leeds and only used for the purpose of this project. The data will be geocoded based on Lower Super Output Area (LSOA) to obtain information on higher aggregated geographical units (Clinical Commissioning Groups). No further linkage to the data will occur. Only summarised and aggregated data will be disseminated in the form of academic presentations and peer-reviewed journals. The data will not be used for commercial purposes, provided in record level form to any third party or used for any direct marketing. The study is funded by the British Heart Foundation. For the avoidance of doubt, the British Heart Foundation will not influence the results or dissemination of the research conducted, and the British Heart Foundation will have no role in the design, analysis or interpretation of the research.

Objectives:

The objective for processing of these data is to perform research into survival following heart attack in England. Over the last decade, there has been a substantial and sustained decline in mortality rates from cardiovascular disease in the UK. Despite this, cardiovascular disease remains the biggest killer in the UK and someone is admitted to an NHS hospital with a heart attack every three minutes. Moreover, improvements in acute myocardial infarction (AMI; heart attacks) survival are likely to be a major cause for the increasing incidence of heart failure (‘transferred morbidity’), which now affects around 900,000 individuals in the UK and accounts for 5% of all emergency hospitalisations. Presently, most patients with cardiovascular disease are elderly and because AMI survival has increased there are more patients living longer with co-morbidities. More frequently, such patients require specialist cardiovascular care in the form of invasive cardiac procedures including high and low voltage and resynchronization pacemakers and coronary revascularisation. Moreover, they frequently re-present to hospital – escalating the burden of admissions with heart failure. Specifically, the research will aim to quantify the long term outcomes and hospitalisation rates for survivors of acute myocardial infarction in England. The objectives of the analysis are: 1. To describe hospitalisation patterns and endpoints (heart failure, cerebrovascular disease, coronary revascularisation, vascular dementia, severe bleeding, acute myocardial infarction, atrial fibrillation, all-cause mortality) for patients hospitalised with non-fatal AMI (i.e. survivors of the index hospital stay) compared to those who have no recorded AMI . 2. To identify factors associated with hospitalisation and endpoints for hospital survivors of index AMI compared to those who have no recorded AMI specifically focusing on geographical variation and the provision of timely percutaneous coronary intervention. In order to quantify the incidence of a range of hospitalisations (cerebrovascular disease for example) following survival from AMI, the study team at the University of Leeds need to ensure they have a clean cohort for analysis to minimise confounding where possible as well as data of the hospitalisations occurring among patients who have no recorded AMI. Detailed justification for the request of this level of data is outlined below. 1. Reasons for requiring hospitalisations amongst patients with no recorded AMI In order to quantify the incidence of a range of hospitalisations (cerebrovascular disease, heart failure and other outcomes) following survival from AMI, the study team needs to compare the number of each hospitalisation type occurring amongst AMI patients to the number of each hospitalisation type which occur in the background population (in this case, the background population is the population of patients admitted to hospital without an AMI in the same study period). The hospitalisations occurring in the non-AMI population are used to determine the expected number of each hospitalisation type for someone hospitalised in the same year, and of the same age and sex as someone who has had AMI. The observed numbers of hospitalisations for those with AMI will then be compared to the expected number of hospitalisations amongst those without AMI to determine whether patients with AMI have more hospitalisations than expected (the excess hospitalisation incidence rate). Without this quantification of the excess hospitalisation incidence rate, the results will have no context as the study team will be unable to ascertain whether those with an AMI are more or less likely to experience certain conditions following their AMI than the background population. Determining this is the primary aim of the study. The study team will require all hospitalisations amongst patients with no recorded AMI (subject to filtering described under “2. Ensuring derivation of a ‘clean’ cohort”) rather than a sample of hospitalisations. The study team considered the feasibility of selecting a reduced number of geographical areas to represent the nation rather than requesting national data but ruled out this approach because it would affect the validity of the findings. This is because an incidence rate is calculated from the observed hospitalisations amongst the AMI patients divided by the observed hospitalisations in the non-AMI population. A sample of hospitalisations (obtained from a reduced set of geographical areas) would change the study design from a population based cohort study to a case-control study, through which it is not possible to calculate incidence rates. Whilst it is possible to obtain relative risks from a case-control study, selecting a sample of ‘controls’ which are representative across a range of hospitalisations (i.e. all the study outcomes) in England could not be guaranteed. Without a representative cohort, the relative risks obtained would be prone to bias. Given the potential impact of the findings on healthcare users and NHS policy, it is essential to minimise uncertainty. In addition, a case-control study design is not suitable for studying multiple outcome measures as proposed by the study team. We would therefore be unable to achieve the study objective of defining the incidence of multiple hospitalisation outcomes following AMI if restricting the non-AMI patient data to a sample of the population, nor guarantee the validity of results which are obtainable under a case-control study design. Finally, the study team aim to additionally determine the extent of geographical variation in hospitalisations and mortality following AMI, which require the calculation of incidence rates (and therefore require a full population denominator) for all areas in England. 2. Ensuring derivation of a ‘clean’ cohort The study team cannot be sure that patients admitted to hospital with AMI in a given period of HES data have not had a previous AMI, or, whether they have had any of the conditions they are considering as outcomes, such as cerebrovascular disease prior to their AMI. Therefore, the study team proposes to derive the cohort for analysis from admissions from 2008/09 to present day, however, has requested for NHS digital to exclude all people from the data who have had a previous AMI or any of the hospitalisations the study team are considering as outcomes. The same filtering of prior conditions will be done by NHS digital for patients who have not had an AMI from 2008/09 onwards. This is a substantial minimisation effort by the study team, as without this step, data from 200-1/02 would have been required as part of the data application. Data from 2008/09 onwards will give sufficient data to look at time trends in hospitalisations and mortality as part of the analysis. Details of all hospital attendances (not restricted to specific conditions with known associations with AMI) are required in order to understand the history of the patient and whether past (non-related) attendances have contributed in any way to that AMI attendance, or to any of the study outcomes including heart and non-heart related outcomes for patients in the AMI or non-AMI cohort. Post attendances also supports this (whether AMI contribute to non-heart related attendances). Additionally, for each individual NHS Digital will provide a vital status indicator (alive/deceased) and, where individuals are deceased, the number of days between the data of admission and the date of death. The date of admission will not be supplied to the University of Leeds making it impossible for the study team to derive the date of death from the data supplied. The research will be undertaken by the established Cardiovascular Epidemiology Research Group within the Leeds Institute of Cardiovascular and Metabolic Medicine at the University of Leeds. This research group has a remit of using large scale routine data and clinical registries alongside advanced analytical epidemiological techniques to better understand and improve the quality of care of patients with cardiovascular disease. The proposed work to study the hospitalisation patterns and outcomes for patients with acute myocardial infarction is part of a larger programme of work funded by the British Heart Foundation (Project Grant PG/13/81/30474) in order to fill an important knowledge gap of the long term hospital burden and non-fatal outcomes for patients with AMI using contemporary, large scale and national observational data.


Project 9 — DARS-NIC-325074-F0J3D

Opt outs honoured: N

Sensitive: Sensitive, and Non Sensitive

When: 2017/03 — 2017/05.

Repeats: One-Off

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

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

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

Benefits:

The SHIFT Trial was commissioned by the Department of Health via their National Institute for Health Research (NIHR) funding stream. The primary purpose of conducting research like this is to inform NHS practice. The trial design is such that the ‘primary outcome’ is hospital attendance following self-harm 18 months after trial entry – what this means is that the University of Leeds will compare hospital attendances for the group of young people who received family therapy and the group who received usual care. The University of Leeds can only make this statistical comparison when everyone in the trial has been involved for 18 months. This is now the case – the last participants completed follow up in June of this year, so statistical analysis is underway. Once the data is analysed the University of Leeds will have the results showing whether or not family therapy was better than usual treatment. Whatever the outcome there will be some benefit to the NHS and to people using NHS services. A review of the NIHR report and other literature this will inform NICE guidance which in turn will influence NHS commissioning. A positive outcome showing that family therapy is more effective than current usual care would influence NICE guidance for best practice within the NHS. This would be established after publication of the main results - in 2016/17. If family therapy is shown to be no better than standard care, the results will provide other valuable insights which will assist commissioners. For example, it may be that family therapy is does not lead to a decreased number of hospital attendances however, the results will show whether it is more or less cost effective overall. If it is worse or better, this will be similarly disseminated to inform practice.

Outputs:

The primary and secondary analyses from the SHIFT trial will be published as an HTA monograph (SHIFT is funded by the Department of Health’s National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme. It is a key funder for a lot of large scale research projects across England. One of the requirements of conducting research funded by HTA is that a 50,000 word monograph (or report) is produced at the end of the study, providing detailed evidence of processes undertaken, trial results, interpretation and dissemination plans. The HTA monograph is due for submission March 2016, with an anticipated publication date of October 2016. The findings will be submitted to relevant peer reviewed journals in the field of child and adolescent mental health and self-harm. There is the intention to more widely disseminate trial results to patient and public groups and to the lay community. The intention is to meet with a young person’s lay group in London (the same group they consulted regarding the newsletter) the National Institute for Health Research’s (NIHR) Young People’s Mental Health Advisory Group. At this forum the results will be presented and questions will be asked for their advice on interpretation from a lay perspective, and also their thoughts on where and how results would best be disseminated. It is a condition of NIHR funded research that results are disseminated widely and appropriately and not just in academic journals – lay dissemination will be initiated at the meeting with the above group and their advice sought. The SHIFT trial will also look at other existing patient forums – local and national – for oral dissemination, as well as via charitable organisations (such as Young Minds) and local / national lay publications in which they might be able to include suitable articles. In all cases this would be a summary of findings including the implications of these. In summary results will be published in usual academic routes, papers in journals, presentations in conferences; Newsletter to all participants (unless they have told us they don't want this) and to all participating staff; Information on study web site; Press releases and possible involvement of national media; A special conference will be organised in Leeds for all the clinical colleagues who were involved. The University of Leeds also want to do something with the young people and their families. Exactly what is still under discussion, consultation is still underway with YoungMinds and the NIHR CAMHS user group. This will influence wording of newsletters/ websites etc. The intention, should the trial show that the intervention is effective, is that the results will ultimately inform NICE guidance and influence NHS practice in this area. No outputs will ever identify any individual and be aggregated with small numbers suppressed, organisation, nor include any record level data.

Processing:

Data will be processed by the trial statisticians at the Clinical Trials Research Unit, Leeds Institute for Clinical Trials Research (LICTR) at the University of Leeds. It will be securely stored on CTRU systems with access only granted to the statistical team. Data will not be accessed by any third parties, nor will it be accessible across multiple organisations. LICTR has IG toolkit status (Code: ECC0010). Identifiers will be sent to the HSCIC (Trial ID, postcode, NHS number, gender and Date of Birth). Data will be linked with existing SHIFT Trial datasets (data provided by participants and researchers in accordance with the REC-approved trial protocol and participant consent), as per the existing agreement with HSCIC that this data will augment the dataset with data that is difficult or impractical to obtain from individual NHS organisations. The linked data will be returned, containing identifiers, back to the University of Leeds and only accessed by a named statistician before it is stored in the networks. Data will not be used for any other purposes: it will not be used for commercial purposes, nor for direct marketing purposes. All individual accessing or processing the data are employees of the University of Leeds.

Objectives:

The SHIFT (Self-Harm intervention Family Therapy) Trial has been designed as a pragmatic, individually-randomised, controlled trial comparing Family Therapy (FT) with Treatment as Usual (TAU) for adolescents aged 11 – 17 years who have engaged in at least one previous episode of self-harm. The trial aims to recruit 832 participants from centres in Yorkshire, Greater Manchester and London. Family therapy will be delivered by qualified family therapists using a modified version of the Leeds Family Therapy & Research Centre Systemic Family Therapy Manual (LFTRC Manual), the development of which was funded by the MRC to support trials of FT. The primary outcome is rate of repetition of self-harm leading to hospital attendance 18 months after randomisation. Secondary outcomes include rate of repetition at 12 months, cost-effectiveness, quality of life, and predictive/process measures. Data will be processed alongside other data collected for SHIFT Trial participants to form the final data set for trial analysis. Specifically: HES APC & A&E data will inform the primary outcome of the trial (hospital attendance following self harm) and the safety profile of participants (hospital attendance for any reason); ONS mortality data will inform the safety and population profile; MHLDDS data will enable a fuller description in the trial results of, and accounting for, services accessed by participants. All publications and reports to external bodies utilising the data will be fully anonymised; participants will only be identifiable to the trial team (who are already in receipt of full identifiers by virtue of existing data provided by and for participants). Data will not be shared with third parties.


Project 10 — DARS-NIC-367152-K6Y1D

Opt outs honoured: Y, N

Sensitive: Non Sensitive

When: 2016/04 (or before) — 2018/02.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report
  • MRIS - Personal Demographics Service

Benefits:

Clinical and scientific progress in managing prostate cancer will only bring benefits in terms of well-being and survival for patients if we develop comprehensive and clinically meaningful approaches to measuring the important patient outcomes. Primary aims • To describe the Health-Related Quality of Life (HRQL, e.g., physical, psychosocial) of men with prostate cancer using qualitative and quantitative methods; • To explore if and how their HRQL is associated (cross-sectional) or is predicted by (longitudinal) disease, treatment and/or patient characteristics with a view to inform development of health care policy and service delivery in ways that better meet the needs of such men and their families; • To describe the levels of patient empowerment and undertake preliminary exploration of the interaction between patient empowerment and HRQL. Secondary aims • To collect data to support, if possible, provider variation and health economic analyses especially for the longitudinal work; • To analyse the questionnaire data collected by exploring and checking the psychometric properties (e.g., reliability, validity) of the newer, less well-established questionnaire measures and to investigate the possibility of developing an item-bank for HRQL assessment for use with men living with and beyond prostate cancer using Rasch models. Qualitative interviews will be used to identify ‘gaps’ in surveys of importance to patients and patient partners with a view to adding additional items/questionnaires in the second surveys. • To explore the acceptability/options of electronic PROMs data collection in this cohort and acceptability of real time feedback to service providers to influence/support direct patient care. The commercial aspect of this application does not, however, detract from the numerous and varied health-related benefits of the project, notably with regards to the insight into life with prostate cancer and intention to improve clinical treatment and policy going forward (see above). This work will ultimately inform clinicians and the NHS about prostate cancer sufferers and in turn help drive improvements to treatment. Picker Institute Europe is a health research charity, and this project supports the organisation's overarching objective to improve patient experience and healthcare.

Outputs:

The first output will be the survey itself. The applicant will send the PROMs questionnaire to prostate cancer patients in England, Scotland, Wales and Northern Ireland asking a variety of questions about their care and their quality of life. Those who respond will be sent a follow up questionnaires on an annual a year later asking the same questions. The target date for the first mailing is 5th October 2015, with the fieldwork continuing for three years. Picker Institute Europe will present the research team at University of Leeds with a finaldata file once the fieldwork is complete from which the research team will carry out various analyses. This data file will consist of case data and will contain sampling information –, NHS trust and reference number - alongside the response data from the questionnaire. The mailing data will not be included in this submission, so names, addresses, year of birth and NHS numbers will not be present. This data will contribute to a report presented to the funders: Prostate Cancer UK and the Movember foundation and a series of papers submitted to peer-reviewed journals. It is not yet known exactly what the articles will be on and where they will appear, but there is a large research team and it is hoped that there will be many outputs from this rich data source.

Processing:

A csv file will be prepared to be sent to HSCIC for list cleaning with details of patient surnames and forenames, NHS numbers and date of births. Picker Institute Europe require HSCIC to identify those patients that have died as well as providing back the patient's NHS Numbers, forenames and surnames, addresses and postcodes.

Objectives:

Objective for processing: To conduct death checks, retrieve patient addresses and data verification (of the data included in the CVS sent to HSCIC) for the purposes of administering a PROM survey of men with prostate cancer.


Project 11 — DARS-NIC-378523-Y5Q9L

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/09 — 2017/11.

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 Admitted Patient Care
  • Hospital Episode Statistics Accident and Emergency

Benefits:

A) For the identifiable, consenting cohort benefits include: • Obtaining reliable ‘safety’ information – i.e. data which will show the reasons for and number of hospital attendances or admissions. This is important to be sure there are no adverse impacts of the intervention. It could also give us an indicator that the new intervention might have some benefits if people from intervention homes attend hospital less. • Obtaining health resource use information – e.g. the number, type and length of hospital admissions - is a key element of NIHR-funded research. It is important to have this ‘health economic’ data which details the full cost of an intervention – for example, an intervention may appear to be effective, but incur many additional NHS costs such as multiple hospital visits or GP call outs. Without collecting data on health service use we cannot undertake this analysis. Collecting hospital attendance data from HSCIC for the feasibility study will inform how to best undertake this for the main trial, as well as giving an early indicator of resource use. B) For the aggregate, non-identifiable cohort benefits include: • An overview of safety at the whole home level. This will help us see the overall safety of the intervention – so we would be able to observe any differences in hospital attendances between ‘intervention’ homes and usual care homes. This would contribute to our decision to proceed with a main trial – i.e. if there are no safety concerns we would be happy to proceed. • Having hospital attendance data for all (or at least ‘most’) residents gives a more representative picture of health resource use, rather than just that used by recruited (consenting) residents. For example it might be that those who consented are less ill than those who didn’t, so we wouldn’t get a true picture of ‘whole home’ hospital admissions from consenting residents alone. It is an important benefit to be able to report the generalisability of research findings – this data would help us to do that. Decreasing mobility and increasing dependency have many adverse effects. For residents in care homes, it may lead to increased incidence of pressure sores, contractures, cardio-vascular deconditioning, urinary infections, and loss of independence. Mobility problems and reduced physical activity compound health difficulties by directly affecting physical and psychological health and reducing opportunities to participate in social activities; social isolation negatively impacts on mood and self-esteem, which can then further adversely affect physical health. Residents identify mobility as of central importance to quality of life and well-being and residents with dementia wish for more day-time activities. Physical ill-health and disability are the most consistent risk factors for depression in later life with reports suggesting that, rather than illness per se, it is the resulting functional limitations (handicap) including social participation and meaningful relationships that increase risk of depression. Physical activity provides positive benefits for older people > 65 years for a range of outcomes: decreased disease risk, mood and overall health. For frail institutionalised older people, systematic reviews indicate that physical training can positively affect fitness for some participants; the level of effect may be related to level of frailty. A recent review of the effects of physical activity for older people with dementia (not all of whom were in institutions) reports some benefits for physical function. Additional benefits may be accrued through enhancing social engagement directly by, for example, participation in communal activities such as exercise sessions, and indirectly by maintaining physical abilities sufficient for the resident to be mobile enough to move around the home and interact with other residents. Such social engagement has been shown to be linked with more successful ageing. The University of Leeds proposed research to enhance routine physical activity supports the aims of the DH report, NICE guidance and BGS6 reports to promote the well-being of older people in long-term care. It is in keeping with the National Care Home Review, which promotes the concept of care homes as community places with emphasis on creating opportunities for meaningful activity, for shared decision making and for building an environment that supports community. The outputs will inform feasibility assessment in relation to a larger definitive clinical trial which would assess incremental cost effectiveness of an intervention to increase physical activity in care homes, compared to usual care. This feasibility assessment will be complete by the end of the current NIHR programme grant (14 February 2018).

Outputs:

The University of Leeds will begin the staged process of developing a complex intervention embedded in the routine of care homes to promote physical activity tailored to the context and environment of individual care homes and thereby enhance quality of life for this neglected group. Ultimately, and if successful, the intervention strategies will be disseminated through the local Care Home Forum, local and national contacts with Adult Social Care and links with national care home providers through co-applicants and the Steering Group. Successful completion of the feasibility trial (the last stage of the programme grant) will inform the application for funding to undertake a definitive Randomised Controlled Trial (as described in 'objective for processing'), to investigate the effectiveness of a physical activity intervention in care homes across England. The outputs will be used to establish a protocol for this trial (or otherwise, as appropriate). The University of Leeds will report the results of the REACH trial to the NIHR (the funder) and, if the feasibility study results indicate that it is reasonable to proceed to a main trial, will apply for further NIHR funding to conduct a definitive main trial. The feasibility assessment will be completed and published at the end of the programme grant (14 Feb 2018). Results for the trial will be presented in a report for the funding body NIHR. Academics will have access to the outputs via anonymised publication in journals. Indicators will not be produced that show the performance of organisations. The outputs of this would inform best practice in care homes, and would be published in relevant academic journals (for example Age and Aging), non-academic platforms accessible to the general public (e.g. a study website), and would be disseminated to the care home community via relevant national and local forums or events. Specifically the results of the feasibility study would be published in academic journals (e.g. Age and Aging, BMC Pilot and Feasibility Studies) and disseminated to the participating care home staff, residents and their relatives. Outputs are expected in January 2018, when the study will complete analysis. Outputs will be disseminated as detailed above, regardless of the findings (and 'success') of the research.

Processing:

1) Consented Record Level Cohort (153) The University of Leeds CTRU will supply the following identifiers of the consented cohort to the HSCIC: - trial ID, NHS no, DoB uploaded by named CTRU statistician to NHS Digital secure data depot. NHS Digital will provide a bespoke extract of HES using APC and A&E datasets for the consented cohort of 153 care home residents. Data is uploaded to the data depot by NHS Digital, and downloaded by a named CTRU statistician. 2) Aggregate Level Cohort The University of Leeds CTRU will provide NHS Digital with Participating Care Homes’ (N=12) postcodes. NHS Digital will provide aggregate data for all residents 65 years old and over at these care homes collated. Tabulations will include the number of A&E visits, the length of these visits between certain data parameters; and for the APC dataset they will include the number of hospital admissions (planned and unplanned) and average length of stay. Aggregate data sets (by care home) uploaded to the data depot by NHS Digital, and downloaded by a named CTRU statistician. Data Storage Data will be stored at The University of Leeds in a secure, limited access folder on CTRU network. Data required for use by health economics will be transferred by the named CTRU trial statistician to the named health economist via the CTRU's secure file transfer system. This data will be stored at The University of Leeds in the Secure Electronic Environment for Data (SEED) system. Data Processing 1) CTRU enter and store data securely on restricted access UoL server (IGT ref ECC0010) - Data required for use by health economics will be transferred from CTRU and stored in the SEED system (IGT ref 8E218). 2) Data collected for the trial + NHS Digital data used for REACH trial analysis (analysis undertaken by CTRU statistician and health economist as per trial protocol) Data will be processed by a named statistician and named health economist who are substantively employed by The University of Leeds. The data will not be used for commercial purposes, and will not be provided to any third party or used for direct marketing.

Objectives:

The University of Leeds primary aim is to assess the feasibility and reliability of routinely collected data on health resource use. Residents of care homes are amongst the frailest in our population with significant health and social care needs. The health requirements of residents place considerable burden on the NHS, in primary and secondary care. Greater demands are placed on the workload of GPs providing care for care home residents than caring for people in their own homes, in face to face contacts and out of hours visits. Care home residents are significantly more likely to attend emergency departments by ambulance and be admitted to hospital compared to the older population generally. Hospital admission exposes residents to risk of hospital acquired infections and falls and is disruptive for this frail population as they struggle to return to their previous health state once discharged. If not appropriately addressed, the burden on NHS primary and secondary care services will continue to rise for this expanding client group. Promotion of health of frail older people in care homes is poorly and inconsistently developed. Despite the potential for reduced NHS expenditure from improved health, provision of programmes to support activity within UK nursing homes (which could promote health and well-being) is only patchily realised. Only 10% of care home residents receive physiotherapy, and just 3% occupational therapy. The feasibility trial proposed by The University of Leeds is the final stage of a programme grant funded by the National Institute of Health Research (NIHR). The programme grant has been divided into 5 work streams, with earlier work involving observation of care home environments; interviews with care home staff, residents and relatives to explore how to best implement change in activity; assessing which questionnaires to use and how to best measure activity levels; and developing an appropriate intervention with the aim of increasing activity (or reducing sedentary behaviour) in care homes. This trial is now testing the intervention in 6 of 12 homes from selected locations within Yorkshire, randomised on a 1:1 basis to receive the REACH intervention plus usual care, or to continue with usual care only. It is anticipated that 8 - 12 residents will be recruited from each of these care homes. Staff working in care homes randomised to receive the REACH intervention will implement the intervention in their care home. Staff working in care homes randomised to the control arm will continue with their usual routine care to residents. All 12 homes will provide data for the trial, either directly in person, from care home records, or via routine data sources such as NHS Digital. Part of the trial aim is to look at the best method of obtaining both safety (i.e. hospital attendance) and health resource use data. If the University of Leeds are able to do this by collecting HES and other data sets this will inform how data is obtained ultimately to run a large scale trial in many homes. This would happen if The University of Leeds feasibility trial was successful. The University of Leeds will seek to establish the number of admissions overall from the 12 participating care homes and assess the completeness of this data. This aggregate data will allow assessment of the effect of the intervention at a whole home level, rather than only being reported for consenting residents (a sub-set of the care home population). This will allow us to assess whether the consenting cohort is representative of the whole home or whether there are differences in the number of hospital attendances and admissions for those who are and are not taking part in the research.


Project 12 — DARS-NIC-40493-G5Y6K

Opt outs honoured: N

Sensitive: Non Sensitive, and Sensitive

When: 2017/12 — 2018/02.

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

Benefits:

The benefit of this data linkage work is that it will allow the study to understand how linked data can be used to explore the intended and unintended outcomes of a transfer of care for people with heart failure. By using this pilot stage to join the data of a small number of patients the study can explore whether their hospital discharge has been followed by a readmission or death and to explore how multi-disciplinary and multi-organisational care works to safely continue NICE-recommended medicines sets after people leave hospital. This work will allow the study to develop the capacity to understand how this linked data can be used to measure important outcomes so that they can evaluate a co-designed intervention that improves patient care across a care transfer. The results of the data linkage work are expected to inform the evaluation methods used in the definitive cRCT integrated care (Domain 4.9). The continuing drive for cost effectiveness is explicitly recognised through the studies accompanying and comprehensive economic evaluation. Furthermore, the studies discussions with health service commissioners have highlighted the need for evidence to support future commissioning of community pharmacy services as well as the need for more effective optimisation of heart failure treatment, with associated health benefits. The overall aim of the research has the potential to reduce the burden of cardiovascular disease by reducing preventable cardiovascular events that occur in the period after patients with heart failure are discharged from hospital through a combination of diminished medication errors and health gains from optimised treatment. Furthermore, the findings and other envisaged outputs may make a valuable contribution to the medicines management of many other NHS patients with long-term conditions.

Outputs:

The study team expect to have a data linkage algorithm in by the end of 2017. The plan is to publish this work in peer-reviewed journals (for example Health informatics, target submission 2018) and present at relevant conferences to inform the wider research community on the lessons learned in data linkages (for example, 'Using electronic health records in clinical trials: rising to the challenge of developing a data linkage pipeline – experience from the ISCOMAT programme' was presented as an at the joint International Clinical Trials Methodology Conference / Society for Clinical Trials 2017 conference in Liverpool on May 7-10, 2017). The study will inform participants and involved clinicians and hospital teams and update the trial website to share the study findings in various formats (e.g. lay summaries). All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

CTRU have receive patient identifiable data (including patient’s name, date of birth, gender, NHS number and patient study ID data) for each consenting participant from the participating site. The total number recruited is 53 participants. This data is processed by the trial data manager and statistician at the CTRU at the University of Leeds. The trial statistician will provide NHS Digital with study ID, NHS number and date of birth for linkage to HES / ONS. The NHS Digital will provide the linkage to HES APC, OP, A&E, CC and ONS for each study participant. All transfer of data between the CTRU trial statistician and the NHS Digital will take place via a secure file transfer system / secure data depot as agreed between the data provider and the data recipient. Upon receipt of the data, data cleaning will be undertaken by CTRU statistician in internal process. This will include processes to ensure no duplicate episodes, no admission after date of death (if deceased). Additional data cleaning, analysis and linkage across each dataset will be in accordance with a Statistical Analysis Plan. This CTRU trial statistician will subsequently link the HES / ONS record level data to participant record level data from primary care clinical systems (SystmOne and EMIS), a national cardiovascular specialist registry (National Institute of Cardiovascular Outcomes Research) and community pharmacy data. Data will also be linked to the ISCOMAT dataset held at the CTRU (consisting of the patient identifiers and details of patient’s study eligibility, admission and discharge medications). This will enable a data linkage pathway algorithm to be developed. The algorithm will not identify at the individual participants, the outputs will identify the variables and pathway. The data items linked between the datasets are those relevant to the primary and secondary analysis of the proposed cRCT have been identified in each provider dataset. The data linked by the CTRU across the databases will establish quality and completeness of the data available from each database and derive the most reliable data pathway for the trial. The same patient identifiers will be used across each dataset, if permitted by the provider, to ascertain information on the same patient is collected. For this data linkage feasibility study, work will be undertaken to provide descriptive statistics which show the extent to which linkage was successful for each data source. No statistical modelling will be attempted. The study will summarise the extent to which can successfully obtain the primary and secondary outcomes of the cRCT. The CTRU statistician will also summarise the covariates that plan to adjust for in the analysis of the subsequent cRCT. These covariates will be pooled from all the data sources. Levels of missing data from each data source will be summarised to demonstrate the acceptability of the data sources for the cRCT. It is anticipated that the study will be able to triangulate data from the various sources to produce a master patient record. The agreement/disagreement between various data sources on common data items will be reported. All individuals with access to the data are employed by the CTRU, University of Leeds and will have undergone CTRU Data Protection training prior to accessing the data. No other third party will have access to the data. Data will not be used for any other purposes: it will not be used for commercial purposes, nor for direct marketing purposes. The University of Leeds is the sole data processor. IT Infrastructure: The CTRU server infrastructure is split between two data centres on the main University of Leeds campus. Backups taken from this infrastructure are replicated to the University of Leeds disaster recovery site at the University of York with tape backups being kept at Iron Mountain. In all locations data is stored encrypted on disk/ tape. The 2 disaster recovery sites provide different recovery options. The site at University of York is a warm online copy on disk that can be retrieved instantly for the last 30 days. The Iron Mountain site holds a cold offline copy on tape for 12 months. At the University of York location, the server is University of Leeds own equipment and connects only to the University of Leeds network. No individual at the University of York can access the data. The University of York only provides the physical space and power to support this. University of Bradford are not part of the agreement and will not be accessing any data through this application. ONS Terms and conditions will be adhered to. 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:

Improving the safety and continuity of medicines management at care transitions (ISCOMAT) is a series of interlinked work packages delivered by a multidisciplinary research collaboration between the Bradford Teaching Hospitals NHS Trust (BHTH) (as NHS lead and sponsor), the University of Bradford and University of Leeds. Each work package has designated lead(s) and is supported by researchers and collaborators from each of the organisations. BTHT has overall responsibility for the delivery of the programme and delegates responsibilities to the University of Bradford and the University of Leeds in a collaborator agreement. University of Bradford are not receiving any data as part of this application. The programme is a series of interlinked projects which will design and test a complex intervention (a Medicines at Transitions Toolkit) to make best use of medicines and reduce harm through effective medicines management for heart failure patients from hospital discharge and into primary care. When a patient moves between care settings (e.g. from hospital to home) medicine problems are common and planned changes are not always followed through. Patients particularly at risk are those with long-term illnesses taking several medicines – especially when medicines have been started or changed in hospital. Patients with heart failure are the focus of our study as they are a public health and NHS priority, are frequent service users (including readmission to hospital), and susceptible to poorly managed medicines. Heart failure is responsible for approximately 5% of medical admissions and the hospital readmission rate within 3 months of discharge has been estimated as being as high as 50%. ISCOMAT aims to help the way patients are supported with their medicines. This may contribute to improving their health through helping them better understand their medicines. It also aims to improve the way medical professionals work together to offer good standards of care to patients when they leave hospital. The specific objectives are listed below: 1. Map and evaluate current medicines management pathways across care transitions, describe the core characteristics of best practice and effective systems at each stage and compare with published evidence.(work package 1a) 2. Devise an underlying data linkage and data collection exercise to measure the effect of the proposed intervention (work package 1b) 3. Synthesise these data to develop a model of best practice that can contribute to a multi-disciplinary intervention (work package 2) 4. Based on a co-design process, integrate a patient-led perspective on the continuity and safety of the medicines management across care transitions to enhance the patient information-giving process as part of a Medicines at Transitions Toolkit (MaTT) intervention (work package 2) 5. Assess the intervention for usability and acceptability, establish an effective implementation process, and determine the feasibility of data collection for economic evaluation (work package 3) 6. Evaluate the effect and cost effectiveness of the intervention in a multi-centre cluster RCT (cRCT), in conjunction with a rigorous process evaluation. (work package 5) This agreement will facilitate objective 2 (data-linkage) which is a data linkage feasibility study and will construct the data linkage foundations for the cluster RCT (objective 6). A future application will be made to access data for objective 6 (multi-centre cluster RCT). The day-to-day running of WP1a and WP1b is the responsibility of the programme manager employed by the University of Bradford. This includes co-coordinating the development of the research protocol, ethics submission and the identification of sites to identify, consent and recruit participants. The lead investigator for this work package 1 is based at the University of Leeds and the data linkage will be transferred, processed and analysed by researchers at the CTRU, University of Leeds. The data linkage feasibility study has recruited in the region of 55 patients from four hospitals who were consented and recruited at the point of discharge from an in-hospital stay for heart failure by a research nurse. The hospitals are Castle Hill Hospital in Hull, Leeds General Infirmary, Calderdale Royal Hospital and Royal Blackburn Hospital. The consent process included consent for information about their health conditions and prescribed medicines held by their hospital and GP to be accessed via the organisations (i.e. Data Providers) which hold this information, for example, NHS Digital, SystmOne, EMIS and the National Health Failure Audit (NICOR), along with community pharmacies. Data will be received, processed, stored and analysed by the by the Clinical Trials Research Unit (CTRU) at the University of Leeds. No patient level data will flow to the Bradford Teaching Hospitals NHS Trust from the CTRU. Upon recruitment from four different hospitals the research nurse (or study research fellow) registered the patient with the CTRU and provided demographic details. Details about the patients study eligibility, admission and discharge medication were also be recorded. Registration was via a secure online database at the CTRU. The data recorded at registration will include patient’s name, date of birth, gender, NHS number and patient study ID. The study team have mapped the data variables available from each data provider onto the primary and secondary endpoints of the proposed definitive cRCT (objective 6) to allow for an accurate assessment of the patient pathway and important co-morbidities. The primary endpoint for the definitive trial will be all-cause mortality + HF rehospitalisation measured over 12 months from hospital discharge. We anticipate that the data required for this outcome will be obtained from HES and ONS. The key secondary endpoint is the number of patients prescribed the correct heart failure medications at 12 months post-discharge. The study anticipate that the data required for this outcome will be from NICOR, primary care and community outpatient pharmacy databases. Clinical and prognostic data such as diagnosis, comorbidities and discharge information, will be collected from the HES, NICOR, and primary care databases. The findings of this data linkage study work package will inform the data variables required for the definitive cRCT and it is anticipated that a reduced dataset will be required for the subsequent DARS application for the cRCT. The CTRU statisticians will provide patient identifiers in line with the requirements of HES analysis Guide Requirements. This may include patients name, date of birth, gender, NHS number and patient study ID. The patient identifiers will be sent to NHS Digital for confirmation that there is a record for the patient in HES / ONS. NHS Digital will provide data extract to the CTRU. The above process will be performed with each of the Data Providers. The identifiers required to accurately identify a study participants will be agreed with each provider as part of the application process, ensuring the minimum identifiers are used. The CTRU statisticians will link the individual patient data items from each of the data providers to allow for the creation of a master patient file.


Project 13 — DARS-NIC-49164-R3G5K

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/09 — 2017/11.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Benefits:

It is expected that there will be benefit to health from the outputs provided to NHS Digital. The benefits will occur as benefits to the health system, either directly (through improvements to data collections) or indirectly (better quality data used in analysis/research). The following headline benefits will arise from NHS Digital’s usage of the tool: 1) Allow NHS Digital to conduct integrity checking to identify occurrences of poor data quality in routinely collected data, for feedback to data providers (target date 30/9/18). 2) Allow NHS Digital to cross-reference variations in data quality issues with external influences (e.g. change in policy, change in priorities, change in resource, change in service provision or structure, coding improvement initiatives) (target date 30/4/19). 3) Allow NHS Digital to conduct bias checking to identify occurrences of poor data quality in routinely collected data, for feedback to data providers (target date 30/9/19). 4) Support NHS Digital in the development of new business rules for automated data profiling and feedback to data providers (target date 30/11/20). 5) Improve NHS Digital’s understandings of biases across time, geography and activity; NHS Digital’s focus has necessarily been on data quality by provider (in order to report back to providers so that data quality issues can be addressed ), with less emphasis given to time, geography and activity. 6) Allow NHS Digital to quantify the impact of data quality issues - e.g., whether the degree of missingness for certain conditions supports linkage match rates. 7) Improve the quality of the data used by NHS Digital when conducting analysis. 8) Improve the quality of the data provided by NHS Digital to third parties when conducting research/analysis. NHS Digital manages many of the nation’s critical health and care data assets. It collects data from a range of care providers and provides secure and controlled access to those data by legally authorised bodies. Better use of health and care data will help those involved to: - manage the system more effectively; - commission better services; - understand health and care trends in more detail; - develop new treatments; and - monitor the safety and effectiveness of care providers. Understanding the quality of data is essential in deciding whether it is fit for these uses. The benefits above will help NHS Digital develop appropriate methods to monitor, challenge and highlight data quality issues to various audiences, giving the them the ability to correct the data if submitting or adjust their findings accordingly if just analysing. NHS Digital also has a statutory responsibility, enacted in the Health and Social Care Act 2012 (section 266), to assess the quality of the data it receives against nationally published standards and to publish the results of those assessments. These benefits are likely to lead to new measurements in the report covering this statutory responsibility. The tools and methodologies developed through the QuantiCode project may also benefit other organisations in other sectors, but the data from NHS Digital is only disseminated on the basis of there being a benefit to health.

Outputs:

The intended outputs relating to health are: 1) Data analysis tool Version 1 (target date 28/2/18). This output will be a visual analytics tool, which allows users to gain an overview of missing data patterns and investigate data integrity in health datasets. 2) Research report 1 (target date 30/9/18). This report will describe the application of the tool to health data, and the benefits that the tool provides. The report will be submitted to a high-impact outlet such as the Journal of the American Medical Informatics Association (the pre-eminent journal for research into methods for analysing health data). 3) Data analysis tool Version 2 (target date 31/5/19). This version of the visual analytics tool will allow users to investigate bias caused by data quality issues in health datasets. 4) Research report 2 (target date 30/11/19). This report will describe the application of the tool for bias investigations, and the benefits that the tool provides. The report will be submitted to the Journal of the American Medical Informatics Association. Outputs 1 & 3 will not contain any data – a user will load their dataset into the tool to analyse missingness. Outputs 2 & 4 will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. That data will be shown in figures that illustrate the usage of the tool. The ultimate beneficiaries of this work will be the general public. For example, CCGs and local authorities analyse data to generate business intelligence for operations and investment, with the aim of providing us all with improved and more cost-effective services. Businesses similarly require business intelligence for operations and investment, which translates to jobs and other economic benefits. To bridge the gap between these indirect benefits from the project and popular interest in big data, the University of Leeds will conduct a range of public engagement activities which include live demonstrations at the annual Leeds Festival of Science, a short film, an on-line tutorial about the ethics surrounding data analytics, and publishing articles in the popular scientific press.

Processing:

The dataset (to be) provided by NHS Digital consists of pseudonymised, record-level data. The dataset will be transferred by the University of Leeds’ Integrated Research Campus (IRC) Data Services Team using NHS Digitals Secure Electronic File Transfer system (SEFT) The data will only be accessed by substantive employees of the University who are contributing to the project. The patient data from NHS Digital will not be linked with any other dataset. The Quanticode project will develop tools and methodologies for investigating data quality across a number of datasets. The aim is to test these tools and methodologies across a variety of datasets, including health data. Although the QuantiCode project as a whole will examine issues around data linkage, that work will not involve the use of data from NHS Digital data. The QuantiCode project will process datasets provided by other organisations involved in the project, and the methodologies/tools will be developed to apply across datasets as much as possible. One of the aims of this is to ensure that techniques are developed which are generically applicable, rather than each sector needing to develop their own tools. The NHS Digital dataset will be used to help design and test a new data analysis tool, which allows users to investigate data quality in health records. The development process for the tool will involve: (a) characterising the dataset so that scalable algorithms and appropriate statistical models may be designed, (b) using the dataset as an exemplar to design and implement new interactive visualization techniques for data quality investigation, (c) running the data to refine the statistical models, and (d) testing of the data analysis tool prior to release to NHS Digital for in-depth evaluation. The NHS Digital dataset will only be used where necessary for the purposes in this agreement. Live data will not be used for early stages of development/testing when it would be more appropriate to use test data. The data from NHS Digital may only be processed in order to produce the outputs detailed below (in the Specific Outputs Expected section).

Objectives:

The University of Leeds are running a project called QuantiCode. This project is funded by the Engineering and Physical Sciences Research Council (EPSRC) from March 2016 to February 2019 (details: http://gtr.rcuk.ac.uk/projects?ref=EP%2FN013980%2F1). The overall QuantiCode project is divided into 3 stages: 1) Data fusion, covering tools for data linkage and visualizing data quality, and thought leadership in data governance. 2) Analytical techniques that allow users to interactively mine longitudinal data for patterns. 3) Governance-aware abstraction techniques that allow users to explore how complex data may (or may not) be simplified to reveal important patterns. The work will be evaluated by the collaborating organisations to ensure that the solutions are applicable in the real world. The QuantiCode project involves a number of collaborating organisations (University of Leeds, Bradford Teaching Hospitals NHS Foundation Trust, Consumerdata Limited, NHS Digital, Sainsbury’s Supermarket Limited, Leeds City Council, Leeds North Clinical Commissioning Group, AQ Limited), who are interested in this work and are supplying datasets and will receive reports and tools as outputs of the project. NHS Digital is one of these organisations, as NHS Digital has a number of very large datasets, and Data Quality is an issue which is particularly important – it impacts directly on areas such as running the NHS (invoice validation, etc.), as well as some indirect benefits (research and analysis to develop policy/clinical guidelines). By collaborating with the QuantiCode project, it is expected that there will be benefits for NHS Digital and benefits to healthcare more widely as a result. Any data provided from NHS Digital to the University of Leeds for this project will not be linked in any way with the other datasets being used. No record-level data from NHS Digital will be shared, or be in any way accessible, to third party organisations (including the other collaborating organisations). Similarly, no aggregated data including small numbers (as defined in the HES Analysis Guide) may be shared with (or be in any way accessible to) third party organisations Within the bounds of the QuantiCode project, the purpose for processing healthcare data from NHS Digital is to allow different designs of visualisation and machine learning technique to be compared for their ability to meet user requirements, and allow the QuantiCode data analysis tool to be tested prior to release to NHS Digital for in-depth evaluation. A single year of HES Admitted Patient Care data is being requested in order to fulfil these objectives. The single year of hospital data is important as there are data quality process which take place at the end of the year, meaning that a sub-set of data (e.g. 6 months)will display different characteristics to the finalised (“Annual Refresh”) data produced after the end of the financial year. Without healthcare data, it is possible that the methodologies/tools cannot be applied to healthcare data, and the opportunity to improve the healthcare data will be lost. In addition, a specific purpose for processing the health data is to make a detailed analysis of patterns of “missingness” in data (the manner in which data are missing from a sample of a population). The goal is to be able to investigate patterns that involve: (a) several (3+) variables missing together, and/or (b) are dependent on the particular value of other variables (e.g., provider code and admission type). These patterns are currently unknown and such missingness may involve any variable in a dataset, which is why all variables (except those deemed sensitive or identifiable) have been requested.