RCGP Research Surveillance Network Observational Research Umbrella (RCGP RSC ORUm)

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).

Flows of data:

• Data are initially extracted from practices that are members of the Royal College of General Practitioners (RCGP RSC) Research and Surveillance Network by Wellbeing. The University of Oxford subcontracts with Wellbeing to do this as part its contractual responsibilities. The data are pseudonymised at source within the Wellbeing extraction process.

• The University of Oxford, on behalf of RCGP RSC, will provide NHS digital with a list of hashed NHS numbers and hashed date of birth for the cohort. NHS Digital will be operating under instruction as a data processor from the Data Controllers in this agreement to process the cohort data as per the details set out in this agreement and return the linked data asset. That data will flow back from NHS Digital using the same hashed algoritham therefore the research team will only be accessing pseudonymised data.

The Hashing process is as follows:

1. An encryption salt is held by a designated staff member of the University of Oxford Medical Science Division who is not a member of the research team.

2. When a data linkage is required, the encryption salt holder sends the encryption salt to the data provider (NHS D)

3. The data provider NHS D in this case will hash personal identifiers (in the data requested under this agreement) using a hashing algorithm

4. The hashing algorithm is SHA2-512.

The data are then linked across the datasets requested in NHS Digital using hashed NHS numbers.

On receipt of the data from NHS Digital University of Oxford will then link the NHS Digital data with the cohort data already held in the University using the same hashed algorithm. The data will be pseudonymised in a consistent manner so that the research team are then only working with a fully pseudonymised dataset. Each individual in the cohort will have a fully linked record.

NHS digital will provide back linked data including hashed NHS Number and hashed DOB:

• 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

• University of Oxford will store the data on the secure network.

• University of Oxford will process and aggregate pseudonymised data to produce approved reports for surveillance (as part of the National surveillance process); and quality improvement.

The data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

All record level data will be held and stored within England and Wales.

Detailed explanation of flows of data:

a) Data flow from RCGP RSC network member practices to University of Oxford: Wellbeing extract the data from the practices. Patients who have opted out of data sharing do not have their data extracted, unless they have consented to a specific surveillance programme or study. This extract provides the study with information about patient’s visits to general practices including the date of the appointment, the reason for the visit and any relevant vaccination information. The University of Oxford also receive patient’s NHS numbers and date of births which are pseudonymised using SHA-512 algorithm. Detailed information about this algorithm is held in a separate location by IT services at the University of Oxford. It is this department who will share the identifiers with NHS Digital for the linkage leaving the research team only access to the pseudo data.

b) University of Oxford Storage and processing of data: The data about patients registered with RCGP RSC general practices is stored on the secure server at the University of Oxford which can only be accessed from the University of Oxford. The data will be processed within secure network and dedicated analysis server of the Surveillance Group. The secure network is located behind a firewall within the University’s network, all in-bounded connections are blocked, but out-bounded connections are allowed. Patient level data are held in the database server within the RSC Group’s secure network.

c) Pseudonymised data will be stored on the database server within the RSC’s secure network once fully linked with the NHS digital returned data. The pseudonymisation algorithm is held in a separate location by IT services at the University of Oxford.

d) University of Oxford process and aggregate pseudonymised data to produce reports. For example, University of Oxford on behalf of RCGP RSC provide a mid-season flu cohort to PHE with data up to the end of December. This is a fully pseudonymised patient-level extract collected by a PHE statistician using a secure drive. The University of Oxford also produce an end of season report, an annual report and weekly reports that are available to the public and use aggregated data on rates of infectious and allergic conditions.

The RCGP RSC data is controlled and processed by a group of staff who are all based at the University of Oxford; all are mandated to complete information governance training. The group is made up of analysts, academic fellows, Structure Language Query (SQL) developers, RCGP RSC practice liaison officers, a project manager and a head of department. The team work from secure workstations or secure laptops with encrypted drives within the group’s secure network.

Data will only be accessed by individuals within the RSC who have authorisation that are substantive employees of University of Oxford. The authorisation process includes: (1) Contractual requirement to follow IG principles; (2) Using the email registered with Human Resources to complete IG training and to return the certificate; (3) Staff’s email is authorised by the IT department for one year to access the secure network and staff’s computers are configured to allow this; (4) At any point the project managers or Head can have access to the secure network turned off. There is special authorisation to have access to the main database.

Only three SQL developers and one senior project manager can access the main database. Surveillance databases are created for approved analyses once they have been agreed by the RCGP RSC approval committee. This agreed protocol includes the list of variables required for the database. The SQL developers create separate databases for individual projects only including the required variables, for the required time interval.

There will be no requirement nor attempt to re-identify individuals from the data by the research team. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide.

Specific Outputs for this study are:

• To track the impact of COVID-19 lockdown, visual descriptions (dashboards) of the number and rates of patients presenting with specific symptoms (primary care data), being tested for specific tests (including DID data), or referred for particular conditions will be presented over time (at weekly frequency) from 2018 will be hosted online. The raw data will be overlaid by 7-day moving averages, adjusted for seasonality. Subgroups of data will be identified to enable display by GP practice, region, age group, gender, and ethnicity.

• Using HES, SUS, ECDS, Mental Health Services Data, and Cancer data, outcomes will be examined through 7-day moving averages and presented graphically over time for the years 2018, 2019 and 2020 onwards to descriptively compare levels of activity.

Findings from this study will also contribute to existing outputs as follows:

• The RCGP RSC weekly report is circulated to a selected list of recipients on Wednesdays and it is publicly available on Thursdays at 2 pm at the RCGP RSC website (http://www.rcgp.org.uk/clinical-and-research/our-programmes/research-and-surveillance-centre.aspx). This report currently covers incidence rates of 37 infectious and respiratory conditions in England. It is expected that, in future, hospitalisation trends will be included. This is incorporated into the syndromic surveillance carried out by PHE on a daily basis, which allow them to determine any urgent priorities for local health protection teams.

• Similar to this, an annual report is published covering the annual trends of the 37 conditions. Each year, this report has a new theme which is explored in a paper submitted to a peer-reviewed journal (usually British Journal of General Practice). Themes explored include demographic disparities in disease presentation, higher rates of consultations for lower respiratory infections for boys, and urban/rural disparities of presentation.

• In January of every year, the University of Oxford provide a mid-season flu cohort to PHE with data up to the end of December. This is a fully pseudonymised patient-level extract collected by a PHE statistician using a secure drive. This data extract contains details of influenza swabbing, chronic conditions, and vaccination status for each patient. It is hoped to be able to include details of emergency attendances or admission around influenza, pneumonia, or lower respiratory tract infection events. At the end of the flu season (varies from March to May), a second extract is provided updating the first, with data recorded after December.

• The data from both of these extracts is used to estimate seasonal influenza vaccine effectiveness, stratified by comorbidities and demographics. HES data allow the University of Oxford/PHE and RCGP to include the impact of any changes in effectiveness, assessed through changes in hospital admissions/emergencies due to respiratory conditions. The results are published at the mid-season and at the end of season stage, on the peer-reviewed journal Eurosurveillance.

• Important results from either of these will be further analysed and presented at the RCGP annual conference, the PHE annual conference, and the PHE annual epidemiology conference.

The surveillance work conducted by the RCGP RSC on behalf of the Data Controllers is used by Department of Health, NHS England and PHE to monitor trends in a number of infectious conditions. Specifically for COVID-19, the RSC aims 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. In addition, the RCGP RSC will 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. Furthermore the analyses conducted under RCGP RSC ORUm will lead to a better understanding of the characteristics of patients being tested for COVID-19 and 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 benefits

The linkages described in this protocol can help assess the severity and mortality of a given condition, thereby alerting PHE on whether larger measures should be implemented. This could lead to improved healthcare and reduced mortality of certain conditions. Additionally, the linkages allow the RCGP RSC to identify how COVID-19 lockdown has put additional pressure on the health system in terms of delayed testing and referral, meaning that plans can be put in place in order to prevent or deal with these pressures during subsequent waves of the pandemic.

By supporting RGGP RSC COVID-19 SURVEILLANCE the researchers will be able to augment direct RCGP RSC COVID-19 surveillance using dedicated national COVID feeds (CHESS/SGSS) and enable full care pathway analysis from presentation to community providers (GP/111) through to secondary/tertiary care (ECDS/HES/SUS).

By supporting DECISION-COVID linked data will allow analyses to determine associations between demographics, comorbidity and medications of patients presenting to GP and the likelihood of developing COVID-19 and subsequent complications such as hospitalisation, admission to an intensive care unit, death (ECDS/SUS/HES/ONS), and to characterise socioeconomic and ethnic disparities in patients being tested (CHESS/SGSS) for COVID-19.

By supporting MAINROUTE-C19 linked data will enable an end-to-end description of the impact of the COVID-19 lockdown on the clinical pathway in terms of presentation patterns (GP RSC), testing (RCGP RSC/SUS/HES/DID) diagnoses (GP RSC/SUS/HES/Cancer/MHDS), monitoring (RCGP RSC) and outcomes (GP RSC/SUS/HES/Cancer/ONS) of common non-communicable diseases, such as cancer, cardiovascular disease, diabetes and mental health.

Not stated in the register.