University of Oxford

Academic Institute

No

DARS-NIC-381683-R6R6K-v1.2

Since the outbreak of COVID-19 in Wuhan, China, and the subsequent pandemic, PHE has commissioned the RCGP RSC to incorporate the monitoring of COVID-19 into its virology surveillance scheme. A vital part of this work has been to monitor the number of suspected COVID-19 cases in the community in a timely way. PHE and the RCGP are Joint Data Controllers for this request. The RCGP Research Surveillance Centre (RCGP RSC) is based at the University of Oxford. The RCGP RSC is a growing network of over 1200 GP surgeries based in England. University of Oxford is the data processor. PHE Public Health England (PHE) holds a contract with the Royal Collage of Practitioners (RCGP) who in turn hold a contract with the University of Oxford to deliver information to support surveillance and monitoring of vaccine efficacy on Influenza. RCGP The Royal College or GPs (RCGP) Research and Surveillance Centre (RSC) has over 50 years’ experience of undertaking surveillance and research activities, predominantly in influenza surveillance. Pseudonymised patient data is extracted from over 1600 practices on a weekly basis, feeding into the disease surveillance and research funded through Public Health England (PHE). The COVID-19 activities set out in this agreement fall within the wider Disease Surveillance activities. University of Oxford – are a data processor. The secure network which holds the physical data is at the University of Oxford. The University of Oxford acts as Data Processor on behalf of the Data Controller (RCGP and PHE). The lead Professor who is the RCGP RSC Director, has moved his main appointment from the University of Surrey to the University of Oxford. Oxford currently provide the academic and clinical informatics input to inform data usages and ensure this adheres to contract held with PHE. Additionally, the study produces research outputs from the University of Oxford (these outputs have small numbers suppressed and Oxford are therefore not listed as a data processor). The surveillance function of the RCGP RSC provides a unique platform upon which to build population based observational epidemiological studies designed to inform the national public health response to COVID-19. Direct COVID-19 analyses will study for example which patient-level characteristics are associated with COVID-19 infection, predictors of adverse outcomes, and potential treatments. Indirect COVID-19 analyses will for example provide near real-time monitoring to inform strategies to mitigate the indirect effects of the national response to COVID-19 on other 'COVID-19 sensitive' non-communicable diseases. Built on high quality primary care electronic health records data, the Joint Data Controllers for this request (PHE and RCGP) hope to add to the existing RCGP RSC HES (Critical Care, Outpatients, A&E, Admitted patient care) and Civil Registration (mortality) Data (CRD) linkages to support the priority observational COVID-19 studies outlined below. OVERALL AIM The study aims to establish an umbrella agreement for data linkages to support the RCGP RSC to conduct observational epidemiological studies inform the national public health response to COVID-19. PRIORITY OBSERVATIONAL WORKSTREAMS The following three priority workstreams outline analyses underway or in set-up using the RCGP RSC dataset. 1. RGGP RSC COVID-19 SURVEILLANCE Aim - to identify whether there is undetected community transmission of COVID-19, estimate population susceptibility, and monitor the temporal and geographical distribution of COVID-19 infection in the community. Specific objectives 1 a. To monitor the burden of suspected COVID-19 activity in the community through primary care surveillance and clinical coding of possible COVID-19 cases referred into the containment pathway 1 b. To provide virological evidence on the presence and extent of undetected community transmission of COVID-19 and monitor positivity rates among individuals presenting ILI or acute respiratory tract infections to primary care. PHE see all specimens (identified by NHS Number within their laboratory department) then pseudonymise this identifier to allow linkage. The PHE data and NHS Digital data will all be pseudonymised using the same algorithm so that a fully linked record for each person in the database will be available for the research team. The analysis will therefore be done by the team at an individual level but without the need to know who that individual is. 1 c. To estimate baseline susceptibility to COVID-19 in the community and estimate both symptomatic and asymptomatic exposure rates in the population through seroprevalence monitoring 1 d. To pilot implementation of a scheme for collection of convalescent sera with antibody profiles among recovered cases of COVID-19 discharged to the community. PHE see all specimens (identified by NHS Number within their laboratory department) then pseudonymise this identifier to allow linkage. The PHE data and NHS Digital data will all be pseudonymised using the same algorithm so that a fully linked record for each person in the database will be available for the research team. The analysis will therefore be done by the team at an individual level but without the need to know who that individual is. 2. DECISION-COVID: DEfining the CharacterIStIcs Of Individuals with suspected Novel COronaVIrus Disease and risk factors for development of the disease. Aim - To better understand the characteristics of patients being tested for COVID-19 and to determine the associations between demographics, comorbidity and medications on the likelihood of developing COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, death). Specific objectives 2 a. Identify patient demographics and co-morbidities that predict the diagnosis of COVID-19 and subsequent complications (e.g. hospitalisation, admission to an intensive care unit, pulmonary events, death). 2 b. Identify medications that are associated with and increased or decreased risk COVID-19 infection and complications (e.g. hospitalisation, admission to an intensive care unit, pulmonary events, death). 3. MAINROUTE-C19: Monitoring Attendance, INvestigation, Referral, and OUTcomEs in Primary Care: impact of and recovery from COVID-19 lockdown Aim - To describe and analyse the impact of the COVID-19 lockdown on presentation patterns, diagnoses, monitoring and outcomes of common non-communicable diseases, such as cancer, cardiovascular disease, diabetes and mental health. Specific objectives 3 a. To produce practice-level data analytics on presentation, management and diagnoses of common non-communicable diseases and preventive health activities before, during and after COVID-19 lockdown, by region, practice, gender, and age 3 b. To examine the effect of the COVID-19 lockdown (and release) on presentation, management and diagnoses of common non-communicable diseases and preventive health activities by region, practice, gender, age and ethnicity 3 c. To determine the effects of the changes in presentation, management and diagnosis on long-term outcomes such as hospitalisation, morbidity and mortality, and some condition-specific outcomes. EXISTING DATASET The main aim of this application is to build on the exiting RCGP RSC database. The RCGP RSC dataset includes individual patient level up-to-date primary and secondary care data which can be easily queried. Primary care/general practice data is rich in terms of diagnosis and information about the process of care. For example, the database contains the following variables for each patient (where present): • Detailed demographic and risk factor data. • COVID-19 appointments: including information on whether or not a virology swab was taken and the outcome of the swab • Non-COVID-19 appointments. • Detailed data for the 32 conditions monitored by RCGP RSC on behalf of PHE • Vaccination status: date of vaccination, type of vaccination • Co-morbid conditions • Medication which may be associated with better or adverse outcomes. • Test results • Referrals made • A & E visits • Inpatient appointments, including critical care • Outpatient appointments • Mortality data (if applicable). Existing linkages include CRD and HES data provide key information about the outcomes of care: • HES: Critical Care • HES: Outpatients • HES: A&E • HES: Admitted patient care • CRD (mortality) data ADDITIONAL LINKAGES REQUESTED Additional individual level linkages to the entire RCGP-RSC cohort will support the priority analyses outlined above. Individual patient level data is required because individual patient level linkage allows much more precise statistical analyses to be made, compared with comparing aggregate data. Additional historical and updating linkages are requested to the following additional datasets: • Cancer Registration Data • Secondary Uses Service Payment By Results Episodes • Secondary Uses Service Payment By Results Outpatients • Secondary Uses Service Payment By Results Accident & Emergency • Secondary Uses Service Payment By Results Spells • Mental Health Services Data Set • Diagnostic Imaging Dataset • Emergency Care Data Set (ECDS) • COVID-19 Hospitalisation in England Surveillance System (CHESS) Dataset • Second Generation Surveillance System (SGSS) Dataset Historic data are needed because longitudinal data better enable the RCGP RSC to predict what might happen in the future; even a small increase in the ability to understand flu and COVID-19 and its associated morbidity and mortality would offer benefits for patients and the NHS. Both historical and future data are needed in order to build a robust database and reporting system using up-to-date primary and secondary care data at the individual patient level, which can be easily queried. This will enable the study group to answer a wide range of questions which will have an impact on the provision of health care in England. For example, the data will be used to answer questions posed by PHE, who make many decisions about healthcare, such as the vaccination programme, or preventative measures. In MAINROUTE, for example, longitudinal data will allow time series analyses to be conducted as part of objcetive 3 b. which will compare primary care activity before, during and after 'lockdown' to establish whether changed in primary care activity are associated with changes in disease presentation and outcome. The same pseudonymisation algorithm will be applied to all data involved in this study (and any other studies) so the researchers can draw scientific conclusions for a study population. The PHE data and NHS Digital data will all be pseudonymised at University of Oxford prior to researcher access using the same algorithm so that a fully linked record for each person in the database will be available for the research team. REGULATORY FRAMEWORK The GDPR Lawful basis for processing the requested data under this agreement are; Public Health England; Article 6(1)(e) (Public Task 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). Article 9(2)(h) (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) and Article 9(2)(i) (processing is necessary for reasons of public interest in the area of public health, such as protecting against serious cross-border threats to health or ensuring high standards of quality and safety of health care and of medicinal products or medical devices). PHE exist to protect and improve the nation's health and wellbeing, and reduce health inequalities. RCGP; Article 6(1)(f) processing is necessary for the purposes of the legitimate interests pursued by a controller, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. This shall not apply to processing carried out by public authorities in the performance of their tasks. Article 9(2)(i) (processing is necessary for reasons of public interest in the area of public health, such as protecting against serious cross-border threats to health or ensuring high standards of quality and safety of health care and of medicinal products or medical devices). Additionally the request for data is supported by PHE as they have an emanation of the Secretary of State for health and social care, to both self-approve the use of Control of Patient Information Regulation 3 and to grant this approval to third parties processing confidential patient information without consent for purposes that fall under the scope of Regulation 3. This authority to has been in existence since PHE was established in 2013 although the large majority of the Regulation 3 approvals granted since that date have been internal to PHE; only a very small number have been granted by PHE to third parties. Specifically the work being undertaken under Reg 3 in this application is limited to Communicable Disease surveillance and other risks to public health’. The data will not be shared with third parties and only used within the data processors listed in this agreement. Data disseminated under this application can only be used for different purposes after those different purposes have been approved by NHS Digital under separate applications and a live DSA is in place. 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).

07/12/2021

De-Personalised

CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002

Statutory exemption to flow confidential data without consent

Recurring

TRE