NHS Digital Data Release Register - reformatted

Imperial College London

Project 1 — DARS-NIC-02077-R7M9C

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/09 — 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 - Cause of Death Report

Objectives:

The aim is: (a) to examine whether measures of emphysema/bronchiestasis collected in 2003/2004 predict mortality (b) to see whether longitudinal changes in airway inflammation, lung function decline and exacerbation frequency predict mortality Previously, it has been shown that frequent exacerbations of COPD are associated with increased mortality but the mechanisms remain to be elucidated. Frequent exacerbations are associated with greater emphysema and bronchiectasis (airway wall thickening). Imperial College’s initial objective is to examine whether indices of emphysema and bronchiectasis previous acquired by CT scanning predict mortality. The data was collected in 2003/2004 on 54 patients. Imperial Colleges were one of the first groups to CT scan COPD patients and therefore this would have one of the longest follow-up periods available.

Expected Benefits:

The results will help physicians to estimate a COPD patient’s life-span. This will be useful in guiding therapeutic intervention and explaining to patients the need to modify behaviour and life-style. The study is academic research only and is in no way commercial.

Outputs:

The results of this study will be published in general and respiratory journals and they will be presented and discussed at national and international meetings such as the British Thoracic Society, European Respiratory Society and American Thoracic Society. The results will be also disseminated at other educational meetings on COPD and respiratory medicine. The results may also be submitted to NICE for the next COPD guideline update

Processing:

Cox proportional hazard models would be used to assess whether bronchiectatic scores of 2 or less were associated with less mortality than scores of 3 or more. Similarly, for emphysema, Imperial College would test whether scores above a median of 15.6% were associated with increased mortality. Data will be visualized with Kaplan-Meier plots; and co-variants included in the cox proportional hazard model might include disease severity and smoking history. Imperial College also would like to know whether a rising trends in airway inflammation is associated with early mortality. The inflammatory markers Imperial College wish to examine are fibrinogen, sputum interleukin-8 and interleukin-6 and C-reactive protein. Imperial College also has extensive historical data on FEV1 and exacerbations, and wish to find out whether lung function decline or exacerbation frequency predicts mortality. Imperial College will examine whether these longitudinal trends are related to survival using cox-proportional hazards and joint models. Joint models investigate how a marker that is repeatedly measure in time is associated with a time to an event of interest, such as death. Imperial College London will submit their Cohort to HSCIC containing full names, date of birth, address, gender and postcode together with a study ID against each patient. The HSCIC provide ONS Mortality Data back to Imperial College London containing study ID, cause of death and date of death.


Project 2 — DARS-NIC-12828-M0K2D

Opt outs honoured: N, Yes - patient objections upheld, No - data flow is not identifiable (Section 251, Section 251 NHS Act 2006)

Sensitive: Sensitive, and Non Sensitive

When: 2016/09 — 2019/09.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(7)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care
  • Civil Registration - Deaths
  • HES:Civil Registration (Deaths) bridge

Yielded Benefits:

Benefits detailed in measurable benefits section are ongoing.

Objectives:

Imperial College London Doctor Foster Unit (ICL DFU) uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. ICL DFU works in collaboration with Dr Foster Limited (DFI) to provide a management information function in the form of analysis for healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. Both ICL DFU and DFI require the full HES datasets to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with lesser fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to Dr Foster Intelligence Limited (DFI) customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers and ALL NHS Trusts. DFI has no access to the patient re-identification service. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. For this purpose, ICL DFU have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Limited Analysis Toolkit. This service allows supply of Provider trusts’ NHS Number and LOPATID using Dr Foster Limited Analysis Toolkit without passing these fields on to DFI. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from DFI. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to DFI. The unit works in collaboration with DFI to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, DFI responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the DFI Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. DFI also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by DFI or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. DFI is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.

Expected Benefits:

Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.

Outputs:

1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005

Processing:

Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.


Project 3 — DARS-NIC-147827-NC2TC

Opt outs honoured: N, Yes - patient objections upheld (Section 251, Section 251 NHS Act 2006)

Sensitive: Sensitive

When: 2016/04 (or before) — 2019/08.

Repeats: Ongoing, One-Off

Legal basis: Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

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

Yielded Benefits:

The generation of the Intermediate Adenoma database has enabled the CSPRG to successfully receive additional funding from the National Institute for Health Research-Health Technology Assessment to extend the analysis to include both the low- and high-risk adenoma groups, as the 'All Adenomas study'. The outcomes of the All adenomas study will help to inform the update of the UK Adenoma Surveillance Guideline, which have not been updated since 2002.

Objectives:

This study is analysing the risk of cancer or advanced adenomas with varying frequency of colonoscopic surveillance for patients identified with intermediate grade adenomas. The overall research objectives are to: - Examine the optimal frequency of surveillance in people found to have intermediate grade colorectal adenomas.* - Examine he risks and benefits to the patient with respect to prevention of cancer and the development of advanced adenomas; anxiety, morbidity and mortality; costs and cost-effectiveness and implications for the NHS.* * aims for which we require data from ONS using the MRIS.

Expected Benefits:

The IA study is required to determine the extent to which the current UK adenoma surveillance guidelines gives rise to unnecessary colonoscopies in people with intermediate-risk adenomas; the elimination of which would minimise risks of complications associated with the procedure and worry to patients, plus ease the current unsustainable economic and workforce pressures on the NHS. For the All Adenomas study, by quantifying CRC risk in all adenoma patients with and without surveillance, the study will benefit patients and the NHS by identifying: • those at sufficiently low risk of CRC after adenoma diagnosis at colonoscopy to render surveillance unnecessary; • those most likely to benefit from colonoscopic surveillance; • the minimum surveillance required to afford adequate protection from CRC, and when it can be safely stopped. This study would be the first to use CRC incidence as an outcome to evaluate adenoma surveillance requirements, and through its large size and longevity of follow-up, would provide new evidence of long-term CRC risk in all adenoma patients. Because the dataset is current, the results generated could have enduring value as a reference against which future interventions for minimising CRC risks in adenoma patients could be compared.

Outputs:

The primary outcomes of the Intermediate Adenoma study have been published in the Lancet Oncology on the 25th April 2017 (electronically); https://doi.org/10.1016/S1470-2045(17)30187-0. The Intermediate Adenoma study NIHR Final Report was published in May 2017 [Atkin W, Brenner A, Martin J, Wooldrage K, Shah U, Lucas F, et al. The clinical effectiveness of different surveillance strategies to prevent colorectal cancer in people with intermediate-grade colorectal adenomas: a retrospective cohort analysis, and psychological and economic evaluations. Health Technol Assess 2017;21(25)]. Secondary outcomes of the IA study will be submitted to journals such as the Lancet Oncology, Gut or Gastroenterology. In addition, an abstract entitled 'The effect of adenoma surveillance on colorectal cancer incidence: a multicentre cohort study' was presented at the Digestive Disease Week conference in May 2017 in the session 'Colon Cancer Biomarkers and Screenings' (Chicago, USA). The All Adenomas study: The results of the All Adenomas study will be written up for publication in similar high-impact, peer-reviewed journals and submitted for presentation at a scientific conference in 2018/2019. The publication of the outcomes of this study in international journals and presentation at international conferences during 2017 will ensure that this data is available for future validation of the recommendations from the study and potential uptake into revised guidelines for surveillance. The University of Oxford will produce reports in the form of summary statistics, and submit abstracts/manuscripts to cancer-specific and/or health economics conferences/journals in order to disseminate the results of the health economic research independently. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

On receipt of the cancer and mortality data from NHS Digital on the IA study cohort, CSPRG will format it in accordance with the existing IA hospital database schema before uploading it to the IA hospital study database. All CSPRG staff members who have access to the data are substantive employees of Imperial College London staff accessing the data at University of Oxford are all substantive employees of the University of Oxford. With the new data in the database, and after basic data cleaning, CSPRG will match the cancers and deaths to the trial participants. This will tell CSPRG which new cancers and / or deaths have occurred since the last data extract was received. It will also tell CSPRG which, if any, deaths or cancers were incorrectly reported in the previous excerpt. The data will be pseudonymised and given to CSPRG statisticians for analysis and to give the required outputs for reporting and publication in accordance with the NIHR-HTA terms and conditions. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. A sub-set of the pseudonymised data will be shared with the University of Oxford for the purpose of conducting the health economic data. The sub set is minimised by the study before sharing with Oxford to ensure that only the required fields are shared which are necessary for the analysis being undertaken. Processing activities by the University of Oxford: The data received from the CSPRG will be used to estimate resource use (for example, number of healthcare appointments), costs, and health outcomes (for example, number of adenomas and cancers detected). This will involve rearranging the data; applying unit cost estimates and resource use estimates from external sources; and producing summary statistics. Secondly, the University of Oxford will estimate long term economic outcomes by fitting statistical models to the data received. The statistical models will be combined with unit costs and quality of life estimates to estimate long terms costs and quality of life for subgroups of individuals in the data (by surveillance strategy as defined by the group at Imperial, and cancer stage as provided in the linked data). The data will not be further linked with other patient level datasets for the analysis by the University of Oxford beyond what is provided by the CSPRG. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data) ONS Terms and Conditions will be adhered to. Data obtained for Deaths & Cancers in Scotland For English patients who have moved to Scotland during the study will not be provided by NHS Digital these data are sourced from the Scottish equivalent organisation.


Project 4 — DARS-NIC-148056-T6T5Z

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

Sensitive: Sensitive

When: 2017/09 — 2019/09.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

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

Yielded Benefits:

Publications resulting from data collected by the Airwave study are available at https://www.police-health.org.uk/publications. An initial results paper that is based on the cancers data provided by NHS Digital was recently published. (Gao, H. et al. Personal radio use and cancer risks among 48,518 British police officers and staff from the Airwave Health Monitoring Study. Br J Cancer 120, 375-378, doi:10.1038/s41416-018-0365-6 (2019). https://www.ncbi.nlm.nih.gov/pubmed/30585256). There is also a list at https://www.police-health.org.uk/research/approved-research of the currently approved set of sub-studies of the main study, and this should give more than a flavour of the use being made of the Airwave resource (data and samples).

Objectives:

The Airwave Health Monitoring Study was established in 2003 to evaluate possible health risks associated with the use of Terrestrial trunked radio (TETRA), a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. It addresses needs raised in a report by the Advisory Group on Non-Ionising Radiation (AGNIR) on the possible health effects from TETRA. There are currently c. 53,000 participants in the Study. The aim of the study is to estimate the risk of all cancers, certain mortality outcomes and various non-fatal, non-malignant health disorders in relation to Airwave use. As well as the focus on cancer incidence, the study will investigate non-cancer health outcomes (including cognitive, neuropsychiatric and neuro-degenerative effects which may be linked to sickness absence and early retirements), as the mechanisms of any putative health effect related to TETRA use are unknown. The cohort consists of police force employees from Great Britain and c. 53,000 participants are enrolled at the present. The study population will be flagged for mortality and cancer incidence using the cancer and mortality data at NHS Digital and Information Services Division (ISD) of the Scottish Health Service, based on personal identifiers such as full name, date of birth and NHS number.

Expected Benefits:

• Safety of Airwave. The primary objective of the Study is to ascertain whether or not there is any link between use of Airwave and the long-term health of its users. Were such a link identified, it would be relevant and important to both Airwave users and management to understand in what circumstances risks might be increased. Alternatively, a finding of no demonstrable effect would be reassuring to the Airwave user community and would place any current and future concerns about possible health effects into proper context based on objective evidence. • Helping future generations. The study is generating new knowledge of benefit not only to police officers and staff as individuals, but to the wider community and to society as a whole. Analyses of data and samples will help to better understand the risks and causes of future diseases and ill health, and thus inform improved preventive and treatment strategies. There are many special aspects of the Airwave cohort including the occupational setting, given the particular nature of police duties and working patterns, the relatively young age of the cohort, and the inclusion of large numbers of women as well as men, which make this study uniquely valuable. The results generated from the use of this resource will inform future policy and practice both for the betterment of police force health and for the health of the public more generally. • Clarity in respect of health effects of long-term use of radio frequency technology. Continuing to gather the data necessary to undertake and report on the Study’s analyses of Airwave use and health will allow the potential long-term health effects to be better understood, and to place any future claims of possible harm into proper context based on the evidence. The findings would be relevant to, and inform, strategic decisions about future investment in radio communications systems within the Police Service. • Responsibility to the health and welfare of the workforce. Policing is a highly complex occupation with specific patterns of working and occupational risks with potential health effects that are not well understood. The Study has established a ‘broad and deep’ biomedical resource with which to continue to monitor the health and well-being of the workforce, and to help understand the causes and risks of ill-health and disease. Results will inform possible preventive approaches and best practice for maintenance of a healthy and engaged workforce.

Outputs:

The aim of the study is to estimate the risk of all cancers and certain mortality outcomes in relation to Airwave use. Cancer and death notifications will be used to determine prevalent cases at baseline and subsequent incident cases for each outcome (e.g. head and neck cancers) under study. Survival analyses will be performed to investigate the association between each outcome and level of Tetra exposure and the risk of incident cases for each disease using multivariable Cox models. The results of the analyses will be published in peer-reviewed scientific journals and in summary form on the study website. The peer-reviewed journals targeted are likely to be similar to those that have already published in (Environmental Research). The data will be used to compile progress reports for the Study funder (the Home Office). However, all such outputs will report aggregated results only, and no individual will ever be identified. Results will be published on aggregate level with small numbers suppressed. It will not be possible to identify the individuals. The recruitment phase was completed on the 31st of March 2015. However, the follow up and data analysis phase continue. The target date for submission of a scientific outcomes paper (in a peer reviewed journal) on any possible long-term health implications for Police personnel related to use of Airwave (the main purpose of the Study) will be December 2017. Recent publications: 13th July 2016 Acute Exposure to Terrestrial Trunked Radio(TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation http://dx.doi.org/10.1016/j.envres.2016.06.031 28th April 2016 Validation of objective records and misreporting of personal radio use in a cohort of British police forces (the Airwave Health Monitoring Study) http://dx.doi.org/10.1016/j.envres.2016.04.018 06th September 2014 The Airwave Health Monitoring Study of police Officers and staff in Great Britain: Rationale, design and methods http://dx.doi.org/10.1016/j.envres.2014.07.025

Processing:

NHS Digital data is only accessed by substantive employees of Imperial College London and only for the purposes described in this document. Directly identifiable data is kept separate from the study data, NHS Digital data is only linked to the AIRWAVE study data and no other datasets held by the applicant. The standard ONS terms and conditions will be adhered to. Airwave data from each police participant will be collected from monthly downloads of relevant data from the Home Office, giving information on Airwave exposure at individual level. These will be combined with questionnaire data about participants' use of Airwave to derive an exposure metric. Health outcome data will be assessed by linking information on individual participants to national records on mortality and cancer incidence, and from absence records supplied by the police force employers. Data, once received, is stored and analysed at Servers in South Kensington campus on servers located in a secured area. Users at St Marys connect to the servers via remote desktop. All IT infrastructure is owned and managed by Imperial college, there are no shared resources, and all network traffic is contained within Imperial College. The servers will be designated as holding identifiable data or anonymised data. All servers are secured to only accepting connections from specified users and workstations. Identifiable data can only be accessed from dedicated workstations that sit alongside the users college PC. These workstations can only be used to connect to the “identifiable servers”, there is no internet access available. Data uploading/downloading can be further restricted to specified users and PCs. All data transfers are recorded and kept for audit purposes by a staff member who has the role of “internal auditor”. The internal auditor monitors compliance of the Information Governance Policy including all agreements the groups have with external groups. The NHS data sharing agreement covers the Imperial college campus, and relies on each group having an environment that conforms to the NHS toolkit standard. Data received will be linked to the existing participant database. The scope of IGT policy whose code is EE133887-SPHTR (Imperial College London - School of Public Health Medical Trials and Research) is the Imperial College network. It uses the Imperial College infrastructure to create isolated enclaves that are used to form the security zones of the network. The IGT policy requires that, “Where possible all servers should be held within Imperial College’s Data centre, and subject to its security policy (currently aiming towards ISO27001). Any group not able to place a server in the datacentre will need to seek approval from the Security Manager.” The Airwave Study activities that are bound by EE133887-SPHTR will use servers located in the Data centre. Users working according to EE133887-SPHTR will be based at the College site in Norfolk Place and will access the Imperial College Data centre at South Kensington according to the security requirements defined in EE133887-SPHTR; the IGT policy therefore covers both the South Kensington and Norfolk Place sites. From time-to-time, consolidated pseudonymised extracts of the database are created and these are used by researchers to investigate the questions addressed by the Study. Those extracts follow the same security rules of the main database and will be kept in the same location. Other than in exceptional cases, namely resolving linkage questions or to contact research participants, data used by researchers is delinked from personal identifiers such as name and address. All researchers complete a detailed written confidentiality agreement with the College, and ONS Linkage Short Declaration of Use. When the Study is completed and closed to further analysis, the data will be archived securely during the life time of the data sharing agreement and for such time as is necessary to provide proper audit for published research. The data will be subsequently securely destroyed. No third parties will be allowed to access any data provided under this agreement. The applicant will supply NHS Digital with name, address (including postcode) and date of birth for linkage. This will ensure that any new members not already flagged by NHS Digital are linked and remove any members who have subsequently opted out of the study.


Project 5 — DARS-NIC-148071-QHNM8

Opt outs honoured: Yes - patient objections upheld (Section 251)

Sensitive: Sensitive

When: 2016/04 (or before) — 2019/05.

Repeats: Ongoing, One-Off

Legal basis: Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

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

Yielded Benefits:

Imperial College London have already demonstrated a significant benefit to participants who received a single flexible sigmoidoscopy (FS) screen compared to those who did not in preventing colorectal cancer (CRC) incidence and mortality after 17 years of follow-up, and have published extensively on the positive uptake, acceptability and impact of a FS screening examination. The study gave rise to two very significant publications in The Lancet in 2010 and in 2017 on the long-term effects of FS screening. In addition, Imperial College London published two additional manuscripts in 2018 and several talks were given referencing published UKFSST data (see outputs section).

Objectives:

The data supplied bt the NHS IC to Imperial College London will be used only for the approved Medical Research Project - MR700 - SINGLE SIGMOIDOSCOPY SCREENING IN PREVENTION OF BOWEL CANCER

Expected Benefits:

The results of the long-term follow-up of the UKFSST in 2022 and 2026, as well as the additional secondary analyses up to 2022, will be made available to inform policy makers via publications and presentations at national and international meetings. Colorectal cancer (CRC) is a very common malignancy that is very treatable when diagnosed at an early stage. However, late stage diagnoses have a very poor prognosis and are more costly to treat. The use of FS as a screening tool can not only identify cancers at an earlier stage but can also remove precursor lesions potentially leading to long-term protection. The study have already demonstrated a significant benefit to participants who received a single FS screen compared to those who did not in preventing CRC incidence and mortality after 10 years of follow up, and have published extensively on the positive uptake, acceptability and impact of a FS screening examination. The UKFSST has been instrumental in providing data to support the use of FS in the National BCSP in England, which began offering FS at age 55 years in 2011: however, there is no data on the long term benefits of this procedure. If the protective effect on CRC incidence and mortality is sustained over the long term, the health economic benefits of FS would be even larger than expected. This research will build on existing knowledge on the impact of FS and will provide evidence to the National BCSP on the long term effectiveness of a single FS examination on the reduction in incidence and mortality from CRC over a period of up to 25 years. This data can then be used to directly inform estimations of patient and economic benefits over a 25 years period. Examining long term follow up is important because the participants will be approaching the highest incidence age group for CRC. In addition, as the participants age, the outcomes of surgery are not as good so it is imperative to prevent cancer in these individuals. Finally, the average life expectancy for those aged 75 years is currently 13 years for women and 11 for men, therefore , quality of life in these older age groups is important. The past and future contribution of the UKFSST was acknowledged at the most recent Trial Steering Committee (TSC) meeting for the study in April 2016. The TSC, which is comprised of external experts in the field, concluded that the UKFSST is a unique resource able to provide vital information to National screening committees on the efficiency of FS, the duration of effect, the number needed to screen to prevent one CRC diagnosis (important for cost effectiveness analyses) and whether the effect is evident in subgroup analysis by gender, age and sub-site. In addition, the UKFSST forms the basis for many value added prospective analyses. The TSC strongly supported the continued follow up of the study cohort to investigate the duration of the effect of a single FS examination.

Outputs:

Imperial College London anticipate a number of publications from the continuation of the UKFSST study, in line with the analyses listed in the ‘objective for processing’ section. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Updates to the main UKFSST paper on the effect of FS on CRC incidence and mortality (Lancet 2010 and Lancet 2017) will be written periodically and at the end of the study, in line with available funding. Imperial College London have recently published two additional papers on the UKFSST, listed below. All additional manuscripts will be submitted to high impact, peer-reviewed journals; for example The Lancet, Lancet Oncology or leading gastroenterology journals such as Gut. In addition, Imperial College London aim to make publications available through ‘open access’. The results from this study will be presented at national and international scientific and clinical meetings as appropriate, such as the British Society of Gastroenterology meeting in the U.K. and the Digestive Disease Week conference in the United States. Outputs published to date: 1. Brown JP, Wooldrage K, Kralj-Hans I, Wright S, Cross AJ, Atkin WS. Effect of once-only flexible sigmoidoscopy screening on the outcomes of subsequent faecal occult blood test screening. J Med Screen. 2018 Oct 3:969141318785654. Doi: 10.1177/0969141318785654. [Epub ahead of print] PMID: 30282520. 2. Pinsky PF, Loberg M, Senore C, Wooldrage K, Atkin W, Bretthauer M, Cross AJ, Hoff G, Holme O, Kalager M, Segnan N, Schoen RE. Number of Adenomas Removed and Colorectal Cancers Prevented in Randomized Trials of Flexible Sigmoidoscopy Screening. Gastroenterology. 2018 Jun 20. Pii: S0016-5085(18)34667-5. Doi: 10.1053/j.gastro.2018.06.040. [Epub ahead of print] PMID: 29935150. 3. Atkin W, Wooldrage K, Parkin DM, Kralj-Hans I, MacRae E, Shah U, Duffy S, Cross AJ. Long term effects of once-only flexible sigmoidoscopy screening after 17 years of follow-up: the UK Flexible Sigmoidoscopy Screening randomised controlled trial. Lancet. 2017 Apr 1; 4. Atkin WS, Edwards R, Kralj-Hans I, Wooldrage K, Hart AR, Northover JM, Parkin DM, Wardle J, Duffy SW, Cuzick J. Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial. Lancet. 2010; 5. Kralj-Hans I, Wooldrage K, Moss S, Patnick J, Duffy S, Atkin W. The UK Flexible Sigmoidoscopy Screening Trial: Uptake and Outcomes of First Round Faecal Occult Blood Testing in the English NHS Bowel Cancer Screening Programme. Gastroenterology, Vol. 144, Issue 5, S-45. Published in issue: May 2013. DOI: https://doi.org/10.1016/S0016-5085(13)60159-6 Planned analyses / publications & corresponding timescales: 1. Effect of variation in adenoma detection rates at screening FS on CRC incidence (17 year follow-up data); submission 2019 2. Flexible sigmoidoscopy screening in colorectal cancer: a pooled analysis of four randomised controlled trials; analyses to be conducted in 2019 3. Validation of the Intermediate Adenomas study findings, submission 2019 4. Validation of the All Adenomas study findings, submission 2020 5. Long term effects of once-only flexible sigmoidoscopy screening after ~22 years of follow-up, submission 2022 5. Long term effects of once-only flexible sigmoidoscopy screening after ~26 years of follow-up, submission 2026 In addition, CSPRG are planning other secondary data analyses as part of PhD studentships funded until the end of 2022 addressing: • The quality of flexible sigmoidoscopy (FS) screening on outcomes and colorectal cancer (CRC) incidence/mortality • Patient and procedural factors affecting performance of FS screening • The safety and acceptability of once-only FS • The effect of family history of CRC on the incidence of CRC after screening • The use of advanced stage at diagnosis of incident cancers as a surrogate for CRC mortality • The efficacy of colonoscopy surveillance for higher-risk adenomas found at screening • The incidence of proximal CRC after FS, by the number and type of polyp detected at FS • The incidence of CRC according to smoking history • Medications in relation to risk of adenomas and CRC • The effect of diet and lifestyle factors on the development of polyps, and CRC by subsite Talks given at external meetings referencing published UKFSST data: 1. Cross AJ. Colorectal cancer screening and early detection. National Cancer Research Institute: Screening, Prevention and Early Diagnosis (SPED) strategy meeting. London, U.K. Apr 2018. 2. Cross AJ. Colorectal Cancer Screening Programmes: UK Experience. 2nd Combined Gulf Cancer Conference. King Faisal Specialist Hospital and Research Centre, Riyadh. Mar 27th-29th 2018. 3. Cross AJ. Colorectal Cancer Screening in the UK. Saudi-International Colorectal Diseases forum. Four Seasons Hotel, Riyadh. Mar 25th-27th 2018. 4. Cross AJ. Early detection of colorectal cancer. Dining with the Stars, Cancer Research UK fundraiser, London, U.K. Mar 2018. 5. Cross AJ. Targeted endoscopy: Who, what, when? Difficult questions in colorectal cancer research workshop. International Agency for Research on Cancer and the U.S. National Cancer Institute. Lyon, France. Nov 2017. Furthermore, this cutting edge research on the benefits of screening will be disseminated to patient groups through contacts of the patient representative for this study, as well as existing relations with support groups such as Maggie’s centre at Charing Cross hospital (https://www.maggiescentres.org/). It will also be summarised on the CSPRG website (http://www.csprg.org.uk/ukfsst/) and disseminated via twitter (@csprg_imperial). All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide

Processing:

On receipt of the data already disseminated by NHS Digital on the UKFSST study cohort, Imperial College London formatted it in accordance with existing UKFSST database schema before uploading it to the UKFSST study database. With the new data in the database, and after basic data cleaning, Imperial College London matched the cancers and deaths to the trial participants. This tells Imperial College London which new cancers or deaths have occurred since the last data extract was received. It also tells Imperial College London which, if any, deaths or cancers were incorrectly reported in the previous excerpt. The data was then pseudonymised and given to CSPRG statisticians for analysis and to give the required outputs for reporting and publication in accordance with Imperial College London grants. Data already held: CSPRG have a number of secondary analyses planned, which will use the existing data. These secondary analyses are covered within the purpose of this application. Any additional analyses / research questions Imperial identify will be defined and approval sought through the amendment process. In 2019 and 2021 CSPRG will request a refresh of the same data items as previously provided, using the existing flagged study cohort. This new data will be combined with the existing UKFSST database and updated data from each of the data providers listed under the ‘purpose’ section and illustrated in the data flow diagram. CSPRG has been approached by external parties to collaborate on additional research questions using the combined UKFSST study database. Data has been requested in an anonymised, aggregated format. Any requests for pseudonymised record-level data will be subject to an amendment request to NHS Digital prior to release. All outputs contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). All CSPRG staff members who have access to the data are substantive employees of Imperial College London or PhD students. As PhD students at Imperial College London are not held to the same contractual clauses as staff, the CSPRG have implemented a ‘student data access agreement’ which they must complete to indicate their obligations for processing personal data. Data will not be accessed or processed by any other third parties not mentioned in this agreement. 2019 Amendment In order to validate the Intermediate Adenomas (IA) study (Atkin W, et al. Lancet Oncol 2017; 18: 823–34) and All Adenomas (AA) study findings, the CSPRG will use a subset of the UKFSST data on its own, and in combination with data obtained from external sources (English data from the UK Faecal Occult Blood test (FOBT) pilot and Kaiser Permanente dataset). No attempt will be made to individually match UKFSST patients to those on the IA/AA studies. This analysis will investigate if the subgroup of intermediate-risk patients who may be eligible for less frequent surveillance identified in the IA study, are also at lower risk in the UKFSST dataset. Once the AA study analysis is complete, any new subgroups identified in the low- and high-risk patients will be examined in the same way to determine whether their risk is similar in the UKFSST to that observed in the IA study.


Project 6 — DARS-NIC-148230-KHMHH

Opt outs honoured: N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2016/08.

Repeats: Ongoing

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

Categories: Identifiable

Datasets:

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

Objectives:

The data supplied will be used only for the approved medical research project - MR1009 - High Risk Period for Patients with Heart Failure: A Population-Based Study


Project 7 — DARS-NIC-172334-W0G2L

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2018/10 — 2019/04.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

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

Objectives:

Imperial College London’s Big Data and Analytical Unit (BDAU) requires an extract of HES data with mortality flags. The quantitative analysis that this agreementrelates to feeds into a wider mixed methods research study: “Effectiveness and Value for Money of Prescribed Specialised Services Commissioning for Quality and Innovation (CQUIN) interventions 2016/17 to 2018/19”. The study is funded by the NIHR/DH Policy Research Programme. NHS England introduced Prescribed Specialised Services (PSS) CQUIN schemes in 2016. The primary aim of this incentive programme is to improve the healthcare quality of specialised services (rare and complex conditions) in NHS hospitals. NHS England intend to allocate £900 million to this programme between April 2016 and March 2019. It is important to understand the effectiveness and cost-effectiveness of such a significant investment. Furthermore, the CQUIN monies are not-recurrent. Empirical evidence is clearly needed that uses the most recent available data from the schemes to inform the contracts negotiation in 2019/20 and future rounds. Finally, financial incentives are increasingly available in NHS England to improve the quality of healthcare. A thorough understanding of optimal contract design with financial incentives as an important element is required. This project aims to explore: i. How best to operate financial quality incentive schemes such as CQUIN in the context of NHS England commissioning specialised services, and ii. Whether, and if so how, the PSS CQUIN innovations can be supported for possible mainstreaming or incorporation into service specifications. What counts as Prescribed Specialised Services are determined by: The number of individuals who require the provision of the service or facility; The cost of providing the service or facility; The number of persons able to provide the service or facility; and The financial implications for Clinical Commissioning Groups (CCGs) if they were required to arrange for the provision of the service or facility. Specific examples include Blood and infection diseases, Cancer, Trauma, and neonatal care. Some of the incentive schemes that the study will evaluate are targeting specific disease areas, while others are focusing on all PSS activity. For example, one scheme is incentivising the implementation of Clinical Utilisation Reviews for reduction in inappropriate hospital utilisation and will be analysed by looking for changes in Length of stay and the number of admissions at the targeted providers. The exact decision on which of the schemes will be analysed will be taken in collaboration with the stakeholder group which includes representatives from the DH and will proceed in two rounds over the course of the project. This study is being undertaken by team of researchers from Imperial College London, the Office of Health Economics, the Manchester Centre for Health Economics at the University of Manchester and the Department of Economics at the University of York. The team has extensive experience in designing and evaluating financial incentives for healthcare providers. They have previously developed a methodological framework to evaluate the cost-effectiveness of financial incentives for healthcare and have significant expertise in the econometric analysis of administrative healthcare datasets. The quantitative analysis which this data will feed into will be conducted by a postdoctoral research associate under the supervision of a researcher, both based at Imperial College London, both of whom are substantive employees of Imperial Collage. The Big Data and Analytical Unit (BDAU) is a multidiscipline team within Imperial College London which collaborates with a large network of researchers across the college with the aim of ensuring the maximum use, impact and dissemination of research using healthcare data. Only substantive employees of Imperial Collage will have access to the record level raw data supplied under this agreement, non-Imperial researchers will only view aggregate outputs/visualisations with small numbers suppressed in-line with the HES analysis guide.

Expected Benefits:

The findings from this project will provide evidence for NHS England to understand how to operate financial quality incentive schemes in the context of commissioning specialised services, and specifically which PSS CQUIN innovations should be supported for possible mainstreaming or incorporation into service specifications. This information will be vital for NHS England in their preparations for the PSS CQUIN contracts in 2019/2020 and beyond. One of questions that Imperial are going to address in this study is about how effectively PSS CQUIN schemes support implementation of interventions. Part of this evaluation is to estimate the impact on and benefits for patients/service users, including possible equalities issues. For instance, whether and how different patient groups benefit differently from the PSS CQUINs schemes. To address the diversity of patients who are affected by the PSS CQUIN schemes, the study will control for the key socio-demographic characteristics of patients in modelling the effectiveness of the schemes. The comparison of the cost-effectiveness of the PSS CQUIN schemes with other quality incentive schemes, such as Best Practice Tariffs, will provide information about the efficiency of healthcare resource allocation. Additionally this project will provide generalisable knowledge about how to design financial incentives within the public healthcare systems.

Outputs:

Research reports: There will be two reports submitted to NHS England. • An interim report by Summer 2018 will help to inform guidance to providers in contracts running from April 2019. The interim report will include (1) analysis of outcomes and costs data for 2016/17 at scheme/intervention level, and (2) qualitative insights from interviewing commissioners and providers on implementation. • The final report will be submitted to NHS England by August 2019. The results will be used to inform future contracting rounds. The final report will record all aspects of the project in details. In addition, the study will produce and distribute a quarterly newsletter to the key stakeholders detailing progress to date. Academic publications: BDAU will publish the findings in two high-profile peer reviewed journals: • A health economic journal such as Health Economics, Journal of Health Economics or similar • A health policy journal such as Health Affairs, Health Services Research or Similar. Both articles will be open access and the study have budgeted appropriate allowances for this. The research team has identified the list of patient representatives (Patient & Public Voice) for each Clinical Reference Group under the six National Programmes of Care for the specialised services commissioned by NHS England (publically available information at https://www.england.nhs.uk/commissioning/spec-services/npc-crg/). The study have invited two patient representatives to join their advisory group. The research team is seeking feedback on all aspects of the study design, implementation, analysis and draft reports from the advisory group. To have patients in the advisory group increases their understanding of contracting as well as appropriate outcome and quality measures for particular PSS CQUIN schemes. The two patient representatives are attending two annual face-to-face advisory group meetings at the Office of Health Economics (OHE) London office. The interim and final reports of this project will be sent to the advisory group for critical review before the submission to NHS England. Imperial will also seek the guidance of the advisory group regarding dissemination activities, e.g. presenting findings to patient groups, and how to maximise the impact of the project. Other dissemination and target audience: BDAU will disseminate the findings at one domestic conference (such as the Health Economics Study Group) and one European conference (such as the European Health Economics Association).The audience includes patient groups, healthcare support groups, and other key stakeholders. BDAU will ensure that all audiences receive a summary of our results in an appropriate and accessible format. Study findings will also be communicated to the wider public. The Office of Health Economics (OHE) will establish a webpage which will provide details on the specifics of the project and the research findings. In addition BDAU will disseminate the findings of this project to the general public via blogs and social media using only aggregate data with small numbers suppressed. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

NHS Digital will securely transfer a pseudonymised extract of HES data with mortality flags to Imperial College London. Imperial College London will store the data on a server in the BDAU Secure Environment (SE). Data access is strictly controlled by the BDAU through a robust dataset registration process. No one other than BDAU staff can authorise access to the data. Access to the data will be only for the purpose outlined in this Data Sharing Agreement, all staff are bound to the policies, procedures and equivalent controls of the BDAU SE and Imperial College London, as substantive employees of the College. The raw data provided by NHS Digital will be analysed solely in the BDAU SE. Any further analysis done outside the BDAU SE (usually for visualisation purposes for output) will be done using data that has been aggregated with small numbers suppressed in line with the HES Analysis Guide. The data will be analysed to examine how variation in scheme design affects performance on the incentivised dimensions. This will involve statistical analysis using standard and innovative econometrics techniques inside the BDAU SE. BDAU SE key identification strategy will be a ‘difference in differences design’. Specifically, BDAU SE will compare the outcomes targeted by the PSS CQUIN scheme and the previous CQUIN scheme, for specialised services at hospitals, that were not previously targeted before the scheme’s introduction. Any differences observed in the intervention group, but not in the control group, will be attributed to the PSS CQUIN scheme. This design requires the observation trends in comparison and control groups prior to the intervention, to ensure that any difference identified to between the two in the post policy period, is due to the intervention and not pre-existing differences. Therefore, justifying, the request for access to data from the 2012/13 period. There will be no linkage with other record level data and Imperial College London will make no attempt to re-identify any individual in the data provided. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). The use of any cloud based solution for data storage is not permitted under this agreement. Any changes must be reflected through an amendment and subsequent approval of the agreement.


Project 8 — DARS-NIC-204903-P1J7Q

Opt outs honoured: Yes - patient objections upheld (Section 251)

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/12.

Repeats: Ongoing, One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care
  • HES:Civil Registration (Deaths) bridge
  • Civil Registration (Deaths) - Secondary Care Cut

Yielded Benefits:

SAHSU has a long record of publishing its research in high quality peer reviewed journals and presenting results at scientific meetings and conferences to inform policy and to empower public debate. SAHSU puts details of its research on its website (www.sahsu.org), including publications and outputs. Scientific publications using SAHSU health data and/or directly arising from SAHSU project work in the last two years are listed below. •Halonen JI, Blangiardo M, Toledano MB, Fecht D, Gulliver J, Ghosh R, Anderson HR, Beevers SD, Dajnak D, Kelly FJ, Wilkinson P, Tonne C. Is long-term exposure to traffic pollution associated with mortality? A small-area study in London. 2016. Environmental Pollution. 208 (Part A); 25-32 •Ghosh RE, Close R, McCann LJ, Crabbe H, Garwood K, Hansell AL, Leonardi G. Analysis of hospital admissions due to accidental non-fire-related carbon monoxide poisoning in England, between 2001 and 2010. 2016. Journal of Public Health. 2016. 38 (1); 76-83. •Ghosh RE, Ashworth DC, Hansell AL, Garwood K, Elliott P, Toledano MB. Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology. Archives of disease in childhood. 101 (5); 451-7 •Fecht, D. Hansell AL, Morley D, Dajnak D, Vienneau D, Beevers S, Toledano MB, Kelly FJ, Anderson HR, Gulliver J. Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies. 2016. Environment International. 88; 235-42 •Douglas P, Bakolis I, Fecht D, Pearson C, Leal Sanchez M, Kinnersley R, de Hoogh K, Hansell AL. Respiratory hospital admission risk near large composting facilities. International Journal of Hygiene and Environmental Health. 2016. 219 (4-5); 372-9. •Bakolis I, Kelly R, Fecht D, Best N, Millett C, Garwood K, Elliott P, Hansell AL, Hodgson S. Protective Effects of Smoke-free Legislation on Birth Outcomes in England: A Regression Discontinuity Design. Epidemiology. 2016. 27 (6); 810-8 •Fecht D, Jones A, Hill T, Lindfield, T, Thomson R, Hansell AL, Shukla R. Inequalities in rural communities: adapting national deprivation indices for rural settings. Journal of Public Health (Oxf). 2017 Apr 27; 1-7 •Smith RB, Fecht D, Gulliver J, Beevers SD, Dajnak D, Blangiardo M, Ghosh RE, Hansell AL, Kelly FJ, Anderson HR, Toledano MB M. Impacts of London's road traffic air and noise pollution on birth weight: a retrospective population-based cohort study. BMJ 2017 Dec 5;359:j5299. doi: 10.1136/bmj.j5299. •Wang Y, Pirani M, Hansell A, Richardson S, Blangiardo M. Using Ecological Propensity Score to Adjust for Missing Confounders in Small Area Studies. Biostatistics 2017 Nov 9. doi: 10.1093/biostatistics/kxx058. [Epub ahead of print] •Ghosh RE, Dag Berild J, Sterrantino AF, Toledano MB, Hansell AL. Are babies getting heavier? Birth weight trends England and Wales (1986-2012). Archives of Disease in Childhood Fetal Neonatal Ed. 2017 Aug 5. pii: fetalneonatal-2016-311790. doi: 10.1136/archdischild-2016-311790. [Epub ahead of print] •Douglas P, Sterrantino AF, Sanchez ML, Ashworth DC, Ghosh RE, Fecht D, Font A, Blangiardo M, Gulliver J, Toledano MB, Elliott P, de Hoogh K, Fuller GW, Hansell AL. Estimating particulate exposure from modern Municipal Waste Incinerators (MWIs) in Great Britain. Environmental Science and Technology 2017;51(13):7511-7519. doi: 10.1021/acs.est.6b06478 SAHSU also present its work at scientific advisory committees where of particular national interest e.g. some of the work on the incinerators project will be considered at forthcoming meetings of the Committee on Carcinogenicity (COC) and the Committee on Toxicity of chemicals in foods, consumer products and the environment (COT). SAHSU take part in a wide range of public facing events. For example, in June 2016, members of SAHSU presented work on the Environment and Health Atlas for England and Wales, bioaerosols and health effects of environmental noise at the ‘Life Bank- Interactive Science Event’ as part of MRC Festival of Medical Research which targeted young members of the public aged 18-40 years old. See video at https://www.youtube.com/watch?v=ml4XyHs720I - A look at how your environment affects your health at the MRC festival. On 14 July 2015, SAHSU attended the launch of the Respiratory Health of the Nation project, which used respiratory hospital admission, mortality and cancer registrations data, at the House of Commons in July. This project presented SAHSU maps of hospital admissions, mortality and cancer registrations of respiratory conditions. In addition to meetings with the MRC-PHE Centre Community Advisory Board, SAHSU also meet with various community organisations e.g. the assistant director attends noise committee meetings of Environmental Protection UK and provides research updates. The following examples below gives an overview of how outputs from SAHSU’s studies have been used to inform health policy: Traffic pollution and health in London study The results of this study allow for a better understanding of the health problems caused by air pollution and noise from traffic in London. Findings to date have received a high media-profile and have the potential to influence air pollution policy and regulatory practices in London and the UK. Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) The study was commissioned to extend the evidence base and to provide further information to the public about any potential reproductive and infant health risks from MWIs and to extend the evidence base with respect to exposures and any potential reproductive and infant health risks from MWIs. Health effects of large airports – the London Heathrow example (Heathrow) The results of this study allow for better understanding of the health problems caused by noise from aircraft in London. The findings had a high media-profile and are directly relevant to the Davies commission and decisions on whether to build a third run-way at Heathrow. Results of this study have been reported widely in local, national and international media and been raised as questions at Prime-ministers question. The results of SAHSU studies are placed in the public domain via peer-reviewed publications and are used to inform the behaviour of health care providers and to inform national public health policy. All studies equally feedback their results into the relevant policy leads within PHE. SAHSU has a long record of publishing its research in high quality peer reviewed journals and presenting results at scientific meetings and conferences to inform policy and to empower public debate. Where results are likely to be of particular interest, SAHSU put out a press release and, if necessary, hold a press conference e.g. for the SAHSU Environment and Health Atlas for England and Wales published in 2014, SAHSU held a press conference and several media interviews, including the Assistant Director of SAHSU being interviewed on the BBC Radio 4 Today programme. SAHSU also respond to media enquiries relevant to SAHSU’s work e.g. the Assistant Director of SAHSU took part in a Newsnight report on health effects of air pollution, broadcast 17 October 2016. SAHSU puts details of its research on its website (www.sahsu.org), including publications and outputs. The website: https://www.sahsu.org/publications shows all publications to date.

Objectives:

Objective for processing: The Small Area Health Statistics Unit (SAHSU) is a long-standing and internationally-recognised centre of excellence assessing the risk of exposure to environmental pollutants to the health of the population, with an emphasis on the use and interpretation of routine health statistics at small-area level. SAHSU was established in 1987 as a recommendation of the Black enquiry into the incidence of leukaemia and lymphoma in children and young adults near the Windscale/Sellafield nuclear power plant. SAHSU has a particular role nationally in carrying out environmental health surveillance of the population in relation to environmental contaminants and point sources of industrial pollution, based on routinely collected health data. This is a highly specialised area of work requiring excellence in computing, database management, geographical information systems (GIS), statistics, environmental exposure assessment and epidemiology. The set of skills and expertise that has been established and built up in SAHSU is a unique resource both nationally and worldwide. SAHSU has a programme of work established which is defined by the following terms of reference :- 1) To develop and maintain databases of health data, environmental exposures as required to meet specific need, and social confounding factors at the small area level; 2) To carry out substantive research studies on environment and health issues including studies of the relationship between socio-economic factors and health, in collaboration with other scientific groups as necessary; 3) In collaboration with other scientific groups, to build up reliable background information on the distribution of environmental exposure, socio-economic data and disease amongst small areas; 4) To develop methodology for analysing and interpreting health outcomes related to small areas; 5) To act as a centre of expertise, disseminating information on developments in spatial epidemiological methods to national and regional groups; 6) To respond rapidly, with expert advice, to ad hoc queries from the core funding bodies (DH and PHE) about unusual clusters of disease, particularly in the neighbourhood of industrial installations To deliver against that Terms of Reference, SAHSU will utilise the data to meet the following purposes: Purpose 1 – maintenance of the SAHSU health research database At the core of the overall programme of work is the maintenance of the SAHSU research database, which combines multiple datasets and has both ethics approval and s251 approval in place Purpose 2 – to carry out a programme of research projects and studies. The research programme includes both methods development and investigation of priority questions in environment and health. A key aim is to improve the science base underlying translation of knowledge on the effects of the environment on health into policy. SAHSU conducts national research studies on environmental factors that may affect health ranging from exposures to electromagnetic fields (such as from electricity power lines) to traffic-related air pollution and noise, using nationally collected patient data including mortality, hospital admissions, cancer registrations and births data. Additional small area analyses are conducted that help support the general remit of the unit e.g. investigating differential hospital admission rates in ethnically diverse small areas. Additionally, SAHSU provides national expertise in cluster and small area statistical methods and has close links with Public Health England including input into their environmental public health tracking programme. Approval of individual projects All SAHSU studies are controlled via the PHE-SAHSU Liaison Committee. New study concepts must initially be approved by either the Director or Assistant director of SAHSU prior to the outline study proposal being created. Consideration is given to whether the study is adequately covered by SAHSU’s existing ethical approval and if not, separate ethical approval must be sought. Once ethical approval is confirmed the outline study proposal is reviewed by the SAHSU-PHE liaison committee who, once approved then take the study for formal minuted approval from the appropriate PHE programme board (attended by a member of the Department of Health). Projects are only approved where they are within the constraints of the SAHSU programme terms of reference. Purpose 3 – to provide rapid ad hoc support to PHE and DH about unusual clusters of disease, particularly in the neighbourhood of industrial installations Such work is carried out on the instruction of DH or PHE, and is approved by the Director of SAHSU. By its nature, such requirements cannot be detailed in advance, but the only outputs would be aggregate with small numbers suppressed, and provided to DH and PHE. SAHSU have a single s251 support in place to cover the above. Any project which may have additional requirements (for example to require the linkage of an additional dataset beyond that previously agreed) must seek an amendment to the existing s251. It would also be outside the scope of this application, and therefore an amendment would be put before DAAG for consideration.

Expected Benefits:

Expected measurable benefits to health and/or social care including target date: The focus of the work under this application is to enable key public health issues associated with environmental factors at a small area. This would by definition therefore not look at care of individuals, but would related to informing public health policy and considering population health risks. The key points in demonstrating the benefit to health and social care therefore are :- - The core SAHSU programme of work is funded by Public Health England, and this includes the service to Public Health England to assist PHE in fulfilling their duties - Individual projects must meet be aligned with the Terms of Reference of the SAHSU programme, which includes addressing environmental and health issues. As part of this application, SAHSU will be amending these conditions to include an explicit requirement to reference that projects must be for the promotion of health - All projects are approved by the PHE Programme Board, which includes membership from DH. In order to assist with standardising approaches across application processes, SAHSU have offered membership of the approving group to the Data Access Request Service. Whilst individual detailed project related benefits cannot be stated at this time, three examples are given below of how outputs have and will be used to inform health policy. Traffic pollution and health in London study The results of this study will allow for a better understanding of the health problems caused by air pollution and noise from traffic in London. Findings are likely to have a high media-profile and have the potential to influence air pollution policy and regulatory practices in London and the UK. Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) The study was commissioned to extend the evidence base and to provide further information to the public about any potential reproductive and infant health risks from MWIs and to extend the evidence base with respect to exposures and any potential reproductive and infant health risks from MWIs. Health effects of large airports – the London Heathrow example (Heathrow) The results of this study allow for better understanding of the health problems caused by noise from aircraft in London. The findings had a high media-profile and are directly relevant to the Davies commission and decisions on whether to build a third run-way at Heathrow. Results of this study have been reported widely in local, national and international media and been raised as questions at Prime-ministers question. The results of SAHSU studies are placed in the public domain via peer-reviewed publications and are used to inform the behaviour of health care providers and to inform national public health policy. All studies equally feedback their results into the relevant policy leads within PHE.

Outputs:

Specific outputs expected, including target date: There are three main types of output from this application :- 1. The maintenance of the research database, and associated record level pseudonymised extracts for use by individual researchers within SAHSU. Such extracts are delivered either through the RIF or the database team. This database will have the ability to provide data for the specific projects which have been approved as set out above. Note that all extracts are subject to the constraints within this agreement, eg: that data will only be held and processed at the processing / storage address. Such work is on-going through the lifetime of this agreement. 2. Individual project research outputs. All such outputs would be aggregate data, and be used within journal papers, research reports and results, presentations at conferences. Such research outputs would be placed into the public domain. Examples of previous outputs and projects are given below. 3. Individual analyses for Public Health England (PHE). Again, all such outputs would be in aggregated form, but would be provided directly to PHE. Examples of outputs produced to date Traffic pollution and health in London study There have been three papers published to date: • Halonen, JI et al. Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. European Heart Journal 2015. • Gulliver, J et al. Development of an open-source road traffic noise model for exposure assessment. Environmental Modelling & Software. 2015. • Halonen JI et al. Is long -term exposure to traffic pollution associated with mortality? A spatial analysis in London. Environmental Pollution. 2015. Future outputs from the study will be disseminated via peer reviewed publication and academic conferences presentations. The outputs of the project will be published in peer-reviewed journals during the course of the study and after completion, which is expected by 2016. Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) There have been two papers published to date: • Font A et al. Using Metal Ratios to Detect Emissions from Municipal Waste Incinerators in Ambient Air Pollution Data. Atmospheric Environment. 2015 • Ashworth DC, et al. Comparative assessment of particulate air pollution exposure from municipal solid waste incinerator emissions. Journal of Environmental and Public Health. 2013 Study information will be provided on the SAHSU website for the public. Health effects of large airports – the London Heathrow example (Heathrow) The results of this study have been published in the peer-reviewed BMJ (http://www.bmj.com/content/347/bmj.f5432 ). All outputs from individual projects will be anonymised (data will only be shared where aggregated with small numbers suppressed in line with the HES Analysis Guide). To confirm, no record level data is provided to any third party organisation and no commercial use is permitted.

Processing:

Processing activities: Processing is consistent across all three purposes, given that they all require the use of the same research database, In summary :- - identifiable data are encrypted and held in a secure area of the database on the SAHSU private network. Access to the identifiable data is limited to a small database team within SAHSU. - The identifiable data is held on a separate encrypted file system with access limited to the database team only. The pseudonymised output (CSV) file is then loaded into Oracle for use by researchers. - Separation is maintained between the database team, who handle data encryption and see identifiable data, and researchers, who only have access to pseudonymised data. NHS numbers, addresses and postcodes are encrypted and replaced by pseudonyms and held in confidential tables to which only the database team has access. Record separation occurs during data loading; the original files are stored on a separate encrypted file system on a separate server. Identifiers are not held on the same server as the clinical data. Further processing standardises names, data types, correct dates, links in geography via the postcode, performs encryption and pseudonymisation and carries out dataset specific bespoke processing (e.g. the detection of potential duplicates). At this point the data is only accessible by the database team, and is fully protected by encryption and pseudonymisation. The next phase creates production tables and sets them up for use by the researchers, granting appropriate permissions and setting up auditing. There then follows an extensive set of quality control checks; these are documented in the database. Finally, documentation is automatically generated. When the process is complete the load tables are dumped to the encrypted file system for reference and then removed. It is therefore practicable to reload SAHSU data at intervals to enhance security and to add processing improvements to pre-existing data (e.g. improved data processing, enhanced security, improved pseudonymisation). It is normal SAHSU practice to reload datasets each time a fresh year is received to ensure the latest processing is uniformly applied to all data. SAHSU operates a hierarchy of data access permission based on user role: 1. General level access to aggregated health data, such as that publicly available from data providers websites e.g. district level mortality counts; 2. SAHSU researcher with access to small area data that is not pseudonymised and non-sensitive; 3. SAHSU researcher level access to pseudonymised sensitive data where required for specific projects; 4. Database team access to identifiable information supplied by data providers e.g. to pseudonymise the data. All access to confidential data is password protected. Data may only be extracted by the database team, or by using a “self-select” tool called Rapid Inquiry Facility (RIF). Note that this tool allows the user to select the nature of data to be extracted, but only at in an aggregated form. The RIF is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. This facility was initially designed as a tool for SAHSU staff to analyse routinely collected health data in relation to environmental exposures in the European Health and Environment Information System (EUROHEIS) project and has also been used by SAHSU to provide aggregated information as part of the US Centers for Disease Control (CDC) environmental public health tracking. In all cases data is extracted using the Rapid Enquiry Facility (RIF) or by the database team, and all extracts are supervised by the database manager. All data extracts are logged and cross checked by the database manager prior to extraction. The RIF will also permit data extraction and will also enforce identical checks. The checks carried out prior to data extraction are: • Projects is approved by the SAHSU liaison committee; • User is under contract to Imperial; • If required, access to event data (date of birth and/or death) has been justified; • SAHSU confidentiality form has been signed ; • The user has been information governance inducted and trained. In addition to the data provided by the HSCIC, SAHSU hold the following record level identifiable datasets: • ONS Births and Still births • ONS Cancer Incidence • Welsh Cancer Intelligence and Surveillance Unit • ONS Mortality • National Congenital Anomaly Register (NCAR from ONS) • Local Congenital Anomaly registries affiliated with BINOCAR • Terminations grounds “E” • NN4B • NCCHD (National Community Child Health Database) Linkage of data between datasets is only permitted with: • Approval via a substantial amendment to SAHSU ethics approval • Approval via a substantial amendment from HRA CAG • Explicit written permission from the data providers concerned. To date, amendments to the s.251 support have been sought and granted for the following three projects requiring specific data linkage: 1. Traffic pollution and health in London; 2. Incinerators; 3. Small area variation in coronary heart disease incidence, mortality and survival and their risk factors and determinants in England. Any projects requiring linkage beyond that covered by this application would also be subject to an amendment for DAAG’s consideration. It would also require support from ethics, and be covered by an amendment to the existing s251. Data minimisation As part of this application, the data required has been rationalised and HES data currently held by SAHSU no longer covered by this agreement will be securely destroyed. The remaining years are required by a number of studies, but the totality of years of data is also required by a single project - a study relating to health at major airports. Whilst the initial study is complete, the data is required to be retained in order to respond to any queries relating to the research (a common requirement for published research). National data is also required in order to provide the ad hoc rapid response service to Public Health England. Given that PHE coverage is across England, and the requirements cannot be predicted in advance, national data is required given that response times do not permit time to request, receive and process individual extracts of data.


Project 9 — DARS-NIC-209174-W2H3G

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2018/10 — 2018/12.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Patient Reported Outcome Measures (Linkable to HES)

Objectives:

Imperial College London’s Big Data and Analytical Unit (BDAU) requires an extract of Patient Reported Outcome Measures (PROMs) data for use in a research study: “Estimating the Impact of Patient Safety Incidents on Quality of Life using Patient Reported Outcome Measures”. This data will link to data provided under study DARS-NIC-172334-W0G2L and a separate dataset will be created, utilised and managed for this study. This study is funded by the National Institute of Health Research - Patient Safety Translational Research Centre. This study is being undertaken by a small team of researchers from Imperial College London, all with substantive contracts with the College. The team has extensive experience in the econometric analysis of administrative healthcare datasets. This study aims to contribute a better understanding of economic costs in relation to patient safety and quality of life. The Big Data and Analytical Unit (BDAU) is a multidiscipline team within Imperial College London which collaborates with a large network of researchers across the college with the aim of ensuring the maximum use, impact and dissemination of research using healthcare data. Imperial College London will provide local access control within the BDAU ISO 27001 certified research environment for this study and ensure the dataset for this study is managed separately to the study for DARS-NIC-172334-W0G2L. There are several papers estimating the impact of patient safety indicators (PSIs) on providers’ resource use and payments. However, there are relatively fewer studies on the estimation of costs in terms of patients’ quality of life (QoL). The sparse literature suggests that estimated patient-reported complications are associated with a reduction in QoL in the short term, but that QoL might recover in a long term (Frie et al, 2012; Bosma et al, 2016). Patient safety events are for the purposes of this study defined as they have been defined by the American Agency for Healthcare Research and Quality (AHRQ) set of patient safety indicators, which includes pressure ulcers, hospital acquired infections, and postoperative sepsis. Aylin and Bottle (2009) have validated that these events can be identified in HES data. In addition, the PROMs questionnaire asks patients if the experienced “complications” (wound problems, urinary problems, allergy or reaction to drug, bleeding). Imperial College London also consider these complications “patient safety events” and will test to which extent there is agreement between patient reported and hospital reported patient safety events. This study attempts to estimate the impact of patient safety events on QoL in elective surgery patients and the monetary value of QoL loss due to patient safety events. In addition, it will compare patient- and hospital reported rates using developed patient safety indicators. Using this data, the researchers will identify a comparable set of patients that do and do not experience a patient safety event using matching methods. Furthermore, based on matching, the researchers will compare quality of life improvement between the two groups, and estimate quality-adjusted life years (QALY) loss attributable to patient safety events.

Expected Benefits:

Information has been taken directly from the benefits section of the NIHR Funding application for this project. Theme 6 of the Imperial College NIHR PSTRC aims to evaluate the value for money in patient safety. This includes examining the impact of adverse events on quality of life outcomes. By evaluating the financial impact of patient safety incidents, routinely collected outcomes and patient related outcomes as reported by the patients themselves, Imperial College London can provide insight into this. The output of this study will enable more informed policy making and clinical practice ensuring that money within the NHS is focused on cost-effective patient safety interventions. This is part of a £7 million-pound investment in the PSTRC to achieve measurable benefits in translational research on patient safety in the NHS. Anticipated target dates for these are covered in Section 5C.

Outputs:

The main outputs for this particular study will be academic publications Findings from this study will be published in two high-profile peer reviewed journals: • A health economic journal such as Health Economics, Journal of Health Economics or similar (target submission date Q3 2019) • A health policy journal such as Health Affairs, Health Services Research or Similar (Target submission date Q3-2019) Both articles will be open access and appropriate allowances have been budgeted for this. Other dissemination and target audience Findings will also be disseminated at one domestic conference such as Health Economics Study Group and one European conference. Through the National Institute for Health Research (NIHR) Patient Safety Translational Research Centre (PSTRC), findings will be disseminated to relevant patient groups, health care professionals and other key stakeholders. All audiences will receive a summary of results in an appropriate and accessible format. All outputs will contain only aggregate level data with small numbers supressed in line with the HES analysis guide. No raw data will be transferred outside the BDAU SE and neither the data nor outputs will be used for commercial purposes.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract, including requirements on the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). NHS Digital will securely transfer a pseudonymised extract of PROMs data to Imperial College London. Imperial College London will store the data on a server in the BDAU Secure Environment (SE). HES Data from study DARS-NIC-172334-W0G2L will be cloned into this dataset and linked with PROMs data provided by NHS Digital. Data access is strictly controlled by the BDAU through a robust dataset registration process. No one other than BDAU staff can authorise access to the data. Access to the data will be restricted to researchers and supervisors only for the purpose outlined in this Data Sharing Agreement. Researchers and supervisors are bound to the policies, procedures and equivalent controls of the BDAU SE and Imperial College London as substantive employees of the College. The raw data provided by NHS Digital will be analysed solely in the BDAU SE. Any further analysis done outside the BDAU SE (usually for visualisation purposes for output) will be done using data that has been aggregated with small numbers suppressed in line with the HES Analysis Guide. The data will be analysed to examine how patient safety indicators, extracted from HES, affect quality of life in various dimension, such as hip surgery, knee surgery, hernia repair and varicose veins as described using PROMs data. This will involve statistical analysis using standard and innovative econometrics techniques inside the BDAU SE. The main econometric technique will be difference in differences, where Imperial College London compare improvements in quality of life after surgery for patients that do and do not experience a patient safety event. This study will compare the quality of life improvements between groups of patients that do and do not experience relevant events according to established patient safety indicators. To accomplish this, all data is required for all requested years for the specified variables. The initial analysis will focus on patients with PROMs conditions that do and do not experience a patient safety events. In practice Imperial College London will not divide the data set, but specify a dummy variable that indicates whether the patient experienced a patient safety event or not which will be used in multi-variable regression analysis to estimate the difference in quality of life gain from operation between the two groups. This difference is equal to the quality of life cost of experiencing an event. In the second part of the analysis we will identify patients with other (non-PROMs) conditions who experienced the same type of patient safety events to estimate the total impact of patient safety events on quality of life. To ensure that differences in quality of life improvements are due to the patient safety events and not other differences between patients Imperial College London will make sure patients are as comparable as possible based on observable data. The variables will include age, sex, diagnosis codes, procedure codes, admission method, discharge method and length of stay. Based on these observable variables Imperial College London will use propensity score matching and to select comparable cohorts. There will be no linkage with other record level data not mentioned in this agreement and there will be no attempt to re-identify any individuals in the data. Any outputs produced from this data will only be aggregated outputs with small number suppression, in line with the HES analysis guide. No patient will be able to be identified from any outputs produced by Imperial College London.


Project 10 — DARS-NIC-278518-F3H0X

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2016/12 — 2017/02.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

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

Objectives:

To provide a robust alternative assessment of the use of health service resources by patients enrolled in IMPROVE (ISRCTN48334791), a randomised trial of an endovascular strategy versus open repair for patients with a clinical diagnosis of ruptured abdominal aortic aneurysm. Since the primary outcome of the trial, 30-day mortality, was not different between the two randomised groups (BMJ 2014;348:f7661), accurate longer-term clinical and cost-effectiveness evaluations of the 2 treatments have particular relevance for the NHS. Imperial College London's current source of information on the use of health services after hospital discharge comes primarily from the specialist vascular centres participating in the trial (where patients had their ruptured aneurysms repaired). Re-admissions to non-trial hospitals are not captured. To enhance the generalisability and robustness of clinical and cost-effectiveness evaluations, IMPROVE trial Management Committee wish to cross-check both aneurysm-related re-interventions and hospital re-admissions (any hospital for any reason) data, to validate and supplement the information collected from relevant trial hospitals with HES admissions/procedures data. The aneurysm-related re-interventions include procedures directly related to the aorta and distal arteries (L procedure codes) and procedures which arise as a result of damaged bowel or abdominal wall during aneurysm repair (including some G, H and T procedure codes). HES data for admissions, procedures, length of hospital stay are requested for specified patients, to provide details of hospital resources used between discharge and 3 years post-operative period. Ethical approvals are in place to support the collection of data. Admitted patient care and critical care datasets from 2009/10 (01/09/2009 onwards) to 2012/13 were received in 2015 as one-off files as part of this DSA and the data crosschecked against aneurysm-related re-interventions captured by participating hospitals: this covered 1-year follow-up for the majority of patients (randomised Sep-2009 to July-2013). Imperial College London identified two major re-interventions in the HES dataset (1 had occurred at a non-participating hospital and the other overlooked by one of the participating centres). After 3 years of follow-up, Imperial College London again need to cross check the data received from trial centres, this time separating re-interventions on the aorto-iliac and distal arteries from those due to damage to the abdominal viscera or aorta wall incurred by either the event or repair of the ruptured aneurysm. Imperial College London will submit details for patients who were recruited to the IMPROVE trial at England hospitals and were alive at primary discharge, with post-operative consent (around 350 patients). Imperial will provide the NHS number, Trial ID, sex, DOB and the date of discharge after index repair date for each patient. For these patients Imperial would like to request all hospital admission dates, discharge dates, diagnosis codes, provider codes, until 3 years following discharge from index repair (or death if this occurs before 3 years). Imperial would also like for these admissions to be linked to certain procedure codes to ascertain aneurysm-related re-interventions, if there were any. To be able to cover 3-year period for all patients Imperial request to receive admitted patient care and critical care datasets from 2009/10 to 2015/16 (current year needed to 22nd July 2016). Imperial plan on using Trial ID and HES patient identifier to link APC and CC datasets and do not require any sensitive/identifiable fields. The pseudonymised data received from NHS Digital will not be linked to identifiable data which is held on a separate unlinked database.

Expected Benefits:

1. To add to the accuracy of the cost-effectiveness and the incremental net benefit of an endovascular strategy for repair of ruptured abdominal aortic aneurysm: 2016-2017. 2. To benefit all future patients admitted to hospital with a diagnosis of ruptured abdominal aortic aneurysm. 3 To drive organisational changes, so that endovascular repair becomes available to all patients with ruptured abdominal aortic aneurysm

Outputs:

1. Clinical and cost-effectiveness of an endovascular strategy versus open repair at 3-years after repair of ruptured abdominal aortic aneurysm [for major journal publication, Health Technology Assessment (HTA) report and reporting to the National Institute for Health and Care Excellence (NICE) for April 2017. 2. The main findings to 3 years will be presented to The Vascular Society of Great Britain and Ireland on 1st December 2016. The major outcomes also will be reported at other national and international conferences (International VEITHsymposium in New York, The British Society of Endovascular Therapy and the European Society for Vascular Surgery), on the IMPROVE trial websites and via social media outlets such as Twitter. All outputs will consist of aggregate data only with small numbers suppressed in line with HES analysis guide.

Processing:

The data will be processed in the offices of the Vascular Surgery Research Group in Room 4N12 at Charing Cross Hospital (Imperial College Healthcare NHS Trust) on a non-networked dedicated computer with built-in BitLocker encryption (i.e. full disk encryption) in a secure office only accessible to the IMPROVE trial manager, who is employed by Imperial College London, but has a Research Passport and Letter of Access with Imperial College Healthcare NHS Trust. The sole trial manager has had IGT training and will comply with the principles of the Data Protection Act 1998 at all times when processing/storing personal information. Identifiable personal information will *only* be viewed by the trial manager on this specific computer (i.e. no mobile or remote working). HES datasets will not be saved on any other trial database in any other location. Once the data has been checked and cleaned, it will be fully anonymised on this same stand-alone computer and all patient identifying details removed (only the trial ID will be carried through during data analysis). Procedure codes received from HES will be summarised into clinically helpful categories (e.g. aorta and distal artery related, damaged bowel or abdominal wall related, etc.) and an anonymised file containing four digit Trial ID and these summary categories of complications will be analysed by the statistician, (based at the University of Cambridge) and two health economists (based at the London School of Hygiene and Tropical Medicine (LSHTM). Both the University of Cambridge and LSHTM have formal subcontracts with Imperial College London, to undertake the statistical and health economic analysis for the trial, respectively. Both the statistician and health economists will only view and analyse the anonymised data file at Charing Cross Hospital at the Imperial College site and under the controls/policies of Imperial College London.


Project 11 — DARS-NIC-28095-S9N3P

Opt outs honoured: N, Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2016/11.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

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

Objectives:

ICL DFU uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. Refer to section (Expected measurable benefits to health and/or social care including target date) to demonstrate the benefits ICL DFU work have brought to the Health and Social Care. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to DFI customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers. Dr Foster Limited has no access to the patient re-identification service. No patient identifiers will ever be passed to Dr Foster Limited or any other organisation except the NHS provider trust from where the data originated. For this purpose, we have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Analysis Toolkit. This service allows us to supply Provider trusts’ NHS Number and LOPATID using Dr Foster Analysis Toolkit without passing these fields on to Dr Foster Limited. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and Dr Foster Limited provide consultant from our analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by Dr Foster Limited in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from Dr Foster Limited. On approval of this application, a sub-licence model between the HSCIC and Imperial College similar to that previously in place, which permits Imperial to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to Dr Foster Limited. The unit works in collaboration with Dr Foster Limited to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by Dr Foster Ltd: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – analyses are provided to NHS organisations via a non-NHS organisation that holds a subscription to the tool, only being able to view aggregate small number suppressed data. 2. Value Added Services As an information intermediary, Dr Foster Ltd responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the Dr Foster Ltd Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. Dr Foster limited also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by Dr Foster Ltd or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. Dr Foster Ltd is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined by Schedule 3 of the Data Sharing agreement.

Expected Benefits:

ICL DFU works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the Health and Social Care Information Centre on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development.(ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Cusum alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Ltd services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. Dr Foster Ltd intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. Dr Foster Limited welcomes the opportunity to work with HSCIC to ensure information captured can support our ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and Dr Foster Limited’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.

Outputs:

1) Research into variations in quality of healthcare by provider: background to proposed work ICL DFU work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Limited is an independent healthcare information company. It provides a research grant to our unit to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with Dr Foster Limited to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes

Processing:

ICL DFU uses hospital administrative data in the form of HES/MMES to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICLDFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the standard HES extracts and sensitive fields. Imperial’s researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The standard HES extracts and sensitive fields are stored in the Research database where researchers are able to access the data to do their analyses. The standard extracts are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid (for Dr Foster Limited) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by Dr Foster Ltd who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both Dr Foster Limited and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.


Project 12 — DARS-NIC-287804-H1T1R

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 - Members and Postings Report

Objectives:

To facilitate complete reporting of mortality for a randomised trial of an endovascular strategy versus open repair for patients with a clinical diagnosis of ruptured abdominal aortic aneurysm (IMPROVE ISRCTN48334791), which is supported by the National Institute for Health Research. The primary outcome of this trial is 30-day mortality and secondary outcomes are 1 year and 3 year mortality and aneurysm-related mortality. The most recent data file was received for these patients in December 2015. To enable full patient follow up for a minimum of 3 years, data are needed for deaths through July 2016 (last patient in the study will complete 3-year follow-up on 21st July 2016). The trial included critically ill patients often in great pain. Either patients or a relative/carer/welfare guardian on behalf of the patient have signed a consent form before aneurysm repair. After recovery patients provided fully informed consent for continued participation in the trial, which included consent to access “their data from NHS Information Centre and NHS Central Register NHS information”.

Expected Benefits:

1. To enable discrimination between aneurysm-related and other causes of death in the mid-term, as well as full patient follow up for mortality, following endovascular or open repair of ruptured abdominal aortic aneurysm: 2015-2017. 2. To benefit all future patients admitted to hospital with a diagnosis of ruptured abdominal aortic aneurysm (from 2015) 3. To drive organizational changes, so that endovascular repair becomes available to all patients with ruptured abdominal aortic aneurysm (from 2015)

Outputs:

Mortality following endovascular strategy versus open repair 3-years after repair of a rupture of an abdominal aortic aneurysm [for major journal publication, HTA report and reporting to NICE] for December 2016 This major outcome also will be reported at conferences and via relevant charities, patient groups and social media outlets.

Processing:

The data will be processed in the offices of the Vascular Surgery Research Group in Room 4N12 at Charing Cross Hospital (Imperial College Healthcare NHS Trust) on a non-networked dedicated computer with built-in BitLocker encryption (i.e. full disk , encryption) password-protected computer in a secure office only accessible to the trial manager, who is employed by Imperial College London, but has a Research Passport and Letter of Access with Imperial College Healthcare NHS Trust. The trial manager has had IGT training and will comply with the principles of the Data Protection Act 1998 at all times when processing/storing personal information. Identifiable personal information will *only* be viewed by the trial manager on this specific computer (i.e. no mobile or remote working) Data will then be fully pseudonymised by removing all identifiable information, and a file containing four digit Trial ID and category (10 used) of underlying cause of death will be analysed by the statistician, who is based at the University of Cambridge, but he will carry out the analysis at Imperial College Charing Cross Campus under the controls and policies of Imperial College).


Project 13 — DARS-NIC-302604-S7H2N

Opt outs honoured: N, Yes - patient objections upheld (Section 251, Section 251 NHS Act 2006)

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2019/07.

Repeats: Ongoing, One-Off

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Scottish NHS / Registration
  • MRIS - Cohort Event Notification Report
  • MRIS - Members and Postings Report
  • Hospital Episode Statistics Outpatients
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set

Yielded Benefits:

The data obtained on the cause of death for the ASCOT flagged patients has shown the long term benefits in the use of statins in the prevention of cardio-vascular disease. This has been influential in the debate around statin use, and UK guidelines. Cholesterol and blood pressure trials have led to major changes in NICE and other international guidelines, by providing the necessary background evidence for the committees. The ASCOT trial is a major contributor to the Cholesterol Treatment Trialists Collaboration and the Blood Pressure Trialists Collaboration both have led to the recommendations for blood pressure and cholesterol lowering targets that are widely used the NHS and around the world. It is very important as it was one of the first trials to use a high intensity statin. It has led to a number of publications e.g. Lancet. 2017 Jun 24;389(10088):2473-248, J Hypertens. 2011 Oct;29(10):2004-13, Eur Heart J. 2011 Oct;32(20):2525-32, J Hum Hypertens. 2013 Aug;27(8):492-6.

Objectives:

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

Expected Benefits:

Long term follow up of the UK cohort of the ASCOT population has shown that, 11 years after randomisation and approximately 8 years after trial closure, there was a significant reduction in all-cause mortality in subjects initially randomised to atorvastatin, suggesting a legacy effect of statins which persists many years after treatment (Sever PS et al, Eur Heart J 2011). Analysis of mortality data a further four years after trial closure showed further persistence of the legacy effect of atorvastatin on all-cause mortality (data presented at British Hypertension Society 2016.). However, no such legacy benefits were apparent for the blood pressure lowering arm of the study. In order to strengthen the information on the legacy benefits of statins, and to ascertain whether such benefits exist for blood pressure lowering, It is proposed to extend the analysis to include all fatal and non-fatal events, such as dementia, diabetes, and fatal and non-fatal vascular events. It is believed that the results of this research will be of benefit to patients considering the long term benefits and harms of vascular secondary preventative medication, for example those in mid-life who are considering taking a statin or blood pressure lowering medication. Determining precisely the roles of higher blood pressure and LDL-cholesterol for dementia prevention is a priority for public health. If the control of vascular risk in mid-life or effective treatment of pre-symptomatic cerebral vascular disease could be shown to prevent or delay dementia, this would have considerable implications for the global burden of vascular-mediated dementia and for public policy. It would expand the number of people eligible for intervention at a younger age; and identify new methods to target interventions (currently targeted based on absolute risk of heart attack and stroke, not dementia). The cost-savings estimated from risk factor control (£60 million saved for every year’s delay in dementia in the 1% of the population with vascular risk factors) have been estimated solely from observational data (nice.org.uk/Guidance/NG16). Data from this analysis might substantially modify these estimates, giving commissioners better evidence to weigh the benefits of different interventions. If, on the other hand, a causal role for vascular risk factors or pre-symptomatic cerebral vascular disease cannot be demonstrated, this would lead to a radical shift in both research and public health priorities.

Outputs:

ASCOT expect the following outputs: 1. An analysis of the effect of each drugs on dementia, hospitalisation, and cardiovascular outcomes in the very long term 2. An analysis to determine whether the effects of the drugs on long term outcomes is affected by their baseline characteristics. When these outputs are available and submitted for publication, ASCOT will provide them to funders and other relevant boards. All outputs are publicised on the ASCOT website. Once data has been received by the ASCOT study team, they expect the initial analyses to take 18 months to 2 years. All outputs will be that will be published will be aggregated with small numbers suppressed in line with the HES analysis guide. The study aim to present the findings of the work at the European Stroke Conference, Alzheimer’s Research UK and European Stroke Organisation Conference within 12 – 24 months. The study have extensive contacts with the Alzheimer’s Society, the Stroke Association and the British Heart Foundation, which they will use to communicate to patient groups, and as a conduit to policy makers such as NICE and other national guideline bodies. The ASCOT trial investigators will publish these outputs in major clinical journals (e.g. Lancet, BMJ, Stroke etc.) and present them in international meetings by study end, target 2019-2020 . it is anticipated these would contribute to national guidelines on reducing cardiovascular risk. Imperial will present outputs at international academic conferences (European Stroke Organisation Conference etc.), as well as at national meeting for people with dementia.

Processing:

NHS Digital already hold the identifiers from participants in the ASCOT trial, from their previous consent to mortality linkage. NHS Digital will use these identifiers to further link participants to other aspects of the EHR. Also, each individual is currently identified by an ASCOT trial number. The ASCOT trial investigators do not hold identifiers for each individual, but do hold trial numbers. Data received by Imperial will include ASCOT trial ID, age at recruitment and age at death or event, outcomes of relevance, and data from: HES admitted patient care, HES outpatients, A&E, mental health, date and cause of death. In addition Imperial are requesting data from a UK-wide dementia audit, that Imperial have previously been informed is linkable. The ASCOT trial investigators will then use the trial ID to link data received from NHS Digital to other non-identifiable data held by the ASCOT team (for example randomised allocation, in-trial events. etc.) The ASCOT trial investigators will receive and link these datasets in a IG toolkit environment. For Imperial's purposes, this data is not identifiable, and investigators will then export the dataset with trial ID and age (to nearest year only) and age at death for analysis. This dataset will be available to bona fide researchers who approach the ASCOT team chief investigator. The long term effect of blood pressure and LDL cholesterol lowering treatments is of great interest. In order to power studies of these questions adequately, meta-analysis of study data may be needed. If such meta-analyses are performed, Imperial will share either ASCOT level summary estimates, or where appropriate anonymised individual level data with approved collaborators. Because the long term follow-up of clinical trials is a matter of great interest, and the utility of clinical trial data many years after collection has often been proved to be greater than at the time of approvals (ASCOT has a number of good examples), Imperial would propose to keep an anonymised dataset indefinitely for the following reasons:. 1. Long term data retention is necessary to allow tabular data sharing which is now a requirement for publishing in high impact journals. Any tabulations would be aggregated with small number suppressed in line with the HES analysis guide 2. The study may wish to request further data depending on the results of this study. For data from the Mental Health (MHSDS, MHLDDS, MHMDS) data sets, the following disclosure control rules must be applied: • National-level figures only may be presented unrounded, without small number suppression • Suppress all numbers between 0 and 5 • Round all other numbers to the nearest 5 • Percentages can be calculated based on unrounded values, but need to be rounded to the nearest integer in any outputs • In addition for Learning Disability data in Mental Health (MHSDS, MHLDDS, MHMDS), the England-level data also must apply the suppression of all numbers between 0 and 5, and rounding of other numbers to the nearest 5. University of Edinburgh will not have access to any record level data. 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).


Project 14 — DARS-NIC-311095-K1Q0B

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

Sensitive: Sensitive

When: 2018/10 — 2018/12.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c)

Categories: Identifiable

Datasets:

  • MRIS - List Cleaning Report
  • MRIS - Cause of Death Report

Yielded Benefits:

This study has had a major impact to date in (a) reinforcing much other evidence that mixed anxiety and depression is better considered as part of a single syndrome than separated into separate moods, (b) helping to generate a completely new classification of personality disorder that will be introduced by the World Health Organisation in 2018 in which all the current categorical labels will be abolished, (c) shown that personality status is the main determinant of long-term outcome in anxiety and depressive disorders. The new classification of personality disorder was published on June 18th this year by the World Health Organisation; it classifies personality on a single spectrum from normal to severe. This is a complete change from the previous classification and its development is described in the last of the papers listed (2018 – in press). The implications are that the outcome of common mental disorders can be predicted accurately using the new classification and that, unless the personality aspects of the disorder are addressed (which currently are not) patients will receive a disservice. This is also reinforced by an editorial published in July 2018 in Psychiatric Bulletin. The scale for the general neurotic syndrome was first published in a book (The Classification of Neurosis) and expanded in detail in 1989. It has now been shown to be the best predictor of outcome in these disorders. Other publications to date: 1. Seivewright, H., Tyrer, P, & Johnson, T. (2002) Changes in personality status in neurotic disorder. Lancet, 359, 2253-2254. (first paper to show that personality status changed significantly between disorders over time – now confirmed by many others) 2. Tyrer P, Crawford M, Sanatinia R, Tyrer H, Cooper S, Muller-Pollard C, Christodoulou P, Zauter-Tutt M, Miloseska-Reid K, Loebenberg G, Guo B, Yang M, Wang D, & Weich S. (2014). Preliminary studies of the ICD 11 classification of personality disorder in practice. Personality and Mental Health, 8, 254-263. (most read paper from the journal in the last two years – shows that the new classification yields much better information than the old (ICD-10) one and is much simpler. 3. Tyrer P (2015). Personality dysfunction is the cause of recurrent non-cognitive mental disorder: a testable hypothesis. Personality and Mental Health, 9, 1-7. (A clear hypothesis supported by our data – you will only get completely better from most mental disorders if you have no personality problems (unless the personality problems are addressed – 90% of the time they are ignored) 4. Tyrer P, Tyrer H, Yang M, & Guo B. (2016). Long-term impact of temporary and persistent personality disorder on anxiety and depressive disorders. Personality and Mental Health, 10, 76-83. (shows that mild personality disorders using the new classification leads to better outcome than moderate or severe disorders). 5. Tyrer P, Mulder R, Kim Y-R & Crawford MJ. (2018). The development of the ICD-11 classification of personality disorders: an amalgam of science, pragmatism and politics. Annual Review of Clinical Psychology (in press). (this is the most prestigious psychological journal in the US and will help to spread the message across the pond that they have to abandon their poor performing DSM classification of the condition)**

Objectives:

The Nottingham study of neurotic disorder (NSND) was set up in 1983 to examine both the short and long term outcome of common anxiety and depressive disorders. In particular it examined one major hypothesis, which is that the outcome of common mental disorders is primarily dependent on personality status and that this becomes more pronounced over time, and that the separate classification of individual neurotic disorders (then recently outlined as discrete syndromes) was of limited benefit to science or practice and that a significant proportion of patients with anxiety and depressive disorders had a mixed anxiety/depression disorder linked to personality disorder mainly in the anxious/dependent cluster, and together this constituted a general neurotic syndrome. It was hypothesised that those with the general neurotic syndrome would have a worse outcome in both the short and long term than those without the syndrome, and that the latter had a generally good outcome similar to that for adjustment disorders (i.e. were self-limiting). As detailed on the study website the following outcome measures will be implemented via the study: http://www.isrctn.com/ISRCTN65727743?q=&filters=conditionCategory:Mental%20and%20Behavioural%20Disorders,ageRange:Not%20Specified&sort=&offset=3&totalResults=140&page=1&pageSize=10&searchType=basic-search Primary outcome measure Comprehensive Psychopathological Rating Scale; at 2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years Secondary outcome measures 1. DSM diagnosis; 10, 16, 32, 52, and 104 weeks, and follow-up at 12 and 30 years 2. Hospital admission; 2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years 3. Hospital Anxiety and Depression Scale - Anxiety section; ,2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years 4. Hospital Anxiety and Depression Scale - Depression Section; 2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years 5. Montgomery-Asberg Depression Rating Scale; 2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years 6. Neurotic Disorder Outcome Scale (NDOS); 5, 12 and 30 years 7. Personality status; 2 years, 12 and 30 years 8. Social Functioning Questionnaire; 12 and 30 years 9. Suicidal behaviour; 2, 4, 6, 10, 16, 32, 52, and 104 weeks, and follow-up at 5, 12 and 30 years The study is aiming to follow up as many as possible of the patients with anxiety and depressive disorders first seen in a research study 30 years ago and subsequently followed up on 10 occasions. The study is requesting date of death to avoid causing distress to relatives and friends of any of the participants by inadvertently trying to follow up those who have died. The Researcher would through this agreement request data on the cause of death for those who have died to support the analytical part of the work. One of the hypotheses of the research is that those who had a certain combination of characteristics called the general neurotic syndrome will have premature mortality. This request covers the last set of observations on this cohort and will be combined with the ones taken on 10 previous occasions to build a comprehensive picture of both short and long-term outcome. In addition to the benefit of not causing any distress the main purpose of the study will be to carry out the analyses posed by the main hypotheses.

Expected Benefits:

There is an immediate benefit in the provision of data to the study which would ensure that the study does not cause distress to relatives and friends by inadvertently trying to follow up those who have died. The expected benefits to health from this research will be to continue to improve the treatment of people with mental disorders. This treatment is known as Nidotherapy and attempts to treat the problems of behaviour by changing the environment to create a better fit between the person and society and in so doing reduce the frequency of such behaviors. Nidotherapy is the formal term introduced to describe the systematic manipulation of the physical and social environment to help achieve a better fit for a person with a persistent mental disorder (Tyrer et al, 2003). It was first used in the treatment of personality disorder, but can also be extended to all forms of chronic disorder, including the full range of non-psychotic disorders (Tyrer, 2009). In addition to the main hypothesis a second hypothesis will be tested that when personality disorder is present it only takes place in the context of major environmental change and this can be achieved. The publications from the follow-up began in 2017 and continue until 2021. The main benefits are improved outcomes for patients if personality assessment is made when they are first seen, and if established interventions for personality disorder are given early.

Outputs:

All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide Publications are planned in academic journals which will continue to influence research and treatment in personality disorder worldwide. This study will provide; (i) valuable data on the new international classification of personality disorder (used in the study) to be published by the World Health Organisation in 2017, (ii) at least five papers in majormedical journals such as the Lancet (who have published three papers already on this study), (iii) guidance from the Royal College of Psychiatrists on the approaches needed for the assessment of personality, (iv) presentations at World Psychiatric Association meetings across the world.

Processing:

Once address data of the living patients is obtained Imperial College London have an agreed and ethically approved process for contacting those participants and asking them for an interview. Mortality information will also be received for those who have passed away since the last follow up was conducted. When the project was first set up in 1982 one of the subsidiary hypotheses was that there would be differences in the cause of death between different diagnoses (and would specifically test whether those with panic disorder were more likely to die of cardiac disease). This could only be tested adequately by having a long time frame. Details of medical problems have been obtained at interview at previous assessments but need to complete this by having cause of death recorded for the much larger number. This will be linked to the previous data on medical pathology. For the purpose of this last follow up the study ID's for only the members who remain active in the follow up will be shared with NHS Digital for the corresponding data to be returned. NHS Digital will remove all other members data from the flagged study. Imperial College London require the identifiable data so there is reduced chances of contacting deceased study members causing distress to relatives. For those who have died and have a cause of death identified the data will be used to test the main personality disorder hypothesis (ie those with more serious personality disorder will have a younger age of death and higher proportion of suicide and accidental death). The outcomes of the study will be analysed by visit using number (%) for categorical outcomes and mean (Standard Deviation) for numerical variables. For statistical inferences, a generalised mixed model will be employed to analyse all outcomes. Choice of the distribution and link function in the generalised mixed model will depend on the type of outcome variable. For example, for a continuous outcome, normal distribution will be assumed and identity link function will be used. In the generalised mixed model, treatment, visit and some selected baseline characteristics will be treated as fixed effects and subject will be treated as random effect. In addition, interaction effects between time and significant predictors of an outcome variable will be fitted in an exploratory way to assess the possible trend of changing effect of a covariate with time. A separate environmental analysis will be carried out on the data to test to what extent changes in symptomatology are related to planned environmental change (nidotherapy) or unplanned (incidental) change. Only data which have been aggregated to the extent that it would be publishable data will be shared with the University of Nottingham. The University of Nottingham is therefore not processing any data which would not be able to be put into the public domain. There is a set of numbers indicating the scores of the patients in the study on 20 different rating instruments on 10 occasions over a 30 year period. There is no possible way in which anybody could identify a patient from these data. An abbreviated form of relevant data on completion of the study will be available for bona fide researchers working on NIHR projects. There will be no data linkage undertaken with NHS Digital data provided under this agreement. There will be no direct sharing of NHS Digital data with third parties who are not named in this agreement. Data will not be accessed by any third parties, other than those permitted under this application. 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).


Project 15 — DARS-NIC-345991-H2F5N

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2016/04 (or before) — 2016/08.

Repeats: Ongoing

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

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

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

Objectives:

To use hospital administrative data to provide measures of quality of delivery of healthcare by providers, or in some instances, by area and to show variations in quality by provider and to support management information function for the NHS

Expected Benefits:

1) Ongoing benefits from our previous work The Imperial unit’s methodological research forms the basis of a near Real-Time Monitoring System called Quality Investigator (QI) produced by Dr Foster Intelligence, currently used by 70% of English NHS acute trusts to assist them in monitoring a variety of casemix adjusted outcomes at the level of diagnosis group and procedure group [1]. Dr Foster Intelligence is an independent healthcare information company to develop indicators and methodologies to assist in the analysis of healthcare. It provides a research grant to the unit to develop indicators and methodologies to assist in the analysis of healthcare performance. The unit works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. We generate monthly mortality alerts, based on high thresholds [2], which we have been running since 2007. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[3]. The resulting Public Inquiry recognised the role that our surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [4]. Key recommendations, [5] reflecting our unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [6]. If Imperial are given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. Imperial will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from our analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Our mortality outlier outputs are used by CQC within their Hospital Inspection framework. As a result of our leading role in the development of hospital mortality measures, in 2010 we were invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [7] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on our HSMR methods) is now a public indicator used by all acute trusts. [8] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [9]. We continue to advise the Health and Social Care Information Centre on methodological issues around the Summary­level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Our research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews (http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/). The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of our published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September. 2) Expected benefits from the proposed work For our future research, analyses of return to theatre for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects. We intend to examine capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. Previous work using HES showed higher mortality risk for asthma in those living in areas further from a hospital than those near it. Using Lower Super Output Areas would enable studies into the effect of distance from home to hospital on patient outcomes and the estimation of hospital catchment areas. This allows geographical access to services to be estimated, as we can calculate how far patients must travel for their treatment. Using larger geographical areas than LSOAs would incur too much measurement error. Our analysis on our mortality alerting system will allow us to improve the alerting process, and provide a better indication of how to investigate them (including what are the key contributing factors in the alerts). Our development of maternity indicators are expected to help monitor quality, and if similar findings around weekend differences in outcomes are discovered, may help to drive improvements in this area. References [1] Real Time Monitoring (RTM). Enabling providers and commissioners to benchmark and monitor clinical outcomes. http://drfosterintelligence.co.uk/solutions/nhs-hospitals/real-time-monitoring-rtm/ [2] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at: http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [3] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [6] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [7] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [8] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [9] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf

Outputs:

1) Research into variations in quality of healthcare by provider: background to proposed work The Dr Foster Unit at Imperial College use hospital administrative data in the form of HES/MMES to provide measures of quality and safety of delivery of healthcare by provider, or in some instances, by area or time. The unit’s work focuses on quality of care and patient safety, including healthcare acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. The proposed work will continue Imperial’s principal themes: i) developing and validating indicators of quality and safety of healthcare, particularly by consultant and hospital; ii) show variations in performance by unit and socio demographic stratum; iii) risk prediction and risk adjustment of such indicators and variations and any other methodological aspects as they arise. Imperial plan the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, we will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of our work with Imperial’s NIHR-funded Patient Safety Translational Research Centre on the use of information for service improvement. Oct 2017. Drivers of return to theatre (reoperation: RTT) in elective hip and knee replacements: correlation between RTT and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. Dec 2015 Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in re-admission and re-operation rates. We have recently found that earlier OPD non-attendance is a strong risk factor for readmission. Previous frequent emergency admissions are also highly predictive. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. Nov 2015. Imperial intend to examine capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. This will require a historical perspective, to look at changes to health service policy, and its impact on capacity. Jun 2016 International comparisons of service use and outcomes: England and the USA. We hold data from Centre for Medicare and Medicaid Services enrolees and from the Nationwide Inpatient Sample from the USA. We have previously set out the methodological issues with using administrative data from multiple countries. We will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. Oct 2017. Imperial are working in collaboration with Professor Aneez Esmail from the University of Manchester and supported by the CQC, to improve understanding of our mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts - acute myocardial infarction and septicaemia. We aim to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. Oct 2016. Imperial are testing a hypothesis that pregnant women who undergo non-obstetric surgery have an increased risk of adverse pregnancy outcomes compared with those not undergoing surgery. We propose to analyse data collected between 2002 and 2013 and identify patients who underwent non- obstetric surgery whilst pregnant. Previous years of data are used to validate and complement maternal parity counts. A preliminary analysis suggests that we will be able to identify around 85,000 such patients out of a total of 4 million pregnancies. We aim to investigate adverse pregnancy outcomes occurring in this group; outcomes we will analyse include miscarriage, stillbirth, preterm labour, low birth weight, prolonged length of neonatal stay and neonatal death prior to discharge from hospital. With the data obtained from our study and subsequent statistical analysis, we aim to examine variation in practice and outcomes, and provide an evidence base with which we can counsel women who face the prospect of undergoing surgery during pregnancy. April 2015. Imperial are developing indicators of maternity care, based on HES which include perinatal mortality, complications following birth, caesarean rates and perineal tears. Imperial propose to examine variation by trust, and by day of the week. September 2015. Examples of key published research that have used HES/SUS data include: Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30 day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2. Supporting a management information systems for the NHS Dr Foster Intelligence is an independent healthcare information company. It provides a research grant to our unit to develop indicators and methodologies to assist in the analysis of healthcare performance. We work in collaboration with Dr Foster Intelligence to provide the NHS with a number of management information systems including: • Quality Investigator (QI) ­ A web-based solution that monitors quality outcomes and patient safety in NHS trusts by assessing clinical, process and coding factors. It is currently used by 70% of acute provider trusts and 50 CCGs (CCGs are not provided with the patient records module, and small numbers are suppressed, hence have no access to the re­identification service) in assisting them in monitoring a variety of casemix adjusted outcomes at the level of diagnosis group and procedure group. • Practice and Provider Monitor (PPM) ­ A strategic planning tool and a joint information resource for providers and commissioners which enables users to quickly identify opportunities for improving operational and clinical outcomes. • TrustView – A dashboard developed in collaboration with NHS organisations, to provide a top level view of benchmarked trust performance around key clinical quality and clinical efficiency metrics. It gives high level, pertinent and timely data on overall trust performance to senior decision makers within trusts. • Care Quality Tracker ­ An online quality monitoring solution designed with and for NHS acute trusts that acts as an early warning system. • Mortality Comparator ­ Compare the two leading mortality indicators in England – SHMI and HSMR. Uncover, investigate and understand variations against peers. Imperial provide standard pseudonymised data extracts about the health care and treatment patients have received in any English NHS hospital in the form of Hospital Episodes Statistics – inpatient and day case admissions, outpatient appointments and Accident and Emergency attendances to Dr Foster Intelligence. These data are supplied by the Health and Social Care Information Centre (HSCIC) to Imperial under license and approved through HSCICs own Data Access Advisory Group (DAAG). The HSCIC has agreed to a sublicense between Imperial and Dr Foster Intelligence. The license permits Dr Foster Intelligence to provide products or services based on the standard extract only to public bodies (including NHS, CQC, Monitor, TDA, DH, PHE and local authorities) with appropriate small number suppression. It prohibits Dr Foster Intelligence from selling services or products derived from the data to commercial companies. Imperial can confirm that no identifiers will ever be disclosed to Dr Foster Intelligence (DFI); in particular LOPATID and NHS number will not be disclosed to DFI. Imperial provide a re­identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the DFI tools. This service allows Imperial to supply Provider trusts’ NHS Number and LOPATID using DFI tools without passing these fields on to DFI. The re­identification service is maintained by Imperial College. DFI have no access to the data held on it.

Processing:

The Dr Foster Unit at Imperial College use hospital administrative data in the form of HES/MMES to provide measures of quality and safety of delivery of healthcare by provider, or in some instances, by area or time. The unit’s work focuses on quality of care and patient safety, including healthcare acquired infections (surgical wound infections and urinary tract infections) and safety indicators. We plan to store the identifiers LOPATID and NHS number separately to our research database which will include the standard HES extracts and sensitive fields. Imperial’s own researchers have no access to identifiable fields. Only two named data managers will have access to the patient identifiable fields within the unit. An extract will be generated from the patient identifiable database and will be loaded to the Re-identification server to provide the service described below. Current CAG approval allows us to hold identifiers (LOPATID and NHS number) for inpatients admitted to NHS provider trusts who are customers of DFI (Dr Foster Limited, trades as Dr Foster Intelligence, registered Company No. 3812015). We require sensitive fields for all records. Sensitive fields Consultant Code We provide consultant from our analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by Dr Foster Intelligence (DFI) in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practicelevel such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. Person referring patient We provide analyses by person referring patient activity which are fed back to the NHS through a range of Management Information Systems provided by Dr Foster Intelligence (DFI) Patient identifiers In our approved Section 251 application (CAG Reference: 15/CAG/0005), we have reduced the number of patient identifiable fields to 2 fields - NHS Number and Local Patient ID number (LOPATID). We have been granted permission to hold NHS Number and Local Patient ID number (LOPATID) to assist local NHS trusts investigating issues around quality and safety of care within their organisation, which have arisen out of Dr Foster Intelligence healthcare performance tools using our methods. We will no longer be holding Homeadd and Date of birth. Historical data processed under PIAG 2-07(d)/2007 will be irreversibly pseudonymised in line with this application. This will be carried out as soon as possible and confirmation will be sent to CAG and HSCIC if required. New identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance, and to help improve the quality and safety of healthcare delivery. Imperial do this by providing a re-identification service to which Dr Foster Intelligence has no access. Imperial only provide this service to acute NHS providers who are customers of DFI. The provision of a re-identification service to non-customers of DFI allowing them to respond to mortality alerts issued by the academic unit has been deferred, pending further information from the applicant, in relation to the disclosure of confidential patient information to provide re-identification service for all trusts. No identifiers will ever be passed to Dr Foster Intelligence or any other organisation except the NHS provider trust from where the data originated. For this purpose we have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the DFI tools. This service allows us to supply Provider trusts’ NHS Number and LOPATID using DFI tools without passing these fields on to DFI. The re-identification service is maintained by Imperial College. DFI have no access to the data held on it. In the last 12 months, there were over 3,600 successful logins from 119 provider organisations. 74 provider trusts use it more than 12 times per year (once a month). One trust has used the service 285 times in the year. (See Supplementary evidence from NHS trusts attesting usefulness of Re-Identification service). The standard extracts will be loaded on to Imperial’s systems and a unique identifier (fosid) will be generated and added to the datasets. A new Extract_hesid (for Dr Foster) will be generated using SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence Pseudonymised data dating back to 1996/7 has been requested for three reasons. • To examine historical trends of treatment practice (e.g. Faiz et al. Traditional and Laparoscopic Appendectomy in Adults Outcomes in English NHS Hospitals Between 1996 and 2006, ANNALS OF SURGERY 2008;248:800-806) and the historical impact of changes in policy (e.g. proposal to examine capacity issues). • To obtain longitudinal data on prior admissions for patients (e.g. to determine prior diagnosis of cancer in Bottle A et al. Association between patient and general practice characteristics and unplanned first-time admissions for cancer: observational study, BRITISH JOURNAL OF CANCER 2012;107:1213-1219 or to validate and complement parity status in maternity fields for non-obstetric surgery proposals). Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. • To increase the power of predictive models for rare diseases, procedures and events (e.g. standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which includes some rarer conditions).


Project 16 — DARS-NIC-366210-V2H5M

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2019/03 — 2019/03.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • Patient Reported Outcome Measures (Linkable to HES)

Yielded Benefits:

Past research using HES data has been highly influential in policy design in the English NHS. For example, in an assessment for the 2014 Research Evaluation Framework (REF) assessment of the impact of research in UK Universities, a former head of policy at the Department of Health and a current member of the NHS England Board commented on the impact of research by Propper on competition policy in the NHS as follows: “In my 20 years’ experience in Government it is most unusual for even the best research work to have such influence as that on competition undertaken by Professor Propper”. Imperial College aim to make a similar impact with the research outlined above (2017). Research has illustrated that the greatest loss of healthy life years, among six safety incidents in English hospitals, is caused by pressure ulcers. They lead to a greater loss of life than central line infections, deep vein thrombosis/pulmonary embolism and sepsis combined. This has come as a surprise to policy makers, because the latter safety events are more prevalent in the public and policy debate. It is interesting that findings are confirmed by US evidence, that highest payouts from litigation involving preventable safety incidents is due to pressure ulcers. The findings of the research will have implications on the cost-effectiveness of novel smart bed platforms for monitoring and ulcer prevention. This technology is expensive, but considering the great potential health gains, are more cost-effective than previously thought.

Objectives:

The agreement is being amended to append the latest years of HES APC, A&E and OP data from 2015/2016 until 2019/2020 along with PROMS data to match the corresponding HES years, released on an annual basis. Imperial College London will continue to use HES APC, A&E and OP data already provided for the same purposes as before. Additional years are required to evaluate the medium run effects of the policy changes occurring in the mid-2000s up to the Health and Social Care Act 2012 (e.g. the lead time for private hospital chains opening new sites is several years). The study will use PROMs linked to HES data to investigate the effects of greater private and voluntary provision of publicly-funded care on outcomes for NHS patients, equity of access to care and overall NHS costs. It will concentrate on the elective procedures most commonly done by private providers, specifically hip and knee replacements. PROMs data are crucial for this study, as they will be the basis for measuring quality of elective surgery at NHS trusts and private hospitals. The additional research will focus on policy 2 detailed further on in this section. Use of the private sector for elective surgery has continued to grow, with private providers now conducting more than a quarter of NHS-funded primary hip and knee replacement (National Joint Registry, 2018). The research will investigate how public providers differ in terms of quality, selection of patients for treatment and efficiency. Original Purpose The data will be used in a programme of on-going research into UK health policy reform undertaken by members of the healthcare management group at Imperial College Business School. The reforms to the NHS in England since 2000 have been some of the most radical in the Organisations for Economic Cooperation and Development (OECD). The reforms consist of a series of policy initiatives, beginning in the early part of the 2000s and carried out for the next 10 years, which were intended to improve care for patients. The broad remit of these reforms was to promote choice for patients and greater competition between providers of care, greater freedom for well managed organizations within the NHS within a tightly regulated system of publicly available standards and central guidance, and improvements in the patients’ experience in the form of enhanced quality of care and reduced waiting times. These reforms thus instigated changes to the (a) organizational and management structure for health care providers and (b) delivery arrangements, mandated or facilitated at the overall health system level, for specific services or specific treatments These reforms were expensive and their effects are highly contested, both in terms of patient benefits and benefit to tax payers. Understanding their impact on efficiency of service provision and the quality of care experienced by patients is therefore important. The research programme focuses on the following policies within this reform agenda. (1) The policy of choice and competition between NHS providers of care. This policy has operated since the introduction of the Chose and Book system for referrals in the mid-2000s, which coupled with a prospective payment system (PbR), gave incentives to increase activity. Imperial College has examined the short-term impact of these policies (pre-2010). The focus will now be on the longer term, and impact and will be analysed until 2017. (2) The policy of promoting a greater role for private and voluntary providers in the provision of publicly funded care. This policy has operated since the introduction of independent sector providers (ISTCs) in the early 2000s, but has subsequently been extended. It currently includes the widespread use of contracting for both hospital and community-based services and franchising of hospital management to private sector bodies (e.g. Circle). Imperial College will examine the impact of this policy on outcomes for patients undergoing joint replacements and other common elective surgeries. (3) The use of networks and guidelines to increase quality of care and patient safety, innovation and the diffusion of good practice in important hospital-based treatments. NICE guidance, in particular clinical guidelines, has transformed clinical practice over the past 10 years, in particular in the treatment of stroke, heart attack and cancer patients, and it has led to reductions in many preventable complications. In addition, there has been the formation of stroke and cancer networks, in which hospitals cooperate to improve care for patients. (4) The policy of granting greater autonomy to NHS providers who perform well on measured outcomes. This policy is most strongly embodied in the NHS Foundation Trust system. This is intended to allow trusts greater control over their working practices (e.g. remuneration of staff and staffing levels), but also requires that they operate within a stronger system of regulation and guidance. The research aims to understand the impact of these high-level policy changes on service providers, health service users, and tax payers. The aim is to make a cost-benefit assessment of the impact of these policies. Imperial College will focus on particular outcomes and treatments, described in the processing activities below. These policy reforms impact on the organisational structure for health care delivery in England, and work continues in all these areas, with new programmes of work identified and explored. In the context of current concerns over the level of hospital funding with in the NHS, and the healthcare delivery system’s ability to cope with shocks to the system under research programme number 4 (the granting of greater autonomy to NHS providers), Imperial College will examine the impact of these changes on resilience to one particular shock. The shock chosen is that of weather variation and what is the impact of weather on the amount and costs of hospital admissions at Trust level.

Expected Benefits:

The outcomes Imperial College examines are of direct interest to patients as they include measures of amenable mortality, patient safety, waiting times and access. They are also of direct interest to providers as they include meeting key targets for quality of care and financial performance and to taxpayers, as they pay for NHS care. But, they are primarily of interest to those charged with getting best value from the NHS i.e. policy makers. Hence the outputs and dissemination strategy outlined above focus at this level. Imperial College intend to influence policy with the work as it proceeds, but most impact will be at the end of the research in 2018 when Imperial College have produced a large body of evidence. Using the data provided under this agreement has resulted in a vast array of outputs aimed at policy makers and the public, the benefits which will be achieved from these out puts are primarily focused on the evaluation of policy decisions and changes which all impact on patient pathways, outcomes and thus providing a benefit to the healthcare system. The reforms were expensive and their effects are highly contested, both in terms of patient benefits and benefit to tax payers, the extensive outputs produced enable the reforms to be assessed. Understanding their impact on efficiency of service provision and the quality of care experienced by patients is therefore important and will bring benefits to patients through more informed choice and providers through better informed care decisions. Ultimately testing the choices and policy decisions gives patients more choice and an ability to make better decisions. The addition of PROMS data permits examination of the gain in health status for patients treated under the NHS, in either NHS or private hospitals. This allows the fact that patients treated in private facilities may be healthier and so easier to treat to be controlled and cannot be done with HES data alone.

Outputs:

The outputs will be a set of contributions intended to input into the assessment of the impact, benefits and costs of the policies listed in the objective for processing. The aim is to prepare several types of output, each type aimed at different audiences. a. Publication in peer reviewed international journals. These will be a mixture of health service research journals and economics journals. The health service research journals Imperial College will target, and in which Imperial College have published analyses using HES data for which Imperial College held licenses in the past, include: The Lancet, the BMJ, Medical Care, Health Services Research, Health Policy. The economics journals include: Journal of Health Economics, Health Economics, European Journal of Health Economics, Journal of Health Services Research and Policy. All these are read by policy makers nationally and internationally who wish to evaluate system reform. b. Publication in outputs aimed at a general readership e.g. the Economics and Social Research Council (ESRC’s) media publications Britain in 2014 and Society Now. The former is sold in WHSmith and other outlets and is published annually. The latter is widely distributed to Whitehall and other policy making bodies. c. Presentation of the research at conferences and events aimed at policy makers. Imperial College have been asked to give presentation of the research findings to statutory bodies including the Department of Health and Monitor; international organizations involved in healthcare policy (e.g. OECD, WHO, The World Bank, The Institute for Health Metrics and Evaluation, the Center for Global Development); all the main policy think tanks in the UK (including the Institute of Government, Reform, Policy Exchange, the Nuffield Trust, the King’s Fund and the Health Foundation); the Royal Colleges; and overseas health economics organizations that have large practitioner membership (e.g. Finnish, Portuguese, Italian, Australian, German, and US health economics societies). d. Presentation of the research to individual policy makers and politicians. Past research by members of the health care group that have used HES data have been presented to the then Secretary of State for Health, to the Prime Minister’s office, to the Prime Minister's delivery and strategy units, to the Treasury, and the Department of Health, Monitor and the Cooperation and Competition Commission (now part of Monitor). e. Presentation of the research, where requested, at individual trusts considering strategic direction and with patient groups. Presentations of the research at small round table events organized by industry (e.g. Arup) Target dates for outputs - some of which have now been achieved. Imperial College list target dates by the policy areas identified above. 1. The policy of choice and competition between NHS providers of care. •Research on the impact of management on NHS acute care provider performance (initial paper 2015 (achieved), later papers to be produced up to 2018/19) •Research on the effect of choose and book on mortality of patients who have had coronary artery graft bypass surgery (CABG) (2016) (achieved with research continuing) 2. The policy of promoting a greater role for private and voluntary providers in the provision of publicly funded care. •Research on the impact of private providers of hip and knee replacements on NHS workload (2016) •Imperial now have plans to use PROMS data to further support this work with research starting late 2018. 3. The use of networks and guidelines to increase innovation, patient safety and the diffusion of good practice in important hospital based treatments (e.g. the use of networks and guidelines for treatment for stroke, heart attack and cancer patients). •Research on the costs to patients and hospitals of patient safety incidents (2015) (achieved) •Research on the impact of the surgical safety checklist (2015) (work continues to produce this) •Research on the impact of stroke networks on patient outcomes (2017) •Research on the impact of networks on the diffusion of laparoscopic surgery for colorectal cancer (2017, 2018) 4. The policy of granting greater autonomy to NHS providers who perform well on measured outcomes. •Research on waiting times in A&E departments of NHS acute care providers (2016) (achieved) •Research on the link between chief executive pay and the performance of NHS acute care providers (2017, 2018) 5. The impact of seasonal weather variation on the number and cost of hospital admissions [This project relates to work being carried out following the August 2016 update]. •Research on whether seasonal climate variations, such as temperature and air pollution, are related to trends in hospital admissions •On impact of climate shocks, such as a heat wave, on the number and cost of hospital admissions. All outputs will be at an anonymised and aggregated level, in line with the HES Analysis Guide. No record level data will be passed to third parties. Data will not be used for commercial purposes. Completed Outputs The following outputs have been achieved using the data so far: 1. Policy of choice and competition between NHS providers a. Management of acute provider performance Bloom N, Propper C, Seiler S, Van Reenen J. The impact of competition on management quality: evidence from public hospitals. The Review of Economic Studies. 2015 Jan 24;82(2):457-89 •One of top five economics journals worldwide Dissemination to public: •Seminar at Imperial Business in the City, May 2017 •Seminar at Melbourne Institute, February 2017 •Seminar at Monash University Centre for Health Economics, February 2017 •Seminar at the Department of Health, January 2017 •Seminar at the Paris School of Economics, January 2017 •Seminar at ISER University in Essex, January 2017 •Microeconomic Insights blog post: “Healthcare: how competition can improve management quality and save lives”: http://microeconomicinsights.org/healthcare-how-competition-can-improve-management-quality/ •City A.M. “From the NHS to Brexit, give people a choice and they'll make a good one”: http://www.cityam.com/253234/nhs-brexit-give-people-choice-and-theyll-make-good-one •The Market and Health Care Production – Project Overview: https://www.imperial.ac.uk/business-school/research/management/management-research/projects-and-centres/the-market-and-health-care-production/ b. Research on effect of Choose and Book on Coronary artery bypass graft patients Gaynor M, Propper C, Seiler S. Free to choose? Reform, choice, and consideration sets in the English National Health Service. The American Economic Review. 2016 Nov 1;106(11):3521-57 •One of the top economics journals worldwide •Won the International Health Economics Association’s (iHEA) 25th Arrow Award, which recognises excellence in the field of health economics, for the best paper published in 2017 •http://healtheconomics.site-ym.com/?page=ArrowAward •http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/newssummary/news_9-5-2017-15-34-58 Dissemination to public: •Microeconomic Insights blog post: “Hospital competition and patient choice can improve healthcare quality”: http://microeconomicinsights.org/hospital-competition-patient-choice-can-improve-healthcare-quality/ c. Research on competition in the NHS Gaynor M, Moreno-Serra R, Propper C. Death by market power: reform, competition, and patient outcomes in the National Health Service. American Economic Journal: Economic Policy. 2013 Nov 1;5(4):134-66 •One of the top economics journals worldwide •Won the 2016 American Economic Journal (AEJ) Best Paper Award Propper et al. Does competition and equality do good things in England. Health Economics, Policy and Law [Forthcoming] Dissemination to public: •Opening plenary at International Health Economics Association Boston 2017 Congress, July 2017 •https://www.healtheconomics.org/page/Livestream •Associated Medical Services (AMS) Healthcare Symposium – Canadian Medicare 2017: Historical Reflections, Future Directions – Toronto, May 2017 •Globe and Mail “There’s room for competition in public health care” •https://www.theglobeandmail.com/opinion/theres-room-for-competition-in-public-health-care/article34956444/ •Keynote speech at South Danish Universities, COHERE Annual Conference, Denmark, May 2017 •Talk given to International Consulting Economists' Association (ICEA) members, London, January 2017 •Invited Knoop Lecture 2016 to general public, University of Sheffield, November 2016 •https://www.youtube.com/watch?v=ZlLEq-WQBGw •Annual seminar at Valtion Taloudellinen Tutkimuskeskus (VATT) Institute for Economic Research to Finnish Policy Makers, November 2016 •http://www.hs.fi/kotimaa/art-2000002928405.html •Keynote at Competent in Competition + Health (CINCH) – Essen Health Symposium, October 2016 – Germany d. Research on equity in health care Cookson R, Propper C, Asaria M, Raine R. Socio-Economic Inequalities in Health Care in England. Fiscal Studies. 2016 Sep 1;37(3-4):371-403. •Presented at Institute for Fiscal Studies Conference, March 2016 2. The policy of promoting a greater role for private and voluntary providers •Policy of greater competition in hip and knee replacement •We now have plans to use PROMS in our research starting late 2018 3. The use of networks and guidelines Hauck KD, Wang S, Vincent C, Smith PC. Healthy life-years lost and excess bed-days due to 6 patient safety incidents: empirical evidence from English hospitals. Medical Care. 2017 Feb;55(2):125. •Paper illustrates that among six safety incidents, the greatest loss in healthy life years is caused by pressure ulcers. They lead to a greater loss of life than central line infections, deep vein thrombosis/pulmonary embolism and sepsis combined. This has come as a surprise to policy makers, because the latter safety events are more prevalent in the public and policy debate. It is interesting that our findings are confirmed by US evidence that highest payouts from litigation involving preventable safety incidents is due to pressure ulcers. •The findings of our research will have implications on the cost-effectiveness of novel smart bed platforms for monitoring and ulcer prevention. These platforms collect information from various sensors incorporated into the bed, and analyzes the data to create a whole-body pressure distribution map, and commands the bed’s actuators to periodically adjust its surface profile to redistribute pressure over the entire body. This technology is expensive, but considering the great potential health gains, are more cost-effective than previously thought. Dissemination to public: •Presented to policy makers at The Health Foundation, October 2015 R Friebel, K Hauck and P Aylin. Centralisation of acute stroke services in London: Impact evaluation using two treatment groups. Health Economics [UNDER REVIEW] Dissemination to public: •Seminar given at The Health Foundation, 2016 •Presented at Health Economics Research Group Meeting, Imperial College London, 2015 Laudicella M, Walsh B, Munasinghe A, Faiz O. Impact of laparoscopic versus open surgery on hospital costs for colon cancer: a population-based retrospective cohort study. BMJ open. 2016 Nov 1;6(11):e012977. Dissemination to public: •Presented at the National Cancer Research Institute (NCRI) conference in Liverpool, November 2014 4. The policy of granting greater autonomy to NHS providers Kosova R, Marini G, Miraldo M, Shaick M. The Impact of Organizational Change on Firm Performance: Evidence from the Healthcare Sector. Management Science. 2017 [UNDER REVIEW] Dissemination to public: •Presented at International Health Economics Association Boston 2017 Congress, July 2017 •Presented at European Health Economics Association (EuHEA) Conference, Hamburg, July 2016 •Industry Studies Association (ISA) Conference, Washington DC, May 2017 Miraldo M, Shaick M, Stieglitz N. Competition, Aspirations & Organizational Change: Evidence from the English NHS. 2017. [IN PREPARATION] Dissemination to public: •Presented at International Health Economics Association Boston 2017 Congress, July 2017 •Presented at European Health Economics Association (EuHEA) Conference, Hamburg, July 2016 •Presented at Strategic Management Society (SMS) Annual International Conference, Berlin, September 2016 5. Exogenous influences (or shocks) on demand for secondary care including temperature extremes and epidemics "Excess Hospital Admissions Due to Seasonality and Temperature Extremes in the UK” – Laure de Preux, Marisa Miraldo and Rifat Atun •Dissemination to public: •Presented at International Health Economics Association Milan 2015 Congress, July 2015 "The Impact of Heatwaves on inpatient admissions to the English National Health Service between 2001 and 2012” – Marisa Miraldo, Dheeya Rizmie, and Laure de Preux Dissemination to public: •Presented at Imperial College Business School as part of an MRes project, July 2017 Research continues in the stated areas, and once the impact of the benefits has been realised specific feedback will be provided.

Processing:

The data will be used to identify the impact of the policies listed above on (a) service providers and (b) service users. This will allow evaluation of the costs and benefits of the policies. The unit of analysis in the research is the provider (for policy areas 1,2 4) and the patient for the work under policy area (3). The provider will generally be the NHS Trust level, but may in some cases be the site level within Trusts. The measures to assess the impact of policies that Imperial College will construct will be robust measures of performance of the provider, and the outcomes directly experienced by patients. The measures of performance include measures of patient safety (e.g. avoidable deaths, adverse incidents), quality of care (including improvement in PROMs, readmission rates, infection rates, adverse events), measures of access (e.g. median or mean waiting times for both elective and emergency care, the distribution of the service across SES of the provider’s catchment area), measures of throughput (e.g. FTEs for particular treatment). Some of these measures are available publicly, but the measures that are made available often change from year to year, and they are not available at patient level. To create a consistent time series covering a number of years, with appropriate controls for patient severity and other factors that may influence outcomes, Imperial College need to be able to use the raw HES data and linked PROMs. These measures will be constructed from the analysis of individual patient level data using appropriate statistical methods to deal with sampling and other statistical issues (detailed below). From HES data and PROMS linked to HES, Imperial College derive measures of outcomes for patients (specifics given below for each policy area) and data on patient severity, to allow the study to control for case-mix. To these data Imperial College will match publicly available administrative data from other sources, where matching is only undertaken at the provider level or the regional level (This will almost always be the trust level as that is the level for which publicly available data is available. In some cases, the patient level data will be aggregated to site level (for example, to conduct sensitivity tests to measures which are available at site level e.g. measures of competition). Imperial College will not be matching to data sources at the patient level. The administrative trust-level data Imperial College will match includes data from Trust financial returns; from providers’ management accounting systems (e.g. cost data); from health care regulators; from the annual staff satisfaction survey of all NHS employees; from local authorities; and socio-economic data at the Middle Layer Super Output Area (MSOA) or Lower Layer Super Output Area (LSOA) level. The data to be matched depends on the specific research question. For example, the customer will match data on trust financial performance for policy areas (2) and (4) but not for policy area (3). For policy area (3) the customer will match data on provider networks for strokes. This will not be used for analyses for policy area (4). All the data Imperial College will match is at Trust or site level and it is all in the public domain (for example, data on Trust financial returns which are available on Trust websites). Matching these data will not increase the risk of re-identification individual patients or clinicians. No data will be published from the research at the Trust, site, or lower level. All published analyses will be statistical or graphical and will be in line with the HES analysis guide. For policy area (1) identified above, Imperial College will use HES and linked PROMs data on hip and knee replacements to examine outcomes for patients under-going these procedures. Imperial College will construct measures of volume, case-mix, waiting times, improvement in functional mobility (measured using Oxford hip/knee scores) and readmissions, and procedure revision rates. Imperial College will also examine HES data on maternity patients and examine within hospital deaths of babies, and foetal and maternal complications. For policy area (2) Imperial College will examine the impact of greater involvement of private and voluntary providers in the provision of publicly funded care on NHS patients’ outcomes, focusing on hip and knee replacement surgery. Imperial College will use PROMs data linked to HES to construct the same measures of performance of NHS providers and private providers as detailed for policy area (1) above. These data will be matched with location data on private and public providers. Measures of market structure (e.g. HHI indices) will be calculated from the patient flow data in HES. The location data will also be used to carry out instrumental variables analysis, to reduce the potential bias from being unable to control fully for case-mix using HES. Specifically, an instrument for patient choice of hospital will be constructed using differential distance of patients from NHS/private hospitals. For policy area (3) Imperial College will examine the impact of the introduction of specific guidelines, including the introduction of the surgical safety checklist, on in-hospital mortality, 30-day mortality and readmissions, and on the occurrence of complications that Imperial College identify from the diagnoses codes of patient records. Among the complications Imperial College investigates are pressure ulcer, death in low-mortality HRGs, deep-vein thrombosis, sepsis, central line infection, post-operative hip fracture, obstetric complications, and some of the rarer events including foreign body left in body after surgery. These indicators will be constructed at the patient level. Imperial College will use controls for potential patient level confounders. Imperial College will also look at the impact of guidelines for various forms of cancer treatment. The customer will begin with outcomes following surgery for colorectal cancer, where laparoscopic surgery has been shown to have patient outcomes advantages over traditional surgery. The outcomes Imperial College will be examining are re-admissions and other subsequent complications that can be extracted from HES data. For policy area (4) the focus is on the impact of management on trust performance and on the role of board level remuneration in this. To examine this Imperial College will construct data from HES on patient outcomes and process measures, aggregated up to the Trust level. The measures Imperial College will focus on are those that have been published by the quality regulators of NHS care (e.g. the Care Quality Commission) but which are not always available on an annual basis. The measures will include amenable mortality, within hospital deaths from emergency AMI admissions and surgery, waiting times in both elective and emergency care, and readmissions following stroke and hip and knee replacements. These data, along with the case-mix of patients undergoing these treatments, will be aggregated to Trust level. These will be matched to publicly available data on NHS trust Board remuneration (from the Trust Annual returns and made available on Trust web sites), data from Monitor and other financial data from the Trust annual returns. The type of analyses Imperial College will undertake for all four policy areas will be statistical. It will account for the fact that in all the analyses Imperial College will be using patient level data to analyse the performance of provider units. Among the statistical methods Imperial College will use to construct provider level measures from the underlying patient are multilevel techniques to account for the hierarchical nature of the data (e.g. the construction of squeezed estimators, and the use of fixed and random effects at the provider level). To identify the impact of policies Imperial College in some cases will have to deal with environments in which more than one policy change may take place at once. An example is the introduction of networks to encourage cooperation between providers at the same time as policies to encourage competition. Techniques and tools Imperial College will use include propensity score matching, difference in difference analysis and other panel data econometric techniques. Imperial College will supplement this with graphical presentation of the results and Imperial College has extensive experience in these techniques. The data will not be used by third parties. No record level data will be made available to any individual provider and from the published research individual providers and patients will not be able to be identified. Imperial College will only present aggregate level data with small numbers suppressed in line with the HES analysis guide. The data will not be used to establish a protocol for a clinical trial. Tied to the new programme of work into weather variations, Imperial College will match publicly available weather station data to the HES information. The weather data will come from the Met Office Integrated Data Archive System (MISAD) database, which provides a large range of weather measures collected by the met office. The daily weather will be approximated at the Trust's location. this matching will not increase the risk of any re-identification and will be done in line with the HES analysis guide. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data Data will only be accessed and processed by substantive employees of Imperial College London and will not be accessed or processed by any other third parties not mentioned in this agreement. PROMS data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS.


Project 17 — DARS-NIC-370843-R6V8T

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2016/08.

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 - Scottish NHS / Registration
  • MRIS - Cause of Death Report

Objectives:

There is extensive public and scientific interest that exposure to RF electromagnetic fields from mobile telephony might increase disease risk. Results from epidemiological studies on RF and disease risk published to date have been inconsistent. A large prospective cohort study of mobile phone users with long-term follow up was required as the best way to resolve current uncertainties The UK cohort study of mobile phone use and health (COSMOS) is a valuable national resource to improve the applicants understanding of environmental exposures and health in the UK. The applicant aims to investigate whether there is any link between long-term use of mobile phones and other radio frequency (RF) technologies and human health, to provide maximum benefit to public health. The UK COSMOS study has recruited a cohort of approximately 105,000 adult participants. It is the UK’s fourth largest cohort study. Data collection also including other environmental exposures e.g. noise and air pollution, information on health, lifestyle, and demographics have already been collected at baseline, and collection continues via prospective follow-up of participants for the next 20-30 years. The UK COSMOS study forms part of the International COSMOS prospective cohort study and together the applicant has recruited 290,000 adult mobile phone users across Europe. This research has been endorsed as a priority by agencies worldwide, including the Department of Health and the World Health Organization. It is important to note that the data requested from HSCIC will be used for research purposes only and that COSMOS is an observational study, with no clinical intervention. The objectives for processing of the Data are therefore: 1. To conduct long-term health monitoring of the UK COSMOS cohort via linkage to national health and mortality datasets, to capture chronic health outcomes as they occur. 2. To pool record-level data of the UK COSMOS cohort (as limited and bespoke datasets containing non-sensitive pseudonymised data) with data for the international COSMOS cohort, to enable pooled epidemiological and statistical analyses on mobile phone use, other RF exposures, other environmental exposures and a wide range of health outcomes. 3. To conduct epidemiological analyses on other environmental exposures (e.g. noise, air pollution) and health outcomes. The COSMOS International Research Group (UK, Sweden, Finland, the Netherlands, Denmark, France) was established and produced a joint international study protocol in 2005 and has collaborated since that time. These collaborators are : • Department of Biostatistics and Epidemiology, Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen , Denmark • Karolinska Institutet, Stockholm, Sweden • Tampere School of Public Health, University of Tampere, Finland • Institute for Risk Assessment Sciences (IRAS), University of Utrecht, Utrecht, The Netherlands • Section of Environment and Radiation, International Agency for Research on Cancer (IARC), Lyon, France Details of these collaboration establishments have been provided on the study website since its inception. The pooling of data from all of these countries is essential to achieve the scientific aims of the COSMOS study and to provide the required statistical power for meaningful analysis of the associated health outcome events. NB – the study will not share whole datasets as provided by HSCIC. Instead, only a bespoke dataset will be shared with the collaborating country leading the group on a certain topic or health outcome (e.g., mobile phone use and cancer). This dataset will include only those variables required to perform the relevant statistical analysis, i.e., main dependent and independent variables as well as covariates/potential confounders (such as age, body mass index, sex etc.) which need to be adjusted for in the statistical models. The bespoke anonymised dataset will not contain variables listed as identifiable and sensitive by the HSCIC (as listed in this application). Data will not be shared beyond this group of collaborators without prior approval, which would be sought through a supplementary application to HSCIC. These pooled analyses would therefore require selected and limited record-level data (as non-sensitive and anonymised in line with the ICO Anonymisation Code of Practice) to be sent outside of the UK to the applicants international COSMOS research partners. These data would also comply with the HES Analysis Guide, including the small numbers requirements. At this stage in this long-term cohort study on the health effects of RF-EMF exposure, the UK group will investigate cardiovascular disease, the Karolinska Institutet (Sweden) cancer risk and the Institute for Risk Assessment Sciences (The Netherlands) reproductive health. However, this may be subject to change and various partners may aid each other in these analyses or other analyses e.g. on neurodegenerative diseases as the number of years of follow-up of the cohort increases and rare health events accrue. The applicant confirms that to protect the confidentiality of UK COSMOS participants, Imperial College will only provide non-sensitive anonymised health data to researchers outside the UK COSMOS research team. This means that: • No sensitive personal information will ever be passed to other researchers outside the UK COSMOS team. • Any variables required for analyses by other researchers that may derive from a personal identifier (e.g. date of birth, mothers date of birth, date of death or health event, cause of death, or type of health event) will be calculated into categories as appropriate by the UK COSMOS research team, so that it is not identifiable by other researchers from the data extract provided by UK COSMOS, i.e. it will be anonymised with regard to the research teams receiving these data. For example, date of birth will be used to calculate age or age-bands. Date of event will be converted to days of follow-up (i.e., days from the start of the COSMOS study to date of event) because this is important information for the study that this is available for inclusion in survival analysis models used to calculate hazard ratios and to estimate health risks associated with RF-EMF exposure. Cause of death or a health event will be coded and included under broad terms/variables such as all-cause mortality, fatal and non-fatal cardiovascular events, cardiac events, cancer etc. For example, if a person had coronary bypass surgery, it will be included in the dataset under cardiac events with 1 or 0 listed for each participant, representing yes or no for a cardiac event. Therefore, broad binomial or dichotomous variables are created which is also the only format in which it is usable in the statistical models. All dates and details of a health event such as ICD codes are removed from any data extract prepared for other researchers. Any release or sharing of study data to international COSMOS collaborators will be subject to approval of the UK COSMOS publication committee. The publication committee consists of the UK Principle Investigators and senior COSMOS International Group members, and decides whether the research question is appropriate. The model for obtaining participant consent for the study was extensive. It took shape over a period of 7 years, alongside the development of the study itself. This included three rounds of testing the consent and associated explanatory materials by 3 rounds of national focus groups, to ensure that information provided and the form of consent were clear and easily comprehensible. These focus groups included adults from across the UK, from all socio-economic groups and ethnic minority groups. These trials indicated that information was best presented in plain English, avoiding unnecessary jargon or technical terms, where these might confuse or cause concern among participants. The resulting materials were subsequently considered by the relevant Medical Research Ethics Committee against the expectations and requirements that were prevalent at that time (2009) and approved for use. This information and form of consent were used throughout the participant recruitment period which completed in 2012.

Expected Benefits:

The results of the applicants studies, which will be placed in the public domain via peer reviewed publications, are expected to provide reliable and robust scientific evidence to: 1. address current gaps in scientific evidence regarding the possible health effects of long-term mobile phone use. 2. inform UK health policy on use of mobile phones and newly emerging RF technologies. Specifically, the detailed research outputs on mobile phone use, and potential associated health risks will allow the UK Chief Medical Officer to review the current precautionary advice regarding mobile phone use, and if required, update this advice. 3. identify specific ways to reduce RF exposure levels, if required, and thus provide more specific health advice to the UK public.

Outputs:

Research outputs from the applicants access to the data will be placed in the public domain by presentations at scientific conferences (presentations, abstracts, posters) and publication in peer-reviewed journals in aggregated and anonymised form. Timing of analyses for specific disease outcomes is dependent upon sufficient statistical power, and thus accrual of sufficient cases of disease over time. The applicant expects to start publishing results of epidemiological analyses of mobile phone use and chronic disease outcomes in 2016. However, this will be an ongoing process especially with the investigation of rare disease outcomes, e.g. salivary gland tumours and amyotrophic lateral sclerosis, which will require much longer follow-up time.

Processing:

Data will be received in from the applicant, and run through the patient status and list cleaning services to a) retrieve any missing identifiers in the cohort where possible (NHS numbers, DOBs, Postcodes, Addresses) and b) update the cohort to the latest data for these variables. The cleaned cohort will then be linked to HES Inpatient, Critical Care Outpatient, and Accident & Emergency data. It will also be linked to ONS mortality data including cancer information, and the information for Scottish patients retrieved. The sensitive and identifiable output will be returned to the applicant. The HES data will be run on a bi-annual basis there afterwards, and the ONS/Cancers run on an annual basis. Health event/mortality data supplied by HSCIC will be linked, using a randomly assigned unique ID number for each participant, to other UK COSMOS data on mobile phone usage, other RF exposures, other environmental exposures e.g. noise and air pollution, green space etc, health and lifestyle to allow epidemiological analyses of exposure and health outcomes. Any personal identifiers such as Name, Address, Postcode that may be supplied by HSCIC will be stored separately from health data. Access to personal identifying information is limited to the COSMOS/SCAMP database team for processing and one COSMOS researcher from the COSMOS research team (to enable participant enquiries or withdrawal requests to be actioned). All COSMOS researchers are employees of Imperial College, who have signed strict non-disclosure agreements for the use of the SAHSU private network and the COSMOS study database. The applicant will also obtain health outcome data from NHS Scotland, NHS Wales (both approved) and the Office for National Statistics (ONS births, approved), as appropriate, under data sharing agreements. Data from these datasets will be matched through the use of anonymised unique record-level identifiers, assigned to each verified study participant on receipt of the datasets by the COSMOS database team, before being provided as pseudonymised data to the COSMOS research team. Name, Address, Postcode are required to verify quality of matching, to improve quality of demographic data, and to ascertain any change in details over the course of this longitudinal study. Over time, these personally identifiable data fields from ‘Latest Patient Information’ which the applicant is requesting as part of ‘Patient Tracking’ may become the most up to date records as the study progresses. Should the applicant find from the data that UK COSMOS study participants’ details have changed, the applicant may use updated names and addresses provided by HSCIC to re-contact study subjects for follow-up questionnaires and/or update information sent to mobile phone network operators to maximise the matching rate to their databases in order to receive mobile phone usage data, which are crucial for ongoing accurate exposure assessment. Similarly, updated name, address, postcode information is provided by network operators (where available and for those consenting) for participants as the study progresses. Written agreements to ensure confidentiality and non-disclosure of data are in place between Imperial College London and mobile phone network operators. The applicant has ethical approval and individual consent from participants to use the personally identifiable data fields (Name, Address, Postcode) from ‘Latest Patient Information’ and from mobile phone network operators for all the purposes stated above. The study requires HES data linked to the applicants cohort participants for all requested years as on the following basis: (i) For prospective epidemiological analyses: HES data provides a more comprehensive medical history, providing information on underlying conditions and treatment of these conditions. This will allow us to perform statistical analysis to enable us to investigate whether a link exists between mobile phone use and future adverse health outcomes. (ii) For retrospective epidemiological analyses: The applicant has collected mobile phone use exposure data for the applicants cohort participants going back to 1985. They will conduct historical analyses of mobile phone use and health outcomes in HES data from 1997 onwards. Imperial College London, and the Department of Epidemiology and Biostatistics, where this study is being undertaken, have considerable experience over a number of years in receiving, holding and analysing sensitive and identifiable data from a wide range of sources. Through this experience, Imperial has developed a robust and effective Information Governance infrastructure, policy and procedures and appropriate culture for the secure handling of these types of data. In general, information governance and especially anonymisation is taken very seriously at ICL and does conform to the Information Commissioner’s Code of Practice. It is important to note that the UK COSMOS researchers and database where the raw identifiable data is received and processed are kept separated. On receipt of the datasets from HSCIC, the COSMOS/SCAMP Database Manager will separate identifiable data for individuals from their health data, by the use of pseudonyms and records identifiers. The COSMOS study researchers then receive this pseudonymised dataset for the purpose of performing statistical analysis for research purposes only. Researchers undertaking epidemiological analysis therefore only have access to pseudonymised data. Access to personal or identifiable personal data requires the use of specialised cryptography and SQL, is restricted to a limited number of specified staff within the COSMOS team, who (for example) may need to check back with raw data provided, for the purpose of ensuring that a participant is accurately identified and linked to their personal information or for the purposes of identifying records to be deleted after a withdrawal request. The SAHSU/COSMOS/SCAMP Information Governance Policy is written to be compliant with ISO/IEC 17799:2005 & ISO/IEC 27001:2005 and has been internally reviewed and risk assessed in accordance with Imperial College policy.


Project 18 — DARS-NIC-383203-Q8B9L

Opt outs honoured: Yes - patient objections upheld (Does not include the flow of confidential data)

Sensitive: Sensitive, and Non Sensitive

When: 2016/09 — 2018/12.

Repeats: One-Off

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

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • Office for National Statistics Mortality Data
  • HES:Civil Registration (Deaths) bridge
  • Civil Registration (Deaths) - Secondary Care Cut

Yielded Benefits:

Benefits detailed in measurable benefits section are ongoing it is anticipated that the benefits over the next 12 months will be a continuation of those already achieved.

Objectives:

The Dr Foster Unit at Imperial College have the following objectives: • To use hospital administrative data to provide measures of quality of delivery of healthcare by providers, or in some instances, by area and to show variations in quality by provider and to support management information function for the NHS. • To compare hospitals' mortality rates for in-hospital deaths with rates for all deaths (to evaluate the effect of differential discharge policies). • To calculate total post-operative mortality rates, e.g. when comparing operative techniques such as laparoscopy and open approaches. • To access potential quality of care issues by comparing the cause of death with the reason(s) for admission, e.g. for surgical patients who are discharged within 30 days of the procedure but who die at home and whether the death is related to their disease process or to complications of treatment? • The Dr Foster Unit at Imperial College are requesting Office for National Statistics (ONS) deaths for Hospital Episode Statistics (HES) 2012/13 and 2014/15 plus 30 days in order to capture all deaths (in and out of hospital) within 30 days of admission or procedure for patients in hospital in March 2015, i.e., so that all patients have the full 30 days of follow-up in the data. Please note that whilst Dr Foster Unit and Dr Foster Limited share a similar name, and the Dr Foster Unit is named in recognition of the funding provided by Dr Foster Limited, the Dr Foster Unit is legally part of Imperial College. It thus is physically and logically separate to Dr Foster Limited, and the agreement and uses within solely relate to activities of Imperial College.

Expected Benefits:

1) Ongoing benefits from our previous work The Imperial Unit’s methodological research forms the basis of a near real-time monitoring system, currently used by 70% of English NHS Acute Trusts to assist them in monitoring a variety of casemix-adjusted outcomes at the level of diagnosis and procedure groups. The unit works with the CQC, contributing to its surveillance remit using the same methods and data. From our monitoring system, the Unit at Imperial College generates monthly mortality alerts, based on high thresholds, which we have been running since 2007. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007. The resulting Public Inquiry recognised the role that our surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier. Key recommendations, reflecting our unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics. If the Dr Foster Unit at Imperial College are given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The Dr Foster Unit at Imperial College will be able to assist the investigation of variations in outcomes at a local level by providing a set of fields from our analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Our mortality outlier outputs are used by CQC within their Hospital Inspection framework. As a result of our leading role in the development of hospital mortality measures, in 2010 we were invited to contribute to a Department of Health (DoH) Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on our HSMR methods) is now a public indicator used by all acute trusts. Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections. We continue to advise the Health and Social Care Information Centre on methodological issues around the Summary­level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Our research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broadsheet coverage, and radio and TV interviews The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of our published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September. Another example of our research that have used HES-ONS Mortality data include one-year survival and readmission in heart failure patients and risk of post-operative death by cause of death over time in patients undergoing general surgery. 2) Expected benefits from the proposed work For future research, it is important to be able to capture deaths occurring following discharge from hospital to assess the full mortality burden relating to that hospitalisation. Out of hospital deaths are particularly useful for surgical outcomes, e.g. for the calculation of total 30-day post-operative death rates, as the effect of premature discharge (in terms of mortality) would otherwise go unnoticed. Longer-term follow-up of hospitalised patients, e.g. using one year survival, necessitates being able to capture all deaths, not just those occurring in hospital. For this reason, the Summary Hospital-level Mortality Indicator (SHMI) specification requires out of hospital post discharge deaths. As described above in relation to the project on heart failure and COPD, mortality is a “competing risk” for important non-fatal outcomes such as readmission. Accurate prediction of the risk of these other outcomes will help with risk stratification and health service planning, and is not possible without total mortality. Knowledge of the cause of death is particularly important for quality improvement. The relation between the cause(s) of death and the reason(s) for admission is of particular interest too. The place of death, including whether it was an NHS institution, is necessary to monitor end-of-life services. Of particular interest is the proportion of patients who die at home. Previous work using HES showed higher mortality risk for asthma in those living in areas further from a hospital than those near it. Using Lower Super Output Areas would enable studies into the effect of distance from home to hospital on patient outcomes and the estimation of hospital catchment areas. This allows geographical access to services to be estimated, as we can calculate how far patients must travel for their treatment. This is of growing importance given the current drive to centralise services, particularly for surgery. Using larger geographical areas than LSOAs would incur too much measurement error when calculating the distance between the patient’s home and the hospital. Ongoing analysis of the mortality alerting system will allow the Dr Foster Unit to improve the alerting process, reducing the number of false positives and unnecessary effort spend by hospitals investigating them. It will also provide advice to hospitals who receive the mortality alerts on how to follow them up and learn, for example, which are the key contributing factors in the alerts. The proposed analysis of variations in treatment and outcomes in TAD patients will shed light on which patients are underserved by current surgical practice, which patients are most likely to benefit from treatment, and what might be the effect of centralisation of surgery.

Outputs:

1) Research into variations in quality of healthcare by provider: background to proposed work The Dr Foster Unit at Imperial College use hospital administrative data in the form of HES/MMES/ONS Mortality data to provide measures of quality and safety of delivery of healthcare by provider, or in some instances, by area or time. The unit’s work focuses on quality of care and patient safety, including healthcare acquired infections and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. Our proposed work will continue our principal themes: i) developing and validating indicators of quality and safety of healthcare, particularly by consultant and hospital; ii) show variations in performance by unit and socio demographic stratum; iii) risk prediction and risk adjustment of such indicators and variations and any other methodological aspects as they arise. The Dr Foster Unit at Imperial College require data dating back to 2000 for two reasons: • To examine historical trends of treatment practice (e.g. Faiz et al. Traditional and Laparoscopic Appendectomy in Adults Outcomes in English NHS Hospitals Between 1996 and 2006, ANNALS OF SURGERY 2008;248:800-806) and the historical impact of changes in policy (e.g. proposal to examine capacity issues). • To increase the power of predictive models for rare diseases, procedures and events (e.g. we build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which includes some rarer conditions). The Dr Foster Unit at Imperial College plan the following analyses: They are working in collaboration with the University of Manchester and supported by the Care Quality Commission (CQC), to improve understanding of our mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts - acute myocardial infarction and septicaemia. The Dr Foster Unit at Imperial College aim to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality by Oct 2016. The Dr Foster Unit at Imperial College have a two-year NIHR-funded project looking at predictors of readmissions and one-year mortality (in and out of hospital) in patients with chronic diseases (heart failure and COPD), following on from our two recently published studies on readmissions in heart failure patients (first two listed below). Mortality acts as a “competing risk” for readmission, and it is therefore essential to know whether a patient has been discharged alive but subsequently dies and is therefore no longer at risk of readmission by Jun 2017. The Dr Foster Unit at Imperial College have begun work using HES with the University of Leicester on thoracic aortic disease (TAD) looking at variations in rates of surgery and mortality between centres. There seem to be wide variations in the rates of treatment for this condition, but it is unclear how this impacts on outcomes. In-hospital mortality only captures part of the effect. With the recent growth in the number of endovascular procedures (TEVARs), post-discharge deaths are vital to assess the impact of these procedures and of TAD services in general by Aug 2016. International comparisons of service use and outcomes: England and the USA. The Dr Foster Unit at Imperial College hold data from Centre for Medicare and Medicaid Services enrolees and from the Nationwide Inpatient Sample from the USA. They have previously set out the methodological issues with using administrative data from multiple countries. They will compare patient casemix, rates of outcomes such as post-operative mortality, infections and readmissions, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA by Oct 2017. Examples of key published research that have used HES/ONS data include: Bottle A; Goudie R; Cowie MR; Bell D; Aylin P. Relation between process measures and diagnosis-specific readmission rates in patients with heart failure. Heart 2015; Jun 11 (epub). Bottle A, Aylin P, Bell D. Effect of the readmission primary diagnosis and time interval in heart failure patients: analysis of English administrative data. Eur J Heart Fail 2014; 16(8): 846–853. Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30 day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/

Processing:

This process comprises the following steps: The Dr Foster Unit at Imperial College use hospital administrative data in the form of HES/Monthly Managed Extract Service (MMES)/ONS to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare acquired infections, mortality and safety indicators. The Dr Foster Unit at Imperial College hold 2 databases to store data – A Research database and a Patient Identifiable Database to provide a Re-Identification service for NHS provider trusts. The ONS Mortality data will be stored in the Research database where named researchers with Approved Researcher status will be able to access the data to do their analyses. Patient identifiers are stored separately to our research database which holds the standard HES extracts and sensitive fields. Imperial’s researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance, and to help improve the quality and safety of healthcare delivery. No record level will be transferred outside of the EEA under this agreement, and is only processed and stored at the addresses given within this application. ONS Data supplied under this Agreement may be linked with HES Data Supplied under NIC-12628 for the purposes of cross HES-ONS mortality analysis. Data may be linked using Encrypted_HESID. All individuals with access to the data are employees of Imperial College London. All outputs (including those shared with collaborators) are aggregated with small numbers suppressed in line with the HES analysis guide. The ONS data provided under this agreement will not be shared with any third party, and (for the avoidance of doubt) specifically not shared with commercial companies including (but not limited to) Dr Foster Limited.


Project 19 — DARS-NIC-39944-D4M0D

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2016/09 — 2016/11.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Objectives:

While predicting the risk of emergency admissions is becoming ingrained in healthcare delivery and financing in England, the models that are being used have limited predictive power. Most models are linear regressions, based on simple, static predictor variables. As such, there is significant scope to improve on these methods. The aim of this project is to develop a new method to predict risk in healthcare, going beyond linear regression, by using data science methods. The aim is to explore the different options such as artificial neural networks and to explore which method offers the most potential to revolutionise risk prediction. Simple risk algorithms that require no advanced data analysis will also be explored. Using HES data, the different techniques will be analysed and compared. If successful, the new risk prediction models will be developed into a tool or manual for the NHS to use.

Expected Benefits:

The preliminary research for this project has identified, through a survey of 150 GPs across England, that currently only 37% of GPs with access to risk stratification think it is actually useful to their practice. Nevertheless, they are financially incentivised to use it though the NHS Enhanced Service specification on Admission Avoidance, which requires GP to identify the top 2% high risk patients on their list, and provide them with special services. There is a need to try and improve the usefulness of risk stratification. This research aims: - to improve the accuracy of risk prediction; and - to provide healthcare professionals with the tools to do the analysis and provide patient-centred, effective care. 1) The research aims to identify new and improved methods to perform risk prediction. Improved risk stratification methods will allow GPs to target their interventions more accurately, to those patients who need it most. This will improve care for high-risk patients and reduce their need for emergency admissions. Preventing emergency admissions will have a positive impact on all three elements of the Triple Aim of healthcare: Patients receive high quality care, the negative experience of an emergency hospitalisation is avoided, and costly emergency care is prevented. 2) If a software tool or manual is developed based on new and improved methods, GPs and CCGs will be able to conduct their own risk prediction analysis. Currently, the majority of GPs (82%, according to our survey) receive only a list of names for the high risk patients, with no additional analysis or insights. If they run their own risk prediction analysis they can understand what is driving the high risk of each patient and see the full picture. This information can then be used to personalise interventions and improve care.

Outputs:

· A PhD on data analysis methods to segment patients - submission Jun 2017 · A paper published in a peer reviewed journal focused on a health professionals audience (e.g. BMJ or Health Affairs) - expected initial submission Dec 2016. This paper will describe the research, and if applicable, provide a reference to where the software tool or manual (as per below) can be found. This will ensure a widespread dissemination of both the knowledge and the actual method. · A software tool or manual for risk stratification, based on the method that was found to be most accurate - March 2017 (Note - this is contingent on the results of the analysis). If a method is found that significantly improves on current methods, Imperial College London will develop either a software solution or a manual that describes how to do the analysis in an existing software package (e.g. SPSS). This will be made available on a not-for-profit basis to CCGs and GPs for use in their practice. Imperial College will share results with the Data team at NHS England. This has already been discussed with the Chief Data Officer, and Imperial expect to share with him the results of the study, to ensure it reaches the right people. This will include any negative results. HES data will only be used to identify the optimal methods for risk prediction, but that the actual tool will contain no HES data. Instead, it will be run from GPs’ or CCGs’ own data.

Processing:

The data will be used to test the different predictive models. Data from the years 2011-2014 will be used to predict emergency readmissions in 2015. The following steps will be taken to create a dataset to which the different predictive models can be applied: • A range of predictor variables (such as hospital admissions, demographics including age and gender, and diagnoses of specific long-term conditions) will be extracted from the 2011-2014 data • These predictor variables will be used to create a patient-level file, with on each line a patient, his/her predictive variables, and whether or not the patient had an emergency admission in 2015 • The patient-level file will be split into a training and a test dataset, consisting of either 10% and 10%, or 50% and 50% of the population (depending on computing power available). The training data will be used to train the predictive methods, and the test dataset will be used to evaluate the predictive power of the developed models in a new population. • The models will be tested for predictive power using the area under the receiver operator characteristic (ROC) curve, and the positive predictive value. • In addition, the different methods will be compared by looking at the high-risk population (e.g. top 5%) they identify.


Project 20 — DARS-NIC-67398-K2Y3T

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2016/12 — 2019/02.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

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

Yielded Benefits:

There have been several publications from Imperial College London created with the use of the HES data NHS Digital have provided prevoiusly. As detailed below these include; Publications under review: • Clarke J, Warren L, Arora S, Barahona M, Darzi A (2018). A Retrospective Observational Review of Inter-Organisational Patient-Sharing in England. Under review by Health Affairs. • Warren L, Clarke J, Arora S, Barahona M, Arebi N, Darzi A (2018). Caring About Sharing: A Review of Transitions of Care Across Secondary-Care Settings in Patients with Inflammatory Bowel Disease in England. Under review by Colorectal Disease • Clarke J, Warren L, Darzi A (2018). Care Fragmentation and Organisational Performance in the NHS in England: Results of a Retrospective Observational Review of Hospital Episode Statistics. Under review by the Journal of the American Medical Association. Presentations given: • Clarke J (2018). The Power of Connections – Mapping the Behaviour of Healthcare Networks. Presentation to the midterm review of the EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London. • Clarke J, Marti J, Barahona M, Darzi A (2017). Predicting Organizational Interdependence in Emergency Care in England. Department of Surgery Annual Research Meeting, Imperial College London. • Clarke J, Warren L, Arora S, Barahona M, Darzi A (2017). Identifying Inter-Organisational Patient Sharing in England Through Network Analysis. Department of Surgery Annual Research Meeting, Imperial College London. • Clarke J (2017). An Ecological Analysis of Seasonal Equity in Access to Emergency Care in England. International Health Economics Association Congress, Boston, USA. • Clarke J, Warren L, Arora S, Barahona M, Darzi A (2017). Identifying Characteristics of Inter-Organisational Patient Sharing in England Through Network Analysis. Sowerby Symposium Imperial College London.

Objectives:

The Department of Surgery and Cancer, based at Imperial College London, is requesting data for use in the following research project: The Power of Connections: Mapping the Behaviour of Health Care Networks The purpose of this study is to examine how care providers in England are connected by virtue of the patients that flow between them. This request for data will, through the application of network analysis, provide insights into the factors determining how patients flow through the network and where the network may be particularly vulnerable will be identified. Strategies to improve the efficiency, equity and safety of the network may be developed and tested using predictive modelling in order to identify to optimal routes for investment and restructuring of the health care providers. This project will use the following data: HES OP 2011/12-2014/15, HES A&E 2011/12-2014/15 and HES APC 2011/12-2014/15. These four years of data are necessary to provide an adequate picture of health care utilization and capture less common events.

Expected Benefits:

The measurable benefits to health and social care are expected to be as follows: 1. The structure of interhospital transfers in the NHS. The transfer of a patient from one hospital to another often occurs at critical periods in a patient’s journey where they can no longer be optimally cared for by their current hospital. Recent centralization of specialist services has increased the need for transfer to another hospital to receive specialist care. This transfer process is a period of increased patient risk, where an often critically ill patient is transferred by ambulance over significant distances and whose care is handed over to an entirely new team of clinicians. Which patients need to be transferred, when and to which hospital remains poorly understood, as do their health outcomes relative to those who do not need to be transferred to receive the same specialist care. Through the publication of this work in relevant academic journals, insights into the movement of patients from one hospital to another may be achieved by clinicians and commissioners. This knowledge may be used to both understand the factors which influence patient and physician choice, and also incorporate these factors into future service design. By understanding the circumstances that lead to patient transfer the aim is to identify patient groups that are particularly likely to undergo interhospital transfer and to focus on the development of local and national strategies to ensure optimal transfer of care for these specific groups. It is expected that this work will be completed within three months of receipt of the data. 2. The impact of patient choice in maternity care on local service supply and demand. Patient choice is an increasingly important factor of care delivery in the NHS. The factors underlying patient choice remain poorly understood, in part because of the many patient and provider factors that influence decision making. Expectant mothers can freely choose which hospital they would like to deliver their maternity care. Maternity care is delivered frequently across the country and as it is generally focused solely on the process of giving birth, the variability in patient and provider factors is far less than for other clinical scenarios. This therefore serves as an excellent setting to model the factors which underlie patient choice. In the context of maternity care, where patients can freely choose where their care is delivered, certain providers may be repeatedly favoured or avoided by expectant mothers in response to a range of factors including individual previous experience, geography or waiting times. This may lead to demand for certain providers becoming too great to be met, while others have unused capacity. Identifying and predicting these factors allows providers locally and nationally to correct imbalance in the supply and demand relationship for maternity care, thereby optimising the effectiveness of maternity provision nationally. It is expected that this work will be completed within three months of receipt of the data. 3. The structure of care networks for patients following trauma, stroke and cardiovascular events, comparing regions with established care networks to those without. The introduction of defined care networks for the treatment of trauma, stroke and cardiovascular disease in parts of the NHS have demonstrated significant improvements in patient outcomes where they have been implemented. In the case of stroke care, networks have been extremely successful in London and Manchester where they have been introduced. The rest of the country currently does not have the same effective network structure. Using the principles of community detection analysis and Markov models is would be possible to identify for the London and Manchester stroke networks whether their structure optimally reflects the distribution of disease and pattern of clinical practice in the geographic areas they cover. Outside of these two networks it would be possible to examine whether similar network structures already informally exist elsewhere in the country, and develop a nationwide stroke network, in a manner like that which was created for the highly successful national trauma network. This knowledge would inform the development of a national stroke network so that the benefits already obtained from its implementation in London and Manchester may be available nationally. Publication of these findings in high impact health policy journals will bring this work to the attention of key stakeholders nationally and locally. It is expected that this work will be completed within nine months of receipt of the data. 4. A network analysis demonstrating the interdependence of secondary care providers in the NHS followed by predictive modelling of patient flows to secondary care providers in response to changing organizational capacity. Demand for health care within the National Health Service continues to rise, and does so in a stochastic fashion. Each hospital has a finite capacity to provide safe care, and therefore a threshold over which harm is more likely to result. The likelihood of the demand being placed on a hospital exceeding the care it can safely provide is dependent upon the local incidence of disease and its intrinsic capacity to provide care, but is also critically dependent on the performance of its neighbouring hospitals. If a hospital is unable to meet the demands placed on it, the burden of care provision falls to its neighbouring hospitals, which therefore see an increase in the demands placed on their services. Hospitals with many nearby hospitals may be less vulnerable to this pattern of behaviour and would therefore be said to have a low degree of interdependence, while a pair of hospitals with no nearby neighbours would be highly interdependent on the behaviour of one another. This principle when applied across hospitals the National Health Service will identify areas of high interdependence within the health care network. Areas of high interdependence of care providers are expected to be less resilient to increases in demand for care or reduction in the capacity to provide care. Identifying these vulnerabilities will assist NHS England in identifying hospitals who require additional investment to ensure the ongoing delivery of high quality patient care. It is intended that the predictive models developed from this work will be published in high impact health policy or general medical journals to reach the widest possible interested audience. Additionally, the methodological insights from this work will be disseminated either in the form of a further journal article or white paper for NHS Improvement and to detail the application of these techniques. It is expected that this work will be completed within 12 months of receipt of data. The proposed work in focusing on the interconnectedness of healthcare providers, represents an exciting, novel and important means by which the efficiency and equity of health care provision may be examined in a new light, with a high likelihood of lasting improvement to the NHS as a whole.

Outputs:

-Specific outputs expected, including target dates: All outputs will contain only aggregate data with small numbers suppressed in line with the HES Analysis Guide. The study will yield a PhD Thesis between October 2019 and October 2020 in addition to published academic papers as follows: 1. The structure of interhospital transfers in the NHS. 2. The impact of patient choice in maternity care on local service supply and demand. 3. The structure of care networks for patients following trauma, stroke and cardiovascular events, comparing regions with established care networks to those without. 4. A network analysis demonstrating the interdependence of secondary care providers in the NHS. 5. Predictive modelling of patient flows to secondary care providers in response to changing organizational capacity. Each of these papers will be targeted at health policy or health informatics journals including; The British Medical Journal Annals of Surgery The Lancet British Journal of Obstetrics and Gynaecology Health Affairs International Journal of Systems Science. It is intended that all work will be presented at academic conferences prior to publication, including; Health Systems Global Symposium – UK International Health Policy Conference – UK. Results will also be directly reported to NHS England and NHS Improvement where appropriate.

Processing:

The Department of Surgery and Cancer confirms that the data under this application would only be used for the project described in this document. Individuals working on this project would only be permitted to access data relating to that project, as identified within the application. Access is granted to the data only to named individuals working on the project under authorized user names. Such access is password controlled (with a password reset required on a regular refresh). Only substantive employees of Imperial College London will use the disseminated data and only for the purposes described in this document. The raw data will be handled only within the Department of Surgery and Cancer to support academic research. The data will be received from NHS Digital and stored on a secure server hosted at the South Kensington campus of Imperial College. Access to data on this server is restricted to authorized individuals only. The data is accessed and processed by researchers who are based in rooms with keyless combination locks that are always locked when not in use. This access is password-based and permitted solely to registered users logging on via permitted IP addresses. Record level data will not be distributed to different parts of the organization. The data will not be made available to third party individuals, institutions or companies. No other data will be linked to this data though data will be compared at aggregate levels if required. The data will be processed as part of the above mentioned research project within the Department of Surgery and Cancer. It will be queried using data analytical tools such as SPSS, STATA, SAS, Microsoft Excel, Matlab, Python etc. to aid in answering specific research questions. Data visualizations will be done to present insights gained using suitable tools including Tableau, Inkscape and others. The specific processing activities will be as follows: 1. A patient level database for interhospital transfers of patients will be constructed, in addition to a range of utilization and outcome variables (e.g. length of stay, additional procedures, readmissions). A patient level database of maternity care will be constructed to examine patient choice in relation to delivery location. In both cases, directed unipartite networks of care transitions from one provider to another will be constructed and the characteristics of the network, providers and patients will be analysed using linear and logistic regression. 2. A patient level database of presentations to acute hospitals will be constructed. This database will be used to identify the probability of presentation of a patient in a particular geographic location to a particular centre with a particular diagnosis. These values will be used to undertake computational community detection algorithms to identify geographically nested networks of care providers to compare to existing predetermined care networks and guide the implementation of novel, more efficient networks of care. 3. A patient level database of outpatient, inpatient and A&E presentations will be created. The aggregate interaction across datasets between a particular geographic region (e.g. postcode, LSOA or primary care provider) and a secondary care institution will be used to generate a unipartite network of acute hospitals linked to one another by the strength of their shared patient activities. The network will then be interrogated to identify how patterns of patient flow will change in response to increased patient demand and altered provider capacity. Clusters of vulnerability in the network will be identified and optimal avenues for intervention will be suggested.


Project 21 — DARS-NIC-72318-M4W8J

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

Objectives:

Imperial College London’s Big Data and Analytical Unit (BDAU) requires an extract of HES data with linked month and year of death (where applicable) for use in a research study: ’ Evaluating the Rate of Deadoption of Interval Cholecystectomy, a Low Value Intervention, and Diffusion of Index Cholecystectomy, a High Value Intervention’. This study aims to investigate the respective values of two ways of treating patients with the same specific health condition. This study is being undertaken by a PhD student within Imperial College London and will contribute to a PhD thesis in addition to other outputs intended to maximise the benefit of the work. The Big Data and Analytical Unit (BDAU) is a multidiscipline team within Imperial College London which collaborates with a large network of researchers across the college with the aim of ensuring the maximum use, impact and dissemination of research using healthcare data. Finding efficiency savings in health care provision is paramount given the pressures on national health care budgets worldwide. This provides motivation to identify and reduce the use of health care interventions that deliver only marginal benefits, be it through overuse, misuse or waste, that could be substituted by less costly alternatives without affecting safety and quality of care. A greater emphasis on value is key and achieving high value for patients must become the goal of health care delivery. A clinical definition of low value interventions has been established as care in the absence of a clear medical basis for use or when the benefit of therapy does not outweigh risks; this encompasses terms such as medical overuse and over-diagnosis. The importance of identifying and studying low value healthcare services is motivated by the concept of ‘opportunity cost’, i.e. that disinvestments in low value procedures and services from the healthcare budget leads to the opportunity for further investments in higher value services. That is, a reduction in low value service results in improved value of care overall. This study aims to investigate the relationship between two interventions for a cholecystectomy – a surgical procedure to remove the gallbladder. Interval cholecystectomy is a low value intervention, while the other, index cholecystectomy is considered a high value intervention. Interval cholecystectomy is the choice to discharge patients following index admission and readmit them for an elective operation whereas index cholecystectomy is performed during index admission. These two interventions would be analysed to inspect the patterns of deadoption and adoption respectively. Cost analysis will be used to compare the two interventions and this will take into account the impact of adverse events, readmissions and excess mortality to ensure that costs and impacts are both analysed. These analyses will then inform outputs which will be used to help change current practices and improve patient care in respect of this particular condition. The study requires the hospital admissions data of any individual who had a procedure (defined by specific procedure codes) indicating a cholecystectomy treated by trusts which had 10 or more operations per year for the relevant procedure codes. Details of all hospital admissions for these individuals over a period of up to 10 years will be required because the study will take into account the possible relationships between the cholecystectomy and other admissions to ascertain the true costs of each type of intervention.

Expected Benefits:

By highlighting the differences in cost associated with the different treatments to key decision makers through the dissemination strategy outlined above, it is the hope and expectation that decisions will be taken to adopt the intervention type that offers the highest value resulting in potentially significant cost savings. Savings of £820 per patient with index cholecystectomy have been estimated (Gutt CN et al. 2013). With 72,572 (http://www.rcseng.ac.uk/healthcare-bodies/nscc/data-tools) non-operative admissions with gallstone disease in 2014, the potential for savings of £59,509,040 exists. Therefore, the opportunity cost of reallocating resources towards higher value services is great and, although this work will not guarantee such efficiency savings, it will contribute to beginning conversations with policy makers and clinicians to optimise treatment and begin change management. This conversation would utilise evidence produced by this work. This project would not only provide knowledge of the cost of persistent interval cholecystectomy but also an understanding of how best to promote a change to index cholecystectomy. By providing a novel model of efficient de-adoption (which would be a specific output of this research) potential benefits may be extended to other low value procedures both surgical (e.g. arthroscopy in osteoarthritis) and non-surgical (e.g. use of antibiotics when not indicated.) The aim with this work is to explore the practice, purpose and experience of deadoption and to develop new tools and insights to help guide those trying to navigate this space. The expectation is that the papers would be published by October 2018, thereby impacting clinical activity by October 2019.

Outputs:

The following outputs will be produced: Models: Q3/2018 - A model for efficient de-adoption will be developed as part of this study Publications: It is intended that this study will lead to the following peer-reviewed publications which will be targeted for Health Affairs, the Lancet and the BMJ: Q3/2018 - Modelling of deadoption of low value procedures Q3/2018 - Adoption of high value procedures and geographical network analysis of diffusion of innovation Q3/2018 - Cost implications of non-deadoption of low value procedures Presentations: It is intended that this study will lead to presentations at the following conferences: Q2/2018 - The Association of Surgeons of Great Britain and Ireland - surgical conference Q2/2018 - Health + Care, Commissioning in Healthcare - conferences directed at healthcare commissioners Q3/2018 - The Association of Upper Gastrointestinal Surgeons - surgical conference Q3/2018 - Road to Rightcare, Overuse Conferences, World Congress on Health Economics - academic meetings Academic outputs: This study will contribute to a PhD thesis which will be published online. Target audience: The outputs of this study are aimed at those who will make use of the findings to decide the best course of care for patients. This includes surgeons who would be performing these operations, clinical commissioners who decide on priorities for funding and healthcare leads who can influence guidelines. This study is part of the Centre for Health Policy at Imperial College London which helps advise on global health policy, the Patient Safety Translational Research Centre which is one of 3 centres in the UK which translates research into clinical practice and the Global Health and Development Group which were formally part of NICE International which helped advise for local and global standards for clinical practice. All data which is used for outputs will be anonymous summary aggregate data. All outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide. No raw data will be transferred outside the BDAU SE and neither the data nor outputs will be used for commercial purposes.

Processing:

NHS Digital will securely transfer a pseudonymised extract of HES data and linked month and year of death to Imperial College London. Imperial College London will store the data on a server in the BDAU Secure Environment (SE). Data access is strictly controlled by the BDAU through a robust dataset registration process. No one other than BDAU staff can authorise access to the data. Access to the data will be restricted to one researcher, a PhD student, and that researcher’s supervisors if necessary (usually not required), only for the purpose outlined in this Data Sharing Agreement. The student and the supervisors are bound to the policies, procedures and equivalent controls of the BDAU SE and Imperial College London as substantive employees of the College. The raw data provided by NHS Digital will be analysed solely in the BDAU SE. Any further analysis done outside the BDAU SE (usually for visualisation purposes for output) will be done using data that has been aggregated with small numbers suppressed in line with the HES Analysis Guide. The data will be analysed to investigate the two interventions and associated cost, utilisation and outcome patterns. This will involve statistical analysis using standard and innovative statistical programmes inside the BDAU SE. Cost data sourced from the freely available ‘National Schedule of Reference Costs’ will be integrated into the raw dataset on an intervention level. Each intervention will be costed according to its relevant healthcare resource group (HRG); which is a reimbursement tariff of the average unit cost to the NHS of providing a defined service in a given financial year. This will not increase the risk of re-identifying individuals. Results will be graphed and compared at an aggregate level. The importance of longitudinal data (10 years) in this scenario is to capture the change in clinician & institution behaviour following the publication of a Cochrane review in 2006 (Gurusamy et al. 2006) which recognised interval cholecystectomy as being low value. Today interval cholecystectomy is still being used despite growing evidence to the contrary. The goal of creating a model of deadoption requires longitudinal data in order to illicit the changes in rates of use and to identify whether changes have been sustained. Without longitudinal observation an incomplete picture would be shown and the study would be unable to formulate recommendations to supply side policy change which is an ambition of this project. there will be no linkage with other record level data and no attempt to re-identify the data.


Project 22 — DARS-NIC-80304-H6P6R

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2019/04 — 2019/04.

Repeats: One-Off

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Yielded Benefits:

Imperial College London have yet to realise any yielded benefits as no data has been disseminated under previous approved versions of this agreement - as no GMC ID's of the colorectal surgeons were submitted to permit the HES data extraction.

Objectives:

Imperial College London’s Big Data and Analytical Unit (BDAU) requires an extract of HES data linked to consented surgeon simulation-based skill assessment data for use in a research study: ‘An evaluation of the relationship between simulation-based training assessment tools and performance in real world settings’. This study aims to establish what, if any, association there is between simulation-based skills assessment and clinical and patient outcomes. This study is being undertaken by a PhD student within Imperial College London and will contribute to a PhD thesis in addition to other outputs intended to maximise the benefit of the work. The Big Data and Analytical Unit (BDAU) is a multidiscipline team within Imperial College London which collaborates with a large network of researchers across the college with the aim of ensuring the maximum use, impact and dissemination of research using healthcare data. Although simulation-based training seeks to improve surgical performance and provides a marked change to traditional methods of assessment there is currently no evidence of whether better performance at these assessments results in improved patient care or improved surgical outcomes. To understand this relationship, the ratings of performance during simulation-based assessments must be linked to data which can be used to assess performance during real-world surgery. This linked data can then be used to investigate the relationship between surgical skills assessments and surgeon performance as determined by outcomes for patients. The study will use a de-identified linked dataset prepared by NHS Digital to compare the performance during simulation as collected by assessment score cards to previous performance as recorded in hospital episode statistics (HES). There have been limited studies linking surgical skills assessment to outcomes and complications. This study will be the first to link data from consenting participants of surgical skills assessment to HES data to investigate performance. Measures such as readmission, mortality and re-operation rates can then be investigated. The benefit of validating these tools in a positive context, i.e. the tools accurately reflect real world practice, is that they can then be used to assess surgeons who are still trainees and would not have sufficient evidence for performance review. In this context, they can also increase engagement of trainees and trainers in simulation training. This study is also beneficial in a negative context, i.e. the tools have no link with actual performance, in that they can then be used to encourage redesign of training. The number of participating surgeons will be 20 which has been shown to be robust enough for these types of findings according to an already published study (Birkmeyer JD, Finks JF, O’Reilly A, Oerline M, Carlin AM, Nunn AR, et al. Surgical Skill and Complication Rates after Bariatric Surgery. New England Journal of Medicine. 2013; 369(15): 1434–42.). The analysis performed as part of this study can be used to improve surgical simulation training tools and to identify if there should be more engagement in existing training tools or if redesign is needed for existing training tools. August 2018 - No data has been disseminated under previous approved versions of this agreement - as no GMC ID's of the colorectal surgeons were submitted to permit the HES data extraction. Imperial College London have therefore submitted an extension request to allow for extension of this agreement to allow them time to send in the ID's (which is due to a delay in the flow of data to receive these which is not related to the NHS Digital data flow), to do the data analysis for the aforementioned project and create the outputs and benefits mentioned below.

Expected Benefits:

Further dissemination of this research will allow healthcare providers to understand the relationship between simulation training and clinical outcomes. This increases the ability to ensure that healthcare providers can accurately deem what training is necessary to provide better care for their patients and provide the appropriate training to keep clinical skills at the highest standard. Through similar methods, surgical skills assessment will identify training requirements, leading to targeted training and improved surgical skills. This is a further benefit in that simulation skills assessment can, if deemed to be linked to real-world performance, assess surgical skills for trainees who have not yet build up enough routinely collected administrative data for analysis.

Outputs:

The following outputs will be produced: Publications: It is intended that this study will lead to the following peer-reviewed publications which will be targeted for Annals of Surgery and British Journal of Surgery: 2019 – Impact of simulation training on performance of surgeons Presentations: It is intended that this study will lead to presentations at the following conferences: 2019 – American College of Surgeons Accredited Education Institutes - annual meeting 2019 – Association of General Surgeons of Great Britain and Ireland Conference Academic output: This study will contribute to a PhD thesis which will be published online. Target audience: The outputs of this study will be directly communicated to surgeons at workshops. Only aggregated results will be used and surgeon identity will be protected. The outputs will also be aimed at those who will make use of the findings to decide the best training of surgeons which will improve care for patients. This includes clinical commissioners and healthcare leads who can influence guidelines. This study is part of the Centre for Health Policy at Imperial College London which helps advise on global health policy, the Patient Safety Translational Research Centre which is one of 3 centres in the UK which translates research into clinical practice and the Global Health and Development Group which were formally part of NICE International which helped advise for local and global standards for clinical practice. All data which is used for outputs will be anonymous summary aggregate data. All outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide. No raw data will be transferred outside the BDAU SE and neither the data nor the outputs will be used for commercial purposes.

Processing:

On approval of this data sharing agreement, the simulation training scores for consented surgeons along with their consultant GMC ID in the appropriate format will be transferred from the BDAU to NHS Digital for the purposes of linking. NHS Digital will link this to the individual episodes in hospital episode statistics (HES) admitted patient care (APC) data for the years between 2006 and 2016 and return a de-identified dataset with minimisation applied as per section 3a. This ensures that the dataset returned to the BDAU is completely pseudonymised as the BDAU will not receive any other identifiable or further linkable data. The data minimisation method applied will ensure that patients are not re-identifiable even with a known consultant. NHS Digital will securely transfer the resulting pseudonymised extract of HES data to Imperial College London. Imperial College London will store the data on a server in the BDAU Secure Environment (SE). Data access is strictly controlled by the BDAU through a robust dataset registration process. No one other than BDAU staff can authorise access to the data. Access to data will be restricted to one researcher, a PhD student, and that researcher’s supervisors if necessary (usually not required), only for the purposes outlined in this Data Sharing Agreement. The student and supervisors are bound to the policies, procedures and equivalent controls of the BDAU SE and Imperial College London as substantive employees of the College. The raw data provided by NHS Digital will be analysed solely in the BDAU SE. Any further analysis done outside the BDAU SE (usually for visualisation purposes for output) will be done using data that has been aggregated with small numbers supressed in line with the HES Analysis Guide. The data will be analysed to investigate the impact of simulation-based training on surgical performance of consented participant surgeons. This will involve statistical analysis using standard and innovative statistical programmes inside the BDAU SE. Results will graphed and compared at an aggregate level. At no point will the data being provided by NHS Digital be used to identify any individual whether patient or consultant.