Good TREs work

Imperial College London projects

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


🚩 Imperial College London was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. Imperial College London may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.

A phase I study to determine safety and immunogenicity of the candidate COVID-19 vaccine AZD1222 delivered by aerosol in healthy adult volunteers (COVAXAER01) — DARS-NIC-637092-P9K6S

Opt outs honoured: Identifiable (Consent (Reasonable Expectation))

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

Purposes: Yes (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2022-03-18 — 2023-03-17

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Permission to Contact

Objectives:

This Data Sharing Agreement authorises the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials. The data will be processed on behalf of the Data Controller, Imperial College London, by NHS Digital as a Data Processor for the purpose of supporting recruitment to participate in a COVID-19 vaccine trial being run by Imperial College London

The following provides background to the Permission to Contact (PtC) Service:

NHS Digital has agreed to work in partnership with the National Institute of Health Research (NIHR) to build and host a first of type online Permission to Contact Service on nhs.uk where members of the public can register their details and give their permission to be contacted by researchers working on NIHR approved UK coronavirus vaccine trials about participating in those trials. This PtC Service, which is called “Sign Up to be Contacted about Coronavirus Vaccine Studies” on the nhs.uk website was launched as a national service on 20th July 2020.

This Service enables participants to:
• Provide permission for NHS Digital to share an individual’s details provided through the Service with the researchers undertaking COVID-19 UK vaccine trials for the purposes of researchers contacting that individual about taking part in those trials.
• Provide their permission to be contacted by NHS Digital about progress and outcomes from CV19 vaccine studies and in relation to the development of the PtC Service, including to inform them of opportunities to participate in other types of health research.

The data collected from individuals who sign up includes sufficient information to achieve the following purposes:
• Matching potentially eligible participants to eligibility criteria provided by the vaccine trials for their specific studies. This data will comprise of age, sex, geographic locations, type of employment, and a number health question e.g. about whether they have long-term health conditions.
• Providing relevant details of potentially eligible participants which have been obtained through the Service to researchers. This will allow the researchers to contact the participants with a view to discussing their taking part in a trial and if so, to obtain their further permission to take part in the trial.
• NHS Digital will provide access to the information obtained from individuals through the Service via the existing Data Access Request Service (DARS) process available to researchers working on UK COVID-19 vaccine trials sponsored by the National Institute of Health Research. The Service will only provide researchers with the data collected directly from individuals themselves through the Service.

The contact details will be used to invite potentially eligible individuals to undertake an eligibility assessment and, if eligible, to give informed consent to participate in this trial. NHS Digital, as Data Processor acting on behalf of Imperial College London, will be sending the communication to eligible participants.

This request relates specifically to a vaccine trial. The full title for this study is: A phase I study to determine safety and immunogenicity of the candidate COVID-19 vaccine AZD1222 delivered by aerosol in healthy adult volunteers (COVAXAER01)

The COVAXER01 phase I study will test the COVID-19 vaccine candidate AZD1222 (produced by AstraZeneca) to investigate its safety, tolerability and capability of boosting immune responses both in the blood and the lung when administered to the respiratory tract in volunteers previously vaccinated by intramuscular COVID-19 vaccination. Using standardised methods, the study team will measure immune responses in the blood, nose and lower airway and compare with data from ongoing clinical trials of intramuscular vaccination. By doing this analysis, the study team aim to demonstrate the effect of the delivery method and provide the critical information required to begin further clinical trials to show the efficacy of this needle-free vaccination strategy for booster vaccination. Recruitment will take place in a step wise manner to ensure volunteers safety. The first volunteer will receive the lowest dose of vaccine and then providing there are no safety concerns after Day 1, Day 3 and Day 7 follow up visits, a further two more volunteers will be invited to receive the lowest dose of vaccine and assessed after Day 1, Day 3 and Day 7 for any side effects. The safety profile will then be assessed by a Data Safety Monitoring Board (DSMB) before escalating to the next dose cohort. This will then be repeated (if no safety concerns) for the medium and high dose of vaccine, until the target recruitment of volunteers is reached.

The aim of this agreement is to recruit a total of 27 participants.

The GDPR legal basis for processing the data is GDPR Article 6 (1) (e). Processing is carried out by a public organisation in the public interest, in order to understand the safety, tolerability and capability of boosting immune responses using the above-stated vaccine candidate and thus potentially helping to protest the UK patient population in the future.

In addition to the above GDPR Legal Basis for Processing, this agreement refers to health data, which is a Special Category of Personal Data and therefore Imperial College London also relies upon Article 9(2)(j) (processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject). NHS Digital is content that the purpose of this study is the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials, thus clearly demonstrating that the study purpose is research into public health and therefore providing benefit to health and social care in the UK.

The data are required for research purposes in the public interest- meeting the conditions in the DPA 2018 Schedule 1 Part 1 (4) - which GDPR Recital 52(2) determines is an appropriate derogation from the prohibition on processing special categories of personal data. The ways in which the processing of data will be of benefit to the public – thereby demonstrating that the processing is in the public interest – are described in section ‘5d. ii. Expected Measurable Benefits to Health and/or Social Care Including Target Date’.

- In accordance with GDPR Article 89(1) processing is subject to appropriate safeguards. These include:
i. The data recipient’s technical and organisational measures to safeguard the data have been assessed and meet NHS Digital’s acceptance criteria (see sections 2 and 5b of this application for further details) although it should be noted that Imperial College London is not receiving data from NHS Digital in this agreement;
ii. The requested data has been assessed as proportionate to the aim pursued (see section 5a of this application for further details);
iii. Controls, data retention and processing activities have been assessed to ensure respect to the essence of the right to data protection;
iv. Measures to protect the rights and freedoms of data subjects have been assessed including transparency (fair processing) publishing subject’s rights.

NHS Digital is content that the purpose of this study is the use of information voluntarily provided to NHS Digital by individuals who have given permission to be contacted about potential participation in COVID-19 vaccine clinical trials, thus clearly demonstrating that the study purpose is research into public health and therefore providing benefit to health and social care in the UK.

In the interests of full transparency, it is noted here that AstraZeneca has supplied the AZD1222 vaccine free of charge to Imperial College London for use in this trial. The data will be used in support of the development of a commercial vaccine(s) that, should the research prove successful, will generate income for the commercial companies involved, to cover the development costs of the vaccine and also generate profit for that organisation.

AstraZeneca will not have any access to NHS Digital record-level data. AstraZeneca do not make any decisions about the means by which any personal data is being processed for this study and therefore they are not considered a data controller.

Expected Benefits:

The primary benefit of using the data will be to allow researchers to identify a suitable cohort and recruit them quickly into the vaccine trial – thus reducing the overall time to recruit into the trials. This is expected to accelerate the delivery of an effective vaccine to treat individuals to manage the COVID-19 outbreak and to save lives.

It is expected that this will reduce the burden on research staff in identifying and contacting potential clinical trial participants. It is anticipated this will also support the Vaccines Taskforce objectives to drive forward, expedite and coordinate efforts to research and then produce a coronavirus vaccine and make sure one is made available to the public as quickly as possible.

Outputs:

The information from NHS Digital will be used to facilitate contact with individuals who are potentially eligible and who have indicated willingness to potentially participate in studies/trials of COVID-19 vaccines.

This is expected to result in individuals entering the trials screening process with a view to them participating in the trial with fully informed consent.

The main results from this trial are expected to inform development of a safe and effective multiple vaccine combination against COVID 19.


Examining the healthcare inequalities in breast cancer screening during COVID-19 — DARS-NIC-422971-B8P2V

Opt outs honoured: Anonymised - ICO Code Compliant (Statutory exemption to flow confidential data without consent)

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-10-18 — 2022-10-17

Access method: One-Off

Data-controller type: NHS ENGLAND (QUARRY HOUSE)

Sublicensing allowed: No

Datasets:

  1. GPES Data for Pandemic Planning and Research (COVID-19)

Objectives:

COVID-19 has had an unprecedented impact upon breast cancer screening services. In addition to the backlog of almost 1 million missed mammograms since March 2020, public health measures have impacted on the functioning of screening hubs. To overcome these stresses, breast screening services have moved from inviting women to a pre-specified mammogram (timed invitations) to an invitation to book an appointment (open invitations), in order to increase the efficiency of screening hubs.

Existing studies have shown that women from Non-White backgrounds are up to 63% less likely to attend screening mammograms compared to their White British counterparts. In addition, women from more deprived areas and those with more medical co-morbidities status are less likely to attend breast screening invitations. These studies have all been conducted with closed, as opposed to, the novel open invitation structure. There are, however, concerns that this open model may exacerbate these existing inequalities by introducing further logistical barriers. For example, studies have highlighted that people from ethnic minorities report significant practical barriers to booking appointments.

NHS England's service evaluation has further highlighted these concerns, with the existing uptake of invitations in London almost 10% lower than in 2019. Moreover, they have shown a negative correlation between uptake of open invitations and Index of Multiple Deprivation (IMD).

Currently, screening services in London are utilising both open and closed invitations, and therefore, the true effect of the new invitations on individual patients is unclear. Furthermore, the effect upon ethnic minorities, those with clinical comorbidities or those who were identified as clinically extremely vulnerable is not known, as this is not collected by screening hubs. Understanding the impact on these groups is essential, as they already constitute low uptake populations and are at risk of this inequality widening.

Understanding this impact is integral to allow services to:
(1) target resources on potentially low attendance groups,
(2) amend invitations to meet the needs of the local populations
(3) maintain pre-COVID breast cancer screening uptake, and
(4) aim to maintain screening levels sufficient for the screening programme as a whole to be of benefit.

This work is of significant public health concern and is led by NHS England. The analysis constitutes a service evaluation of the existing invitation framework introduced as a response to COVID-19 in London. The primary aim of which is to ascertain whether the new open invitation type exacerbates healthcare inequalities and affects screening uptake. No existing data on this work has been published, given the recent introduction of such measures. The results from this work will feed into NHS England. NHS England are commissioning this work to determine the impact of COVID upon screening inequalities within the breast screening programme. The NHSE screening lead is leading this evaluation (and this data request), as this will inform the future COVID recovery of the breast screening programme in London specifically:
1) the impact of invitation types on inequalities
2) whether to change policy to improve attendance (which remains low)
3) inform national practice.

This service evaluation is designed to determine the impact upon healthcare inequalities of a new invitation type to breast cancer screening. In order to minimise data requested only pertinent variables such as co-morbidities shown to have an impact from the literature, ethnicity, Index of Multiple Deprivation and COVID diagnosis flags/vaccine indicators have been selected. These variables have been shown to impact on either 1) patient-level attendance or 2) screening service-level resources.

The Data Controller (NHS England) confirms that the purpose of the work outlined in this agreement is for service evaluation and not research. The primary project aim is to determine the impact of open invitations on attendance to the NHS Breast Screening Programme in London during the COVID-19 recovery phase. This will be achieved through the completion of three project objectives:
1) which factors predict overall attendance at Breast cancer screening appointments during the secondary phases of the COVID-19 pandemic,
2) do the factors in (1) differ between open and timed invitation-types,
3) do the populations that do not attend each invitation type differ significantly, to potentially represent a significant healthcare inequality.

Secondary aims will involve examining the spatial accessibility of screening services/hubs in London during the pandemic. This will examine how the effect of changing loco-regional COVID19 positive cases, the type of screening service (mobile versus fixed) and hospital activity, impacts
open the perceived accessibility of individual screening services in London.

NHS England will provide a cohort to NHS Digital to include patients in London asked to screen between August 2020 and July 2021. Imperial College London estimate approximately 1.2 million patients will be invited to screen over this period. The recovery of the health and screening service following its emergency footing where several emergency measures have been taken, continues with screening services in England. Therefore, Imperial College London will be undertaking a further extraction in Autumn 2021 (approx. September/October 2021) to see how dynamic environmental factors (lockdown/Tier* responses) also impact upon the drivers to uptake the new versus old invitation to breast screening, providing legislation permits.

*Whilst the Tiering system is not so relevant now, the environmental factors such as local rates of COVID and impact on local health systems still are relevant. The screening service is predominantly undertaken within fixed centres based within hospitals, and the impact of local rates (freely available to the public) is an important co-variate in understanding attendance.

To evaluate the ongoing impact of Covid-19 upon breast screening and the inequalities caused by open invitations the study team hope to undertake a further data drops for this cohort using GPES data for Pandemic Planning and Research (GDPPR) in the event that COPI Legislation is extended beyond end of March 2022.

In this agreement, NHS England are the sole Data Controller and Imperial College London will be undertaking the data processing. Funding for this service evaluation is from NHS England. Agreements between Imperial College London and NHS England are in place for this activity. No other NHS (including NHS Improvement) or academic institution is involved in this evaluation work and therefore this agreement.

DATA MINIMISATION
Imperial College London intend to use a data set formed from the linkage of a cohort provided by NHS England to NHS Digital's GPES data for Pandemic Planning and Research (GDPPR). No additional data sets are necessary and therefore this is the minimum data required.

The existing NHS England cohort incorporates NHS Numbers and as the screening service does not collect co-morbidity or ethnicity data directly, a linkage is required. As the cohort Imperial College London intend to be linked with GDPPR is a breast cancer screening population in London - the area of evaluation for NHS England - the data will be filtered by:
- geography (London),
- age (50 to 70 years) and
- gender (female).

The variables of interest include co-morbidities, however these may be provided as cluster-level diagnoses to minimise the data provided. The fields requested have been minimised to provide the information required. Of particular interest to this evaluation are the COVID-codes and co-morbidity status of individuals which may impact upon their healthcare engagement during the COVID recovery[a] period.

[a] The 'COVID-19 recovery phrase' refers to the recovery of the health service following its emergency footing, where several emergency measures were taken in the interest of public safety. These include the halting of screening programmes and changes to invitation systems.

The fields requested in the GDPPR data set have been minimised for only information required. Of particular interest are the COVID-codes and co-morbidity status of individuals as determined by fields within the Charlson Co-Morbidity Index (heart failure, myocardial infarction, Chronic obstructive pulmonary disease (COPD), dementia, peripheral artery disease, kidney disease 3 to 5, liver disease, haematological cancer, solid cancer and immuno-suppressive states). The Charlson Co-Morbidity Index is a validated measure of co-morbidity burden, and is prognostic to survival. Data on healthcare encounters are not required, as this is determined by the individual's episode with the breast cancer screening service, which will be included in the cohort provided. The study team only require diagnoses codes, COVID-codes, IMD and ethnicity correct at the time of the screening appointment date.

The study team do not require data on healthcare encounters, as this is determined by the individual's episode with the breast cancer screening service, which will be included in the cohort provided. They therefore only require diagnoses codes, COVID-codes, IMD and ethnicity correct at the time of the screening appointment date.

The study team requires an extraction based on one cohort for the period August 2020 to March 2021 which includes the current cohort invited by the new open invitation type, and a comparative group from the year previously, and then a further cohort submission in August/September 2021 to review the the ongoing recovery response to the pandemic. The cohort will be submitted by NHS England. Two drops of a record-level pseudonymised GDDPR dataset will be sent to the Big Data Analytical Unit (BDAU) at Imperial College London, who will undertake evaluation on behalf of NHS England.

LAWFUL BASIS FOR PROCESSING
The lawful basis for processing data under GDPR has been reviewed and been assessed as acceptable. NHS England, as the Data Controller, process data under Article 6(1)(e): "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller" as they are a Public Authority. The Data Protection Act 2018 s7(1)(a) defines ‘public bodies’ for the purpose of the GDPR as “a public authority as defined by the Freedom of Information Act 2000”.

The Data Protection Act 2018 defines 'public authority' as that defined under the Freedom of Information (FOI) Act 2000. The FOI Act was amended by the Health and Social Care Act 2012 schedule 5 paragraph 99(b) to include the NHS Commissioning Board (also known as NHS England) as a named public authority.

The Health and Social Care Act 2012 section 23 13E(1) states that the NHS Commissioning Board 'must exercise its functions with a view to securing continuous improvement in the quality of services provided to individuals for or in connection with (a) the prevention, diagnosis or treatment of illness, or (b) the protection or improvement of public health.

Additionally, NHS England process Special Category Health Data under Article 9(2)(j): "processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject" as the data are required for statistical purposes in the public interest - meeting the conditions in the DPA 2018 Schedule 1 Part 1 (4) - which GDPR Recital 52(2) determines is an appropriate derogation from the prohibition on processing special categories of personal data.

Expected Benefits:

Open invitations have been introduced as part of the COVID-19 recovery programme, aimed at reducing the backlog of breast cancer screening appointments. The unintended consequences of introducing these measures upon attendance during the COVID era, and populations who traditionally have low-uptake of screening, is unknown.

This evaluation, led by NHS England, aims at determining this impact with a view to re-evaluate the current screening service practice in London. This is of prime public health concern, and disseminating the findings crucial, to prevent potentially exacerbating healthcare inequalities by continuing to employ current measures in screening services. In London, approximately 268,000 women were screened in 2018/9, however the potential magnitude is greater given the open invitation model, has now been employed across services in England. The need to disseminate the findings to other regions, screening hubs and public health officials is important to ensure they can re-evaluate local policy, as appropriate. Failure to disseminate the findings could result in lower screening uptake or widening health disparities and outcomes amongst subsets of the population.

The findings from this project are hoped to:
1) inform loco-regional policies regarding the use of open invitations during the COVID recovery
2) provide evidence to revert to timed appointments, if appropriate, and
3) allow screening services to tailor resources to increase attendance amongst low uptake groups including those with COVID diagnoses or shielding.

The analysis hopes to achieve the purpose by determining the effect of invitation type (open versus timed) on attendance. Co-variates known to affect attendance such as ethnicity, socioeconomic status and co-morbid status will be adjusted for in this model. Further, this should give an indication on whether the invitation type disproportionately impacts upon these subgroups. Following this analysis, NHS England aim to implement policy based upon these findings, the outcome of which hope to be be determined through screening hub metrics (e.g. uptake and coverage rates).

The work will also inform a PhD student who is substantively employed at Imperial College London, investigating the use of data analytics on the breast screening programme in London which remains in COVID-19 recovery phase. The results from this hope to be used to inform current practice in screening hubs and help guide the national policy on COVID-19 recovery in screening. This is particularly important as the prospect of a third wave approaches and screening services need to adequately prepare. None of the results or methods will be used for/published in any PhD or research work. The involvement of a PhD student is only to facilitate data access and provide infrastructural support as a member of the study team and share their experience and does not affect the purpose of this work as an evaluation.

Outputs:

The following outputs are aimed to be produced from this work:

1. A report from NHS England regarding the impact of open invitations on attendance and healthcare inequalities to guide health policy. The primary output aims to be a report made by NHS England regarding the impact of open invitations on attendance and healthcare inequalities to guide health policy. This report will primarily be made available for NHS Breast Screening Hubs in London, and across the country. It is hoped that this will enable these officials to alter their practice in direct response to the result found from this evaluation. The output report hopes to also be made available to the public who remain important stakeholders in screening services and wider health policy makers who will be able to utilise our findings to alter national practice and inform COVID-19 recovery efforts in other regions.

2. Publications in peer-reviewed scientific journals targeted to the British Medical Journal. Estimated in late 2021, these publications aim to detail the association of varying invitation type and socio-demographics with screening attendance.

3. Presentations in late 2021 which hope to be delivered at international conferences to clinicians, public health workers and experts in preventative care. In addition, Imperial College London intend to present this work directly to screening hubs and public stakeholders to inform practice and delivery of screening regionally. This will be achieved through collaboration with NHS England.

Imperial College London intend to also use non-traditional dissemination through social media and blog posts directed by both NHS England's and Imperial College London's communications department. This will allow the findings to reach relevant stakeholders including patients, clinicians and the broader interested audience.

The work is being conducted through the Patient Safety Translational Research Centre which is one of 3 centres in the UK, as the findings from this work are foreseen to translate into clinical practice. All processes will be compliant with Imperial College London's policy on data publication and dissemination.

All outputs will contain only aggregate anonymous summary 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 insurance, marketing or commercial purposes.

Processing:

Use of Control of Patient Information (COPI) Regulation - Identifiable information flowing to NHS Digital:
Regulation 3 (3)(b) confirms who is able to process patient information which includes: ‘persons employed or engaged for the purposes of the health service.' The Data Controller for this work is NHS England. NHS England have commissioned Imperial College London (ICL) to deliver this piece of work and ICL are therefore the Data Processors in this agreement. NHS England would be considered as falling under the regulation 3(3).

METHODOLOGY
- NHS England provide a filtered cohort of 1,200,000 individuals from the London area via a Secure Electronic File Transfer account (SEFT). The cohort will contain the following identifiers:
1. Pseudo-Study ID (a cryptic identifier created in the Breast Screening service)
2. NHS Number
3. Date of Birth
- NHS Digital will link this cohort of individuals to the GDPPR data set, and extract a data set using the fields requested. NHS Digital will then remove all identifiers from the extract (except the study ID which is a key placed on encounters at the screening service which cannot be used to re-identify the individual outside the hub)
- NHS Digital will then send the resultant pseudonymised record level data extract via SEFT to Imperial College London’s Big Data Analytical Unit (BDAU) for their analysis.

Two drops of data:
Drop 1 - Upon approval of agreement, a cohort of 1,200,000 individuals will be submitted for the period August 2020 - March 2021 (dissemination in August 2021).
Drop 2 - Approx. August/September 2021, a new cohort (estimated numbers are unknown as this is a new invitation system, but smaller than the 1st cohort) will be submitted for the period April 2021 - end August 2021 (dissemination by approx. October 2021).

NHS England will provide a Study ID (a cryptic identifier created in the Breast Screening service placed on encounters at the screening service which cannot be used to re-identify the individual outside the hub).

The resultant pseudonymised dataset will be transferred to the secure data environment at Imperial College London for processing, called the Big Data Analytical Unit (BDAU). To analyse the data, the study team will be using statistics to examine the effect of the new invitation type, which was developed due to the restrictions of COVID-19, on whether individuals attend their appointment. To understand this effect properly and fairly, it is also important to account for other factors that are known to be associated with lower attendance, for example increased deprivation level. By accounting for all these factors statistically (called multivariate regression) the study team will be able to determine the true impact of the new-invitation type.

No attempt to re-identify individuals will be undertaken.

GDPPR data will be used for the specific purpose stated in this agreement. Due to the use of a cohort, it is not currently feasible to utilise the Trusted Research Environment (TRE), and therefore an extract from NHS Digital to Imperial College London is required.

Although the GDPPR data is pseudonymised, the data is treated as confidential patient data due to this being a restricted data set that is collected only for the purpose of COVID-19 management.

Data processing of pseudonymised data will be undertaken by Imperial College's Big Data and Analytical Unit Secure Environment (BDAU SE), which will act as the secure storage and processing location. Data access is strictly controlled by the Imperial College's Big Data and Analytical Unit (BDAU) with stringent procedures including dataset registration process, limiting access to the data to the minimum numbers of the research team required and BDAU approved staff access facilities. Data analysis will only be undertaken through the BDAU. The data will only be used for the purpose outlined in this Data Sharing Agreement. Imperial College London staff are bound by all policies and regulations as substantive employees of the College.

All data will be stored and analysed within the Imperial College's Big Data and Analytical Unit Secure Environment (BDAU SE). The BDAU SE is located in Imperial College London's Data Centre.

Data Processing is only carried out by substantive employees of the data processor (ICL) and who have been appropriately trained in data protection and confidentiality. All personnel will abide by Imperial College London's as well as national data privacy regulation. Imperial’s Big Data and Analytical Unit Secure Environment (BDAU SE) is a secure research environment, providing a standard operating/access model, secure data storage and processing environment and analysis software. It is ISO 27001 certified and also compliant with NHS Digital’s Data Security and Protection Toolkit. The BDAU SE is located in Imperial College London’s Data Centre and can be accessed remotely by users using multi-factor authentication once the user registration process is completed. Data will only be provided once the appropriate dataset registration process is completed. All data files and directories will be encrypted using advances encryptions standards (Encryption is the process where data is encoded for privacy and a key is needed by the data owner to access the encoded data).

Virtus SDC Ltd are not considered a Data Processor. Virtus are a data centre co-location provider (providing data centre space), not a cloud services provider. Imperial College rents space in Virtus data centres to host BDAU equipment. All data processing are performed by BDAU’s staff. Virtus provide physical security, power and environmental controls. Virtus have a number of quality credentials including ISO27001, certificate copies of which can be accessed or downloaded on the following page: https://virtusdatacentres.com/why-virtus/quality-credentials.

GDPPR Disclosure Controls / Suppression Rules
Whilst there are no specific GDPPR disclosure controls, outputs for public consumption should follow the Government (ONS) Statistical Service disclosure controls.  NHS Digital require that users review and follow the disclosure control guidelines as set out within the HES Analysis Guide. Some, but not all requirements are outlined below:
 
Disclosure control only needs to be applied to values relating to individuals. No rounding or suppression is required for values not relating to individuals, such as a count of providers.
No small number suppression is required for national totals.
For any sub national geographies e.g. NHS Commissioning Region / Government Office Region or smaller, then the following apply:
• Zeroes can be shown.
• Values between 1-7 to be displayed as “*”.
• Any other numbers rounded to nearest 5.
• Percentages calculated from rounded values


Quarterly HES Extract - Health Policy HES projects — DARS-NIC-315716-L0F4M

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2018-08-02 — 2020-08-01

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Outpatients

Objectives:

The Department of Surgery and Cancer, based at Imperial College London, is requesting to retain existing data for use in the following research project:

Project 2 - Quality of care for elderly patients with chronic conditions study:
The aim of the research is to evaluate risk factors that lead to functional health decline in elderly population with chronic conditions. There is no strong evidence on the pattern, sequence and interaction of risk factors that lead to functional health decline. The research question is: “Is there any pattern and sequence of occurrence of risk factors that lead to functional health decline? If there is any geographic variation in risk factors due to variation in quality of care provided to elderly population?” This project can use HES to answer these questions.

2018 Update - Imperial College London would like to amend this study, using existing data and without further release of data, to investigate the impact of government policies for improving quality and patient safety for frail patients. Frailty bears close relation with chronic conditions in the sense that both are crucial drivers of a person’s health trajectory in later life. In England, different policies were introduced to improve the quality of care for patients in general, for example: payment by results (PbR) reform (see Department of Health’s Payment by results – Guidance for 2012-13, Emergency readmissions section) and freedom of choice of healthcare providers (see Martin Gaynor’s Death by market power: Reform, competition and patient outcomes in the national health service). However, there has been limited existing evidence in research literature of how these government policies would specifically affect frail patients and this invokes concern that frail patients are less likely to benefit from such policies.

This project will use the following data: HES APC 2003-2014 and HES A&E 2007-2014. The project will use 10-year inpatient data to understand risk factors that lead to long-term functional health decline, which was not assessed by previous clinical studies.

Project 1 - Patient safety alerts study:
Project Completed
This is an evaluation of the effectiveness and economic efficiency of system-level interventions. The national reporting and learning system (NRLS) and selected patient safety ‘alerts’ that have been produced for the NHS during its existence will be evaluated retrospectively to inform the configuration of the system in future. The study will investigate whether there are reductions in patient safety incidents after the publication of an alert, which is expected to reduce health service resource use and indicates that the intervention is successful. The evidence from this study will support decisions about the types of incident reporting activities the system should prioritise in future. This is important given the important role of incident reporting in the NHS and the economic constraints within which these systems must function.

This project used the following data: HES APC 2003/4-2013/14; HES OP 2003/4-2013/14; HES A&E 2007/8-2013/14 and HES Critical Care 2008/9-2013/14. The 11-year period is specified for this evaluation in order to capture sufficient baseline trends before and after any alert is published. Safety alerts take a general form i.e. the interventions they recommend may have implications for any number of clinical settings, which is why all HES setting-specific data is requested.

Project 3 - Hospital-acquired infections study:
Project Completed
The objective of the project is to assess the magnitude and nature of the discrepancies between incident rates as implied by frequency in the National Reporting and Learning System (NRLS) - derived both from the NRLS metadata and analyses of the textual description of incidents - and the frequency in HES.

This project used the following data: HES APC 2003-most recent; HES A&E 2007-most recent, to correlate with the live updates from the NRLS.

Project 4 - Health information exchange evaluation:
Project Completed
The project aims to evaluate the impact of the implementation of a patient-accessible personal health record, provided by the Imperial College NHS Trusts to patients and their other carers (GPs, community and social care, relatives) through a website. This Health Information Exchange (HIE) will be piloted in the autumn of 2015 and the Institute for Global Health Innovation will evaluate the impact of the HIE in this pilot population.

This project used the following data: HES APC 2013/14 and going forward; HES OP 2013/14 and going forward; and HES A&E 2013/14 and going forward.

In the event that other purposes are envisioned, a new request or amendment will be submitted to NHS Digital to obtain appropriate authorisation for these additional projects.

The Department of Surgery and Cancer confirms that the data under this application would only be used for the 4 projects listed in this Data Agreement, and any additional project for which the use of data provided under this agreement has been separately approved by NHS Digital and is subject to a valid Data Sharing Agreement with NHS Digital. Equally individuals working on each project will only be permitted to access the data relating to that project, as identified within this application. Access is granted for each project only to the named individuals associated with that project under authorised user names. Such access is password controlled (with a password reset required on a regular refresh).

The controls enable a single copy of the data to be held, reducing security risk associated with multiple copies being provided per project. This model is aligned with similar arrangements for other sizeable research institutions.

The raw data will be handled only within the Department of Surgery, to support academic research, subject to obtaining the required ethical approval where applicable. However none of the specific projects mentioned in this form needs ethical approval.

The HES data will be used alongside the National Inpatient Survey, which will be available from 2003 going forward, and the National Reporting and Learning System (NRLS), which is housed at IGHI and contains data going back to 2003 and is continuously updated.

Expected Benefits:

The benefits to health and/or social care from the expanded knowledge base of academic research are by their nature not specifically measurable. Besides these intangible benefits, laid out below are a number of additional potential benefits which could result from the four studies described above.

2. Quality of care for elderly patients with chronic conditions study

a. The publication (see output 2B) will be targeted at epidemiologists and public health commissioners. By publishing in journals read by these stakeholders, they can use the newly developed techniques to estimate functional impairment burden in the population and assess various factors affecting it in different settings.
b. The publication (2C) and the white paper (2E) will suggest various outcome measures based on hospital administrative data that will can be used to identify risk. Presentation at different conferences and engagement of primary care policymakers will help spread information about pattern of preventable risk factors leading to functional health decline to front-line primary care stakeholders. This will identify elderly population at high risk of functional health decline, and allow for preventative actions to be taken.

2018 Update - Since it is unclear if the recent NHS policies targeting quality and safety of care are equally effective for different patient groups, or whether vulnerable groups such as frail patients miss out on quality improvements for example due to gaming of quality indicators, the project will assess the performance of these policies e.g. penalties for readmissions and hospital competitions on frail patients. This will benefit Health and Social Care by providing validation of efficacy for such policies both retrospectively and going forward. This could also lead to more efficient budget allocations for policy interventions related to frail elderly patient. Lastly it will provide information which can be used to assess future programmes for quality and safety improvement taking into account this vulnerable patient group.

At this point in the process it would be not be appropriate to attach specific improvement goals or commit to expected savings figures, as the size of the potential impact on the health and/or social care system is a portion of what Department of Surgery & Cancer are seeking to discover in the various projects.

Outputs:

It is expected that these studies will lead to 3 theses submitted in fulfilment of the requirements for the degree Doctor of Philosophy at Imperial College London (“PhD theses”), 10 articles in peer-reviewed journals (“published academic papers”), and 6 policy papers for public consumption (“white papers”).

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

2. Quality of care for elderly patients with chronic conditions study

a. A PhD thesis (October 2017)
b. A published academic paper on the novel methods used to analyse deterioration (December 2016). This will be submitted for publication in the Journal of Clinical Epidemiology, read by epidemiologists and other healthcare planners.
c. A published academic paper on factors contributing to deterioration (May 2017). The applicant hopes to publish this in the BMJ (British Medical Journal), and will request any journals to have open access to the articles. These journals have wider audience and are read by health policy makers, epidemiologists, and primary care teams.
d. The Department of Surgery & Cancer aim to present the findings at international conferences and health policy meetings such as such as the UK Faculty of Public Health annual conference and the National Health Policy conference held by Academy Health, and GP conferences organised by the Royal College of General Practitioners and the BMA.
e. A white paper publication evaluating pattern and variation in preventable risk factors that leads to functional health decline in elderly population with chronic conditions.

Update 2018

a. A PhD thesis (December 2020)

b. A published academic paper on quantifying the costs of care for frail patients (March 2019).

c. A published academic paper on the association of frailty syndromes and patient safety incidents (June 2019).

d. A published academic paper on the reliability of using penalty as an incentive to reduce hospital readmission rates for frail patients (December 2019).

e. A published paper on the relationship between competitions and quality in healthcare markets for frail patients - assessing the change in the quality of care for frail patients after the 2006 NHS reform in giving patients more freedom on choice of hospitals and healthcare service providers (May 2020)

f. The applicant aims to have all four papers published in the Journal of Health Economics. We hope to present the study findings in international health economics conferences such as: Health Economists’ Study Group (HESG) Conference, European Conference on Health Economics by the European Health Economics Association (EuHEA).

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

Processing:

The data was received from NHS Digital and is stored on a secure server hosted at the South Kensington campus of Imperial College. Access to data on this server is restricted to authorised individuals only. The data is accessed and processed by researchers in the Department of Surgery & Cancer at the St Mary’s campus of Imperial College in Paddington, West London, 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 organisation. The data will not be made available to 3rd party individuals, institutions or companies.

The data will be processed as part of the above-mentioned research project within the Department of Surgery & Cancer. It will be queried using data analysis tools such as SPSS, STATA, SAS, macros and pivot tables in Microsoft Excel, Microsoft Access etc. to aid in answering specific research questions. Data visualisations will be done to present insights gained using suitable tools including Tableau, Inkscape, NodeXL, Qlikview and others.

Data will be moved to the Big Data & Analytical Unit (BDAU) within the duration of the agreement.

Two senior members of the Big Data & Analytical Unit (BDAU) create and make available (via the secure server or very occasionally on an encrypted data storage device) the customised extract specific to the needs of each project.

The specific processing activities for each project will be as follows:

Project 2: Quality of care for elderly patients with chronic conditions study: This project is ongoing.

• This project will include all the patients over the age of 55 years that will be diagnosed with COPD, Stroke and dementia for the first time. Department of Surgery & Cancer will select patients for the years 2007/2008 who had been diagnosed with stroke, dementia or COPD. In order to check that it was their first time diagnosed, Department of Surgery & Cancer will check in the preceding five years (till year 2003/2004) their secondary diagnosis and any previous diagnosis of stroke, dementia and COPD. The patients will be followed up until 2013/2014
• Department of Surgery & Cancer will use HES inpatient data and HES A&E data to check for the following information:
i. Annual and overall all-cause readmission rate
ii. Annual and overall cause-specific readmission rate (common causes of readmission every year will be identified)
iii. Annual and risk adjusted overall mortality rate
iv. Annual cumulative LOS
v. Annual discharge destination (that is discharge to nursing home, residential care)
vi. Annual rate of recurrent falls (use data from HES A&E and HES inpatient)
• Department of Surgery & Cancer plan to publish these same metrics in future publications as a result of this research.

2018 Update - Imperial will use patient level data to flag admissions where adverse outcomes occur and use econometric methods such as multivariate regression models to estimate the impact of government policies on quality of care for frail patients compared to non-frail patients. Frailty will be defined in line with the literature, for example patients with related diagnoses, e.g. anxiety and depression (F320), senility (R54X), dementia (F000), delirium (F050), dependence (Z741), falls and fracture (R55X), incontinence (R15X), mobility problems (R260), and pressure ulcers (L890). The list of ICD codes for these frailty related diagnoses is referenced from a paper Developing and validating a risk prediction model for acute care based on frailty syndromes. In addition to the listed outcomes we will also analyse information relating to patient demographics, hospital service utilization, and hospital types.

Data required for project 2 will be extracted from the master dataset for 315716-L0F4M which is currently only accessible to BDAU staff. This data will be made accessible in a ring-fenced dataset (in the same method that all datasets are provided in the BDAU) which will only be accessible by researchers assigned to the approved update.

It has been agreed with all academic supervisors that any further work going forward will require a new HES application. Once project 2 has completed all data for 315716-L0F4M will be destroyed.

Controls related to point 2 are handled through a robust security mechanism within the BDAU secure environment which is ISO 27001 certified and 100% compliant with Level 3 of IG Toolkit v14.1. This includes restriction of access to data to only researchers who require access with yearly renewal and accompanying robust audits.

The following projects are now closed, data will be retained in the event of any queries or challenge related to the research findings

Project 1: Patient safety alerts study:

The effectiveness and economic efficiency of system-level interventions will be evaluated using multi-level modelling of patient safety incidents before and after intervention(s) are disseminated. HES endpoints of interest are diagnostic and procedural codes, which will be used to measure health service utilisation and associated resource use in order to create an economic model justifying investment in a national reporting system for patient safety. To achieve this ICL will manipulate the data, as follows:
• The HES record will be used to generate patient and/or hospital trust level panel data.
• The monthly rates of safety incident related diagnoses will be calculated
• Hospital resources used at either patient or hospital level will be estimated(unit cost data will be derived from secondary sources)
• In any publication, monthly and annual safety incident rates, resource use, and associated costs will be published at an aggregate (national) level. Rates are likely to be reported by hospital or patient characteristics / clusters.
• Incident rates are unlikely to be reported by geography / region.

Work on project 1 was assigned to DS002 within the BDAU. Access for DS002 is currently restricted to BDAU staff only. There are no further yielded benefits on this work as there has been no new work done since the last renewal.

Project 3: Hospital-acquired infections study:

Analysis of historic trends in patient safety events, comparing the reported incidence of C.diff and MRSA infections in the National Reporting and Learning System with the calculable counts in HES and official counts from Public Health England
Department of Surgery & Cancer will use HES to calculate time trends for rates of
• Clostridium difficile infection
• MRSA infection/bacteraemia
• ICD10:T80-88 range of medical/surgical complications.

Work on project 3 was closed due to departure of primary researcher. No dataset was produced from the 315716-L0F4M for this project and only theoretical benefits of linking HES and NRLS were outlined as described in previous yielded benefits. No further work will be done for this project and no further data will be extracted.

Project 4: Health information exchange evaluation:

• GP practice populations will be analysed (except where small numbers are present) to build a baseline of system performance which may be affected by an implementation of a health information exchange and their participation in the pilot.
• These populations will be analysed for a change in clinical outcomes (e.g. readmissions, avoidable admissions, emergency admission, length of stay) before and after the implementation of a health information exchange.

Work on project 4 has completed and no new work will be done using the existing dataset. This dataset has been destroyed and yielded benefits were updated last year. Due to delays in CIE rollout item 4b was not produced.

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)

There will be no data linkage undertaken with NHS Digital data provided under this agreement.

Data will only be accessed and processed by substantive employees of Imperial College London.


MR1108: CT colonography, colonoscopy, or barium enema for diagnosis of colorectal cancer in older symptomatic patients: SIGGAR1 (Special Interest Group in Gastrointestinal and Abdominal radiology). Plus SOCCER (Symptoms of Colorectal Cancer Evaluation Research). — DARS-NIC-291981-Y7J2F

Opt outs honoured: Identifiable (Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006)

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-09-01 — 2021-03-31

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report
  4. MRIS - Members and Postings Report

Objectives:

The study sponsor for the Special Interest Group Gastrointestinal and Abdominal Radiology (SIGGAR) and Symptoms of Colorectal Cancer Evaluation Research (SOCCER) studies is Imperial College London. The research group responsible for the studies at Imperial is the Cancer Screening and Prevention Research Group (CSPRG). Imperial College London are the data controller and the data processor for the SIGGAR and SOCCER studies.

When a person visits their doctor for symptoms that are suggestive of bowel cancer, the doctor might refer them to hospital for further investigations. Often people will be offered a test to examine the whole of the large bowel to look for abnormalities. Prior to the SIGGAR study, the tests that had proven most suitable for ‘whole colon’ examination were colonoscopy and barium enema.

Colonoscopy is a procedure during which, usually under sedation, a tube (endoscope) with a small camera attached is inserted into the rectum and guided through the large bowel to examine its surface for abnormalities. The barium enema examination involves passing a white liquid containing barium into the bowel through the rectum and then taking x-rays of the bowel.

More recently, a new imaging test for examining the bowel has been developed. This test is called Computed Tomographic Colonography, which you might see abbreviated as CTC or called ‘virtual colonoscopy’. Virtual colonoscopy uses x-rays to take images of the bowel, and creates 3-dimensional pictures to aid diagnosis. It does not require barium.

Prior to the SIGGAR study it was thought likely that virtual colonoscopy was a useful test for examining people with bowel cancer symptoms, and might have some benefits over barium enema and colonoscopy. However, the strong evidence that was needed to adopt virtual colonoscopy more widely had not been collected.

The SIGGAR study aimed to provide the robust evidence needed to support the use of virtual colonoscopy in bowel cancer diagnosis. To achieve this, Imperial College London compared virtual colonoscopy with barium enema or colonoscopy in two randomised trials, to see how effective and safe virtual colonoscopy was compared with the other two tests.

The SIGGAR study recruited patients from 2004 to 2007. The aim of the study was to investigate the use of computed tomographic colonography (CTC) as an imaging technique for the diagnosis of colorectal cancer, compared against the then accepted alternatives of colonoscopy and barium enema. Patients within the SIGGAR cohort (n=8,484 in total) were divided into two categories: those who were randomised and consented in the trial (n=5,448), and those who were eligible to participate and were registered but were ultimately not randomised (n=3,036).

Flexible sigmoidoscopy, which is an examination of the lower part of the bowel, has many potential benefits over tests that examine the whole of the large bowel, both for patients – who may find it more acceptable – and also for health services – that need to ensure resources are being used in the most effective way.

Prior to the SOCCER study, scientists had carried out a study which showed that certain bowel cancer signs and symptoms are more common in people with bowel cancers in the lower part of the large bowel, and so within reach of the flexible sigmoidoscopy, and that other symptoms are more common in people with cancer in parts of the bowel that are out of reach of the flexible sigmoidoscopy. This was a study at only one hospital, but it provided early evidence that specific signs and symptoms could be used to predict when flexible sigmoidoscopy could be a safe and effective test to diagnose bowel cancer, and when it is less likely to be effective.

The SOCCER study follows on from this SIGGAR work on bowel cancer symptoms. Imperial College London are aiming to provide evidence that is needed to show whether flexible sigmoidoscopy is an effective and safe alternative to whole colon examinations for many people. If the results confirm the earlier study, Imperial College London anticipate that this will change how doctors diagnose bowel cancer in their patients based on their symptoms.

The SOCCER study was a retrospective analysis of data accrued from the SIGGAR study. The SOCCER study cohort was made up of patients referred to hospital with symptoms suggestive of colorectal cancer and who were registered as potentially eligible for the SIGGAR study (whether they were subsequently randomised or not). Anyone who did not wish to take part in the SIGGAR trials, or withdrew their consent following randomisation, were excluded from the SOCCER study cohort. The aim of the SOCCER study was to determine whether whole colon investigation (WCI) by colonoscopy, CTC or barium enema was necessary for all patients with colorectal cancer symptoms, and for which patients a flexible sigmoidoscopy (FS) would suffice.

The CSPRG at Imperial College London wish to extend this agreement to enable further analysis of the SOCCER data to conduct additional analyses included in the current SOCCER protocol and to be able to respond to questions from the scientific community post-publication.

The CSPRG at Imperial College London would like to continue to keep the SIGGAR cohort flagged as they will be reviewing the SOCCER protocol in 2020 and, if an amendment is made, this may result in the need for additional follow-up data.

The CSPRG do not have any plans to conduct additional analyses on the SIGGAR trials; however, it is a requirement of Imperial College London that the CSPRG hold data from all clinical trials for 10 years after the study is closed.

Ethical approval for the SIGGAR study was obtained from Northern and Yorkshire Multi-Centre REC in January 2004. The SIGGAR study was funded by an NIHR-HTA award (Project No: 02/02/01) from 2003 until June 2011.

Ethical approval for the SOCCER study as an add-on study using data from the SIGGAR cohort was obtained from Northern and Yorkshire Multi-Centre REC in April 2013. The SOCCER study was originally funded by the NIHR-HTA from January 2013 until the end of November 2015 (Project No: 11/136/120). The SOCCER study continues to be funded by the Cancer Research UK Population Research Committee programme grant until December 2022. No other funders are involved. The funders were not involved in the study design, data collection, data analysis, manuscript preparation, or publication decisions.

Mortality and cancer information for the patients in the SIGGAR cohort was required to answer the objectives of the SOCCER study as detailed in the SOCCER Protocol v1.0. The primary objective of SOCCER was to investigate the link between patients’ symptoms at presentation and the risk of cancer in the proximal colon, to determine whether there were particular symptoms or symptom combinations which indicated that a patient could be adequately cared for by a distal colon exam (FS) rather than more extensive WCI.

The justification for obtaining and processing data for both the SIGGAR and SOCCER studies is covered by the General Data Protection Regulation Article 6 (1e Public task: the processing is necessary for you to perform a task in the public interest or for your official functions, and the task or function has a clear basis in law. and Article 9 (2j processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes).

Yielded Benefits:

The SIGGAR trials showed that computed tomographic colonography (CTC), also known as virtual colonoscopy, was better than barium enema for the diagnosis of colorectal cancer and was significantly better value for money for the NHS. On the basis of preliminary results from the trial, the NHS Bowel Cancer Screening Programme recommended that only CTC should be offered as an alternative to colonoscopy. This is described in the Guidelines for the use of imaging in the NHS Bowel Cancer Screening Programme, Second Edition. NHSBCSP Publication No 5, Sheffield: NHS Cancer Screening Programmes, 2012, available at: https://www.gov.uk/government/collections/bowel-cancer-screening-commission-provide-inform. Guidelines for the management of patients with symptoms suggestive of colorectal cancer were also updated in light of these findings, which now recommend CTC over barium enema as the first line alternative to colonoscopy: Colorectal cancer: diagnosis and management. NICE guideline (CG131). (Last updated 2014). London: NICE, 2011. https://www.nice.org.uk/guidance/cg131. The SIGGAR trials also showed that CTC was generally as good as colonoscopy at finding colorectal cancer and had the added benefits of being associated with fewer complications and being more acceptable for many patients. These findings demonstrated that patients can be safely diverted towards CTC as an alternative to colonoscopy, which may help to alleviate pressure on overburdened endoscopy services. Conversely, the risk of proximal cancer is very low in patients without broad definition anaemia and/or abdominal mass but who present with any rectal bleeding or solely a change in bowel habit to increased frequency. Patients with this symptom profile can be examined safely by flexible sigmoidoscopy alone. If incorporated into guidelines, this strategy would greatly reduce the number of whole colon investigations (WCIs) performed in patients at low risk of proximal cancer; this would alleviate the burden of WCIs on patients and endoscopy and radiology services.

Expected Benefits:

No new or further data will be provided under this version of the agreement.

Both the SIGGAR and SOCCER studies were funded by the NIHR-Health Technology Assessment Programme.

The SIGGAR HTA Project No: 02/02/01 from January 2003 until June 2011.

The SOCCER HTA Project No: 11/136/120 from January 2013 until November 2015.

Currently SOCCER is funded through the Cancer Research UK Population Research Committee grant until December 2022.

The research for the SOCCER study utilised a significant amount of data collected within the SIGGAR trial to determine whether all patients with symptoms suggestive of colorectal cancer require a whole colon examination (i.e. colonoscopy or CTC), or whether flexible sigmoidoscopy, which examines just the rectum and distal colon, might suffice for some patients. The CSPRG found that the risk of cancer in the proximal colon was very low in patients without anaemia and/or abdominal mass, but who presented with rectal bleeding or solely a change in bowel habit to increased frequency. The CSPRG therefore concluded that flexible sigmoidoscopy would suffice for these patients.

The SOCCER study also newly demonstrated that the probability of missing proximal cancers can be significantly reduced by adopting a broad definition of anaemia when deciding upon diagnostic investigation. The CSPRG found that 80% of proximal cancer cases could be identified using a broad definition of anaemia (Haemoglobin (Hb) <13 g/dL in men and <12 g/dL in women), compared to 39% using the narrow definition (Hb <11g/dL men; <10g/dL women) with requirement for iron deficiency anaemia. Given the importance of anaemia as a marker for proximal cancer, the SOCCER study concluded that all patients referred with suspected colorectal cancer should have a full blood count, unless an emergency investigation is required.

In 2019, the CSPRG disseminated these findings to researchers, clinicians, policy makers, interested groups, and members of the public in order to influence policy and see that the recommendations are incorporated into future National Institute for Health and Care Excellence (NICE) referral guidelines for suspected colorectal cancer.

In all dissemination channels, the CSPRG emphasised how the recommendations would benefit the provision of health care. Specifically, the CSPRG described how adoption of the criteria for flexible sigmoidoscopy would save the NHS time and money by minimising the number of unnecessary whole colon examinations. A reduction in demand for whole colon examinations would also mean that endoscopy and radiology resources could be utilised elsewhere; for example, in the NHS bowel cancer screening programme. Furthermore, the CSPRG outlined how the recommendations to take full blood counts and adopt a broad definition of anaemia would minimise the risk of missing proximal cancers.

The SOCCER study dataset has the potential for use by a PhD student in the future - if any additional research questions are identified that are not already included in the current SOCCER study protocol, the CSPRG will go through the amendment process with REC / HRA-CAG / NHS Digital.

Outputs:

No new or further data will be provided under this version of the agreement.

The Cancer Screening and Prevention Research Group (CSPRG) at Imperial College London do not have any plans to conduct additional analyses on the SIGGAR trials at this time.

The SOCCER study is an add-on study to the SIGGAR trials and additional processing is anticipated in the period to 31-Dec-2022 which will generate further publications.

Publications for the SIGGAR and SOCCER studies using the data already received from NHS Digital are:

SIGGAR.
The National Institute for Health Research (NIHR) final report for SIGGAR in July 2015: Halligan S, Dadswell E, Wooldrage K, Wardle J, Von Wagner C, Lilford R, Yao GL, Zhu S, Atkin W. Computed tomographic colonography compared with colonoscopy or barium enema for diagnosis of colorectal cancer in older symptomatic patients: two multicentre randomised trials with economic evaluation (the SIGGAR trials). Health Technol Assess. 2015 Jul;19(54):1-134. doi: 10.3310/hta19540. The report is available online at: https://www.journalslibrary.nihr.ac.uk/hta/hta19540#/abstract.

The manuscript for the SIGGAR trial of CTC vs colonoscopy is available online at: http://www.ncbi.nlm.nih.gov/pubmed/23414650 (Atkin W, Dadswell E, Wooldrage K, Kralj-Hans I, von Wagner C, Edwards R, Yao G, Kay C, Burling D, Faiz O, Teare J, Lilford RJ, Morton D, Wardle J, Halligan S; SIGGAR investigators. Computed tomographic colonography versus colonoscopy for investigation of patients with symptoms suggestive of colorectal cancer (SIGGAR): a multicentre randomised trial. Lancet. 2013 Apr 6;381(9873):1194-202. doi: 10.1016/S0140-6736(12)62186-2).

The manuscript for the SIGGAR study of CTC vs BE is available online at: http://www.ncbi.nlm.nih.gov/pubmed/23414648 (Halligan S, Wooldrage K, Dadswell E, Kralj-Hans I, von Wagner C, Edwards R, Yao G, Kay C, Burling D, Faiz O, Teare J, Lilford RJ, Morton D, Wardle J, Atkin W; SIGGAR investigators. Computed tomographic colonography versus barium enema for diagnosis of colorectal cancer or large polyps in symptomatic patients (SIGGAR): a multicentre randomised trial. Lancet. 2013 Apr 6;381(9873):1185-93. doi: 10.1016/S0140-6736(12)62124-2).

The CSPRG were also involved in the preparation of a news article that was published on the Imperial College London website in February 2013: http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/newssummary/news_14-2-2013-12-20-5.

SOCCER.
The NIHR final report for SOCCER in November 2017: Atkin W, Wooldrage K, Shah U, Skinner K, Brown JP, Hamilton W, Kralj-Hans I, Thompson MR, Flashman KG, Halligan S, Thomas-Gibson S, Vance M, Cross AJ. Is whole-colon investigation by colonoscopy, computerised tomography colonography or barium enema necessary for all patients with colorectal cancer symptoms, and for which patients would flexible sigmoidoscopy suffice? A retrospective cohort study. Health Technol Assess. 2017 Nov;21(66):1-80. doi:10.3310/hta21660. PMID: 29153075. The report can be viewed online at: https://www.journalslibrary.nihr.ac.uk/hta/hta21660/#/abstract.

In addition, CSPRG produced a manuscript describing the main findings from the SOCCER study for publication in a peer reviewed journal. The manuscript was published online in December 2018: Cross AJ, Wooldrage K, Robbins EC, Pack K, Brown JP, Hamilton W, Thompson MR, Flashman KG, Halligan S, Thomas-Gibson S, Vance M, Saunders BP, Atkin W. Whole-colon investigation vs. flexible sigmoidoscopy for suspected colorectal cancer based on presenting symptoms and signs: a multicentre cohort study. Br J Cancer. 2018 Dec 19. doi: 10.1038/s41416-018-0335-z. [Epub ahead of print]. The manuscript is available online at: https://www.nature.com/articles/s41416-018-0335-z.

Presentations:
In February 2017, a member of the CSPRG gave a presentation on the SOCCER study at the Cancer Research UK (CR-UK) Early Diagnosis conference, discussing the study rationale, methodology, and main findings. In March 2018, a presentation was given at the Saudi-International Colorectal Diseases forum in Riyadh on ‘Colorectal Cancer Surveillance and Early Detection’. As part of this presentation, the presenter discussed the SOCCER study, to communicate the results with an audience of researchers, scientists, and clinicians.

Other:
CSPRG have been using social media (i.e. Twitter) to promote the SOCCER manuscript following publication online in December 2018. This has helped to engage and share the findings with a wide audience, including researchers, clinicians, interested groups, and civil society. CSPRG also prepared a lay summary of the findings which was published as an online press release on Imperial College London News in December 2018. This is available at: https://www.imperial.ac.uk/news/189612/quicker-safer-test-could-accurately-detect/. In addition, CSPRG have prepared a blog about the study results for the NIHR website as well as a summary of our findings for the Imperial BRC website.

All outputs only contained aggregate level data with small numbers suppressed in line with the Hospital Episode Statistics (HES) Analysis Guide.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). No new or further data will be provided under this version of the agreement.

Imperial College London is the data processor and data controller for this study. Data was processed previusly by colleagues at The University of Birmingham who were responsible for the health economic analysis for the SIGGAR studies. The University of Birmingham is not involved with the processing of the NHS Digital data currently.

Data historically requested and approved for the SIGGAR and SOCCER studies from NHS Digital include:

February 2009
-Follow-up of trial cohort with the Office for National Statistics (ONS) and local cancer registries (in addition to NHS Information Centre (NHSIC) to enable CSPRG to obtain cancer and death registrations.
-Approval to allow cancer registries to match CSPRG data with the Hospital Episode Statistics (HES) database.

May 2009
-Approval for data on randomised patients in SIGGAR (Approval under Section 251 of the NHS Act 2006 – formerly Section 60 of the Health and Social Care Act 2001.
-Approval to identify study population/control group for SIGGAR using death records and cancer registrations.
-Variables to be supplied by NHS Central Register included; Patient ID variables (Patient’s name, DOB, sex and NHS No.), site of cancer (ICD codes), morphology of cancer (ICD codes), cancer registration no. (to include cancer registry codes), date of death (if applicable) ICD codes and description of causes of death (where applicable).
-Approval to flag records within the study population on the NHSCR (NHSCR – administered by NHSIC).
-Details to be flagged on the death records included; Date of death, cause of death.
-Details to be flagged on the cancer registration records included; Date of cancer diagnosis, site of cancer (ICD codes), morphology of cancer (ICD codes), cancer registration number (to include cancer registry codes).
-Approval to trace study population using date of death and cause of death.

March 2011
- Received SIGGAR HES dataset

May 2011
-Approval to receive data relating to registered but not randomised patients for use in SOCCER (Approval under Section 251 of the NHS Act 2006 – formerly Section 60 of the Health and Social Care Act 2001).
-Approval to link data for approximately 4000 patients, who were eligible to take part in the SIGGAR trial but did not do so, with the national cancers and deaths databases held by the ONS and the NHSIC.
-Approval to allow patient identifiers to be sent to the NHSIC (now NHS Digital) and ONS by participating hospitals and permit these organisations to match this data with their cancer and death databases.
-Approval to use list cleaning service

2015
-CSPRG received data relating to registered but not randomised patients (approved in May 2011) in a pseudonymised form for the SOCCER study (MR1108A; DARS-NIC-291891-Y7J2F).

The SIGGAR cohort used in SOCCER consists of two groups of patients:

1) Randomised patients (flagged as MR1108 by NHS Digital) - these patients consented to SIGGAR and did not subsequently withdraw consent (n=5,345 after exclusions). Identifiable data was sent from NHS Hospitals to NHS Digital only. All data sent from NHS Digital to the CSPRG was in a pseudonymised form but included the unique study number.

2) Registered but not randomised patients (flagged as MR1108A by NHS Digital) - these patients were registered for SIGGAR but were not randomised and were therefore not offered the opportunity to give consent (n=2,030 after exclusions). Identifiable data was sent from the NHS Hospitals to NHS Digital only. NHS Digital flagged these patients and sent only pseudonymised cancers/mortality data to the CSPRG. The legal basis for obtaining this data for SOCCER was Section 251 approval (14/CAG/1043).

There was no requirement to minimise data when SIGGAR began (other than to request the right kind of data for the right people and the right time period). The studies covered the whole of England. The data dates as far back as 1971 and goes to at least 2014.

The participating NHS hospitals were:
• Royal United Hospital, Bath
• Bradford Royal Infirmary
• Queen Elizabeth Hospital, Birmingham
• Charing Cross Hospital and Hammersmith Hospital
• Leighton Hospital, Crewe
• Royal Cornwall Hospital
• Frimley Park Hospital
• Royal Lancaster Infirmary/Furness General Hospital
• Queen’s Medical Centre/Nottingham City Hospital
• University Hospital of North Tees
• The Royal Oldham Hospital
• John Radcliffe Hospital, Oxford
• St Mary’s Hospital, Paddington
• Derriford Hospital, Plymouth
• Queen Alexandra Hospital, Portsmouth
• St Mark’s Hospital, Harrow
• Withington Community Hospital/Wythenshawe Hospital, Manchester

The data received to date has been analysed to support the NIHR Final Report and publications (see output section).

Additional processing in 2019 – 2022 will be restricted to analyses relating to the existing SOCCER study objectives or analyses of the SOCCER database required to respond to questions from the scientific community following the CSPRG’s publication of the SOCCER study results and associated dissemination activities. No additional follow-up of the SOCCER study cohort is anticipated at this time; however, the CSPRG will be reviewing the SOCCER protocol in 2020 to determine if an amendment is required. If any additional secondary study objectives are identified, the protocol will be updated and submitted for approval to the Research Ethics Committee and the Health Research Authority - Confidentiality Advisory Group.

Explanation of the Data Flow Diagram:
In 2015, the CSPRG did not request data on the 10% of patients who originally dissented to take part in the study (834 of the 3,036 registered but not randomised patients -> 2,202), nor the 64 patients who subsequently withdrew consent (5,448 randomised patients -> 5,384). In addition, a further 113 patients were excluded from the registered but not randomised patients as, after reviewing the study records, it was not clear whether these patients may have been approached to provide consent and dissented or may not have been capable of providing informed consent (2,202 -> 2,089). One further patient was excluded from the registered but not randomised patients as duplicate records were found (2,089 -> 2,088). Therefore, the cohort for the SOCCER study was reduced to a subset of 7,472 patients of the SIGGAR study on whom the CSPRG planned to base their analyses . The legal basis for obtaining this data was Section 251. NHS Digital sent pseudonymised cancers and mortality data (including causes of deaths) for the 7,472 patients.

During the statistical analysis of the SOCCER study, additional exclusions were applied such that the final eligible numbers were reduced to 7,375 patients (5,345 randomised and 2,030 registered but not randomised patients). Reasons for exclusion were no signs or symptoms at baseline (n=32), duplicate records (n=9) and not being traced by NHS Digital (n=56).

The data received in 2015 was uploaded to a secure SOCCER Oracle database and linked to the patients from the SOCCER cohort by the unique patient study number. No patient identifiers are held for any of these patients on this SOCCER database. The pseudonymised cancers and mortality data were not uploaded to the SIGGAR database, which contains the patient identifiable information and therefore any new pseudonymised cancers/mortality data received from NHS Digital were not linked back to the identifiable data on consented patients already held by the CSPRG.

The data received in 2015 was combined with data on performed procedures and presenting signs/symptoms collected directly from the participating English NHS Hospitals as part of SOCCER.

This data was analysed to determine if there was a correlation between cancer diagnoses/deaths occurring within three years of patients presenting at a clinic with specific symptom(s).

The data controller must ensure that there are appropriate contracts and controls in place between the organisation and all persons accessing NHS Digital disseminated data. NHS Digital have the right to audit the controls in place under the data sharing agreement. There are currently no active contracts with third party data processers.

For the SIGGAR study, patient identifiable data is stored in a dedicated and secure database server only accessible to specific CSPRG employees - data analyst/developer and the data clerk. Patient identifiers are removed before providing the data to any other employees for analysis. For the SOCCER study, all data is stored in a separate database to the SIGGAR trial data. All data on the SOCCER database is pseudonymised and is only accessible to a limited number of individuals (data analyst/developer and approved study researchers). Access to the database servers is tightly controlled and limited to specific IP client addresses in the CSPRG office. This access is granted only to specific individuals on a study-specific basis and passwords to access the database are changed every 90 days. All access to the system is logged and recorded.

The identifiable patient data held by the CSPRG is only accessible by a limited number of staff (Data Analyst/Developer and data clerk) to enable them to manage the collection and secure storage of the data. The data is provided in anonymised or pseudonymised subsets and encrypted using the AES encryption standard, before transferring to other approved employees as required. Data is pseudonymised by replacing normal personal identifiers (such as names, addresses and NHS numbers) by a unique identifier. The key linking the pseudonymised data to the personal information is only accessible by the CSPRG Data Analyst/Developer.

All individuals handling the data are required to complete training courses in Good Clinical Practice, Information Security Awareness, Records Management, Freedom of Information and Data Protection and Research Data and Confidentiality. The CSPRG has its own Information Governance Policy which provides employees extensive guidance on the legal obligations involved with sensitive data held within the CSPRG.


Hospital level aggregate NDA data for young adults (16-25) with diabetes in England — DARS-NIC-228637-P6N0L

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2019-06-20 — 2020-06-19

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. National Diabetes Audit

Objectives:

Diabetes care in the UK is among the best in Europe (eg UK is 4th out of 30 countries in the Euro Diabetes Index). However, good overall performance may mask poor clinical outcomes and patient experience within specific patient groups.
The TOGETHER study is a collaboration between clinicians and researchers at Queen Mary's University London, University College London and Imperial College London. This agreement relates to the national quantitative analysis aspect of the study only, for which Imperial College London is the lead, and therefore the sole data controller who will process data. More specifically, only Imperial College London will define the means and purpose for which the NHS Digital data will be used for this agreement, and will be the only organisation to access this data.

Previous work by Imperial College London has shown that mortality among young adults with diabetes in the UK is significantly worse than in other European countries and rose significantly between 1990 and 2010. This is consistent with other data Imperial College London have published showing that young adults report the worst NHS experience of any age group, and have distinct healthcare needs and priorities compared to other age groups. Recent advances in neuroscience and psychology also suggest that peer influences are likely to be particularly important for assessing risk and guiding
behaviour at this stage of the life-course. This is supported by evidence of successful introduction of a group clinic for young adults following renal transplant, which led to a significant reduction in the incidence of graft loss. However, recent systematic reviews have found that group clinics often had a positive effect on clinical and patient-reported outcomes but identified uncertainty about the mechanisms and pathway through which some groups were effective in improving outcomes.

Study aim
The overall aim of this NIHR-funded study is to explore the scope, feasibility and potential scalability of group clinics for young adults with diabetes and complex health and social care needs.

Study evaluation plan
As part of this study, Imperial College London are conducting a mixed-methods evaluation of the impact of group clinics on young adults' engagement with services, and their confidence and success in managing diabetes.

The main quantitative element of this evaluation includes detailed analyses of individual-level, longitudinal data from controls and participants in the group clinics: these data are collected through questionnaires and from hospital records and are not part of this application.

To complement the individual-level analyses, Imperial College London wish to analyse Hospital level data on young adults (16-25 years) with diabetes in the National Diabetes Audit. The legal basis for processing the NHS Digital data under
GDPR is Article 6 (1)(e) - "processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller" and Article 9(2)(j) - "processing is necessary for archiving purposes in the public
interest, scientific or historical research purposes or statistical purposes".

There are three main objectives in doing these additional analyses.
1. To investigate how the clinic population in this study compares to other services across the country (for example, in terms of age, sex, deprivation, and ethnicity).
2. To investigate how this study compares to other services on the three treatment targets and eight care processes included in the National Diabetes Audit.
3. To study change over time in performance on the three treatment targets and eight care processes in the study sites, and how these changes compare to changes in other services across the country.

Depending on the findings of this evaluation, Imperial College London anticipate applying for funding to perform a randomised controlled trial of group clinics for young adults with diabetes.

Imperial College London requires National Diabetes Audit data for use as part of the NIHR-funded TOGETHER study to study group clinics for young adults with diabetes.

Papoutsi,C, Hargreaves DS, Colligan G, et al. Group clinics for young adults with diabetes in an ethnically diverse, socioeconomically deprived setting (TOGETHER study): protocol for a realist review, co-design and mixed methods, participatory evaluation of a new care model. BMJ Open. 2017;7(6).

Role of Imperial and other organisations
The TOGETHER study is a collaboration between clinicians and researchers at Queen Mary's University London, UCL and Imperial College London. This agreement relates to the national quantitative analysis aspect of the study only, for which Imperial College London is the lead, and therefore the sole data controller who will process data. More specifically, only Imperial College London will define the means and purpose for which the NHS Digital data will be used for this agreement, and will be the only organisation to access this data.
This agreement solely relates to quantitative analyses at Imperial College London. As part of the wider TOGETHER study, some qualitative research is being done at Oxford University, but this does not involve any application to NHS Digital or any other body and Imperial College London is not involved in collection, storage, or analysis of any qualitative data.

No record level data are requested. Hospital level data (aggregated with small numbers unsuppressed) from the National Diabetes Audit will only be accessed by authorised members of the research team, who are substantive employees of Imperial College London.

Expected Benefits:

If this trial were to prove successful impact of group clinics on diabetes outcomes, then Imperial College London could expect to see significant improvement in diabetes outcomes for young adults in the NHS within the next 5-10 years.

Analyses of Hospital level National Diabetes Audit data will be an important part of the overall findings - both as key research findings in themselves, and also in supporting interpretation of findings from the qualitative work and analyses of clinical records of study participants (not covered by this application).

The expected measurable benefits of the study overall are listed below

• Assessment of current needs of service users and stakeholders and review of evidence and current use of group clinics and non-traditional care models in diabetes will be performed. The outcome of this realist review and scoping process will, (i) identify baseline needs of local users and stakeholders and, (ii) identify the potential need for new care models in the wider UK population of people living with diabetes.

• The outcome of the co-designed group clinic model will be evaluated against the complex health and social care needs of the participating young adults with diabetes in Newham. Qualitative assessment using individual semi-structured interviews of all stakeholders will derive outcomes on
(i) acceptability/feasibility of delivering group clinics including impact on staff offering them, training needs, resource implications,
(ii) active ingredients/key mechanisms in the intervention,
(iii) contextual factors relevant to successful implementation at macro/meso/micro level,
(iv) sustainability of successful care delivery through group clinics. Quantitative assessment, using national benchmarking and audit data, will be performed from the new care model and compared to current care at Newham, two comparable external units, and national audit data (NDA).

• Mechanisms by which the co-designed group clinics work will be investigated by analysis of differences in patient experience and behaviour before implementation, during iterative revision, and after implementation of the new care models.

• Economic evaluation will identify outcomes related to, (i) costs of this new care model from the health care provider perspective, (ii) comparative costs between new and standard pathways, including direct and substitution costs, (iii) incremental cost-effectiveness, (iv) cost/benefit from the perspective of patient users.

• Outcomes from the qualitative analysis and economic evaluation will have wider impact via guiding commissioners and policy members in the future development of group-based diabetes care. Information from local and national scoping will guide the scaling up and generalisation of this new care model to other geographic locations and long-term conditions.

• The outcomes from this study will also provide a platform for a future cluster-randomised controlled study of integrated group clinics in diabetes, their effect on quantitative clinical outcomes, cost effectiveness and generalisability across different locations.

Specific points about the benefit of findings from NDA data

- Previous work has suggested that group clinics can reduce costs and can improve patient experience and engagement with care. Imperial College London know from their engagement work with commissioners and others that many commissioners and professionals are interested in introducing group clinic models for young people with diabetes and other long-term conditions. However, one important barrier to doing so is concerns that clinical outcomes may deteriorate where such clinics are introduced, particularly for patients that do not attend the groups.

- Ethics protocols for the study prevent individual-level analyses of clinical records on patients who decline participation in the TOGETHER study. Analyses of Hospital level data from NDA will allow Imperial College London to investigate whether any clinic-level change is seen after introduction of group clinic models. Data on all Hospitals will allow Imperial College London to use a sophisticated difference-in-difference analyses rather than simple before-and-after analyses.

Outputs:

Specific outputs of Hospital level analyses of National Diabetes Audit (NDA) data

1. Final report to NIHR (due December 2019)
2. Peer-reviewed journal article of quantitative evaluation of group clinics (planned submission date December 2019/January 2020)
3. Since Spring 2017, Imperial College London have submitted regular updates about progress in applying for NDA data to NIHR, and continue to do so.

Outputs from this project to date include

1. Papoutsi,C, Hargreaves DS, Colligan G, et al. Group clinics for young adults with diabetes in an ethnically diverse, socioeconomically deprived setting (TOGETHER study): protocol for a realist review, co-design and mixed methods, participatory evaluation of a new care model. BMJ Open. 2017;7(6).

2. A realist review of group clinics for young people with long-term conditions is currently under review.

3. This work has been presented at 3 conferences (focusing on diabetes and/or health services research).

4. Preliminary findings were presented to project partners, commissioners and national policy makers at a celebration event in London in November 2018.


Overall dissemination plans and outputs for the TOGETHER study

Findings from this analyses of NDA data will be an important component of the final project findings and be included in all major outputs from this project.

Imperial College London will publish briefing statements, summaries and academic papers to disseminate the findings of this study to a range groups with roles in service use, service delivery, policy making and health service design. These will include:

Local networks:
• Institution-based: e.g. Imperial College London, Queeny Mary University of London,
• Newham CCG have agreed to support dissemination activities, via their Patient and Public Engagement Officer, and their existing network of Diabetes Champions and community neighbourhoods
• Local Transforming Services Together programme - Imperial College London will regularly update this commissioning group which is signed up to by four inner London CCGs and has prioritised YAs.

National organisations
• One of the researchers attends the National Children and Young Adult Working groups chaired by the National Leads for Children and Teenagers and Young Adults and will report to them regularly on the project.
• The study team will engage the Association of British Clinical Diabetologists and Diabetes UK in the national scoping exercise in this study and will plan to disseminate and scale up the study via their key contacts.
• The study team will engage the Young Adult and Adolescent Steering Group of the Royal College of Physicians, as well as other relevant committees and patient groups at the Royal Colleges of Paediatrics and Child Health, and General Practice.
•The study team will also engage with Improvement Science London toshare results across wider academic partnerships and cross-sector groups e.g. NHS England, Greater London Authority
• This study has been adopted onto the North Thames Cancer Research Network portfolio and hope to use their existing structures to disseminate output to their members and lay committees. Imperial College London also have the support of the North Thames CLAHRC (Collaborations for Leadership in Applied Health Research and Care).

International
• Social networks: Imperial College London will use twitter groups of people living with diabetes for the journal Diabetic Medicine, to disseminate the findings.
• Academic: Imperial College London plan to present and publish their work to an international audience interested in diabetes, pregnancy, young adults with long term conditions, new models of care, e.g. via the NIHR Health Services and Delivery Research journal, conference

Processing:

Data access is strictly controlled by the Big Data and Analytical Unit (BDAU) at Imperial College London, 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 Secure Environment (SE) and Imperial College London, as substantive employees of the College.

The National Diabetes Audit (NDA) is part of the National Clinical Audit Programme.
NHS England have commissioned NHS Digital to deliver the NDA Programme for a further 3 years covering 2017-2020
NDA is held by NHS Digital under controllership and no additional data is received and disseminated.

Many units have a relatively small number of patients aged 16-25 and diabetes complications such as eye and kidney problems are relatively uncommon among younger patients. In combination, these two factors mean that small number suppression would effectively exclude a significant number of services from the analyses and would prevent meeting the objective of these analyses, which is to place the local findings in a national context.

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.

Summary of data processing for desired outputs

Step 1: Data will be received, stored and analysed within the Imperial Big Data and Analytical Unit (BDAU)

Step 2: Analyses of aggregate, Hospital level data will be performed. No individual level data will be requested or analysed.

Step 3: Three sets of analyses will be performed on Hospital level socio-demographic characteristics, audit performance, and change in audit performance over time (corresponding to the three objectives above).

- Descriptive analyses of how the sociodemographic profile of the clinic population in Imperial College London's study sites compares to other services across the country (with regard to sex, age, deprivation, ethnicity)

- Comparison of Hospital level audit performance for young adults on the three treatment targets and eight care processes in Imperial College London study sites compared to other services across the country. This will involve descriptive analyses of mean values/proportion of patients receiving each care process.

- Analyses of change in Hospital level audit performance in Imperial College London study sites between 2016/17 and 2017/18 on the three treatment targets and eight care processes, followed by comparison of changes in Imperial College London study sites to changes in the whole population. Differences will be assessed using t tests and chi-squared tests.

Please see the published study protocol for further details of Hospital level analyses and how these will link to other strands of the study evaluation (individual-level analyses of study participants and controls, health economic analyses and qualitative work). This is available to the public as an open access article in British Medical Journal (BMJ) Open.

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.


UK Small Area Statistics Unit - Research Database, NN4B data request previously supplied by ONS — DARS-NIC-208561-Z1M7V

Opt outs honoured: Identifiable (Section 251 NHS Act 2006)

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2019-07-01 — 2020-06-30

Access method: Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Birth Notification Data

Yielded Benefits:

Smoking ban: This study examined the impact of the smoke-free legislation on rates of preterm birth, low birth weight (LBW) and small for gestational age (SGA). As a consequence of the Health Act 2006, England successfully implemented comprehensive smoke-free legislation in July 2007, prohibiting smoking in all enclosed or partially enclosed public spaces. Few studies have examined impact of interventions to reduce exposure to second-hand smoke on pregnancy outcomes. Protective effects of smoke-free legislation on birth outcomes in England - a regression discontinuity design. Bakolis, Ioannis; Kelly, Ruth; Fecht, Daniela; Best, Nicky; Millett, Christopher; Garwood, Kevin; Elliott, Paul; Hansell, Anna L; Hodgson, Susan. Published ahead of print - doi: 10.1097/EDE.0000000000000534 available http://journals.lww.com/epidem/Abstract/publishahead/Protective_effects_of_smoke_free_legislation_on.98989.aspx Also Susan Hodgson ‘video abstract’ on this paper available here: https://journals.lww.com/epidem/Pages/videogallery.aspx?videoId=33&autoPlay=true Posters presentations: Poster (Regression discontinuity design and policy evaluation: an evaluation of smoke free legislation on birth outcomes. Ioannis Bakolis, Ruth Kelly, Daniela Fecht, Nicky Best, Anna Hansell, Christopher Millett, Kevin Garwood and Susan Hodgson) - 8th UK and Ireland Occupational & Environmental Epidemiology Meeting, London, April 2014 Poster (Smoke-Free Legislation and Adverse Pregnancy Outcomes in England Ioannis Bakolis, Ruth Kelly, Daniela Fecht, Nicky Best, Anna Hansell, Christopher Millett, Kevin Garwood, Paul Elliot , Susan Hodgson) International Society of Environmental Epidemiology, Seattle, Washington, August 2014 Poster (Regression Discontinuity Design and Policy Evaluation: an Evaluation of Smoke Free Legislation on Adverse Pregnancy Outcomes Ioannis Bakolis, Nicky Best, Daniela Fecht, Anna Hansell, Christopher Millett, Paul Elliot, Kevin Garwood, Ruth Kelly, Susan Hodgson, International Society of Environmental Epidemiology, Seattle, Washington, August 2014 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 (http://www.imperial.ac.uk/school-public-health/epidemiology-and-biostatistics/small-area-health-statistics-unit/), including publications and outputs. 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 also present its work at scientific advisory committees where of particular national interest e.g. some of SAHSU's 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 puts details of its research on the SAHSU website (http://www.imperial.ac.uk/school-public-health/epidemiology-and-biostatistics/small-area-health-statistics-unit/), including publications and outputs. For the SAHSU Environment and Health Atlas for England and Wales, SAHSU developed a public-facing website with interactive searchable maps and explanatory text (www.envhealthatlas.co.uk) working with the NGO Sense About Science. SAHSU take part in a wide range of public facing events, both general and project specific For example, in June 2016, members of SAHSU presented work 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, including on the Environment and Health Atlas for England and Wales (involving nine cancer outcomes mapped at small area level across England and Wales), bioaerosols and health effects of environmental noise. See video at https://www.youtube.com/watch?v=ml4XyHs720I - A look at how your environment affects your health at the MRC festival. SAHSU present work and also approaches to data handling to the MRC-PHE Centre for Environment and Health Community Advisory Board. 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 and meetings with government departments. SAHSU also present its work at scientific advisory committees where of particular national interest. Besides details of SAHSU research can be accessed via its website http://www.imperial.ac.uk/school-public-health/epidemiology-and-biostatistics/small-area-health-statistics-unit/), including publications and outputs, SAHSU's Environment and Health Atlas for England and Wales, SAHSU developed a public-facing website with interactive searchable maps and explanatory text (www.envhealthatlas.co.uk) working with the NGO Sense About Science. Policy makers. In 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 the respiratory conditions. In January 2018, the Assistant Director presented information on SAHSU noise and health studies to the All Party Parliamentary Group on Heathrow Expansion. In 2017 and 2018, members of SAHSU also presented work at Imperial Festival, the university’s annual public engagement showcase. In 2017, SAHSU members contributed to exhibits on both the health benefits of greenspace, and more generally on the health effects of environmental pollutants such as air pollution and noise. In 2018 the Centre for Environment and Health organised exhibits on both exposomics, and more specifically on work related to SAHSU in an exhibit entitled “Big data, small areas”, which involved demonstrating the Environment and Health Atlas for England and Wales, outlining how SAHSU uses routine health and environmental data, and also covering noise and air pollution in London. SAHSU members also took part in the MRC Festival of Medical Research in June 2017, in a public event for the community of Deptford demonstrating work on bioaerosols and health effects of environmental noise. The Centre for Environment and Health and the Health Protection Research Unit in Health Impacts of Environmental Hazards have a joint community advisory board, a stakeholder group that provides input to research to and guidance on research projects – in 2017 SAHSU members have provided presentations on the estimation of costs to the NHS and social care due to the health impacts of air pollution, the use of personal data and noise as a public health problem.

Expected Benefits:

The Small Area Health Statistics unit forms a major part of the national MRC-PHE Centre for Environment and Health – it is the only such specialist unit in the UK and its studies span surveillance and research. SAHSU is core- funded by Public Health England and has a programme of work agreed with Public Health England, with terms of reference in the contract between PHE and the Centre as mentioned earlier.

The work of SAHSU helps progress PHE’s remit to protect the public’s health from public health hazards, particularly in relation to environmental hazards, and also in surveillance of non-communicable disease. For example, SAHSU have published neighbourhood and postcode searchable maps in the SAHSU Environment and Health Atlas for England and Wales(www.envhealthatlas.co.uk) and published in recent years’ substantive studies on health effects of environmental air pollution and noise and future impacts of climate change on mortality.

SAHSU’s work includes investigation of inequalities in health and inequalities of environmental exposure, which help inform policy to reduce inequalities in health. Data analyses may also indirectly support service development, for example, adapting national deprivation indices to better define inequalities in rural areas.
The following projects will go ahead as soon as NN4B data is made available to SAHSU

1. Evaluation of birth weight trends:
Birth weight centiles updated with the data 2012-18 (up to the available year) and small for gestational age centiles to be used as a threshold to define small for gestational age (SGA) outcome in future environmental epidemiology studies
2. Evaluation of stillbirths in relation to birth weight charts:
Analysis of the effect of stillbirths in relation to Birth weight centiles.
3. Construction of Birth weight charts sex-specific and ethnic-specific:
Birth weight trends to investigate the evolution of trends in England and Wales, (including data from 1986-2012) and to identify change points and analysis in the relation of ethical differences.
4. Validation of the existing birth weight charts and evaluation of the misclassification rates:
Validation of the actual birthweight chart with the most recent data, to investigate based on the birth weight trends if updated are required due to changes in population distribution

Maternal residential exposure to aircraft and railway noise and risks of adverse birth outcomes: (2019)
A PhD student is the lead analyst working on this study. Her work will lead to a publication in a leading peer-review journal and to international conference presentations. The outcome of the study is likely to influence national and international policy on aircraft traffic and inform further research in this area.

Green Space and Birth Outcomes (on-going)
The impact of potentially confounding variables, including maternal age, parity and air pollution, as well as area-level socio-economic deprivation and smoking will be explored. The outputs of this research will inform society's growing understanding of the impact of the natural environment on human health. Peer-reviewed publications and academic presentations at relevant conferences will be the main planned outputs.

Traffic in London: (on-going)
The results of this study will allow for better understanding of the health problems caused by air pollution and noise from traffic in London. Any 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. Output from this work 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 was expected by 2016 and delayed due to unavailability of most up to date data.

Incinerators: (on-going)
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. Study information will be provided on the SAHSU website for the public. The outputs of the project will be published in peer-reviewed journals during the course of the study and after completion.

Small for gestational age (SGA): on-going
Small for gestational age is used by many studies looking at birth outcomes in relation to environmental exposures. Identifying the best method to define small for gestational age for a study population is therefore of great importance, both for SAHSU studies and for the wider epidemiological community interested in prenatal health. This project has been undertaken as an MSc project, and written up as a Masters dissertation. Outputs are also envisaged to be written up as a peer reviewed publication.

Outputs:

SAHSU's proposed work will continue with the same themes of SAHSU’s programme of work agreed with Public Health England.
Some of SAHSU’s studies for which ONS Birth registration linked to NN4B data are required:

Maternal residential exposure to aircraft and railway noise and risks of adverse birth outcomes: (2019)
The study aims to investigate whether there is an impact of aircraft noise exposure from Heathrow airport on birthweight of babies living near the airport. There is good biological plausibility through noise-induced stress and also the known associations of environmental noise on blood pressure that may affect blood flow to the placenta. The analysis follows on from the SAHSU ‘Traffic in London’ study recently published in the BMJ (Smith RB, Fecht D, Gulliver J, Beevers SD, Dajnak D, Blangiardo M, Ghosh RE, Hansell AL, Kelly FJ, Anderson HR, Toledano MB. Impact of London's road traffic air and noise pollution on birth weight: retrospective population-based cohort study. BMJ. 2017 Dec 5;359:j5299), which examined road traffic noise using the NN4B dataset. The study suggested potential impacts at highest levels of road traffic noise exposure. The impact of aircraft noise on birthweight are important to assess, as aircraft noise exposures are higher in some areas than road traffic noise and that the quality of the noise of an aircraft flying overhead means that noise is more intrusive, also in London aircraft overfly heavily populated areas.

Green Space and Birth Outcomes (on-going)
The availability of green space has been linked to multiple health benefits, with recent studies in the USA and Europe suggesting a link between improved birth outcomes and residential exposure to green space. The aim of this SAHSU study is to explore the associations between green space and birth outcomes in London.
The ONS NN4B dataset for 2006-2012, provides information on approximately 700,000 births per year across England. Outcomes of interest include: 1) birth weight; 2) gestational age at delivery; and 3) small for gestational age 4) preterm delivery, all leading causes of perinatal mortality and morbidity, and associated with increased morbidity in later life. Maternal address at delivery will be linked to relevant measures of green space exposure/access using LiDAR data on ground cover vegetation and trees for Greater London and OS MaterMap.
The impact of potentially confounding variables, including maternal age, parity and air pollution, as well as area-level socio-economic deprivation and smoking will be explored.

Traffic in London: (on-going)
The objectives of this study are to investigate population exposures in London to traffic pollution (including air and noise) and relationships with health, including examining traffic-related air pollution, aircraft noise and road noise in relation to adverse birth outcomes such as low birth weight.
This MRC-PHE centre study lead by Kings College London aims to describe and understand the patterns of exposure of the population in London to traffic pollution (including air and noise) and their relationships to health (http://www.kcl.ac.uk/lsm/research/divisions/aes/research/ERG/research-projects/traffic/index.aspx). The consortium of over 20 investigators has a wide range of work packages. SAHSU related deliverables are to quantify relationships between traffic noise and air pollution and the following health outcomes: mortality, hospital admissions and adverse birth outcomes including low birth weight in the study period 2003-10.
Air pollution data come from the Environmental Research Group at Kings College and noise exposures have been modelled at Imperial. ONS birth registrations are linked with NN4B data to generate an enhanced and added-value ONS birth dataset with gestation age and ethnicity which is important for accurate exposure estimation and outcome interpretation. Postcodes are used in order to accurately estimate exposures at residential address.

Incinerators: (on-going)
This national study investigates whether emissions from solid municipal waste incinerators (MSWis) in operation after implementation of the EU Waste Incineration Directive (WID) (2000176/EC) pose a risk to reproductive and infant health. Modelled ground level concentrations of emissions from 22 MSWis in 2003-2010 will be investigated in relation to birth weight low/very low birth weight, preterm delivery, stillbirths and small for gestation age.

Small for gestational age (SGA): on-going
Small for gestational age (SGA) is normally defined as a baby with a weight below the 10th percentile for their sex and gestational age. Defining the gender-specific birth weight for gestational age can be done in two ways, 1) a within study distribution or; 2) using pre-defined centile charts based on other studies. The main pre-defined reference curves currently in use in the UK are based on the British 1990 (UK90) reference which is based on measurements from white British children born between 1983 and 1994. Additional ethnic specific centile charts have since been produced. A recent study investigated whether the UK90 centile charts predicted neonatal outcomes better than an ethnic-specific one using data from 9102 births in the Born in Bradford cohort. This project will use national data for the whole of England and Wales (2006-2014) to calculate national centile charts, including ethnic-specific centile charts. These will be used to calculate the risk of being SGA as compared to using the UK90 and ethnic specific centile charts.
Other projects that will require NN4B data are:
1. Evaluation of birth weight trends:
2. Evaluation of stillbirths in relation to birth weight charts:
3. Construction of Birth weight charts sex-specific and ethnic-specific:
4. Validation of the existing birth weight charts and evaluation of the misclassification rates:

Smoking ban:
This study examined the impact of the smoke-free legislation on rates of preterm birth, low birth weight (LBW) and small for gestational age (SGA). As a consequence of the Health Act 2006, England successfully implemented comprehensive smoke-free legislation in July 2007, prohibiting smoking in all enclosed or partially enclosed public spaces. Few studies have examined impact of interventions to reduce exposure to second-hand smoke on pregnancy outcomes.

Processing:

This agreement permits SAHSU to continue to hold data but not to otherwise process the data. No new data will flow under this agreement. The wording below (until the end of this ‘Processing Activities’ section) describes the data processing established under the last approved agreement V0.6.

Data extracts will be downloaded from the NHS Digital’s SEFT System and copied to an encrypted USB drive and immediately transferred to SAHSU’s secure environment – “Private Network” with no connectivity to other networks – which is located at St Mary’s Campus in Paddington. SAHSU has extensive mechanisms in place to secure the health data including a robust information governance framework, privacy enhancing technologies and physical security measures.
Data are loaded to an Oracle Database with Advanced Security implemented. This allows SAHSU to protect the data at the source using Transparent Data Encryption (TDE) by enforcing data-at-rest encryption in the database layer which prevent unauthorised access from bypassing the database and reading sensitive information directly from storage. After the data are encrypted, data are transparently decrypted for authorised users when they access the data. ORACLE Transparent Data Encryption (TDE) helps protect data stored on media (also called data at rest) in the event that the storage media or data file is stolen. To prevent unauthorised decryption, TDE stores the encryption keys in a security module external to the database, called a keystore - a built-in encryption key management which eliminates the complex task of managing and securing encryption keys.
Only the database team have access to the Oracle database which holds patient-identifiable and sensitive data. Data loading, cleaning, validations, derivations and any authorised data linkage are performed by the database team. All data extracts are logged and cross checked by the database manager prior to extraction.
The following checks are carried out prior to data extractions:
1) The project has been approved by the SAHSU Liaison Committee;
2) User is under contract with Imperial College;
3) Any access to sensitive or patient identifiable data e.g. (date of birth and/or death) has to be strongly justified;
4) SAHSU confidentiality form has been signed; and the user has undertaken mandatory information governance training.
5) ONS NN4B data access - only Researchers/statisticians/PHD students who have Approved Researcher accreditation under sections 23 and 39 (4) (i) of the SRSA 2007 amended by s287 of the Health and Social Care Act 2012 can request extracts relating to ONS data as per specifications required to undertake their studies for research projects not related to surveillance.

Pseudonymised extracts are generated from the Oracle database and copied to the specific user folder on a research server within the “Private Network” which has a range of statistical software installed to enable them to do further analysis. Users requesting access to SAHSU’s data have had Information Governance training and have a prior understanding of the sensitivities around using administrative data, how to handle sensitive data safely, lawfully and responsibly. They are required to apply the rules and regulations for disclosure risk analysis for their studies' outputs. All outputs will be aggregated with small numbers suppressed in line with NHS Digital’s Disclosure Control Procedure and ONS disclosure procedure guidelines.

Researchers/statisticians/PHD students do not have access to patient identifiable data and can only request pseudonymised extracts.

No record level data will be transferred outside of the EEA under this agreement, and is only processed and stored at the addresses given within this application and data provided under this agreement will not be shared with any third party.

In addition to this data request SAHSU also hold the following datasets:
• HES (APC, A&E and Critical Care) (1990 – 2017)
• HES linked to Civil Registration data (1997-2017)
• Cancer registrations (England (1974 - 2017) and Wales(1974 - 2016))
• ONS deaths registrations (1981 - 2018)
• ONS birth and still birth registrations (1981 - 2018)
• British Isles Network of Congenital Anomaly Registers (2000 - 2010)
• Scottish births, mortality and congenital anomalies (2003 – 2010)
• NN4B (2006-2012)
• NCCHD (National Community Child Health Database) (2000 – 2010)

SAHSU understand that linkage 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 s251 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 IGARD’s consideration which would also require support from ethics, and be covered by an amendment to the current s251.
The following linkage criteria will be used for the linkage of NN4B data to ONS birth registrations
1. DOB, sex, postcode, sbind (live/still), DOBM, valid birth weight
2. For singletons only: DOB, sex, postcode, sbind, DOBM
3. DOB, sex, postcode, sbind (live/still), valid birth weight
4. DOB, sex, postcode, sbind, invalid birth weight

There will be no data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the previous paragraph.

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

A PhD student will be the lead analyst working on this study and needs assess to the NN4B data set. All PhD students have substantive employment contracts in place with Imperial College London.

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.


MR1249 - European Community Respiratory Health Survey III (ECRHS) — DARS-NIC-147944-KJQBS

Opt outs honoured: Identifiable (, )

Legal basis:

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2011-12-21 — 2026-12-20

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Flagging Current Status Report
  4. MRIS - List Cleaning Report
  5. MRIS - Members and Postings Report
  6. MRIS - Personal Demographics Service

Objectives:

The ECRHS is a large (n=10000) multicentre international cohort study of adults specifically addressing development and prognosis of respiratory and allergic disease.

In the UK, the cohort were initially selected by random sampling from the Family Health Services Authority register. Detailed information on health status, lifestyle, occupational exposures and environmental exposures has been collected by postal questionnaire and by clinical assessment in two phases a) baseline (1991-1994) b) ten years later (2000-2002). We are currently recontacting the cohort to progress with a third follow-up. Similar protocols are being followed in over 20 research centres in 12 other countries.

This is one of the longest running cohorts with detailed information on respiratory and allergic symptoms, lung function measures and allergic status. Respiratory disease is a major cause of morbidity in the elderly and the ECRHS provides an opportunity to investigate factors that influence the development and prognosis of disease. The prevalence of allergic disease has increased over the last fifty years for as yet unknown reasons and its evolution in older adults is poorly understood.

Expected Benefits:

The specific aims are to
1) Describe change in respiratory symptom prevalence in adults as they age
2) Assess change in IgE sensitisation to common allergens in adults as they age
3) Determine whether the prognosis of asthma is influenced by any observed change in atopic status
4) Assess whether atopic status and asthma as measured over a twenty year period is associated with lung function decline or the development of COPD in older adults
5) Describe the association of obesity and physical exercise with asthma, lung function, lung function decline and the prognosis of asthma
Throughout we will assess whether observed effects are similar in men and women.

Data generated through this project will also be analysed to provide observational evidence of associations of lifestyle and environmental factors (eg occupation and air pollution) with atopy, respiratory symptoms, lung function decline and the development of COPD.

Outputs:

We will approach new PCTs to request new addresses for our participants and we can then send them our questionnaire. Overall the data provided by the MRIS will 1) improve response 2) enable us to derive correct response rates to survey and 3) allow description of mortality in the cohort. Information provided will be stored until we are provided by the participant with further information long term in our databases to assist with follow-up of the cohort in future years. We are currently seeking ethical permission to invite responders to the postal survey for further investigation

Processing:

The UK arm of the ECRHS will contact all eligible participants in Norwich and Ipswich and request they complete a twenty-item postal questionnaire asking about respiratory/allergic symptoms. The cohort was recruited in 1992, and from 1992-1993 1105 underwent detailed investigation. Participants provided the name/address of two friends/relatives who were likely to know their whereabouts in ten years time. In the year 2000, participants were contacted using last recorded address and completed a postal survey. If they had moved, new addresses were located by search of electoral rolls and from information provided by nominated contact. From this, 980 completed the postal questionnaire. Responders were invited to undergo detailed investigation (n=819) as in 1992, and those agreeing consented to 'electronic database follow-up' in the future (please see attached forms). Nominated contacts were also provided. In the last year we have re contacted our cohort using last known address. We would like to know the vital status and new PCT of those who have not responded. We would also like to know the cause of death of all those who have died. Amongst those who gave permission to be traced electronically in 2002 we request vital status and new PCT for about 250 subjects who we believe may have moved. We ask the committee to consider providing, in addition, new PCT for about 100 participants who expressed willingness to be contacted again in 1992 (by providing relatives names) and who completed the postal survey in 2002 but not the clinical assessment. This group have shown willingness to be followed up twice but because of personal circumstances were unable to complete the clinical interview in 2002 (and therefore were not given the opportunity to complete our consent forms.


Imperial College London/Dr Foster Limited Standard Extract Service Feed (HES Amendment, Renewal/Extension) — DARS-NIC-12828-M0K2D

Opt outs honoured: N, Yes - patient objections upheld, No - data flow is not identifiable, Anonymised - ICO Code Compliant, Identifiable, No, Yes (Section 251, Section 251 NHS Act 2006, Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s), )

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), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: Yes, No (Academic)

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

When:DSA runs 2019-08-14 — 2020-08-31 2017.06 — 2022.07.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Outpatients
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Admitted Patient Care
  5. Civil Registration - Deaths
  6. HES:Civil Registration (Deaths) bridge
  7. Emergency Care Data Set (ECDS)
  8. Civil Registration (Deaths) - Secondary Care Cut
  9. HES-ID to MPS-ID HES Accident and Emergency
  10. HES-ID to MPS-ID HES Admitted Patient Care
  11. HES-ID to MPS-ID HES Outpatients

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.

Yielded Benefits:

Benefits detailed in measurable benefits section are ongoing.

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.


SAHSU annual renewal and amendment - HES — DARS-NIC-204903-P1J7Q

Opt outs honoured: Yes - patient objections upheld, Identifiable, Anonymised - ICO Code Compliant, Yes, No (Section 251, Section 251 NHS Act 2006)

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), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

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

When:DSA runs 2018-04-01 — 2021-03-31 2017.06 — 2022.06.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Critical Care
  4. HES:Civil Registration (Deaths) bridge
  5. Civil Registration (Deaths) - Secondary Care Cut
  6. Civil Registration - Deaths
  7. Emergency Care Data Set (ECDS)
  8. HES-ID to MPS-ID HES Accident and Emergency
  9. HES-ID to MPS-ID HES Admitted Patient Care
  10. GPES Data for Pandemic Planning and Research (COVID-19)

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. 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 and surveillance activities.

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

The rapid response function is in our contract with Public Health England (i.e. mandated and funded by PHE). 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.

The Rapid Inquiry Facility (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.

A newer version of the Rapid Inquiry Facility (RIF) software is currently being developed. This version will allow us to test on various scenarios to help detect clusters or increased rates of disease near industrial installations. As a result of the RIF being redeveloped, we have been doing less on this function (due to less capacity/resources available) but we are expecting to be doing more over the next 1-2 years.

As an example of the most recent request we have just responded to a request from the Committee on the Medical Aspects of Radiation in the Environment (COMARE) to use the RIF to provide ongoing surveillance of cancer around nuclear power stations using cancer registrations.

SAHSU’s Welcome Trust seed award on “Public health surveillance of chronic diseases: suitability of spatio-temporal methods” starting June 2017 for two years will be testing capacity of HES and HES monthly to look at clusters and appropriate statistical methods to use which may vary by disease type.

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.

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.

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 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.

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
Recent papers published include:
• 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 was expected by 2016 but will now be in 2017.

-Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators)
There have been three papers published to date arising from work on this study:
• 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
• Ghosh R, et al. Ghosh RE, Ashworth DC, Hansell AL, Garwood K, Elliott P, Toledano MB. Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology. Arch Dis Child Fetal Neonatal Ed. 2016
Further papers are expected in 2017. Study information is 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 - e.g. in a study relating to health effects in relation to environmental exposures from 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.


Patient Choice and Provider Quality - Why Patients Change GPs — DARS-NIC-218380-R8L2R

Opt outs honoured: Identifiable, No (Does not include the flow of confidential data)

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2021-07-01 — 2024-06-30 2022.05 — 2022.05.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Demographics

Objectives:

The research team presenting this data application are based in the Economics and Public Policy (EPP) Department within the Business School at Imperial College London.

Informed choice is a key element of the rights and pledges outlined within the NHS constitution. Accordingly, the NHS has invested in a variety of online systems and programs to better assist patients in making informed choices about their healthcare. Among these is a review system that allows patients to view reviews of prospective General Practitioner Practices (GPPs), hospitals, and other services (this system was formerly called “NHS choices” and is currently called “The NHS website”).

Despite the focus on informed choice, and increasing efforts to utilize technology, relatively little is known about the efficacy of online systems in impacting patient choice and behaviours. Do patients consider reviews in their decision-making process? Does this lead them to choose practices that are better rated on the basis of other metrics? Understanding these questions is key to the design and development of new systems that allow the NHS to maintain informed choice. Understanding these questions is key to the improvement of existing systems and development of new systems that could allow the NHS to further promote informed choice by patients.

In January 2020, the NHS website removed the summary of reviews for each GPP, which was previously shown prominently at the top of each GPP’s page. The introduction, sustained use, and later removal of ratings and reviews information on the NHS website provides a rare opportunity to begin to understand the relationship between online information on healthcare quality and patient choice.

Detailed and continuously updated data on online reviews at the GPP level is available via NHS digital. Information on GPP registration is available via the Patient Demographics Service. The combination of these two data sources allow for the estimation of associations between the public availability of online reviews and GPP enrolment growth rates.

The research team’s project will study how individuals use online reviews to choose their GPP. The analysis will provide a first test for the relationship between access to online patient reviews and better patient choices. Moreover, the analysis will consider differences in the strength of this relationship across various demographic characteristics defined at the Lower Layer Super Output Area (LSOA) level. By doing so, the study will be able to understand if, for instance, individuals in wealthier areas tend to benefit more from online information relative to individuals from poorer areas, even after taking into account the differences in GPP choices available in each area. In other words, the study will also shed light on how online information systems influence inequality in access to quality healthcare.

More specifically, the analysis will address the following questions:
1) To what extent do patients consider online reviews in their decision-making process?
The data provided will allow the research team to assess whether individuals choosing a new GPP (e.g., because of a change of address) are more likely to choose a GPP that has a higher star rating, amongst those GPPs available to them. There are many confounding factors that may explain the correlation between enrolment and GPP star ratings. The data provided will allow the research team to obtain credible causal estimates of the effect of the website start ratings on patient enrolment using two methods. First, the team will examine GPPs changes in star ratings over time. Second, the team will examine GPPs at the boundary of ratings.

2) How do different individuals use the information in the NHS website? For instance, do individuals in low income or low education LSOAs respond more or less to changes in reviews? What implications does this have for inequality in access to high-quality healthcare?
The data provided will allow the research team to answer this question by assessing whether individuals in high and low-income LSOA are equally likely to choose GPPs with higher star ratings, amongst those GPPs available to them.

3) Does this lead patients to choose practices that are better rated on the basis of other metrics (for instance, on the basis of clinical QOF scores)?
To answer this question, the research team will determine whether start ratings are correlated with objective measures of clinical quality like clinical QOF ratings and LSOA level improvements in health outcomes. Then, the data provided will allow the research team to determine whether the determinants of patient enrolment are strongly correlated with clinical quality.

4) What has been the effect on patient choices and outcomes of the website’s change in format in January 2020?
The data provided will allow the research team to answer this question by comparing the rate at which patients choose GPPs with high star ratings and high clinical outcomes measures, in the period immediately before and immediately after the change in the website format (the NHS website discontinued displaying overall star ratings in January 2020). In this context, the team will be particularly careful to avoid confounding effects due to the first wave of COVID-19 which occurred shortly after the website format change.

5) Besides the online rating, what other characteristics of GPPs do individuals value the most when choosing their GPP?
The data provided will allow the research team to answer this question by determining what characteristics of GPPs are predictive of patient enrolment. For instance, if patients are very likely to enrol in the GPP closest to the LSOA in which they reside, even if that GPP offers fewer services and has a lower start rating, this would allow the researchers to determine the extent to which patients value distance over and above GPP services and star ratings. In practice, the team will consider average demographic characteristics of each individual’s LSOA, since the data will be pseudonymized so that no individual can be identified in the data. The research team will take particular care to ensure that the model accounts for the fact that 1) different individuals have access to different sets of options from which to choose their GPPS (and some individuals might have a single option) and 2) switching GPPs is rare unless there is an event requiring an individual to choose a new GPP, such as moving to a new area or a GPP closure.

The answers to these questions have important consequences for the NHS. Appropriate design of systems to convey information is key to promoting informed choices by patients [b]. The study’s findings would give the NHS insight on how the previous NHS website influenced patient choices. The study will also determine the consequences of the January 2020 website format change and suggest ways to improve how information is conveyed to patients, with a view to improve patient outcomes and reduce inequality in access to quality primary care in the future. The study will also highlight which features of GPPs are most valued by patients, which can be used by GPPs to improve the quality of the services they provide. The authors have discussed their preliminary findings with members of an NHS Digital team responsible for the NHS website. The study has not been commissioned by NHS Digital but the NHS Digital team involved in the work area are interested in understanding the findings from the study, particularly understanding the consequences of the website’s change in format.

[b] See, for instance, Marsh C, Peacock R, Sheard L, Hughes L, Lawton R. Patient experience feedback in UK hospitals: What types are available and what are their potential roles in quality improvement (QI)? Health Expect. 2019;22:317–326.

The research team has produced some preliminary results, using publicly available but much less granular data aggregated at the GPP level. The initial analysis indicates a strong relationship between publicly observed quality (i.e., the star rating awarded to a GPP) and enrolment growth. The team’s preliminary analysis also suggests that patient reviews are correlated with other measures of GPP quality such as clinical QOF indicators, and that online information is most useful in areas where there are many GPPs to choose from.

However, the publicly available data is too aggregated to answer the team’s research questions. The requested granular level data is indispensable to answer the questions described above for the following reasons:
• The publicly available aggregate data contains only the total number of individuals registered at a GPP at each point in time. This aggregate data conceals a large number of the changes of GPPs done by patients, which biases the study’s results.
• Individuals switch GPPs relatively rarely, so an individual level data set is necessary to understand the determinants of such relatively rare events.
• The requested data will allow the research team to identify the effect of online information and those patients who are actively searching for a new GPP (e.g., due to a change in address). This population is the most relevant one since these patients are the ones most likely to benefit from additional information.
• The requested data will allow the research team to address confounding factors as described above. In addition, the research team will be able to distinguish GPP enrolment growth from broader population trends, which make it impossible to obtain clean statistical results when using aggregated data alone.

DATA MINIMISATION
The requested data will provide the minimal level of information necessary to track the choice of provider at the individual level over time, allowing the team to estimate the underlying drivers of GPP enrolment. The project requires a sample from the Personal Demographics Service (PDS) data for the period from April 2015 to March 2021. This time period would allow the team to study how patients use online information in general, but also to determine the specific effect of the change in website design that occurred in January 2020. The project requires individual level data on GPP choices because the statistical analysis relies on tracking individuals as they move from one GPP to another. For these reasons, there is no alternative data set which would allow for a robust and credible analysis of how individuals choose their GPP. The analysis is particularly concerned with individuals that switch GPPs, including those that switch residences. Given this, the data requested would identify one of 3 changes in the PDS data along with the month and year of the change:
1. Postcode is the same, GPP Code has changed
2. Both Postcode and GPP Code have changed
3. Postcode changed, GPP Code is the same

The researchers have minimized the data request as follows:
• The project requires data only for the period from April 2015 to March 2021. This is the period for which information is available regarding the NHS Choices website. In order to study the effect of the change in website format (which occurred in January 2020) the researchers require data for the period following the change, hence the request for the sample period to extent to March 2021.
• The data set requested does not require linking to Hospital Episode Statistics (HES) data set.
• The researchers do not require information on postcode (the research team will use information about an individual’s LSOA to link average demographic information from the 2011 Census).
• In order to avoid the possibility of identifying individuals that have had a changed address for such reasons as going on a witness protection programme or child protection programme, Data Production is requested to remove all such cases where this sensitive ‘suppression’ flag has been applied (Stop Note indicator).
• The project requires each patient’s GPP code only. The project does not require individual Practitioner codes.
• The project requires only the address LSOA for each individual. The project does not require full addresses. Unfortunately, the first 3 digits of postcode is not enough since this would give too coarse a view of individual choices.
• The project requires data only for England. However, it is necessary to obtain data for all of England since individuals are likely to move across the country.
• The researchers require only a Pseudo-ID for each individual. The project does not require individual NHS numbers and individuals will not be identifiable from the data.
• The project requires the month and year in which an individual changes GPP Codes or Postcode. The project does not require the exact day.
• The project requires only the month and year of death for each individual in the data who has died during the period covered by the sample. The project does not require the exact day of death. The project requires data on deaths because, without this data, the analysis would consider deceased individuals as actively choosing the same GPP, which would bias the results.
• The project requires age and sex for each individual. All ages are required from birth to death, but only in 5 years age bands. The sex variable will allow a better understanding of whether men and women choose GPPs differently.
• The project requires Country of Birth, as the analysis will explore the hypothesis that foreign nationals may make choices differently to those born in the UK.
• The project does not require records from individuals that are in small practices with less than 10 individuals. This is done to ensure the anonymity of individuals in the data.

Imperial College London is the sole Data Controller who also processes the data.

The legal basis for data processing is the GDPR Article 6(1)(e) processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller, and Article 9(2)(j). processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

Expected Benefits:

The research programme is expected to be directly beneficial to the NHS for several reasons. Most concretely, research outputs, including conference presentations and academic papers, will provide an explicit evaluation of the efficacy and impact of digital efforts by the NHS to provide accessible and relevant information to patients on GPPs.

The NHS has included the principle of informed choice within its Constitution for England. In addition to the legal right of patients to choose a GPP (unless there are reasonable grounds to refuse), the constitution notes the NHS’s pledge to
(i) inform patients about available healthcare services locally and nationally and
(ii) offer easily accessible, reliable, and relevant information in a form patients can understand, as well as support to use it.

However, there is no agreed-upon best practice for providing information about GPPs to patients. This is reflected in the changes in format to the NHS website, with a view of making the information more useful to patients . The research team’s study aims to be directly informative to NHS Digital as they seek to further improve the provision of accessible, reliable and relevant information to patients in an understandable form.

Furthermore, this research hopes to shed light on whether easily accessible information on GPPs helps to promote a well-functioning and high-quality provision of primary care. The study hope the analysis will also help understand to what extent does online information provision exacerbate or mitigate inequality in access to healthcare.

The study team at Imperial College London will remain in contact with the NHS in order to ensure that the study is as useful as possible.

The study’s results are hoped to have a number of important benefits for the NHS, as discussed above. The study’s findings would give the NHS insight on how the previous NHS website influenced patient choices. The study may also determine the consequences of the January 2020 website format change and suggest ways to improve how information is conveyed to patients, with a view to improve patient outcomes and reduce inequality in access to quality primary care in the future. The study may also highlight which features of GPPs are most valued by patients, which can be used by GPPs to improve the quality of the services they provide.

In sum, the benefits of the analysis are the following:
1) The analysis aims to provide empirical insight into the role of information barriers in patient choice of GPPs.
2) The analysis aims to determine the effect of the NHS website format change in January 2020, and thereby provide NHS digital with feedback and recommendations to maximize the website’s efficacy.
3) The analysis hopes to give researchers and policy makers insight into what features of GPPs are most valued by patients, so that GPPs can use this information to provide better services to patients.
4) This study hopes to provide insights on how online information provision can be used to improve the quality of information provided to patients, so that patients can make better decisions about the GPP services they require and choose the best GPP to provide those services.
5) The study hopes to provide insights on how the provision of online information affects inequities in utilization of healthcare, so that the most disadvantaged patients can have improved access to the GPP services they require.

Outputs:

The outputs of data processing will include summary statistics and the outputs of the statistical analysis (for instance, regression analyses and figures illustrating the study’s results). The study team expect to have an article ready for circulation to conferences within 12 months of receiving the data, with initial submissions to journals coming shortly thereafter. In all output, papers, working papers, and seminar slides only aggregate data will be shown, with small numbers suppressed in line with the HES Analysis Guide.

These outputs aim to be described and disseminated through a number of channels, including articles prepared for submission to peer-reviewed journals in economics, health economics, and industrial organization, presentations at academic seminars at Universities in the UK and abroad, and presentations at academic and policy conferences in the US, UK, and elsewhere. Conference submissions are particularly aimed at targeting health economics conferences within the UK to ensure maximum interaction with members of the policy making community.

To ensure information is available as freely as possible, working papers aim to be be circulated through the Social Science Research Network (SSRN), the National Bureau of Economic Research (NBER), the Center for Economic Policy Research (CEPR), or similar series. Additionally, economic models and results will be made available to the NHS and NHS Digital, for use in improving websites and other tools aimed at providing accurate and accessible information to patients.

Expected conference submissions include the International Industrial Organization Conference (IIOC), Econometric Society, The National Bureau of Economic Research Summer Institute in Industrial Organization, The National Bureau of Economic Research Summer Institute Health, The American Economic Association Annual Meetings, Health Economists' Study Group, ASHEcon.

Potential journal submissions include the Review of Economic Studies, the American Economic Review, The Rand Journal of Economics, Econometrica, The Journal of Health Economics, The Journal of the European Economic Association.

Processing:

STUDY DESIGN
This study will use pre-existing pseudonymised historical data on patients registered in GP practices in England from April 2015 to March 2021 using the NHS Personal Demographics Service (PDS), extracted by NHS Digital using the following filters :
• GPP practice codes (we require only practice level identifier, not the individual physician identifiers)
• LSOA
• Pseudo-ID (individuals Study ID) for each record (we do not require individual’s real NHS numbers)
• Month and year in which individual’s change GPP Code or Postcode,
• Month and year of death for each individual in the data (if the individual has died within the period covered by the sample)
• Age (in 5-year bands) of each individual
• sex for each individual
• Country of birth

This will be linked to time series data on
1) average patient ratings of each GPP and
2) the aggregated (and rounded) summary ratings displayed on the NHS website between April 2015 to March 2021.

This information will be combined with baseline GPP registration statistics to calculate overall and LSOA specific enrolment growth rates. This linkage will not be at record-level and therefore will not add any identifiable information and hence will not generate an opportunity for re-identification. There will be no requirement or attempt to re-identify individuals. The data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with HES analysis guide.


METHOD FOR DATA EXTRACTION
- Leeds Data Production team extract a record-level cohort of patients registered in General Practitioner Practices (GPPs) in England only from PDS for each of 6 data periods according to bespoke filters and fields.
- Leeds Data Production remove all records with a sensitive suppression status (on a witness programme, etc).
- Leeds Data Production to map all the GPP codes (8 characters) to GPP Code using the date the record changed, so they are all in one format
- Leeds Data Production to filter out all cases where Stop Note indicator applied.
- Leeds Data Production team create a record level pseudonymised data extract for each PDS period and send this via SEFT to the research team at Imperial College London

The sole flow of data out of NHS Digital will be the one-time extraction of historic Personal Demographics Service (PDS) data outlined in this agreement. There will be no subsequent flows of data in or out of NHS Digital.

FINAL DATA FORMAT
The resulting data would consist of one row (per individual) for each change in Postcode or LSOA. This will mean multiple rows per each individual if the individual switches GPP and/or postcode multiple times.
Each row would contain the month and year of the event being recorded (for instance, change of GPP or change of address), as well as the variables described in the objective for processing section.

The primary statistical analysis will be of the relationship between GPP enrolment growth rates and average and summary reviews as displayed online. Both are continuous. The study team will primarily use linear regression analyses. Given the lack of any preliminary evidence on potential effect sizes, the study team cannot make meaningful estimates of the statistical power of the prospective study. Supplemental analyses will include tests of changes in these associations when summary reviews were taken offline, as well as subgroup analysis on the basis of LSOA and GPP level characteristics.

DATA PROCESSING
Data processing will be undertaken within Imperial College's Big Data and Analytical Unit Secure Environment (BDAU SE) which is hosted by Virtus SDC Limited in Slough, which will act as the secure storage and processing location. Data access is strictly controlled by the Imperial College's Big Data and Analytical Unit (BDAU) with stringent procedures including dataset registration process, limiting access to the data to the minimum numbers of the research team required and BDAU approved staff access facilities. Data analysis will only be undertaken through the BDAU. The data will only be used for the purpose outlined in this Data Sharing Agreement. Imperial College London staff are bound by all policies and regulations as substantive employees of the College.

All data will be stored and analysed within the Imperial College's Big Data and Analytical Unit Secure Environment (BDAU SE).

Data Processing is only carried out by substantive employees of Imperial College London who have been appropriately trained in data protection and confidentiality. All personnel will abide by Imperial College as well as national data privacy regulation. Imperial’s BDAU SE is a secure research environment, providing a standard operating/access model, secure data storage and processing environment and analysis software. It is ISO 27001 certified and also compliant with NHS Digital’s Data Security and Protection Toolkit. The BDAU SE can be accessed remotely by users using multi-factor authentication once the user registration process is completed. Data will only be provided once the appropriate dataset registration process is completed. All data files and directories will be encrypted using (ZFS) AES-256 encryption.

Virtus SDC Ltd are not considered a Data Processor as they do not access data held under this agreement. Virtus SDC Ltd are a data centre co-location provider (selling data centre space), not a cloud services provider. Imperial College rents space in Virtus data centres to host BDAU equipment. All data processing are performed by BDAU’s staff. Virtus SDC Ltd provide physical security, power and environmental controls. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. Virtus have a number of quality credentials including ISO27001, certificate copies of which can be accessed or downloaded on the following page: https://virtusdatacentres.com/why-virtus/quality-credentials.’


A retrospective cohort study investigating reintervention rates and pregnancy rates in women undergoing myomectomy and uterine artery embolisation in 2010-2014 in England — DARS-NIC-403356-Z0X1D

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive

When:DSA runs 2021-08-12 — 2022-08-11 2022.03 — 2022.04.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care

Expected Benefits:

The first results for this study are expected within a year of the download of the NHS digital data. This study may hopefully lead to recommendations which may have an impact on the following categories.

1 Clinicians
If the data demonstrates differences in short-term and long term outcomes between the two important treatments, uterine artery embolization and myomectomy, then this may lead to improved patient care by directing patients towards the treatment with the more favourable outcomes in a evidence-based manner . Clinicians may have robust data to identify best practice and to inform recommendations for fibroid treatment. This work may help to understand whether certain treatments are associated with an increased risk of hospitalisation, readmission, reintervention, pregnancy and diagnosis of uterine cancer. There may be improved pre-procedure counselling. This work may also lead to the development of specific treatment algorithms and care pathways to help guide clinicians in their recommendations. Alternative treatment modalities and optimal care can be planned to minimise adverse outcomes. This work may also be generalisable to other high-income countries.

This study may provide data on non-emergency reintervention hysterectomy rates over 8 years in a large cohort of women in England, but importantly it may allow improved understanding of the rates of women undergoing multiple uterine-sparing treatments, of which there is sparse data. It could importantly allow a comparative assessment of both uterine-sparing treatments, of which there is sparse data.

This study may provide increased understanding regarding subsequent rates of leiomyosarcoma malignancy in women undergoing myomectomy or uterine-artery embolisation. Unfortunately there is no current effective and accepted method of diagnosing leiomyosarcoma pre-procedurally. There is significant overlap with how these cancers appears on imaging with fibroids. As such, for any women undergoing a non-resective or uterine-sparing treatment of presumed fibroids, there is a risk of undiagnosed sarcoma and potentially worse outcomes associated with missed or delayed diagnosis. Furthermore, there are concerns regarding upstaging an undiagnosed sarcoma with power morcellation during myomectomy which led to a ban on this technology in 2014. The prevalence of leiomyosarcoma varies with age, but there is poor understanding and poor consensus regarding overall rates. As it is likely an uncommon outcome in women of premenopausal age, it is likely to be met with relative rarity in randomised controlled trials. By undertaking this large cohort study, there may be improved understanding of rates of diagnosis after these fibroid treatments.

This study may provide increased understanding regarding pregnancy outcomes following myomectomy and uterine artery embolisation. Uterine fibroids are common in women of reproductive age and therefore impact of treatments on fertility are an important consideration. Myomectomy is often reserved for women intending on fertility conservation, due to theoretical concerns that embolisation affecting the endometrium or ovarian vascular supply might lead to adverse fertility or pregnancy outcomes. Given these demonstrated effects, it may be difficult to design studies investigating fertility and pregnancy outcomes in randomised controlled trials or randomized prospective studies because of ethical considerations. It is important to more fully elucidate such outcomes as uterine artery embolisation may have some demonstrable benefits when compared to myomectomy such as quicker return to normal activities and reduced risk of major complications such as blood transfusion or visceral injury. A small number of women may undergo pregnancy after UAE. This nationwide cohort study may help to capture data relating to pregnancy outcomes in such women to provide greater understanding.

In addition, this work may lead to the development of auditable standards as it may enable clinicians to compare local outcomes against national benchmarks and then improve care.

2 Patients
Through collaboration with the patient group British Fibroid Trust, Imperial College London will provide a layperson summary of research findings which may be accessed by women with fibroids. Currently little has been published regarding long term outcomes following these fibroids treatments and this data may allow patients to estimate risk of reintervention before undergoing these treatments. This work may increase satisfaction for patients as it may allow patients to understand their potential patient treatment pathway with increased clarity and improve expectations. Patients may have improved understanding of short term and medium-term outcomes, so that they can be better informed as to whether they wish to proceed with treatment. As fibroids are a serious, but benign condition, the risk-benefit profile of myomectomy and uterine artery embolization may be better understood. Patients may derive significant benefit by avoiding treatments that have a worsened outcome profile. On the other hand, patients may be reassured by more favourable risk profiles then anticipated if both treatments are shown to have low risk of readmission or reintervention. Women with uterine fibroids have been demonstrated to have reduced health-related and quality of life, and improved understanding of the treatments and associated risks may help to alleviate some concerns and anxieties. Identifying the potential risk of readmission may help patients to appropriately plan and manage their resources relating to employment and childcare.

3 Impact of uterine fibroid treatment provision
If patients are guided against treatments with increased reinterventions after recommendations from this study, then this may reduce potential future use of services for women with fibroids who would otherwise experience symptom or fibroid recurrence after index treatment. This then frees up access for other women requiring use of gynaecological services.

4 Commissioners
This work may lead to improved financial planning. There may be more robust data on hospital activity workload relating to fibroid treatment and NHS costs associated. Reintervention data could also be useful in understanding the overall cost-effectiveness of treatments. This may generate important knowledge that could inform the allocation of resources for fibroid treatments leading to better commissioning and value for money.

There may also be improved understanding as to whether there is equity in access to treatment, particularly minimally invasive treatment from a geographical perspective in England. This could enable commissioners of healthcare services commissioners to identify patients with the highest risk of poorer outcomes – leading to better care and better delivery of services. This study may provide increased understanding regarding potential variations in outcomes following myomectomy or UAE relating to ethnicity. A number of studies have demonstrated adverse healthcare outcomes following fibroid treatment in Black and ethnic minority women in the US. This may relate to a number of different factors including varying severity of fibroid burden across ethnicities, varying associated co-morbidities across ethnicities and systemic racism. This data may help to understand if outcomes such as reintervention rates and leiomyosarcoma rates vary according to ethnicity in the UK

The data may also identify whether outcomes vary according to surgeon/commissioners of healthcare services volume/caseload. This might inform a need nationally for the development of specialist centres for treatment of fibroids.

5 Impact on the NHS and society
Any recommendations for change resulting from this study may therefore be evidence-based and considered from both the health benefit and cost benefit viewpoints, whilst attending to the needs of women with fibroids. Uterine fibroids are a highly prevalent condition and a substantial proportion of women will seek treatment for fibroids, therefore this data is likely to have significant impact on a national level within the field of gynaecology.

The study has the potential to reduce the direct and indirect costs relating to uterine fibroids by increased understanding of treatments, since fibroids exert significant effect on quality of life, absenteeism, presenteeism and social interactions.

Outputs:

Published work describing the readmission rates, reintervention rates, pregnancy rates and leiomyosarcoma rates is to be completed by the Imperial College London and submitted to the American Journal of Obstetrics and Gynaecology in Feb 2022.

Following publication Imperial College London aims to collaborate with the patient group British Fibroid Trust by providing a layperson summary of research findings. Additional work investigating the variation in outcomes after UAE and myomectomy relating to ethnicity is to be submitted to the Fertility Sterility Journal in May 2022.

Work investigating the relationship between surgeon caseload/volume and commissioners of healthcare services caseload/volume and subsequent readmission and reintervention rates after myomectomy and uterine artery embolisation is to be submitted to the British Journal of Obstetrics and Gynaecology in May 2022.

Additional work reporting on the variation in access to fibroid treatment according to geographical location will be submitted to the British Journal of Obstetrics and Gynaecology in January 2022.

Work will further be disseminated via national conferences including the British Society of Gynaecological Endoscopy, European Society of Gynaecological Endoscopy and American Association of Gynaecology Laparoscopists.

It is anticipated this data may inform future studies regarding cost-effectiveness.

Summary reports of the work and research undertaken will be compiled and also made available on the Imperial College London website.

All outputs will be aggregated with small number suppression in line with the HES Analysis Guide.


Extension of Imperial College London Agreement — DARS-NIC-366210-V2H5M

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

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); Other-Health and Social Care Act S261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; Other-Health and Social Care Act S261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

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

When:DSA runs 2017-07-01 — 2020-06-30 2019.03 — 2022.04.

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Outpatients
  4. Patient Reported Outcome Measures (Linkable to HES)
  5. HES:Civil Registration (Deaths) bridge
  6. Hospital Episode Statistics Critical Care
  7. Emergency Care Data Set (ECDS)
  8. Civil Registration (Deaths) - Secondary Care Cut
  9. HES-ID to MPS-ID HES Accident and Emergency
  10. HES-ID to MPS-ID HES Admitted Patient Care
  11. HES-ID to MPS-ID HES Outpatients

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.



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.

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.


AIRWAVE HEALTH MONITORING STUDY (MR837) — DARS-NIC-148056-T6T5Z

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

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

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-06-06 — 2022-06-05 2017.09 — 2022.03.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. MRIS - Flagging Current Status Report
  5. Demographics
  6. Civil Registration - Deaths
  7. Cancer Registration Data

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.

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).

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.


SCAMP: Study of Cognition, Adolescents, and Mobile Phones MR1439 — DARS-NIC-27085-C5L5G

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2019-03-01 — 2022-02-28 2021.11 — 2022.02.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset
  2. Diagnostic Imaging Dataset
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Admitted Patient Care
  5. Hospital Episode Statistics Critical Care
  6. Hospital Episode Statistics Outpatients
  7. MRIS - Cause of Death Report
  8. MRIS - Cohort Event Notification Report
  9. MRIS - Flagging Current Status Report
  10. MRIS - List Cleaning Report
  11. Birth Notification Data
  12. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  13. Cancer Registration Data
  14. Civil Registration - Deaths
  15. Demographics
  16. Emergency Care Data Set (ECDS)
  17. Mental Health and Learning Disabilities Data Set
  18. Mental Health Minimum Data Set
  19. Mental Health Services Data Set

Objectives:

In the UK, approximately 70% of 11-12 year old children own a mobile phone. However, scientists remain uncertain as to whether children’s developing brains might be more vulnerable than adults to radio frequency electromagnetic field (RF-EMF) exposure.

Study of Cognition, Adolescents, and Mobile Phones (SCAMP) is a prospective cohort study which aims to address current scientific uncertainties by investigating whether the use of mobile phones and/or other technologies that use radio waves e.g. portable landline phones and wireless internet, might affect adolescents cognitive (e.g. language comprehension, memory, attention) or behavioural development (e.g. anxiety and hyperactivity), as well as exploring wider health outcomes.

Baseline and Follow-up Assessments consist of standardised neurocognitive tests, validated behavioural screening scales and questionnaires. In addition, a broad range of information is being collected from children and parents on their health, lifestyle, social and physical environments via school- and home-based questionnaires. Parents are invited to provide consent for their child’s school assessment data to be linked with their child’s routine records including mobile traffic data, health data and educational data. Biological samples (e.g. urine, saliva) are being collected in a subset of SCAMP participants to provide additional information about potential con founders such as puberty. An Radio Frequency (RF) personal monitoring study is also being conducted in a subset of the cohort when they are in Year 8 (aged 12-13) to gain an in-depth understanding of children’s personal RF exposure and the relative contribution of each RF source.

The SCAMP study has already recruited and gathered baseline data from ~7,000 Year 7 pupils (age 11-12 years) from 39 schools across London. Follow-up took place for the pupils in Year 9 and 10 (age 13-14 years).

Of the ~7,000 participating pupils, SCAMP has received consent from parents for >1600 pupils authorising SCAMP to access their child’s routine health data to be used for research purposes these are those participants for whom data are being requested from NHS Digital. SCAMP is an observational study, with no clinical intervention. Data requested from NHS Digital will be used for research purposes only.

The study requires HES data linked to the cohort participants for all requested years as on the following basis:

For prospective epidemiological analyses:
HES data provides a more comprehensive medical history, providing information on underlying conditions and treatment of these conditions. SCAMP requires HES data linked to cohort participants for all requested years to allow the study to perform statistical analysis to enable the study to investigate whether a link exists between mobile phone use (and other environmental exposures such as air pollution, noise and light) and neurocognitive and behavioural development outcomes, and to explore the possible implications of these exposures and new emerging technologies on wider health outcomes.

The study requires mortality data linked to the cohort participants on the following basis:

In the unlikely event that a participant passes away during the course of the study, it is important that the study is notified of this to ensure that the participant’s family are not contacted regarding study developments. In addition, it is important to know the cause of death so that this can be taken into account during analysis and the potential contribution of exposure to radio frequency sources and/or other environmental exposures can be explored.

The study requires cancer data linked to the cohort participants for all requested years as on the following basis:

SCAMP requires cancer data linked to the cohort participants for all requested years to allow the researchers' to perform statistical analysis to enable SCAMP to investigate whether a link exists between mobile phone use (and other environmental exposures such as air pollution, noise and light) and cancer in young people. In addition, cancer data information is important since such diagnoses could impact upon the child’s behavioural, cognitive or educational development, which are primary outcomes of interest in SCAMP.

The study requests the inclusion of diagnostic imaging data (DIDs) as this will include data appertaining to brain scans due to concussion or injury to the head, or even due to brain cancers. When combined with the study's other data, such scans would provide vital information to account/adjust for when analysing the relationship between mobile phone use and any health outcomes related to healthy brain function or neurological development.

The objectives for processing of the Data are therefore:

1.To provide additional information about a child’s health status e.g. establishing accurate diagnosis for autistic spectrum disorders, Attention deficit hyperactivity disorder (ADHD), or any other psychological or psychiatric referrals, and/or cancer diagnoses and treatments (which could impact upon the child’s behavioural, cognitive or educational development) that may have occurred either prior to establishing the cohort or afterwards.

2.To enable epidemiological and statistical analyses on mobile phones and other RF 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.

4.To maintain contact throughout the course of the study.
Data will be restricted to only those consenting participants within the study cohort (to date SCAMP have parental consent to access the routine records, including health data, of ~1,200 SCAMP pupils). SCAMP have requested all the datasets and have not restricted the data request to specific health outcomes since SCAMP is a prospective cohort investigating the potential impact of mobile phone use, other radio frequency wave sources, and other environmental exposures (e.g. noise, air pollution) on health. SCAMP is therefore undertaking ‘health surveillance’ in response to these environmental exposures and new emerging technologies.

Parents were provided with study invitation materials and information packs, all of which had been user-tested to ensure that information provided and consent methods were comprehensible. All pupils in Year 7 from a participating SCAMP schools took part in the SCAMP school-based computer assessment unless their parents informed the school/the SCAMP team otherwise i.e. the SCAMP school-based assessment was ‘opt-out’ consent. Parents were invited to provide additional and optional ‘opt-in’ consent for SCAMP to access their child’s routine records, including health data, and link these to their child’s school assessment data.

This research will enable review of current UK health policy precautionary advice to limit children’s mobile phone use. With an improved understanding of UK children’s radio frequency exposures from mobile phones and other radio frequency technologies, specific ways to reduce exposure levels may be identified to provide targeted advice to parents and children as appropriate.

Yielded Benefits:

Expected Benefits:

In the UK, 70-80% of 11-12 year olds own a mobile phone. Scientists remain uncertain as to whether children’s developing brains might be more vulnerable than adults to radio wave exposures emitted from mobile phone and other wireless devices. SCAMP is the first research project aiming to answer this important question. Technologies that use radio waves e.g. portable landline phones and wireless internet, might affect children’s health including neurocognitive or behavioural development e.g. memory, problem solving skills. By linking a child's mobile usage data with their educational records and health data from NHS Digital, researchers will be able to determine the effects of using mobile devices during adolescence on long term health outcomes.

Research findings of SCAMP will enable review of current UK health policy precautionary advice to limit children’s mobile phone use, which has not been updated for over a decade. With an improved understanding of UK children's RF exposures from mobile phones and other RF technologies, specific ways to reduce exposure levels may be identified to provide targeted advice to parents and children as appropriate.

The major benefit of SCAMP project is understanding of the impact of mobile phone use in adolescence on health and development. Hence, it is mainly the general public who will benefit from this knowledge. It is expected that the main findings would be published within the next few years.

The results of the 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 mobile phone use and other RF-EMF exposure on neurocognitive and behavioural outcomes in children and adolescents.
2. Provide the evidence base with which to enable review of current UK health guidelines which advise children under 16 to limit their mobile phone calls and are based on the precautionary principle.
3. Identify specific ways to reduce RF-EMF exposure levels, if required, and thus provide more specific health advice to parents and their children.

Outputs:

To ensure dissemination to a wide academic audience, the findings of this research will be published in high-impact, peer-reviewed, international journals and will be presented at national and international conferences spanning education, neurocognitive science, toxicology and environmental epidemiology.
The findings will also be disseminated among wider stakeholders via the MRC-PHE Centre for Environment & Health’s Community Advisory Board (CAB) – comprising Non Government Organisation (NGOs), politicians, and representatives of press and industry – and Outreach Committee, which will facilitate public and school engagement with the proposed research.

At the end of the SCAMP study, the team will hold a presentation evening for the head teachers of all participating schools and significant stakeholders, with live video feed via the study website, live Twitter feed allowing questions from parents throughout, and a video podcast made available on the study website.

The study team recently published a paper on the validation of self-reported mobile phone use and a cohort profile paper. Details of these papers can be found here:
https://www.sciencedirect.com/science/article/pii/S001393511731664X
https://academic.oup.com/ije/advance-article/doi/10.1093/ije/dyy192/5132994.

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

Processing:

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

There will be no data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the agreement.

This is an application for bespoke data linkage of the SCAMP cohort to HES Admitted Patient Care, Critical Care, A&E, Outpatient and DIDs data plus patient tracking to deaths and cancers for the purpose of academic research.

Data will be received from Imperial College, and run through the patient status and list cleaning services to a) retrieve any missing identifiers (NHS numbers, DOB, Postcode, Address) and b) update the cohort to the latest data for these variables. Automatic and manual matching will be carried out.

Imperial College wish to receive NHS number in order send to ONS to link birth data to the cohort. To do this, we will provide personal contact details to NHS Digital to identify the participants. Then NHS Digital will send back their NHS number and updated contact details. This data linkage is outlined in the Participant Information Booklet. “In order to link your data, we will send your name, gender, date of birth, and address to NHS Digital or ONS who will then link the information on our behalf to your NHS number, update details where appropriate and return the data to us.”

The cleaned cohort will then be linked to HES Inpatient, Critical Care, Outpatient, Accident & Emergency data., and DIDs.

The sensitive and identifiable output will be returned to Imperial College. The HES data will be run on an annual basis there afterwards, and the Civil Registration data/Cancers run on an annual basis.

Health event/mortality data supplied by NHS Digital will be linked by Imperial College London, using a randomly assigned unique ID number for each participant, to other SCAMP school assessment data and any other data to allow epidemiological analyses of exposure and cognitive and behavioural outcomes.

Any personal identifiers such as Name, Address, Postcode that are supplied by NHS Digital will be stored separately from health data. Access to personal identifying information is limited to the database team at Imperial College London, who have all signed strict non-disclosure agreements.

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 Imperial College London are requesting as part of ‘Patient Tracking’ may become the most up to date records as the study progresses.

Should the study find from the data that the SCAMP study participants’ details have changed, they may use updated names and addresses provided by NHS Digital to re-contact study subjects. No data provided under this agreement is shared with any of the mobile phone companies. Where data is shared this is data which was sourced at consent and written agreements to ensure confidentiality and non-disclosure of data are in place between Imperial College London and mobile phone network operators. Imperial Collage have ethical approval to use the personally identifiable data fields (Name, Address, Postcode) from ‘Latest Patient Information’ for all the purposes stated above.

It is important to note that the SCAMP researchers and the database where the raw identifiable data is received and processed are kept separate. On receipt of the datasets from NHS Digital, the SCAMP Database Manager will separate identifiable data for individuals from their health data, by the use of pseudonyms and records identifiers. The SCAMP study researchers then receive this pseudonymised dataset for the purpose of performing statistical analysis for research purposes only. Researchers therefore only have access to pseudonymised data.

Access to personal or identifiable personal data requires the use of specialised cryptography and SQL, which is limited to a limited number of specified staff within the Department, 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.

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.

Any collaborators working on the study will not have access to record level data shared under this agreement and will only access aggregated data with small number suppressed in line with NHS Digital guidelines.


How can NCS healthcare data be connected with wastewater surveillance of COVID-19 in a privacy-preserving fashion to inform epidemiological models and democratise data access? — DARS-NIC-435753-D4J0Y

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

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

Purposes: No (Academic)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2021-04-22 — 2021-12-31 2021.07 — 2021.12.

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. HES-ID to MPS-ID HES Admitted Patient Care
  2. Hospital Episode Statistics Admitted Patient Care
  3. SUS plus - Admitted Patient Care (beta version)

Objectives:

COVID-19 is a serious disease caused by a virus called SARS-C0V-2. To protect communities, there is a need to find out how many people have this virus, and who they might infect. Most people get tested when they start to feel ill. However, some people never feel ill when they are infected with the virus. But they can pass it on to their families without knowing. So, there is development in new ways to identify how many people have the virus but don't have symptoms, so communities can be protected.

Fortunately, traces of the genetic material of the virus can be found in the poo of everyone who is infected, even if they don't feel ill. The poo travels through the sewerage network to sewage treatment centres in their local area. Here, samples of the sewage are taken to measure the amount of genetic material it contains. This allows an estimation of how many people in the community are infected.

Imperial College London want their estimates to be as accurate as possible. The study team therefore need to compare them with information from local hospitals to understand how many people get sick and need medical help. The study team will collect the information needed for the comparisons in this project. In the end, it will allow the team to use the sewage measurements to predict whether hospitals will get busy and need extra help to keep the community safe.

The main objectives of the proposal are;

- to develop the methods required to aggregate healthcare records to wastewater catchment areas.
- to generate data products that can be shared with researchers working on wastewater-based epidemiology.
- to calibrate wastewater-based epidemiological models and better predict the pandemic.

The Article 6 (1)(e) justification is that data will be processed in the public interest to aid the national COVID-19 response through wastewater-based surveillance.

The Article 9(2)(j) justification for processing special category data is that ‘processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes …’ as the data are required for research purposes in the public interest.

The data requested will achieve the aim identified. Healthcare data pertaining to COVID-19 related symptoms, tests and hospital admissions will be collected from the requested datasets. All records include:

- temporal information, e.g. the date of a COVID-19 test or a hospital admission.
- geospatial information, e.g. the super lower output area that a patient is resident in.
- health-related information, e.g. symptoms, tests or hospital admissions.

These data will be combined with geospatial wastewater infrastructure data (such as the list of data zones or the area served by each wastewater treatment plant) to aggregate healthcare records. Summary statistics for each catchment area and date will be recorded as the output of the analysis, e.g. the number of cases associated with a particular catchment area.

Only datasets and data fields strictly required for this analysis have been requested. The following datasets are requested:

- Hospital Episode Statistics Admitted Patient Care. This database contains details of all hospital admissions in England.
The requested data fields and justification are as follows:

Diagnosis: required to determine whether the patient is admitted for complications arising from COVID-19.

Date of admission: required to evaluate the number of new admissions on a given day.

Date of discharge: required in conjunction with Start Date (Hospital Provider Spell) to evaluate the number of hospitalised patients on a given day.

Discharge method: required to evaluate whether the patient is discharged back to the community where they may continue to shed viral RNA (ribonucleic acid).

Provider code: required to evaluate the number of patients in hospitals in a given catchment area in case the patient's usual address is in a different catchment area than the hospital they stay in.

Lower Super Output Area: required to geolocate patients so the corresponding catchment area can be identified. Also required to calibrate SUS data (smaller lag but lower data quality) against HES data (longer lag but higher data quality) because SUS does not provide statistical reporting units from the census (such as output areas or lower-layer output areas).

Output area: required to geolocate patients so the corresponding catchment area can be identified. The team would like to access output areas so the team can check whether future requests for data could be made at output area or lower-layer super output area level.

Encrypted HES ID/Token Person ID - this will enable the team to group multiple records that are for one individual (to mitigate duplication).

- Secondary User Service (SUS) Plus - Admitted Patient Care. This provides a complete record of hospital admission data in England.
The requested data fields and justification are as follows:

Diagnosis (This is the diagnosis (ICD) codes from the episode which contains the dominant procedure or if no dominant procedure is determined - this will be the dominant diagnosis (looking across all the primary diagnosis in the episodes) based on the diagnosis hierarchy.): required to determine whether the patient is admitted for complications arising from COVID-19.

Start Date (Hospital Provider) (This identifies the admission date of a Hospital Provider Spell): required to evaluate the number of new admissions on a given day.

End Date (Hospital Provider) (This identifies the discharge date of a Hospital Provider Spell). This value is taken from the last episode for multi-episode spells.): required in conjunction with Start Date (Hospital Provider Spell) to evaluate the number of hospitalised patients on a given day.

Discharge Method (Hospital Provider) (This identifies the method of discharge from a Hospital Provider Spell. This value is taken from the last episode for multi-episode spells.): required to evaluate whether the patient is discharged back to the community where they may continue to shed viral RNA.

Provider site code: required to evaluate the number of patients in hospitals in a given catchment area in case the patient's usual address is in a different catchment area than the hospital they stay in.

Encrypted ID - this will enable the team to group multiple records that are for one individual (to mitigate duplication).

These datasets will be used to evaluate the number of patients admitted to hospital that are resident in each of the English wastewater catchment areas.

Individuals of all ages with a positive diagnosis or related symptom are to be included in the analysis. To minimise the data requested, the team would only like to access records where the diagnosis involves COVID-19. Excluding children or other vulnerable individuals from the analysis would lead to biased results, limiting the potential benefit of wastewater-based surveillance of COVID-19. The age of patients associated with healthcare records will not be available to researchers.

The time period required for the data is from 01/01/2020 to the most recent data available. This is to obtain an exhaustive picture of disease prevalence in the United Kingdom. The datasets will be used to evaluate summary statistics aggregated to the level of wastewater catchment areas, i.e. the area served by a given wastewater treatment works.

The sole data controller is Imperial College London who will also process data and the Office fo National Statistics (ONS) will be a joint Data Processor.

The project is funded by HDR UK under their Rapid funding programme for National Core Studies.

Expected Benefits:

This project will deliver a public benefit to the UK by providing an evidence base to improve public service delivery. The data generated as part of this project will allow us to calibrate wastewater-based epidemiological models. Current approaches can identify changes in the level of infection in the community, but they cannot be used to estimate the number of people infected. Having access to high-quality healthcare data at the level of wastewater treatment works will allow us to calibrate epidemiological models and use the information extracted from wastewater samples to better understand the pandemic.

Subject to satisfactory statistical disclosure checks, the summary statistics at the level of wastewater treatment plants will be made available to research groups, government departments, and citizen scientists for wastewater-based epidemiology. This will also provide an evidence base for decisions likely to benefit society or quality of life for people in the UK. Well-calibrated models should allow these groups to infer disease incidence and predict healthcare needs so resources can be better allocated (e.g. whether hospitals will get busy and need extra help to keep the community safe).

This project will help to provide an evidence base for public policy decision making. The team will conduct a small public engagement study to understand attitudes and perceptions regarding the use of healthcare data combined with measuring genetic traces of the virus in poo at sewage treatment works. The recommendations from the focus group discussions will be used to inform the development of an information resource to communicate the aims of the research project in a clear and accessible manner to members of the general public. This research will make wastewater-based epidemiology more accessible to the general public.

Outputs:

Outputs will be disseminated in two formats:
First, methods and high-level results will be published in peer-reviewed journals. The intention is to publish the findings in one of the following peer-reviewed journals. Science of the Total Environment, Environmental Science and Technology or Water Research.

Second, aggregated healthcare data will be released to other research groups, government departments, and citizen scientists subject to satisfying disclosure checks. These data will be disseminated in a machine readable format, such as CSV or JSON, together with a technical report describing the data. Aggregated (with small numbers suppressed) datasets will be disseminated via the Health Data Research Innovation Gateway (http://healthdatagateway.org) which provides an access hub for health data in the context of the COVID-19 response. The data will allow Imperial College London, other research groups, government and interested citizen scientists to develop and calibrate wastewater-based epidemiological models.

Only health data aggregated to the level of wastewater infrastructure will be disseminated. Prior to being made available for download from the Trusted Research Environment (TRE), both accredited researchers and a member of the TRE team (ONS Research Services) will assess the outputs for disclosure risks following the NHS Digital Statistical Disclosure Control Protocol. Therefore, all outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Assessing the data products for disclosure risks is key to the success of the project so the team can share the aggregated data with other research groups to calibrate wastewater-based epidemiological models.

The study team will report summary statistics at both daily and weekly cadences. Daily summary statistics can provide a signal for the pandemic with high temporal resolution but may have to be censored frequently due to low counts. Weekly summary statistics provide a coarser temporal resolution, but they will be less likely to suffer from censoring due to low counts.

The team want to ensure members of the public are informed of this research. The team aim to do this by conducting a small public engagement study to understand attitudes and perceptions regarding the use of healthcare data combined with measuring genetic traces of the virus in poo at sewage treatment works. This will involve working together with 4 focus groups to gain a better idea of the current level of knowledge regarding wastewater-based epidemiology and where a greater understanding would be beneficial to improve public communication.

The target date for the production of outputs is 31/12/2021.

Processing:

Once the required variables have been extracted from the datasets, data will be transferred from NHS Digital to the Office for National Statistics (ONS) Secure Research Services (SRS). The ONS SRS was chosen because it provides the software required for geospatial analysis. HES data will be transferred by Secure File Transfer Protocol (SFTP) and SUS Plus data will be transferred via Message Exchange for Social Care and Health (MESH). The ONS SRS team will liaise with NHS Digital once this application has gained approval from NHS Digital and also, from the ONS Research Accreditation Panel. The ONS Secure Research Services will be used for all analysis.

Transfer of data into the TRE will be arranged by the organisation administering the TRE (e.g. the Office for National Statistics Secure Research Services) and the data controller (e.g. NHS digital) without involvement of the researchers.

The ONS will make the data available to Imperial College London researchers via a Trusted Research Environment (TRE). Imperial College London researchers will process the data within the TRE. Only data fields strictly required for the analysis have been requested and no data linkage is required.

Imperial College London researchers will be granted access to the datasets via a remote connection to Trusted Research Environments (TREs) in the United Kingdom. No record-level data will be transferred out of or stored outside the TREs which are secure environments administered by the Office for National Statistics Secure Research Services.

Within the ONS TRE the Lower Super Output Area (NHS Digital data) will be used to identify the wastewater treatment catchment area. The number of patients with COVID-19 in that catchment area identified and the data will then be aggregated. The NHS Digital data is not being linked to any other data.

These data will be combined with geospatial wastewater infrastructure data (such as the list of data zones or the area served by each wastewater treatment plant) to aggregate healthcare records. Summary statistics for each catchment area and date will be recorded as the output of the analysis, e.g. the number of cases associated with a particular catchment area.

NHS Digital reminds all organisations party to this agreement of the need to 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).


Imperial College London - REACT Data and Connectivity National Core Studies — DARS-NIC-431352-G7F1M

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

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

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2021-06-01 — 2022-05-31 2021.12 — 2021.12.

Access method: One-Off

Data-controller type: DEPARTMENT OF HEALTH AND SOCIAL CARE, IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Civil Registration - Deaths
  2. COVID-19 Hospitalization in England Surveillance System
  3. COVID-19 Second Generation Surveillance System
  4. Covid-19 UK Non-hospital Antibody Testing Results (Pillar 3)
  5. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  6. COVID-19 Vaccination Adverse Reactions
  7. COVID-19 Vaccination Status
  8. GPES Data for Pandemic Planning and Research (COVID-19)
  9. Hospital Episode Statistics Admitted Patient Care
  10. Hospital Episode Statistics Outpatients

Objectives:

The Real-Time Assessment of COVID Transmission (REACT) Study was established in May 2020 and provides monthly estimates of the prevalence of SARS-CoV-2 virus and bi-monthly estimates of the prevalence of antibodies to SARS-CoV-2 virus in the general population of England. To date, there are over 1.5 million people (including 30,000 adults who have tested positive for SARS-CoV-2 antigen or antibodies) in the REACT cohort and over 90% have provided consent for linkage of their study data to records held by NHS Digital.

This application is to enhance the existing REACT study research cohorts by linking the study data from adult participants (who have provided consent) to their health records held by the NHS. This enhanced dataset will then be used to advance understanding of the risks of infection and reinfection with COVID and people’s future health following a COVID infection. The outputs from this study will be delivered by June 2021 and directly feed into the UK government’s pandemic response through the partnership between Imperial College London and the Department of Health and Social Care.

Specifically, this DARS application is to request linkage between participants and the data held about them in the following data assets:

- COVID-19 Hospitalization in England Surveillance System
- COVID-19 Second Generation Surveillance System
- GPES Data for Pandemic Planning and Research (COVID-19)
- Covid-19 UK Nonhospital Antibody Testing Results (Pillar 3)
- Covid-19 UK Nonhospital Antigen Testing Results (pillar 2)
- COVID Vaccine status
- COVID Vaccine adverse reactions
- Hospital Episode Statistics Outpatients
- Hospital Episode Statistics Admitted Patient Care
- Civil Registration – Deaths

Department for Health and Social Care and Imperial College London are joint data controllers of the REACT study. IPSOS MORI is a data processor in the REACT study but will not participate in data processing for the data requested in this application. This application will allow completion of the research study which is funded by HDR UK Data and Connectivity funding scheme for National Core Studies. Imperial College London will be the only data processor on this research study.

Aim and purpose:

The aim of this application is to achieve linkage between the adult participants in the REACT study to information held by the NHS about their health, utilisation of healthcare, COVID antigen and antibody testing and COVID vaccination status. To achieve this, Imperial College will provide NHS Numbers and agreed demographic identifiers plus a study identification number (for participants who have provided consent for data linkage) to NHS Digital to provide pseudonymised data with Study ID rather than patient identifiers in the dataset.

The REACT study has two arms. REACT-1 provides near-real time estimates of the prevalence of SARS-CoV-2 virus in the general population of England (aged 5 and over) using a self- or parent-administered throat and nose swab tested by RT-PCR. Around 150,000 people have been recruited into the REACT-1 cohort each month since May 2020 and the most recent findings are from Round 9 in February 2021. REACT-2 provides estimates of the prevalence of IgG antibodies against SARS-CoV-2 spike protein using a self-administered lateral flow immunoassay (LFIA). Around 150,00 people are recruited during a two-week window every 2 months since June 2020 and the most recent findings from the Round in January 2021. To date, there are over 1.5 million people (including 30,000 adults who have tested positive for SARS-CoV-2 antigen or antibodies) in the REACT cohort and over 90% have provided consent for linkage of their study data to records held by NHS Digital.
This enriched cohort will be used for research to deliver urgent information for healthcare planning on individual-risk of COVID-19 and medium-term health outcomes of COVID-19 across the spectrum from asymptomatic to severe disease. The overarching research question is “What are the biological, social and environmental drivers of medium-term health outcomes following infection with SARS-CoV-2?”. This agreement is for an initial data extract at the start of the study with a second updated extract in May 2021 to update the study findings with a larger dataset with a longer follow-up period. This study is funded by ONS HDR UK Data and Connectivity National Core Study until 30th June 2021.

The IPSOS MORI/Imperial REACT I Antigen study latest DARS agreement is # DARS-NIC-393650-B7J6F-v2.3 and the IPSOS MORI/Imperial REACT II Antibody Study latest agreement is # DARS-NIC-389914-N9R8R-v3.2. These studies are under the data controllership of the Department of Health and Social Care and Imperial College London with IPSOS MORI as a data processor.

This study is relying on Article 6(1)(e) processing is necessary for the performance of a task in the public interest, the data controller Imperial College London are a public authority carrying out the research in the public interest. Article 9(2)(j) (processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes) the data are required for research purposes in the public interest, the research on the biological, social and environmental drivers of medium-term health outcomes following infection with SARS-CoV-2 is of public health importance. The study is performing research on a disease of high public health importance and the data request is to support a high profile National Core Study.

In this application, data is requested for individuals who have participated in the REACT study since May 2020 and provided consent for linkage of their data held by the REACT study with information the NHS holds about them.

Data requested:

Data is requested at the individual level from the following datasets:

1. COVID-19 Hospitalization in England Surveillance System: this dataset has national coverage of clinical and administrative data collected from people during a hospital admissions with COVID-19. Its purpose is for surveillance of secondary care cases of COVID-19. It was established on 15th March 2020 adapted from the system in pace for Influenza surveillance and provides a core dataset that enhances Hospital Episode Statistics through the inclusion of detailed, coded clinical information relevant to COVID-19 disease risk and outcomes. It will be used to provide information about the clinical status of people within the cohort who are treated in hospital for COVID-19.

2. COVID-19 Second Generation Surveillance System: this dataset is the national reporting system for routine laboratory testing for SARS-CoV-2, it builds on the existing SGSS for laboratories. Its purpose is for surveillance of confirmed cases of SARS-CoV-2 and testing began on 24th January 2020. It contains positive test results from Pillar 1 and Pillar 2 antigen testing. It will be used to define the COVID exposure status of participants in the cohort by providing dates when people had a positive test.

3. GPES Data for Pandemic Planning and Research (COVID-19): this dataset has been established to support research and planning to inform the UK pandemic response. All GP practices in England are required to submit a defined, coded dataset every two weeks and the national data-opt-out may not apply. Collection commenced in May 2020. It will be used to provide information about the clinical status of people within the cohort following a diagnosis of COVID. People who do not get diagnosed with COVID will also be followed up to allow comparison between health and healthcare in those who do and do not have episodes of infection.

4. Covid-19 UK Nonhospital Antibody Testing Results (Pillar 3): This dataset is national (UK-wide) surveillance of serology testing, both positive and negative test results. It draws in data from multiple sources. It will be used to define the COVID exposure status of participants in the cohort by providing information about whether people have antibodies or not. It will be specifically interpreted in the context of (6).

5. Covid-19 UK Nonhospital Antigen Testing Results (pillar 2): this dataset is surveillance of the national swab testing for the general population (excluding patients in hospital; health and social care workers; test processed by PHE or hospital laboratories) undertaken in venues such as at home, drive through or mobile test centres. It will be used in combination with (2) to define the COVID exposure status of participants in the cohort by providing dates when people had a positive test.

6. COVID-19 Vaccination Status Data: this dataset has recently been established and contains records of COVID vaccination events in any setting. It will be used to identify the dates when participants in the cohort received doses of a COVID vaccine. People will be followed from this point onwards and their post-vaccine COVID exposure status as obtained from (2); (4) and (5) will be characterised and explored.

7. COVID Vaccination adverse reactions: this dataset is related to dataset (6) and provides information about adverse reactions that occur immediately following the administration of a COVID vaccine.

8. Hospital Episode Statistics Outpatients and Admitted Patient Care: these datasets contain a complete record of all episodes of hospital healthcare in English NHS Hospitals. They will be used to provide information about the clinical status and healthcare utilisation of people within the cohort following a diagnosis of COVID. People who do not get diagnosed with COVID will also be followed up to allow comparison between health and healthcare in those who do and do not have episodes of infection.

9. Civil Registration – Deaths: this dataset is obtained via the Office for National Statistics and contains a complete record of all deaths in the UK with information about date, place and cause of death. This dataset will be used to identify death as a health outcome and as the date of leaving the cohort (for time-to-event analyses).

These data will be linked to the data from the REACT study and used to construct a longitudinal record for the participating members of the REACT cohort that described pre-pandemic health status; COVID-19 testing and results; healthcare utilisation (quantity and reason) since COVID exposure status was determined. It is necessary to request linkage for all consenting participants in the REACT study as this is a representative population-based cohort and it will allow robust comparisons across the longitudinal health profiles of participants with all possible histories of COVID exposure, including across the spectrum from asymptomatic to severe disease. Data is being requested from before entry to the cohort (one year, from 1st March 2019). The rationale for “looking back” over the 12 months before the pandemic is to establish a “baseline” health status for individuals before people were infected with SARS-CoV-2 and before disruptions to healthcare may have introduced bias into data capture. This will allow comparison of health outcomes to be adjusted for pre-exisiting health and healthcare utilisation. Participants will be followed up into the future for as long as the data allows. This initial research is funded to June 2021 and it is anticipated that extensions to these data will be requested to permit longer duration of follow up (up to 20 years) subject to appropriate research needs as more is learnt about the long term natural history of COVID-19. Any further data requests would be subject to an amendment to this agreement being approved.

It is necessary to request individual level data as the data will be linked to individual participants in the REACT cohort and analysis will be conducted taking into account variables including age, sex and location of residence (postcode in cohort data). The data that will be in the extract can be pseudonymised through a study ID that can be linked to NHS number in the original cohort and will be provided by Imperial College. Follow up is currently a maximum of 12 months given the timing of the origin of COVID-19 and will extend as time since infection/vaccination increases as we follow the health outcomes post-infection/vaccination (subject to further DARS applications/extensions and research need). Data is requested on all health outcomes as this research aims to advance understanding of future health status of people characterised by their exposure to COVID-19. As more information becomes available about COVID-19 natural history, this request will be reviewed. We have selected the fields relevant to this research and minimised the number of identifiable/high-risk fields requested.

Expected Benefits:

There is a direct public health benefit to this research. It will be used to inform public health planning by looking at the connection between testing positive for COVID-19, vaccination, antibody status and future health and healthcare visits. It will help establish inequalities (where they exist) in disease risk, vaccination status and future health taking into account participants socio-demographic characteristics and environment. This will inform the design of the public health response and identify particular at-risk groups. The outputs from this study will inform the public health and healthcare planning.

The REACT programme has been used to inform the UK Government on the pandemic, to date it has produced over 20 reports, pre-prints and publications, multiple media appearances and wide-ranging media coverage.

A key benefit may ultimately be the reduction of morbidity and mortality from COVID-19.

Outputs:

The processing of this data during the conduct of the proposed research will produce preliminary study findings that will be shared internally with the REACT study team and DHSC (Joint Data Controller of REACT study) to achieve timely impact from study insights and findings.

Final outputs of this processing will include working papers, reports, blogs, social media (including institutional twitter accounts e.g. @Imperial_PERC and @ImperialSPH), educational seminars. pre-print journal-style articles and peer-reviewed publications with press-releases, as appropriate (likely to appear in similar journals to those current REACT research is published in i.e. British Medical Journal, Nature Communications etc.). This study is required to produce outputs (including reports and peer reviewed publications) by July 2021.

The study has a Public and Patient panel comprised of 4 members of the existing REACT Patient Advisory Group and 4 new members. The Panel will be involved in the production of outputs and the dissemination strategy to ensure that materials are fit for purpose and meet the needs of participants and the wider public. The dissemination strategy will link into the existing programme of activity for the wider REACT programme.

All outputs will have appropriate statistical disclosure control applied and no identifying information will leave the secure research environment. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

The REACT Investigators will also apply for future funding to maintain and extend the research undertaken on this enhanced research cohort. As part of this activity, a Data Access Committee is in development to oversee future research direction.

Processing:

Flows of data:
- Imperial College London will provide the cohort identifiers of NHS Number, Date of Birth and Sex, along with a Study ID to NHS Digital.
- NHS Digital will link the cohort to the requested datasets and send the linked datasets securely back to Imperial College London. NHS Digital will only flow back the Study ID and the linked datasets.

REACT Study data is currently processed on statistical servers at Slough (that are backed up at South Kensington Campus of Imperial College London). Servers holding REACT data can only be accessed by approved REACT users who by definition hold a substantive employment contract with Imperial College and have completed appropriate data protection and security training. By default, users have “screen view” only. Specified users do have the ability to transfer files, but this is for the upload of scripts and the download of nonpersonal data (such as graphs and aggregated tables). Dedicated hardware firewalls surround all servers that hold the REACT data (known as the REACT enclave), this blocks outgoing traffic as well as incoming. All access into and out of the REACT enclave is via “gateways” that are controlled by the Security Manager. The gateways determine which enclave and services the users have access to, and which users can transfer files. Once connected to a REACT server users cannot “browse the web”, send/receive emails, connect to Imperial college network drives etc., everything must be through the gateways.

Users outside of the college access the gateways via pulse secure VPN software. Before connections are allowed the software checks that the users operating system is patched and has an up to date virus checker is installed and operating. The authentication is multi-factor with users also needing a smartphone as well as username and password. All VPN connections are monitored, and the Security Manager receives alerts if non-UK connections are detected. Once authenticated the gateway determines if the user has appropriate rights to access the react servers.

Network traffic between Slough and South Kensington is encrypted. All network infrastructure at Slough is owned and managed by Imperial staff in space dedicated to Imperial. Access into the Imperial area is via swipe cards, CCTV covers the area and is monitored by Imperial and Virtus staff (24 hour).

To date, there are over 1.5 million participants in the REACT cohort and around 90% have provided consent for data linkage. The estimated size of the cohort for data linkage is 1 million people. The REACT cohort will increase at each subsequent round of the study as around 150,000 new participants provide their data. Imperial College will flow NHS number, age, sex, postcode and a novel Study identification number for each participant to NHS Digital. NHS Digital will return the pseudonymised data using study ID as the identifier for linkage within the database at Imperial College.

NHS Digital reminds all organisations party to this agreement of the need to 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).


COSMOS: Cohort Study of mobile phone use and health (MR1367) — DARS-NIC-370843-R6V8T

Opt outs honoured: N, Identifiable, No (Consent (Reasonable Expectation))

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

Purposes: No (Academic)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2016-08-01 — 2020-12-31 2016.04 — 2021.11.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No, Yes

Datasets:

  1. MRIS - Flagging Current Status Report
  2. MRIS - Scottish NHS / Registration
  3. MRIS - Cause of Death Report
  4. MRIS - Cohort Event Notification Report
  5. MRIS - List Cleaning Report
  6. Cancer Registration Data
  7. Civil Registration - Deaths
  8. Demographics
  9. Emergency Care Data Set (ECDS)
  10. Hospital Episode Statistics Accident and Emergency
  11. Hospital Episode Statistics Admitted Patient Care
  12. Hospital Episode Statistics Critical Care
  13. Hospital Episode Statistics Outpatients

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.

Yielded Benefits:

Imperial College London has already published number of peer reviewed reports so far, including: Toledano et al (2018) An international prospective cohort study of mobile phone users and health (COSMOS): Factors affecting validity of self-reported mobile phone use International Journal of Hygiene and Environmental Health, 221 (1) 1-8 Toledano et al (2015) How to Establish and Follow up a Large Prospective Cohort Study in the 21st Century - Lessons from UK COSMOS. PLoS ONE 10(7): e0131521 However, no yielded benefits have yet been achieved for the general public or NHS patients because it is integral to the design of a long-term cohort study, such as COSMOS, which starts with a ‘healthy’ population, that Imperial College London have to wait for sufficient cases of incident disease to accrue in the cohort before they can conduct health analyses that are sufficiently statistically powered. It is crucial that analyses are sufficiently statistically powered, in order to ensure that this produces reliable and robust evidence to inform policy. The research has now reached the stage at which health analyses can commence for specific health outcomes, as reflected in the Expected Outputs section.

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.


COVID Oximetry At Home - (CO@H): Imperial — DARS-NIC-421524-R0Y3P

Opt outs honoured: No - data flow is not identifiable, No - Statutory exemption to flow confidential data without consent, Anonymised - ICO Code Compliant, No (Statutory exemption to flow confidential data without consent)

Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Other-Health and Social Care Act 2012 - s263 (b)

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2021-04-07 — 2021-09-30 2021.04 — 2021.07.

Access method: Ongoing

Data-controller type: NHS ENGLAND (QUARRY HOUSE)

Sublicensing allowed: No

Datasets:

  1. Covid Oximetry @ Home (CO@H)
  2. Shielded Patient List
  3. Covid-19 UK Non-hospital Antigen Testing Results (pillar 2)
  4. COVID-19 Second Generation Surveillance System
  5. Civil Registration - Deaths
  6. Hospital Episode Statistics Admitted Patient Care
  7. GPES Data for Pandemic Planning and Research (COVID-19)
  8. Emergency Care Data Set (ECDS)
  9. Hospital Episode Statistics Critical Care
  10. Personal Demographic Service

Objectives:

The COVID Oximetry @home (CO@H) programme involves the remote monitoring of patients with coronavirus symptoms. Patients use a pulse oximeter, a small monitor clipped to their finger, to measure their oxygen saturation levels three times a day. They record their results using a smartphone app, web portal or paper diary. The paper-based option is available at all sites for patients who are uncomfortable with or unable to use a digital solution to record their readings. Patients are supported by clinical staff locally, so that if they need further treatment they can be admitted to hospital at the right time. Currently, services are delivered by a range of provider organisations, including Clinical Commissioning Groups, Primary Care Networks and acute hospital trusts. Following a successful pilot evaluation - conducted in-part by Imperial College London - NHS England, NHS Digital and NHSX are collaborating on the national implementation of this service, called COVID Oximetry @ Home (CO@H). In November 2020 it was agreed to extend the service across England from 1st December 2020 to date.

In addition to Imperial College London, the CO@H evaluation consists of three other organisations working independently on separate programmes of work which collectively evaluate the qualitative and quantitative aspects of delivery of the CO@H service. A team from University College London (UCL) and the Nuffield Trust will qualitatively evaluate the CO@H service through a range of survey and interview studies and will also undertake evaluation of the effectiveness of the CO@H programme using aggregated data (with small number suppression applied), while the Improvement Analytics Unit (IAU) of the Health Foundation will evaluate the effect of the CO@H programme on patient outcomes.

Imperial College London have been asked by NHS England to evaluate the implementation of the CO@H service to identify emerging issues relating to safety or equity of the service as it is implemented. To undertake this evaluation, Imperial College London require patient-level pseudonymised data for patients enrolled into the CO@H programme, and also for patients not enrolled in the programme either before or after implementation. Imperial College London will be responsible for providing quantitative evaluation of the CO@H service according to three different analytical strategies described in section 5b. Additionally, Imperial College London will provide the Nuffield Trust with data aggregated at the level of the Clinical Commissioning Group with suppression of small numbers (<=5) in order to enable their evaluation of the CO@H service as detailed in 'Processing Activities - Section 5b'.

The purpose of this work is derived from the need to evaluate national roll out of the NHS England Covid Oximetry @Home (CO@H) programme. The aim of this work is to quantitatively assess the cost effectiveness and clinical effectiveness of the CO@H intervention as well as variation in access and outcomes. The data requested is uniquely capable of achieving this aim as it contains necessary information about patients who are enrolled onto the CO@H programme as well as their outcomes. Furthermore, the data requested will support critical analysis regarding inequalities to accessing the pathway, patient-level outcomes after being onboarded into the pathway and CCG-level performance of the CO@H programme. Only this data, inclusive of the requested variables, will be able to support these analyses and therefore achieve the aim identified.

The data is required for service evaluation purposes - meeting the conditions outlined as per Article 9 (2)(H) of the GDPR. “processing is necessary for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, the provision of health or social care or treatment or the management of health or social care systems and services on the basis of Union or Member State law or pursuant to contract with a health professional and subject to the conditions and safeguards referred to in paragraph 3”. Imperial College London are carrying out service evaluation work (under the instruction of NHS England) as described in this agreement to investigate how the quality of care can be improved.

As such, this work is of significant public health concern undertaken by a service evaluation team conforming to General Data Protection Regulation (GDPR) Article 6(1)(e) and Article 9(2)(H).

The quantitative evaluation of CO@H requires understanding the clinical outcomes of patients onboarded into the CO@H programme as well as key features of their medical history. These datapoints are included in the data collection specification and directly enable an evaluation of the CO@H programme which would not be possible with any other data. Given the unprecedented need to understand how best to manage Covid-19 in the community, the data required for this evaluation will enable a statistically robust analysis which can inform and direct national policy making.

The anticipated cohort size for this evaluation will be c3,7 million people - including those with a CV19 Positive Test (control) and those on the CO@H Programme (case).

For this dissemination - the legal basis to disseminate the data is the Control of Patient Information Notice (COPI) Regulations. NHS Digital has chosen to pseudonymise the confidential information in accordance with the COPI regulations.

Expected Benefits:

Sharing this data enables the evaluation of a major pathway of care for patients with Covid-19. Understanding the cost effectiveness and clinical effectiveness of the CO@H programme is critical to building safe remote monitoring pathways for Covid-19 patients in the community. Delivering effective, safe Covid-19 care is vitally important to patients and their care, as well as to NHS staff and the future of how the pandemic is managed.

In terms of measurable benefits, this work will quantifiably demonstrate the clinical effectiveness of remote oximetry for Covid-19 at a patient and CCG-level. These outcomes will drive how the NHS cares for and monitors Covid-19 patients in the community. They will also reveal any changes needed to make remote oximetry services more equitable and more effective.

The pipeline from outputs to national policy is explicit in this piece of work: the results from this evaluation will be shared regularly with NHS England specifically with those involved in the policies and standards surrounding the provision of CO@H services. Therefore, the results from this evaluation will directly inform national policy makers about how best to roll out, modify and improve home oximetry for Covid-19 patients. This pipeline from evaluation to decision making to community benefit has already been demonstrated in the pilot phase of this work, whereby an evaluation of safety of the ‘Covid Virtual Ward’ programme led to the national roll out of the CO@H programme.

The pilot work revealed a large proportion of patients using home oximetry pathways were of low clinical risk and had low rates of mortality and secondary care presentation. This finding contributed to the definition of eligibility criteria for the national rollout of CO@H. Additionally, The finding of low rates of all-cause mortality in those enrolled into home oximetry programmes through primary care indicate the overall safety of the pathway for national implementation.

The quantitative evaluation of CO@H requires understanding the clinical outcomes of patients onboarded into the CO@H programme as well as key features of their medical history. These datapoints are included in the data collection specification and directly enable an evaluation of the CO@H programme which would not be possible with any other data. Given the unprecedented need to understand how best to manage Covid-19 in the community, the data required for this evaluation will enable a statistically robust analysis which can inform and direct national policy making.

The results from this work will be made available to key stakeholders, namely NHS England, in regular reports. Furthermore, the final results will be made public with targeted publications for NHS England but also within the public academic literature, as a scientific contribution.

The evaluation will guide the ongoing implementation of the CO@H programme at national and local level. It will provide evidence as to the equity of access to the CO@H programme and the safety of the CO@H programme. These findings will be used during the implementation of the programme to support emerging evaluation concerns raised by local sites and the national delivery partners. Actions arising from the evaluation will be implemented centrally by NHS England or locally by Clinical Commissioning Groups and other care providers as required.

Findings from this analysis will benefit all patient eligible for the CO@h programme which include:

• All registered patients in England aged 65 and over
• All registered patients in England aged 18 and over who are deemed clinically extremely vulnerable in relation to COVID-19

In particular, findings from this evaluation will serve the following purposes:

• To improve health service delivery (by evaluating policies and reporting feedback to the NHS and policy makers)
• To make health policymaking more effective (the work packages look at specific policy implementation and how this has been effective, and feedback will be given to policy makers)

These individuals will benefit through the evaluation indicating areas of inequity in provision or safety concerns in the CO@H programme thereby supporting NHS England to maintain and improve the quality of the CO@H service.
The programme may be rolled out more widely, depending on the service evaluation findings.

The outputs of the evaluation are intended to directly support the provision of the Covid Oximetry at Home programme of care by NHS England and will therefore meet the stated objective. Additional academic journal outputs will support the sharing of the findings of the evaluation with other healthcare providers nationally and internationally, and will thereby meet a secondary objective to guide future remote monitoring programmes within the NHS in England.

Outputs:

Imperial College London expect to produce the following outputs, which are described in detail below:

• Monthly reports with interim findings prepared for the CO@h evaluation workstream
• Final internal briefing for key stakeholders in NHS England, NHS Digital and NHSX.
• Open access paper in peer review journal on equity of access to CO@H
• Open access paper in peer review journal on the impact of CO@H on patient outcomes and secondary care utilisation
• Publicly available policy briefing published through the Imperial College London Institute for Global Health Innovation website
• Conference presentation on CO@h evaluation, including at the 2021 Health Services Research UK conference where a workshop abstract has already been submitted collectively by evaluation partners.

As a result of the data processed described above, Imperial College London will share interim findings with the CO@H evaluation workstream on a monthly basis (on top of regular reporting in weekly meetings). Imperial College London will prepare a briefing on the findings for circulation to key stakeholders in NHS England, NHS Digital and NHSX. All outputs will only contain aggregated data with small number suppression applied.

Imperial College London will submit two academic papers to peer-reviewed journals and upon submission will also publish a pre-print version of the paper, for immediate dissemination.

The findings of all three proposed evaluation work packages, as well as the wider literature will be combined in a policy briefing that will be made publicly available through the Imperial College London Institute for Global Health Innovation website.

Additionally, the Imperial College London evaluation team will present findings from the evaluation at relevant conferences, not limited to, but including HSR UK. Additional conferences will be identified in the coming months as they are announced.

Where possible the Imperial College London evaluation team will collaborate with evaluation partners in the CO@h programme, to synthesise evidence across multiple studies, and maximise impact form the work. The intent is to share the outputs of the work to the general public in the form of peer reviewed publications in open access journals in addition to reports to NHS England and other forms of dissemination.

The overall timeline of this work will be dependent on the development of the COVID-19 pandemic in England, and the duration of the CO@h programme as a result of this. The suggested timeline below may be subject to change.

Timeline:
• Monthly reports to CO@h programme from February 2021 to April 2021
• Briefing to key stakeholders – April 2021
• Submission to peer review journal on equity of access to CO@H – May 2021
• Submission to peer review journal on the impact of CO@H on patient outcomes and secondary care utilisation – May 2021
• Dissemination of both papers through pre-print server – May 2021
• Publication of both papers will depend on peer review process – December 2021
• Policy briefing – June 2021
• Conference presentation at HSR UK – July 2021

Processing:

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

Processing this data is in the public interest as it will provide evidence as to the clinical effectiveness of home oximetry as a clinical pathway for patients with Covid-19. This evidence will support the health service to more effectively treat and manage Covid-19, and therefore is of substantial public interest. The data will be analysed within given secure environment. The programme and and it's evaluation work-streams has been reviewed by the Imperial College Research Integrity Office, which concluded that it did not require ethical approval. Furthermore, Service Evaluation Approval also noted the lack of ethical concerns. The evaluation will be conducted using pseudonymised data entirely, and therefore there is minimal risk of identification. Given the size of the dataset request, it would not be reasonably feasible to seek individual consent for this project.

The purpose of this work is derived from the need to evaluate national roll out of the Covid Oximetry @Home (CO@H) programme. The aim of this work is to quantitatively assess the cost effectiveness and clinical effectiveness of the CO@H intervention as well as variation in access and outcomes. The data requested is uniquely capable of achieving this aim as it contains necessary information about patients who are enrolled onto the CO@H programme as well as their outcomes. Furthermore, the data requested will support critical analysis regarding inequalities to accessing the pathway, patient-level outcomes after being onboarded into the pathway and CCG-level performance of the CO@H programme. Only this data, inclusive of the requested variables, will be able to support these analyses and therefore achieve the aim identified.

This request supports a funded programme of work, commissioned by NHS England, with additional funding from the National Institute for Health Research (NIHR), with data co-ordination provided by NHS Digital. It is a six-month piece of work beginning in December 2020 and due to finish in May 2021.

The proposed evaluation work is overseen by the CO@H evaluation working group led by NHS England. The working group has contributed to a common understanding of the evaluation needs, as well as worked-up a CO@H specific dataset for evaluation purposes. This dataset, common across studies led by Imperial and the Health Foundation, is what is described in this application. Although both Imperial and the Health Foundation will lead on their own work and evaluation stream (and as such apply for these data separately), there is commitment to share learning between the two pieces of work to ensure robust findings, a common definition of key metrics, and improved efficiency in tackling any issues arising from the data. The intent is to share the outputs of the work to the general public in the form of peer reviewed publications in open access journals in addition to reports to NHS England and other forms of dissemination.

This evaluation will use routinely collected data from patients who have tested positive for COVID or are clinically suspected of having COVID and have been enrolled into the CO@H programme. Eligibility criteria for the programme are further described in the protocols attached to this agreement but will NOT include patients under the age of 18. These data will be used comparitvely, by comparing against patients testing positive for COVID, but not in receipt of the CO@H programme.

The purpose of this work is derived from the need to evaluate national roll out of CO@H. The purpose of this work is to quantitatively assess the cost effectiveness and clinical effectiveness of the CO@H intervention as well as variation in access and outcomes.

Work package 1: Identifying inequalities in access in CO@H
Objective 1:
To identify inequalities in access to the CO@H programme based on location, demographic and clinical traits.
Methods used:
• In the first work package, all patients with a positive Covid test from the date of implementation of CO@H in each site will be included.
• The probability of inclusion in CO@H conditional upon being eligible for the program based on features in GDPPR will be examined across a range of regional, demographic and clinical features.
• Binary logistic regression will be used to identify statistically significant differences in likelihood of inclusion according to these features nationally, and where possible at the level of individual sites.
• Some patients not onboarded onto CO@H may have been too unwell for onboarding, and so may not provide a suitable comparator group. A range of sensitivity analyses will be conducted to examine different assumptions relating to whether mortality or hospital presentation around the time of testing precluded enrolment, using secondary care and ONS mortality data. The ability to undertake this component will be determined by the availability of both date of test and date of result for Covid tests.
• Absence of oxygen saturations for those not onboarded precludes comparison of clinical acuity in the community.
Output:
• This work package will deliver ongoing regular surveillance of the occurrence of inequality in access to CO@H in relation to geographic or demographic traits

5.2 Work Package 2: Quantifying the Impact of CO@H on Patient Outcomes and Secondary Care Utilisation
Objective 2:
To identify national and site-specific mortality and secondary care utilisation effects of CO@H on patients with a positive Covid test result
Methods used:
• This evaluation examines the effect of the CO@H programme on those individuals eligible for the programme and those not eligible for the programme. Outcome measures will include A&E presentation, hospital admission, ICU admission and mortality.
• Propensity score matching will be used to match pre and post implementation populations testing positive for Covid according to known demographic and clinical features extracted from GDPPR. Additionally, local measures of Covid incidence and nearby secondary care burden will be incorporated into the matching.
• In addition to reporting the effect of the intervention on the eligible population, the evaluation will be repeated for those not eligible for CO@H based on the above criteria derived from GDPPR. This will identify any ‘background’ trends in disease severity and mortality in the population not eligible.
• Depending on the characteristics of implementation across sites, post-implementation analysis may begin after a transition period determined according to whether sites implement from a standing start, or already have a remote monitoring programme in place. A stepped-wedge approach design will be used, accounting for different roll-out timelines across sites.
• This evaluation requires knowledge of patients who would be eligible for CO@H prior to the initiation of the intervention, and as such can only report on those who have a positive Covid test.
• A start date of 1st October generally coincides with the Autumn acceleration of Covid incidence and provides approximately two months of pre-implementation data, the precise time at which may vary by site.
• Patients eligible for the CO@H programme prior to implementation will be defined as those who are ages 65 years or more, or those who are classified based on diagnoses held in GDPPR as being ‘clinically extremely vulnerable’.
• Evaluation will be performed nationally and the possibility to evaluate at regional and site levels will be explored based on the volume and quantity of data.
Output:
• Analysis quantifying the effect of CO@H on mortality and secondary care activity

5.3 Work package 3: Identifying Variation in Practice and Performance Between CO@H Sites
Objective 3:
To describe variation in the patient populations, routes of onboarding and patient outcomes between sites, and to use these findings to derive near- to real-time identification of outliers.

Methods used:
• This evaluation will begin by describing rates of uptake between sites over time, and examine variation in the characteristics of patients being onboarded and their routes of onboarding. The following five indicators will be the main outcomes of interest at each site. calculated for each site: A&E presentation, hospital admission, ICU admission, oxygen saturations at hospital presentation and mortality.
• In order to control for differences in the characteristics of patients cared for between sites, regression models will be constructed using data from all sites to predict local expected values of the outcome variables. Ratios of the expected to observed outcome variables will be calculated as a means of readily identifying outlying sites to enable more detailed local evaluation as needed.
• This evaluation does not incorporate information from the pre-implementation period, or from patients testing positive but not onboarded onto CO@H after implementation.
Outputs:
• Descriptive reports of overall uptake and characteristics of patient populations and outcomes nationally and for individual sites.
• Regularly updated measures of outlying site in terms of secondary care utilisation and mortality.

(RSET) Rapid Service Evaluation Team and BRACE = Birmingham, RAND and Cambridge Evaluation Centre.
As part of the RSET/BRACE evaluation, the Nuffield Trust request aggregated data on the CO@H service to be sent via the evaluation team at Imperial College. Data will be aggregated at the level of the Clinical Commissioning Group (CCG). Within this data, small number counts (<= 5) will be suppressed before transfer. The Nuffield Trust require the numbers of people onboarded onto the CO@H service each week, specifically. Numbers onboarded each week within each CCG split by:
- Tech-enabled or manual
- Age band (<65, 65-74, 75-84, 85+)
With each possible combination this will result in 8 data items within each CCG each week.

The RSET/BRACE evaluation of CO@H is a mixed-methods programme with four workstreams with the following aims:
1) Explore the effectiveness of COVID Oximetry @home (e.g. in relation to mortality and use of hospital services)
2) Identify the costs and benefits of implementing COVID Oximetry @home
3) Analyse patients’ experiences of, and engagement with the COVID Oximetry @home service
4) Analyse staff’ experiences of delivering and implementing COVID Oximetry @home

The data will be required to help answer the first of these, and, in particular the specific question as to how hospitalisations and mortality due to COVID-19 compare before, during and after implementation within each implementing area and between areas. We are sourcing data from a number of sources, for example, PHE for mortality data and HES for use of hospital data. The level of analysis will be each CCG and we will explore time series regression models to account for varying dates of implementation, the development of implementation plans over time and changing pressures on hospital services. If uptake is variable between sites, we will also investigate dose-response models during the implementation period. This approach is unique to this evaluation.

The Nuffield Trust plan for the suppressed, aggregated data to be created by the evaluation team at Imperial College, on a periodic basis, and transferred by Imperial College London through secure data transfer (SFTP) to a secure server within the Nuffield Trust. Only named individuals within the Nuffield Trust responsible for analysing data have access to this server. The Nuffield Trust has accreditation with DSPT v2 and ISO 27001.
The Nuffield Trust will only access aggregated data with small numbers suppressed - therefeore are not considered a processor of NHS Digital data.

In terms of the datasets required, the following justifications apply:

General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR) will be used to determine the demographic features and clinical comorbidities of patients enrolled into the CO@H programme. Additionally, these data will be obtained for individuals not enrolled into CO@H, both before and after the start of the CO@H intervention (those who have tested positive for CV19). This will enable construction of appropriate control populations to address key evaluation questions regarding equity of access to the service and the safety of the service.

Covid-19 testing data from the Second Generation Surveillance System (SGSS) and National Pathology Exchange (NPEx) Covid-19 testing datasets will be used to identify those patients enrolled into CO@H following a positive test for Covid-19, and the time between a positive test and enrolment. Additionally, Covid-19 testing data will be used to identify those patients who had a positive Covid-19 test prior to the start of the CO@H intervention as a means of establishing a matched preintervention control population. Patient with a positive Covid-19 test after the start of the CO@H intervention will be identified in order to evaluate the equity of allocation of the intervention across all those with a positive Covid-19 test.

CO@H Onboarding data (collected from sites when a patient begins care in a CO@H programme) will be used to identify the time and routes of enrolment of patients (primary care or secondary care), in addition to the clinical acuity of patients at onboarding. These data are required to determine the duration between enrolment and presentation to hospital or mortality, and to evaluate variation in clinical acuity and routes of enrolment between sites.

CO@H Offboarding data (collected from sites after a patient leaves the CO@H programme) will be used to determine the time spent on the home oximetry programme across patient groups and between sites. It will also be used to determine whether patients used a standard or ‘tech enabled’ intervention during their CO@H programme.

Hospital Episode Statistics Admitted Patient Care (HES APC) /Critical Care (HES CC) data will be used to identify presentations to critical care departments and hospital admissions for patients enrolled into the CO@H programme, and also those within relevant control populations. Evaluating the rates of secondary care interaction are a critical aspect of the evaluation. HES data is requested from March 2017 to provide diagnosis and treatment information which may be missing from GDPPR but important to identify variation in patient populations.

Emergency Care Data Set (ECDS) data will be used to identify presentations to accident and emergency departments. Evaluating the rates of secondary care interaction are a critical aspect of the evaluation.

Civil Registration Mortality Data are required for all individuals in order to identify mortality rates in the CO@H intervention population, and also in control populations. Date of death is required to determine the time from enrolment and Covid-19 testing to death in order to conform to Public Health England definitions of Covid-19 mortality. Cause of death will allow non-Covid-19 mortality to be distinguished from deaths attributed to Covid-19.

All data will be required in pseudonymised format to enable patient-level evaluation across datasets.

Different date ranges will be used for different datasets based on their availability and role in the evaluation. GDPPR data, Civil Registration Mortality Data and Personal Demographic Service data are requested from March 2020, as marking the start of the Covid-19 pandemic in England, and will therefore be relevant to the programme. Some datasets (CO@H onboard, CO@H offboarding and Oxygen saturation levels) are only collected as part of the CO@H programme, and will not be available before October or December 2020. The date ranges for the HES and ECDS data goes back in time further. This historic data will be used to help characterise the patient population eligible for the CO@H programme by looking at previous hospital utilisation.

Data will be required for all of England in order to evaluate performance of the national implementation of CO@H.

Following discussion between the evaluation partners, NHS Digital, NHS England, and NHSX, it has been determined that there is no alternative, less intrusive way (using less information) of achieving the purpose of the evaluation.

In order to minimise the data requested, Imperial College London has only included variables needed to carry out the analysis that has been agreed by NHS England. As part of a consultation with NHS Digital and clinical and academic experts, the evaluation partners have narrowed the dataset to focus only on information that can demonstrate the clinical effectiveness of the CO@H programme.

NHS England is the sole data controller for this data sharing agreement given their role in determining the scope, and purpose of the evaluation , although all processing will be carried out by Imperial College London.

The commissioner of this work is NHS England. The role of NHS England is to provide overall project support and management. They will not carry out any of the analysis, but will ensure the delivery of the work to time and make sure evaluation partners have the tools they need to carry out the work. They have instructed the evaluation partners to carry out this work. The role of NIHR is solely to provide funding to the evaluation partners. They do not act in a data controllership capacity.


Bespoke Extract - HES/Civil Registration Mortality Extract — DARS-NIC-383203-Q8B9L

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

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

Purposes: No (Academic)

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

When:DSA runs 2019-09-20 — 2020-09-19 2017.12 — 2021.04.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Office for National Statistics Mortality Data
  2. HES:Civil Registration (Deaths) bridge
  3. Civil Registration (Deaths) - Secondary Care Cut
  4. Civil Registration - Deaths

Objectives:

Imperial College London Doctor Foster Unit (ICL DFU) requires HES ONS data to identify measures of quality and safety in healthcare.
Although ICL DFU is funded by Dr Foster Limited, this application defines ICL DFU as the data controller. Any ONS data supplied as part of this application will not be shared with Dr Foster Limited.

ICL DFU undertakes its research and analysis to provide measures the quality of healthcare delivery by healthcare providers. For certain healthcare specialities or areas variations can be shown by provider to support the management information for the NHS.

This work also aims to:
• Compare hospital mortality rates for in-hospital deaths with rates for all deaths to evaluate the effect of differential discharge policies
• Calculate total post-operative mortality rates, e.g. when comparing operative techniques such as laparoscopy and open approaches
• Assess 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

With this application renewal ICL DFU will continue the unit’s long running work and its principal themes of:
• Developing and validating indicators of quality and safety of healthcare, particularly by consultant and hospital
• Showing variations in performance by unit and socio demographic stratum
• Predicting risk and adjusting risk of indicators and variations and any other methodological aspects as they arise

ICL DFU requires continued use of ONS data already held and used for its work. Additionally ICL DFU requires ONS data for 2016/2017 plus 30 days in order to capture all deaths in hospital and in the community within 30 days of admission or procedure for patients in hospital in March 2017. This will provide the full 30 days of follow up data.

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.

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 Unit's monitoring system, they generate monthly mortality alerts, based on high thresholds, which they 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 the Dr Foster Unit at Imperial College London's surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier. Key recommendations, 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. With continued access to the data, this monitoring tool from Dr Foster Unit at Imperial College 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. The Unit's mortality outlier outputs are used by CQC within their Hospital Inspection framework.

As a result of Dr Foster Unit at Imperial College leading role in the development of hospital mortality measures, in 2010 they 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 the Unit's 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. Dr Foster Unit at Imperial College continue to advise NHS Digital on methodological issues around the Summary ­level 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. 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 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 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.

Another example of Dr Foster Unit at Imperial College 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 the Unit 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/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.
Proposed work will continue with the Unit'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.

The Dr Foster Unit at Imperial College hold 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. Dr Foster Unit at Imperial 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:
Collaborative working with the University of Manchester and supported by the Care Quality Commission (CQC). This work aimed to improve understanding of the Unit mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts by focusing on two conditions commonly attributed to mortality alerts: acute myocardial infarction and septicaemia. The Dr Foster Unit at Imperial College provided a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. This report was submitted to the funders for approval in October 2016. After approval DFU will publish peer reviewed papers from this work.

The Dr Foster Unit at Imperial College completed 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). This work followed on from DFU’s previously published studies on readmissions in heart failure patients. 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. This report was submitted to the funders in June 2017. After approval DFU will publish peer reviewed papers from this work.

DFU ICL worked with the University of Leicester on thoracic aortic disease (TAD) looking at variations in rates of surgery and mortality between centres. There seems 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. The first paper as a result of this work was published in January 2017 (doi:10.1161/AHA.116.004913).

Examples of key published research that have used HES/ONS data include:
Bottle, A., Mariscalco, G., Shaw, M. A., Benedetto, U., Saratzis, A., Mariani, S., . . . Murphy, G. J. (2017). Unwarranted Variation in the Quality of Care for Patients With Diseases of the Thoracic Aorta. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 6(3), 66 pages. doi:10.1161/AHA.116.004913
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://www.imperial.ac.uk/dr-foster-unit/publications/

Processing:

HES and ONS data are transferred from NHS Digital to ICL DFU. These data are used to identify measures of quality and safety of healthcare.

The HES data includes bespoke identifiable data (NHS Number and local patient identifier) under the separate DARS NIC 12828. The data sets are:
• Admitted patient care
• Outpatient
• Critical care
• A & E

This application is a renewal for continuing the flow of ONS mortality data from NHS Digital to ICL DFU: DARS NIC 383203.

ICL DFU stores the ONS data on a database hosted on a private network located at 3 Dorset Rise. The private network is not connected to any other networks and does not have incoming or outgoing internet access. 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 the research database which holds the standard HES extracts and sensitive fields. Imperial􀍛s researchers do not have 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 data will be transferred outside of the EEA under this agreement. The data is only processed and stored at the addresses specified in this application.

ONS Data supplied under this Agreement may be linked with HES Data Supplied under NIC-12828 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, commercial company or Dr Foster Limited.

The ONS Terms and condistions of use will be adhered to.

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


MR700 - SINGLE SIGMOIDOSCOPY SCREENING IN PREVENTION OF BOWEL CANCER (The UK Flexible Sigmoidoscopy Screening Trial; UKFSST) — DARS-NIC-148071-QHNM8

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

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), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(2)(c), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-01-02 — 2022-01-01 2016.04 — 2021.03.

Access method: Ongoing, One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration
  4. Civil Registration - Deaths
  5. Cancer Registration Data
  6. Demographics
  7. MRIS - Flagging Current Status Report
  8. MRIS - Members and Postings Report

Objectives:

The UK Flexible Sigmoidoscopy Screening Trial (UKFSST) is a randomised controlled trial examining the long-term effect of a single flexible sigmoidoscopy (FS) screen on colorectal cancer (CRC) incidence and mortality in the United Kingdom. It is now in long-term follow-up. Results published after 10 and 17 years showed that a single FS examination resulted in a substantial reduction in CRC incidence and mortality (Atkin WS, et al. Lancet. 2010; 375: 1624–33; Atkin WS, et al. Lancet. 2017; 389: 1299–1311). The Cancer Screening and Prevention Research Group (CSPRG) now wish to see if this reduction in CRC incidence remains over a longer period or if it begins to diminish.

The CSPRG, Imperial College London, are responsible for the long-term follow-up of the study participants, combining demographic data, mortality data and cancer type / site data from NHS Digital with cancer staging data (from cancer registries in England, Wales and Scotland), clinical data directly from participating hospitals and screening data from the Bowel Cancer Screening Programme (England).

Primary Aim of the UKFSST:
To quantify the reduction in incidence and mortality from CRC resulting from a single FS screen at age 55-64 years with colonoscopy surveillance for those found to have high-risk polyps.

Secondary Aims:
1. To examine efficacy of FS screening in each of the specific age bands 55-59 years and 60-64 years.
2. To quantify the reduction in incidence and mortality from distal CRC (rectum, sigmoid colon) resulting from a single FS screen at age 55-64 years.
3. To quantify the reduction in incidence and mortality from proximal colon cancer (descending colon to caecum) resulting from a policy in which colonoscopy surveillance is offered only to those found at the screening FS to have high risk polyps.
4. To determine the duration of efficacy of a single FS.
5. To determine the optimum age for a FS screening examination.
6. To undertake an on-going evaluation of health service research issues to permit an informed decision at the end of the trial about its long-term suitability within national screening programmes.

In addition to the original study objectives listed above, the UKFSST study team identified the following potential questions to be addressed by further analysis of the UKFSST dataset. These analyses have been or will be conducted by the CSPRG medical statistician(s) or PhD students and published in peer reviewed journals.

• The quality of FS screening on screening outcomes and 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 colon cancer after FS, by the number and type of polyp detected at FS
• The incidence of CRC according to smoking history
• The potential contamination of UKFSST by Faecal Occult Blood Tests (FOBTs) given as part of the Bowel Cancer Screening Programme (BCSP)
• Medications in relation to risk of adenomas and CRC
• The effect of prior FS on participation and outcomes of FOBTs as part of the BCSP

2019 Amendment

The UKFSST Protocol has been amended to add one more study objective (to investigate the effect of diet and lifestyle factors on the development of polyps, and colorectal cancer by subsite) and to include the use of UKFSST data to validate findings of the Intermediate Adenomas study and All Adenomas studies (DARS-NIC-147827-NC2TC).

In addition, the study have now been awarded additional funding from the National Institute for Health Research-Health Technology Assessment (NIHR-HTA) to continue to follow-up the UKFSST cohort for a further 10 years to March 2027, until all the participants have reached the age of 80 years.

Mortality data which is held by the study from 1994 to 2012 was originally disseminated by Office of National Statistics (ONS). NHS Digital are now data controllers for that data and so this agreement covers the retention of that data period also.

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).

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.


Effectiveness and Value for Money of Prescribed Specialised Services Commissioning for Quality and Innovation (CQUIN) — DARS-NIC-172334-W0G2L

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

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

Purposes: No (Academic)

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

When:DSA runs 2018-05-17 — 2021-05-16 2018.10 — 2020.11.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Critical Care
  3. Hospital Episode Statistics Outpatients
  4. Hospital Episode Statistics Accident and Emergency
  5. HES-ID to MPS-ID HES Admitted Patient Care
  6. HES-ID to MPS-ID HES Outpatients

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.

Yielded Benefits:

Benefits have not yet been achieved as work is still underway and has not been completed, but a final report is expected by August 2020.

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.


Community-based VIrtual Electronic Wards for remote monitoring in suspected cases of COVID-19 (coronavirus): C-VIEW Study — DARS-NIC-396113-N9L4L

Opt outs honoured: No - data flow is not identifiable, Identifiable, Anonymised - ICO Code Compliant, No (Statutory exemption to flow confidential data without consent)

Legal basis: Health and Social Care Act 2012 - s261(5)(d), CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 - s261(5)(d), Health and Social Care Act 2012 - s261(5)(d); Other-CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002

Purposes: No (Academic)

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

When:DSA runs 2020-08-27 — 2021-03-30 2020.09 — 2020.10.

Access method: One-Off, System Access
(System access exclusively means data was not disseminated, but was accessed under supervision on NHS Digital's systems)

Data-controller type: IMPERIAL COLLEGE LONDON, NHS ENGLAND (QUARRY HOUSE)

Sublicensing allowed: No

Datasets:

  1. GPES Data for Pandemic Planning and Research (COVID-19)
  2. Covid 19 - Virtual Wards (Pilot)
  3. Civil Registration - Deaths

Objectives:

Community-based Virtual Electronic Wards for remote monitoring in suspected cases of COVID-19 (coronavirus): C-VIEW Study.

The pandemic of SARS-CoV-2 (coronavirus, COVID-19) remains a global health problem; to date over 9,277,214 cases have been reported across 216 countries with 478,691 deaths by the World Health Organisation (https://covid19.who.int/. Published 2020. Accessed June 25, 2020). Within in the United Kingdom (UK), 307,980 cases with 43,230 deaths have been reported (https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public. Published June 25, 2020. Accessed June 25, 2020). This undoubtedly has stretched resources, created pressures within the National Health Service (NHS) and accelerated a change of how hospitals operate in preparation for efficacious resource management.

Vital signs trends (oxygen saturations, heart rate, respiratory rate, blood pressure, temperature) are routinely used for monitoring hospital patients. Clinical deterioration may be recognised through changes in these parameters, and often precedes an adverse event. The rate of deterioration for individuals suffering with COVID-19 remains an unknown entity; given that novel digital technologies have enabled remote monitoring solutions, ‘virtual wards’ may provide a safe strategy for approaching this pandemic in appropriately selected patient groups.

Virtual wards can be established to manage patients remotely, freeing up staff, avoiding overwhelming hospitals, and reducing patient anxiety by allowing recovery at home. Healthcare professionals in virtual wards can track vital signs of those suspected of COVID-19, in near real-time, receiving alerts for clinical deterioration. Pulse oximeters combined with digital innovation (i.e. mobile applications) allow for systems to recognise early deterioration in vital parameters and self-reported symptoms, supporting clinical decision making. Indeed, pilot work trialling the virtual model demonstrated a saving of 300 bed spaces over a three week period.

The Institute of Global Health Innovation (IGHI) at Imperial College London (ICL) are supporting a programme of urgent COVID-19 work regarding new pathways of care for COVID-19 patients. The work will explore the value of a new care pathway using virtual wards with remote monitoring in suspected cases of COVID-19 in the community to improve
1) health resource utilisation (e.g. hospital admissions, ITU admissions),
2) clinical outcomes and
3) cost-effectiveness through early detection of clinical deterioration.
The work is led by NHS England (NHSE) with NHS Digital assisting on data set provision.

NHS England have a programme called NHS@Home, part of which has been asked to trial a remote monitoring pathway for COVID-19 patients. The NHS@Home programme is summarised below:

NHS @home provides an important opportunity to enhance NHS services, utilising the best technologies available to enable personalised clinical support to be delivered virtually to people in the setting of their own home including care homes.

Who the programme targets/ is open to
The initial activities are focusing on three groups, in response to the pandemic and to assist preparations for this winter:
• Group A – All care home residents
• Group B - Deteriorating COVID-19 patients, initially at 2-3 pilot sites, building on work of various other community of practice sites and aligned with work taking place with 8 sites separately, supported by NHS X
• Group C - People identified as higher risk of COVID19, including initially:
- People with a learning disability and diabetes (reaching 3000 people)
- Respiratory conditions (scoping use of peak flow meters to support people with asthma and COPD)
- People with heart failure (initially within 3 STPs, providing self-management support)
- Hypertension (22,000 blood pressure monitors to be distributed)
- Support for 5000 unpaid carers of those with learning disabilities to recognise early deterioration of COVID-19 (scope could be widened)

Group B is the focus of this agreement.
For Group B, the uptake/aims of the project are to pilot virtual wards as a way to manage COVID19 patients in the community through remote monitoring, with the aim of reducing hospital and intensive care stay, and patient mortality. This involves home oximetry monitoring, wearable sensors, medical diary apps and/or phone calls from clinicians to patients – essentially a variety of approaches to monitoring COVID-19 patients at home, referring them into the appropriate health service when needed and avoiding deterioration in the community. There are a number of measures in place to help patients with the remote monitoring and for those who have difficulty with entering data themselves the virtual ward are able to telephone the patient and input the data on their behalf.

Throughout the crisis, a series of ad hoc pilots have been conducted using oximeters and apps (namely the Huma Medopad app) to monitor at home. These pilot sites were disparate and uncoordinated, therefore NHSD have come in to collect data from those pilots so that it can be analysed retrospectively. The role of Imperial College is to access data collected from pilot sites for retrospective analysis and they have also developed a trial protocol and minimum dataset requirements for collecting prospective data. There are 3500 patients that will be included in the retrospective analysis and the prospective analysis cohort is unlikely to exceed 300 patients.

The COVID-19 National Incident Response Board (NIRB) have approved three pilots in London, Slough and South Tees. This has been established to ensure an evaluation can take place. It is recognised there are other initiatives across the country and the programme will look to maximise data from all locations. These are called Communities of Practice.

Tees Valley
Population size 700,000. NHS Tees Valley CCG. Mixed urban & rural with a complex health and care environment that gives the option of assessing scalability of the model to a large population. The Tees Valley has seen some of the highest COVID-19 infection rates in the country; with a rate of 484 per 100,000 population in Middlesbrough; twice the national average in May 2020

Slough
Population coverage 172,000. 4 PCNs. 54% BAME population, most diverse in the UK. 27% don’t speak English and 15.5% have no one in the household speaking English. High deprivation (over 50% fall in deciles 2-4, high population density, multigenerational and larger households (so shielded patients living alongside non-shielded). High Covid-19 rates and high transmission rates.

North West London (also a community of practice)
Allows further assessment in a metropolitan area

Imperial College have developed the Data Set. They are leading on the evaluation to inform the NHS England decision making on whether a national programme should be mandated and run centrally.

NHSX are leading on a procurement platform based on their experience with MedoPad (Huma) pilot. This would provide a platform for the NHS to buy equipment required if there is a national roll out. National roll out will not be determined until the results of the pilot are made available and analysed. NHS X are not part of this data sharing agreement, they will be involved in some follow up work should the pilot be successful and it's deemed that the NHS needs to buy equipment to facilitate use of these monitoring apps.

The purpose of the data request described in this agreement is to determine the value and viability of using virtual wards for Covid-19 patients from a clinical, administrative and cost effectiveness perspective. In order to achieve this, there are four specific service evaluation objectives:
1. To roll out the use of virtual wards in a selected location for symptomatic COVID-19 suspected or swab positive patients
2. Integrated retrospective analysis of quantitative outcomes for previous pilot virtual ward/remote community monitoring observational trials across the UK. These will be applied as a source for power analysis and prioritisation of primary trial outcomes.
3. To use quantitative data returned from remote monitoring devices and routinely collected health data to determine whether virtual wards improve clinical outcomes, healthcare utilisation and cost effectiveness as compared to traditional pathways
4. To determine the optimal thresholds for referral to hospital across different patient groups
5. To gather qualitative insights from clinicians and patients involved in virtual wards to assess their viability for future roll out.

The study design tests the effectiveness of the new care pathway of virtual wards site for healthcare delivery for individuals suspected of COVID-19. Furthermore, questionnaires and semi-structured interviews of participants will provide insight into wider implementation of this technology and provide feedback for improvements; semi-structured interviews of staff will provide a healthcare perspective, particularly thoughts on reducing potential infection risk through remote monitoring services.

The study population comprises patients managed on a pilot virtual ward/remote community monitoring observational trial for proven or suspected high-risk of COVID-19 between March 2020 and July 2020.

Setting
The evaluation will be conducted in community regions (such as in London, Slough, South Tees and the North of England) as a continuation of established pilots in association with NHS Digital and the NHS@Home programme.

Participants:
Eligible individuals (i.e. those suspected of COVID-19) will be identified by general practitioners, emergency department teams, or 111 staff for study participation.

The data collection and analysis will enable several key service evaluation questions to be answered.

• Which patients should be in a virtual ward based on risk factors and initial physiological readings?
• How long should they be monitored for and how frequently?
• What are the thresholds for admission to hospital or stopping monitoring?
• Does it work? i.e. improve outcomes and/or reduce length of stay, long-term disability

Once these key service evaluation questions are explored the outputs will be used to inform a decision on national roll out. Early indications are that virtual wards could reduce mortality and/or reduce length of stay in hospital.

The Virtual Wards dataset encompasses a cut of General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR data). This data has had the Type 1 Objections applied to it before it was released to NHS Digital. That objection will continue to be upheld when the dataset is made available as part of the Virtual Wards Dataset as part of the dissemination from NHS Digital to Imperial College London.

Imperial College London and NHS England are joint data controllers for this project.

Expected Benefits:

This is a remote monitoring study of a new pathway of care for COVID-19 focusing on community-based healthcare delivery of a virtual ward to achieve:
(i) increased efficiency of health system resource use and
(ii) enhanced health outcomes through (a) earlier detection of clinical deterioration and
(b) earlier management of morbidity. Apart from reducing infection risk to healthcare staff, the innovation in this trial has the potential to detect earlier clinical deterioration allowing for earlier intervention and provide further insight into the clinical course of COVID-19.

The results of the study could offer data to demonstrate the value and effectiveness of applying a new care pathway through virtual wards and remote monitoring during a pandemic, and may offer a methodology to introduce and manage remote monitoring systems to increase the capacity of community-based health management. The results of this study will inform national policy on the treatment of Covid-19. If evidence from communities of practice is seen more widely the concept has the potential to improve outcomes and reduce length of stay in hospital.

The collection will provide insight into the impact of COVID-19 Virtual Wards on hospital outcomes and hospital length of stay.

Benefits are likely to include:

- using the data to inform a national decision on the potential rollout of Virtual Wards.

- evidence of improving outcomes where patients, especially those at high risk use remote monitoring in a virtual ward.

- insight to enable more refined development of a virtual wards data set to support national rollout.

This information will be timely in preparing for a potential second peak and winter pressures.

Outputs:

All results will be published at aggregate level with small number suppression and in accordance with NHS Digital’s usual statistical disclosure control practices.

The principle output is Imperial College London's evaluation - provided to NERVTAG (New and Emerging Respiratory Virus Threats Advisory Group) to enable them to evaluate whether a national programme should be mandated and run centrally.

Reports on the uptake and effectiveness of the intervention will be presented to internal NHS stakeholders as part of Imperial College London's role as their evaluation partner.

Academic articles describing the uptake and effectiveness of a remote monitoring programme will be submitted to leading peer reviewed journals (e.g. BMJ).

Results will also be disseminated in internal and external academic meetings and shared with other learned bodies. Similarly, only aggregate data with small number suppression will be used.

Findings will be reported in 1-2 peer-reviewed journal publications. These publications would have an academic audience and include findings from the analysis and evaluation of Virtual Wards. Publications would be made open-access to ensure dissemination of learning from this analysis.

 

Processing:

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

NHS Digital will provide the data to Imperial College London, specifically to the team working at the Big Data Analytical Unit. NHS Digital will provide a linked, pseudonymised extract of data including the Virtual Wards Dataset, GPES Data for Pandemic Planning and Research (GDPPR) and Civil Registration Data.

Imperial College London will be the data processor.

Data access is strictly controlled by the Big Data and Analytical Unit (BDAU) at Imperial College London, 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 Secure Environment (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 primary outcome measure is to evaluate any hospital admissions or attendances by days 14 and 28. These time points have been chosen as it is reported that most COVID-19 cases have recovered by day 14. The additional time point allows evaluation of more severe cases.

Secondary outcome measures include: (i) intensive care transfer, (ii) hospital length of stay, (iii) mortality/survival, (iv) oxygen therapy (v) requirement for invasive/non-invasive ventilation and (vi) cost-effectiveness.
Clinical variables for measurement as secondary outcomes:
- Time from first symptom to hospital admission
- Predictors: Comorbidities, age, sex, BMI, ethnicity
- Thresholds/trigger values for face to face or hospital review
- Disability at 3 months after hospital discharge

Quantitative analyses:
Descriptive statistics will be obtained for the baseline characteristics of participants. Continuous variables will be presented as mean ± standard deviation and median (with range) and categorical variables will be reported as numbers and percentages. The total number of alerts, proportion of actioned alerts, and resultant actions will be measured. Outcome measures will be retrieved using the documented notes within the portal and in local hospitals by accessing electronic health records and case notes, if required. Cost-effectiveness and cost-utility analysis will also be performed on aggregate health and resource utilisation data.

Qualitative analyses:
Qualitative data will be word processed and uploaded into a proprietary qualitative analysis package. All interviews will be recorded digitally, fully transcribed, and pseudonymised; uploaded and stored for coding and analysis. To counter analysis bias, a random selection will be reviewed and coded by a second researcher, with any disagreement resolved through discussion.


MR1201 - Frequency of follow-up for patients with low-, intermediate- and high-risk colorectal adenomas — DARS-NIC-147827-NC2TC

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

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), 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(7), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: No (Academic)

Sensitive: Sensitive

When:DSA runs 2019-02-27 — 2022-02-26 2018.06 — 2020.02.

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. MRIS - Scottish NHS / Registration
  4. MRIS - List Cleaning Report
  5. MRIS - Flagging Current Status Report
  6. MRIS - Members and Postings Report

Objectives:

This application covers the amendment of the ‘Intermediate Adenoma study’ to the ‘All Adenomas study’.

The Intermediate Adenoma study:

The Intermediate Adenoma study (the IA study) was set-up in 2006, funded by the National Institute Health Research Health Technology Assessment (NIHR-HTA) following a commissioned call (reference 04/01/33), to investigate the most suitable frequency of follow-up for patients with intermediate-risk adenomas detected during colonoscopy.

The IA study was conducted by the Cancer Screening and Prevention Research Group (CSPRG) at Imperial College London in collaboration with 17 participating NHS hospitals in England and Scotland.

The study included a ‘hospital’ dataset and a ‘screening’ dataset. The hospital dataset was established from consecutive routine gastrointestinal endoscopy examinations in the participating hospitals to identify the study cohort – i.e. patients who were diagnosed as having an intermediate-risk adenoma at ‘baseline’.

CSPRG, Imperial College London has utilized this baseline investigation, plus subsequent colonoscopy and pathology investigations to determine the risk of advanced adenomas (large (≥ 1cm) or with advanced pathology) and colorectal cancer (CRC) in the study cohort. The hospital data was combined with data received from NHS Digital on colorectal cancer incidence and deaths, to ensure all incidences of colorectal cancer were reported for the study cohort.

The combined IA hospital dataset enabled the CSPRG to investigate:
- The optimal frequency of surveillance in people found to have intermediate-risk colorectal adenomas.
- The 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

The primary outcomes of the IA study have been published (see outputs section) and IA study is now in long-term follow-up and secondary analyses will be conducted to explore the data further through 2018 (manuscripts in preparation).

Amendment to the All Adenomas study:

CSPRG were awarded further funding by the NIHR-HTA in March 2017 (reference 15/80/13) to expand the remit of this study to investigate the same endpoints in all adenoma risk-groups, not just the intermediate-risk group. To achieve this, the IA study protocol was amended to the ‘All Adenomas study’.

The All Adenomas study protocol amendment has three main components:
1. To extend the analysis to investigate the ideal frequency of follow-up for patients with any adenoma detected at the initial investigation (low-, intermediate- or high- risk, as defined by the UK adenoma surveillance guidelines, 2002), using the same study cohort

2. To extend the follow-up period to at least 10-years post initial endoscopic investigation
1.3. To add a health economic analysis, to be conducted by the Health Economic Research Centre (HERC), University of Oxford, in collaboration with the CPSRG to determine the cost implications of any changes to the number of colonoscopies required as a result of the investigations of the low-, intermediate- and high-risk adenoma groups (either an increase or decrease).


The CSPRG has has recently received research ethics committee and health research authority approvals (including section 251 support) to include these additional analyses.

The CSPRG would now like to receive an updated data extract on all records to enable them to follow-up the whole cohort for up to 10 years post the initial endoscopic investigation.

A new health economic analysis will be conducted by the University of Oxford to determine the cost implications of any changes to the number of colonoscopies required as a result of the investigations of the low-, intermediate- and high-risk adenoma groups (either an increase or decrease). The University of Oxford have been asked by Imperial to do the economic analysis which will result in the provision of a chapter for the proposed 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.

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.


MR735 - Anglo-Scandinavian Cardiac Outcomes Trial — DARS-NIC-302604-S7H2N

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

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

Purposes: No (Academic)

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

When:DSA runs 2019-08-24 — 2022-08-23 2019.01 — 2019.11.

Access method: One-Off, Ongoing

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. MRIS - Cause of Death Report
  3. MRIS - Cohort Event Notification Report
  4. MRIS - Scottish NHS / Registration
  5. Hospital Episode Statistics Outpatients
  6. Mental Health Minimum Data Set
  7. Mental Health and Learning Disabilities Data Set
  8. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  9. Hospital Episode Statistics Accident and Emergency
  10. Hospital Episode Statistics Admitted Patient Care
  11. Mental Health Services Data Set
  12. MRIS - Flagging Current Status Report

Objectives:

There is insufficient data comparing new with conventional hypertension therapy, particularly relating to effects on morbidity and mortality. At least 50% of high risk hypertensives require two or more drugs to provide adequate blood pressure control in the long term. Previous studies have allowed a wide range of possible drug combinations to be used making it impossible to make recommendations about specific combinations. In ASCOT the allowed combinations are clearly specified as are subsequent add-on drugs which will be common to both limbs of the trial, and the agents used have been established as producing effects 24-hour BP control. In the UK, Ireland and Scandinavia, over 40% of those on two drugs for hypertension use diuretics and beta-blockers and hence represent an appropriate standard against which other combinations should be compared.

Although hypertensives have been included in previous lipid lowering trials, to date no trials of lipid lowering have been carried out specifically among hypertensives, and particularly among those whose total cholesterol is <6.5mmol/l. Standard clinical practice in most of Europe does not, in primary prevention, usually involve the treatment of hypertensives with lipid lowering therapy.

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.

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).


An evaluation of the relationship between simulation-based training assessment tools and performance in real world settings — DARS-NIC-80304-H6P6R

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

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-03-07 — 2020-03-06 2019.04 — 2019.04.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care

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.

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.

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.


The Power Of Connections: Mapping the Behaviour of Health Care Networks — DARS-NIC-67398-K2Y3T

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

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2017-01-31 — 2020-01-31 2017.02 — 2019.02.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Outpatients
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Accident and Emergency

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.

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.

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.


Estimating the Impact of Patient Safety Incidents on Quality of Life using Patient Reported Outcome Measures — DARS-NIC-209174-W2H3G

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

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

Purposes: No (Academic)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2018-10-15 — 2020-10-15 2018.10 — 2018.12.

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Patient Reported Outcome Measures (Linkable to HES)
  2. Hospital Episode Statistics Accident and Emergency
  3. Hospital Episode Statistics Admitted Patient Care
  4. Hospital Episode Statistics Critical Care
  5. Hospital Episode Statistics Outpatients

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 28 — DARS-NIC-311095-K1Q0B

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

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

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2018.12.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - List Cleaning Report
  2. MRIS - Cause of Death Report

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.

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)**

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 29 — DARS-NIC-72318-M4W8J

Opt outs honoured: N ()

Legal basis: Health and Social Care Act 2012

Purposes: ()

Sensitive: Non Sensitive

When:2017.09 — 2017.11.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. 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 30 — DARS-NIC-278518-F3H0X

Opt outs honoured: N ()

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

Purposes: ()

Sensitive: Non Sensitive

When:2016.12 — 2017.02.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Critical Care
  2. 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 31 — DARS-NIC-287804-H1T1R

Opt outs honoured: N ()

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

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2017.02.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. MRIS - Cohort Event Notification Report
  3. 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 32 — DARS-NIC-39944-D4M0D

Opt outs honoured: N ()

Legal basis: Health and Social Care Act 2012

Purposes: ()

Sensitive: Non Sensitive

When:2016.09 — 2016.11.

Access method: One-Off

Data-controller type:

Sublicensing allowed:

Datasets:

  1. 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 33 — DARS-NIC-02077-R7M9C

Opt outs honoured: Y, N ()

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

Purposes: ()

Sensitive: Sensitive

When:2016.09 — 2016.11.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. 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 34 — DARS-NIC-28095-S9N3P

Opt outs honoured: N, Y ()

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

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2016.11.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. 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 35 — DARS-NIC-148230-KHMHH

Opt outs honoured: N ()

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

Purposes: ()

Sensitive: Sensitive, and Non Sensitive

When:2016.04 — 2016.08.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. MRIS - Cause of Death Report
  2. 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 36 — DARS-NIC-345991-H2F5N

Opt outs honoured: N, Y ()

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

Purposes: ()

Sensitive: Sensitive

When:2016.04 — 2016.08.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. 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).