QResearch Data Linkage Project
Queen Mary University of London · Academic
In term In term in the September 2026 edition: the latest version runs to 27 November 2027.
- Reference
- DARS-NIC-382794-T3L3M
- Current version
- v12.5
- Term of current version
- 15 June 2026 to 27 November 2027
- Start date
- 1 June 2020
- Data controller
- Sole Data Controller
- Commercial purposes
- Yes
- Sublicensing
- Yes
- Files released to date
- 564
Why the data was released
Objective for processing
The Data will be used to support a research database: Q Research
The permitted purpose for which the data may be used are set out below.
The Q Research database is used for observational medical research into the causes of disease, its natural history, treatment and outcomes. Research projects suitable for undertaking on the database are which generate or test hypotheses which are intended for publication in peer-reviewed academic journals. In line with the Health Research Authority definition of research (https://www.hra-decisiontools.org.uk/research/docs/DefiningResearchTable_Oct2022.pdf), only studies which attempt to derive generalisable or transferable new knowledge to answer or refine relevant questions using scientifically sound methods which are likely to lead to benefits for patients or the benefit of the health service are allowed.
The database contains general practice (GP) medical records linked to secondary care data and administrative data (e.g hospital, mortality, cancer registry, maternity and births data). Approximately 1,500 GP across the UK, acting as independent controllers have consented to the sharing of this data for the inclusion within the database.
The following types of organisations/individuals can be given access to the Data:
- QMUL substantive employees
- QMUL undergraduate, master’s and PhD students
- Individuals from NHS trusts with an approved honorary contract or secondment with QMUL
- Other UK university researchers and masters/PhD students via sublicence
The Data cannot be used for:
- Requests from pharmaceutical, tobacco and insurance companies
- Solely commercial uses of data
- Interventional studies
- Studies involving identifying or contacting patients
- Studies which are is unlikely to lead to any benefits for patients or the health service
- Studies where the results will not be published or made available
The Chief Investigator for QResearch approves applications following the advice of the Advisory and Scientific Committees. They are responsible for ensuring that data access is provided in accordance with the protocol, ethics and section 251 approval for the research database, and relevant data sharing agreements. https://www.qresearch.org/about/scientific-committee/
Requests are assessed against the following criteria:
Research team
https://www.qresearch.org/information/information-for-researchers/
- The CI and researchers who access the data are either based employed by UK universities or registered students on courses run by UK universities
- The research team may include individuals from NHS Trusts with an approved contract secondment with QMUL
- At least one member of each team must be a medically qualified academic registered with the General Medical Council.
- At least one member of each team must be a statistician who contributes to the design of the study and will advises on the analysis.
- At least one member of the research team is a co-applicant based at Queen Mary University of London
- All team members must be able to publish its findings i.e. publication not restricted by a contractual conflict.
Research Project
The application is reviewed by the science committee against the following criteria https://www.qresearch.org/about/scientific-committee/scientific-committee-review-questions/
1. Is the protocol clearly written? Is the reviewer able to easily understand what the researchers intend to do, why, and how? If not, the reviewer can ask the researchers to revise the application to improve clarity before further review.
2. Is there at least one clear research question or hypothesis which is likely to lead to a generalizable finding, capable of publication in a peer reviewed medical journal?
3. Are the researchers likely to be able conduct the study and analysis?
4. Is QResearch the appropriate database to be used to conduct the research?
5. Are the methods appropriate to answer the question including the possibility of biases and confounding, and dealing with each missing data?
6. What patient and public involvement has there been, or will there be, in this research?
7. Are there any potential direct or indirect benefits for patients, or the public, or the health service?
8. Are there any potential risks to the ethical position of QResearch in undertaking this research (including the potential identification of patients or practices?)
All requests to use the data will proceed according to the following process:
1) Researchers originate a research question and write an outline protocol.
2) Researchers submit a pre-submission enquiry
3) If feasible, QResearch provides a cost estimate and letter of support
4) Researchers secure funding
5) The researchers work with QResearch to produce a detailed data specification based on a template supplied by QResearch including consideration as to whether there are any restrictions associated with certain datasets i.e. COVID 19 datasets
6) The researchers produce a lay summary with Patient and Public Involvement (PPI) groups
7) The researcher submits an Application to the QResearch Science committee
8) The application is considered by the QResearch science committee and feedback is give to the researchers
9) The researchers make any necessary modifications to the protocol and resubmit to the Science Committee.
10) The researchers obtain QResearch science committee approval
11) Timeline agreed for extract
12) If the application is not from QMUL, an associated sublicense agreement is put in place between QMUL and the researchers organisation before NHS England data is released.
13) The researcher is provided access to the data extract and has one month to confirm the data meets their requirements.
QResearch provide summaries of approved research programs and projects on the QResearch website. QResearch will publish a register of approved research programs and projects that would be under the sub-licence as requested by NHS England. Additionally, all subsequent research outputs from QResearch are made publicly and freely available on the website.
Use of AI in relation to the data will be subject to strict controls. Any AI tools proposed for analysis will only be transferred to the secure server following a security review and formal approval. Any AI-derived tools, models, code, or related outputs created using the data will only be downloadable from the secure environment after human review has verified that they contain no individual patient-level data and are severable from the source data. Resulting tools will also be published.
Processing activities
No data will flow to NHS England for the purpose of this DSA.
NHS England will provide QMUL with the relevant records from the datasets listed in this DSA.
Prior to disclosure to QMUL, NHS England will pseudonymise the NHS England data at source using Open Pseudonymiser software. A project-specific salt key will be used to ensure that the pseudonymised identifiers are specific to QMUL.
NHS England will retain control of the salt key. QMUL will not be permitted to retain or copy the salt key. Accordingly, QMUL will not be able to re-identify individuals from the NHS England data. However, as described below, QMUL will be able to link the NHS England data with GP data that has been processed using the same Open Pseudonymiser software and salt key.
The data disclosed to QMUL will contain no directly identifying fields. Solely for the purpose of pseudonymising GP data at the point of extraction from the IM1 platform, NHS England will make time-limited access to the salt key available through an AWS Secret to one named individual at QMUL. That individual will not have access to the NHS England data during the period of access to the salt key. While this arrangement creates a limited technical means by which re-identification could occur, no attempt will be made to re-identify individuals.
Upon receipt of the Data, QMUL will link the GP Data and NHS England data using a common pseudonymised version of NHS Number provided by NHS England.
Sub-sets of the data will be made available to researchers who will only be able to access the sub-set of Data in the researcher’s network within the QResearch Trusted Research Environment.
The Data will be stored on servers at QMUL.
Data extracts are created on a case-by-case basis and made available exclusively within the QResearch Trusted Research Environment (TRE), where technical controls prevent any local download, storage, or copying of data, including disabling copy/paste functions. All outputs are subject to a strict airlock process whereby researchers must request release and each file is independently reviewed by the QResearch team to ensure it contains only non-disclosive aggregate results (e.g. summary tables or graphs) and no individual-level data or small cell sizes (≤5). Only approved outputs are released to researchers. No NHS England individual level data can be extracted or downloaded from the server. At project closure, data are securely archived, with any further use limited to the original approved purpose and named researchers; reuse for new research questions requires a separate application and governance approval from QResearch.
QMUL uses offsite back-up services provided by Dancing House Consulting.
The Data will be accessed by authorised personnel via remote access.
The Controller(s) must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.
For remote access:
• Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA.
• Access controls granting users the minimum level of access required are in place.
• Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data.
• Multifactor authentication (MFA) is required for remote access.
• Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access.
• All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.
The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).
EMIS Health (commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to QMUL. EMIS is not able to access or process any GP data once it is located at QMUL.
EMIS Health is neither a processor nor a controller for the Data provided by NHS England under this DSA. EMIS Health is not permitted to access the NHS England Data under any circumstances. GP practices (controllers) have given permission for the GP data it supplies to be linked with the Data from NHS England for purposes determined by the Chief Investigator at QMUL and described in this DSA.
The datasets are linked to ICNARC (intensive care data), congenital abnormalities and lung cancer screening data as well as COVID-19 medications used to prevent or treat covid including but not limited to vaccinations, antivirals and antibody treatments.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; annual conferences run by cancer charities Cancer Research UK; local and regional conferences.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results were published in late 2023.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
Expected measurable benefits
The findings of research studies using the QResearch linked database are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment and experience of health care users relevant to the subject matter of the study.
The use of the data could:
- help the system to better understand the health and care needs of populations.
- lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
- advance understanding of regional and national trends in health and social care needs.
- advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as obesity and diabetes.
- inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
- inform decisions on how to effectively allocate and evaluate funding according to health needs.
- provide a mechanism for checking the quality of care. This could include identifying areas of good practice to learn from, or areas of poorer practice which need to be addressed.
- support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work)
- understanding the safety of medicines in pregnancy, both for the mother and her baby/child over time.
The QResearch database is widely used by researchers to help understand patterns of disease, safety or medicines, development of prediction tools, research into health inequalities and other similar research questions where the results are likely to have benefits for patients, the NHS or to improve understanding of disease. The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice.
A complete list of research papers using QResearch database is published at http://www.qresearch.org/SitePages/publications.aspx
Research arising from the QResearch database including the linked data has been used to inform national policy. For example, research findings have been included in NICE guidelines on fragility fracture, diabetes, suspected cancer and lipid modification. Research findings have informed the NHS Health Checks programme and Department of Health guidelines on health checks.
Examples of research include assessment of the safety of antidepressant drugs and novel anticoagulants; investigation of potential links between diabetes drugs and cancer; quantification of the risk of thrombosis associated with various types of the oral contraceptive pill.
Covid-19 Work:
The aim for the Covid research is to provide useful knowledge that patients, GPs and intensive care doctors can use to identify patients at high risk of severe outcomes from COVID-19 and reduce the risk of severe COVID-19 infection within this pandemic and assess the uptake safety and effectiveness of COVID-19 treatments such as COVID-19 vaccine, antivirals and monoclonal antibodies. The results of the analyses may help inform policy regarding which patients are likely to benefit most from these treatments.
Specifically, it may help research to understand whether drugs commonly taken for chronic conditions such as hypertension or diabetes may exacerbate or reduce the severity of COVID-19 disease. It is hoped this study will be able to identify alternative drugs for patients with chronic conditions, as well as possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
Around 14 percent of the adult population in England take anti-hypertensive medications, and around five percent receive medication to treat diabetes. The prevalence increases with age, making usage particularly common in those at risk of for severe COVID-19 infections. In many cases drugs from a different class could be used instead. If these drugs are increasing the risk of severe infection, they represent one of the few modifiable risk factors for severe COVID-19 infection. Medical and research communities need rapid large-scale accumulation of data on the outcomes of patients who develop COVID-19 infection whilst taking these drugs to allow appropriate risk assessment and clinical decision making for these patient groups. Other drugs in common use in primary care patients are believed to have anti-viral activity to COVID-19, such as hydroxychloroquine, used in rheumatoid arthritis, and lopinavir-ritonavir, used in the treatment of HIV.
There are also immune-suppressive therapies that may either increase the risk of severe illness by preventing the body’s response to infection or attenuate the hyperinflammation syndrome associated with COVID-19 disease, so preventing severe disease.
The incidence of severe disease in patient groups taking these medications urgently needs to be established to guide both their management and investigation of COVID-19 treatment strategies.
ICNARC is already providing up-to-date information on the admission characteristics and outcomes of all patients with severe COVID-19 infection treated on an Intensive Care Unit (ICU) in England, Wales and Northern Ireland.
It is hoped that through publication of findings in appropriate media, the findings of this research will add to the body of evidence that is considered by the bodies, organisations and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.
Benefits reported so far
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Winter 2024 newsletter
https://www.qresearch.org/media/3rllbkhx/qresearch-newsletter-10-2024.pdf
Datasets on the current version
Legal basis for provision: Health and Social Care Act 2012 - s261(5)(d)
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Birth Notification Data | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Civil Registration - Births | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Civil Registrations of Death | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| COVID-19 Hospitalization in England Surveillance System | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| COVID-19 SGSS First Positives (Second Generation Surveillance System) | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| COVID-19 Therapeutics Programme Data Set | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2) | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| COVID-19 Vaccination Adverse Reactions | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| COVID-19 Vaccination Status | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Demographics | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Emergency Care Data Set (ECDS) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| HES-ID to MPS-ID HES Accident and Emergency | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| HES-ID to MPS-ID HES Admitted Patient Care | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| HES-ID to MPS-ID HES Outpatients | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| Hospital Episode Statistics Accident and Emergency (HES A and E) | Identifiable | Non-Sensitive | One-Off | Section 251 NHS Act 2006 |
| Hospital Episode Statistics Admitted Patient Care (HES APC) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Hospital Episode Statistics Critical Care (HES Critical Care) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Hospital Episode Statistics Outpatients (HES OP) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Maternity Services Data Set (MSDS) v1.5 | Identifiable | Sensitive | One-Off | Section 251 NHS Act 2006 |
| Maternity Services Data Set (MSDS) v2 | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| Medicines dispensed in Primary Care (NHSBSA data) | Identifiable | Sensitive | Ongoing | Section 251 NHS Act 2006 |
| NDRS Cancer Registrations | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| NDRS National Radiotherapy Dataset (RTDS) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| NDRS Systemic Anti-Cancer Therapy Dataset (SACT) | Identifiable | Non-Sensitive | Ongoing | Section 251 NHS Act 2006 |
| SUS plus - Admitted Patient Care (beta version) | Identifiable | Sensitive | One-Off | Section 251 NHS Act 2006 |
Files released
Files released counts only files released externally by DARS. Access granted in NHS England's own systems, such as its Secure Data Environment, is not included.
This agreement permits sublicensing: the applicant may pass data on to others. Anything passed on is not recorded in this register.
Patient opt-outs were applied to 84 of the 564 files released under this agreement, across every version. About opt-outs
Files released against version 12.5 of this agreement, summarised by dataset.
| Dataset | Files | First released | Last released | Opt-outs applied |
|---|---|---|---|---|
| Maternity Services Data Set (MSDS) v2 | 84 | July 2026 | July 2026 | Yes |
Version history
The register lists each renewal of this agreement as a separate row. This site has 13 versions.
DARS-NIC-382794-T3L3M-v12.5 15 June 2026 to 27 November 2027
- Title
- QResearch Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 25
- Files released
- 84
Datasets: Birth Notification Data; Civil Registration - Births; Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Demographics; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; Medicines dispensed in Primary Care (NHSBSA data); NDRS Cancer Registrations; NDRS National Radiotherapy Dataset (RTDS); NDRS Systemic Anti-Cancer Therapy Dataset (SACT); SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v11.4
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2026-06-15 | |
| End date | 2027-11-27 | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Hospitalization in England Surveillance System: type of data | Identifiable | |
| COVID-19 Hospitalization in England Surveillance System: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): type of data | Identifiable | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| COVID-19 Therapeutics Programme Data Set’: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Therapeutics Programme Data Set’: type of data | Identifiable | |
| COVID-19 Therapeutics Programme Data Set’: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): type of data | Identifiable | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| COVID-19 Vaccination Adverse Reactions: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Vaccination Adverse Reactions: type of data | Identifiable | |
| COVID-19 Vaccination Adverse Reactions: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| COVID-19 Vaccination Status: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Vaccination Status: type of data | Identifiable | |
| COVID-19 Vaccination Status: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Civil Registrations of Death: type of data | Identifiable | |
| Civil Registrations of Death: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Demographics: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Demographics: type of data | Identifiable | |
| Demographics: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Emergency Care Data Set (ECDS): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Emergency Care Data Set (ECDS): type of data | Identifiable | |
| Emergency Care Data Set (ECDS): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| HES-ID to MPS-ID HES Accident and Emergency: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| HES-ID to MPS-ID HES Accident and Emergency: type of data | Identifiable | |
| HES-ID to MPS-ID HES Accident and Emergency: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| HES-ID to MPS-ID HES Admitted Patient Care: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| HES-ID to MPS-ID HES Admitted Patient Care: type of data | Identifiable | |
| HES-ID to MPS-ID HES Admitted Patient Care: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| HES-ID to MPS-ID HES Outpatients: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| HES-ID to MPS-ID HES Outpatients: type of data | Identifiable | |
| HES-ID to MPS-ID HES Outpatients: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): type of data | Identifiable | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): type of data | Identifiable | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Hospital Episode Statistics Critical Care (HES Critical Care): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Critical Care (HES Critical Care): type of data | Identifiable | |
| Hospital Episode Statistics Critical Care (HES Critical Care): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| Hospital Episode Statistics Outpatients (HES OP): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Outpatients (HES OP): type of data | Identifiable | |
| Hospital Episode Statistics Outpatients (HES OP): common law duty of confidentiality | Section 251 NHS Act 2006 | |
| MSDS (Maternity Services Data Set) v1.5: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| MSDS (Maternity Services Data Set) v1.5: type of data | Identifiable | |
| MSDS (Maternity Services Data Set) v1.5: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| MSDS (Maternity Services Data Set) v2.0: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| MSDS (Maternity Services Data Set) v2.0: type of data | Identifiable | |
| MSDS (Maternity Services Data Set) v2.0: common law duty of confidentiality | Section 251 NHS Act 2006 | |
| SUS plus - Admitted Patient Care (beta version): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| SUS plus - Admitted Patient Care (beta version): type of data | Identifiable | |
| SUS plus - Admitted Patient Care (beta version): common law duty of confidentiality | Section 251 NHS Act 2006 |
Datasets: + Birth Notification Data; + Civil Registration - Births; + Medicines dispensed in Primary Care (NHSBSA data); + NDRS Cancer Registrations; + NDRS National Radiotherapy Dataset (RTDS); + NDRS Systemic Anti-Cancer Therapy Dataset (SACT)
Objective for processing
QMUL requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
The Data will be used to support a research database: Q Research
1) for use by QMUL for specific research purposes, as described in this Data Sharing Agreement (DSA).
The permitted purpose for which the data may be used are set out below.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA).
The Q Research database is used for observational medical research into the causes of disease, its natural history, treatment and outcomes. Research projects suitable for undertaking on the database are which generate or test hypotheses which are intended for publication in peer-reviewed academic journals. In line with the Health Research Authority definition of research (https://www.hra-decisiontools.org.uk/research/docs/DefiningResearchTable_Oct2022.pdf), only studies which attempt to derive generalisable or transferable new knowledge to answer or refine relevant questions using scientifically sound methods which are likely to lead to benefits for patients or the benefit of the health service are allowed.
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database contains general practice (GP) medical records linked to secondary care data and administrative data (e.g hospital, mortality, cancer registry, maternity and births data). Approximately 1,500 GP across the UK, acting as independent controllers have consented to the sharing of this data for the inclusion within the database.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
The following types of organisations/individuals can be given access to the Data:
The QResearch database is a dynamic database of over 40 million patients with new patients registering with the practices all of the time. The NHS England Data is for all citizens and is not minimised to Optum patients or patients of a practice using the Optum electronic health record system. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the Optum data cannot flow under sub-licence.
- QMUL substantive employees
QMUL is the controller for the QResearch database. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
- QMUL undergraduate, master’s and PhD students
QMUL is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the QMUL’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
- Individuals from NHS trusts with an approved honorary contract or secondment with QMUL
1) Use by QMUL for specific research purposes:
- Other UK university researchers and masters/PhD students via sublicence
QMUL will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via QMUL QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the QMUL QResearch governance and approvals route.
The Data cannot be used for:
Although it is acknowledged that COVID-19 has been ongoing for over three years, QMUL is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
- Requests from pharmaceutical, tobacco and insurance companies
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
- Solely commercial uses of data
Examples of COVID-19 specific research projects:
- Interventional studies
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
- Studies involving identifying or contacting patients
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
- Studies which are is unlikely to lead to any benefits for patients or the health service
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
- Studies where the results will not be published or made available
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
The Chief Investigator for QResearch approves applications following the advice of the Advisory and Scientific Committees. They are responsible for ensuring that data access is provided in accordance with the protocol, ethics and section 251 approval for the research database, and relevant data sharing agreements. https://www.qresearch.org/about/scientific-committee/
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
Requests are assessed against the following criteria:
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
Research team
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
https://www.qresearch.org/information/information-for-researchers/
Example of a general medical research project:
- The CI and researchers who access the data are either based employed by UK universities or registered students on courses run by UK universities
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
- The research team may include individuals from NHS Trusts with an approved contract secondment with QMUL
This is just one example of projects which can only be done using the linked HES and mortality Data. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
- At least one member of each team must be a medically qualified academic registered with the General Medical Council.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA):
- At least one member of each team must be a statistician who contributes to the design of the study and will advises on the analysis.
Requests to use the linked NHS England Data come from researchers within QMUL and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the QMUL (as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
- At least one member of the research team is a co-applicant based at Queen Mary University of London
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
- All team members must be able to publish its findings i.e. publication not restricted by a contractual conflict.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties for organisations unable to use the open source.
Research Project
A summary of the QResearch Application Governance Process is detailed below:
The application is reviewed by the science committee against the following criteria https://www.qresearch.org/about/scientific-committee/scientific-committee-review-questions/
1. Is the protocol clearly written? Is the reviewer able to easily understand what the researchers intend to do, why, and how? If not, the reviewer can ask the researchers to revise the application to improve clarity before further review.
2. Is there at least one clear research question or hypothesis which is likely to lead to a generalizable finding, capable of publication in a peer reviewed medical journal?
3. Are the researchers likely to be able conduct the study and analysis?
4. Is QResearch the appropriate database to be used to conduct the research?
5. Are the methods appropriate to answer the question including the possibility of biases and confounding, and dealing with each missing data?
6. What patient and public involvement has there been, or will there be, in this research?
7. Are there any potential direct or indirect benefits for patients, or the public, or the health service?
8. Are there any potential risks to the ethical position of QResearch in undertaking this research (including the potential identification of patients or practices?)
All requests to use the data will proceed according to the following process:
[1 paragraph unchanged]
2)
Pre-submission
Researchers submit a pre-submission
enquiry
3)
IF
If
feasible,
QResearch provides a
cost estimate
&
and
letter of support
[1 paragraph unchanged]
5) A detailed data specification is produced
5) The researchers work with QResearch to produce a detailed data specification based on a template supplied by QResearch including consideration as to whether there are any restrictions associated with certain datasets i.e. COVID 19 datasets
6)
Co
The researchers
produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
7) The researcher submits an Application to the QResearch Science committee
8) Review by Scientific Committee & feedback is given
8) The application is considered by the QResearch science committee and feedback is give to the researchers
9) Revisions if needed
9) The researchers make any necessary modifications to the protocol and resubmit to the Science Committee.
10) Obtain approval
10) The researchers obtain QResearch science committee approval
[1 paragraph unchanged]
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if
12) If
the application is not from
QMUL)
QMUL, an associated sublicense agreement is put in place between QMUL and the researchers organisation before NHS England data is released.
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
13) The researcher is provided access to the data extract and has one month to confirm the data meets their requirements.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
QResearch provide summaries of approved research programs and projects on the QResearch website. QResearch will publish a register of approved research programs and projects that would be under the sub-licence as requested by NHS England. Additionally, all subsequent research outputs from QResearch are made publicly and freely available on the website.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 23/EM/0166. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
Use of AI in relation to the data will be subject to strict controls. Any AI tools proposed for analysis will only be transferred to the secure server following a security review and formal approval. Any AI-derived tools, models, code, or related outputs created using the data will only be downloadable from the secure environment after human review has verified that they contain no individual patient-level data and are severable from the source data. Resulting tools will also be published.
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of QMUL substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on QMUL servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via QMUL servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). Type 1 opt-outs have not been upheld for this Data. This Data will only be used to support projects (a) and (e) detailed above in the Covid-19 specific projects and will only be analysed by QMUL researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
QMUL is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the QMUL to generate an indicative quote for QResearch data access and service costs.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
QMUL may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for QMUL based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for QMUL research projects, individuals substantively employed by QMUL, students registered with QMUL and individuals from other universities that have an honorary contract or secondment agreement with QMUL which NHS England has confirmed are acceptable before access is granted.
- for other UK Universities research projects this will be under the sublicence data sharing model.
Data for QMUL research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the QMUL. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the QMUL’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of QMUL. QMUL would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with QMUL.
QMUL consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Processing activities
No data will flow to NHS England for the
purposes
purpose
of this
DSA.
DSA.
NHS
NHS
England
provides
will provide QMUL with
the relevant records from the
Covid-19, SUS, HES, ECDS, MSDS and Mortality
datasets
to QMUL.
listed in this DSA.
The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.
Prior to disclosure to QMUL, NHS England will pseudonymise the NHS England data at source using Open Pseudonymiser software. A project-specific salt key will be used to ensure that the pseudonymised identifiers are specific to QMUL.
Before providing Data
NHS England will retain control of the salt key. QMUL will not be permitted
to
QMUL, NHS England use
retain or copy
the
Open Pseudonymiser software (www.openpseudonymiser.org)
salt key. Accordingly, QMUL will not be able
to
pseudonymise
re-identify individuals from
the NHS England
Data at source. NHS England use a project specific ‘salt’ key to ensure that the identifiers are specific to QMUL. NHS England retains the salt key, meaning that QMUL are unable to re-identify the Data but
data. However,
as described
below they are
below, QMUL will be
able to link
the NHS England data
with GP data that
was
has been
processed
by other data suppliers
using the same Open Pseudonymiser software and salt key.
QMUL will not be provided with a copy of the pseudonymisation salt key.
NHS England provide pseudonymised Data to QMUL via Secure Electronic File Transfer (SEFT) which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the NHS England Data. The data linkage is undertaken by a substantive employee of QMUL. The NHS England Data and GP data are linked to health care data such as hospital admissions and attendances, mortality data, COVID-19 data and cancer, SACT, and RTDS data. The datasets are also linked to ICNARC (intensive care data), congenital abnormalities and lung cancer screening data as well as COVID-19 medications used to prevent or treat covid including but not limited to vaccinations, antivirals and antibody treatments. In order to develop risk stratification models (see section 5a, project (a)) and assess uptake, effectiveness and safety of COVID-19 (see section 5a, project e), the data will also be linked to the COVID-19 therapeutics cohort. No data items which would identify the data subjects are received by QResearch as the Data is pseudonymised-at-source by NHS England. Date of birth is rounded to year of birth before receipt by QMUL.
The data disclosed to QMUL will contain no directly identifying fields. Solely for the purpose of pseudonymising GP data at the point of extraction from the IM1 platform, NHS England will make time-limited access to the salt key available through an AWS Secret to one named individual at QMUL. That individual will not have access to the NHS England data during the period of access to the salt key. While this arrangement creates a limited technical means by which re-identification could occur, no attempt will be made to re-identify individuals.
The Data will be stored on servers at QMUL.
Upon receipt of the Data, QMUL will link the GP Data and NHS England data using a common pseudonymised version of NHS Number provided by NHS England.
QMUL uses offsite back-up services provided by Dancing House Consulting.
Sub-sets of the data will be made available to researchers who will only be able to access the sub-set of Data in the researcher’s network within the QResearch Trusted Research Environment.
The Data will be accessed by authorised personnel via remote access.
The Data will be stored on servers at QMUL.
The Controller(s) must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.
Data extracts are created on a case-by-case basis and made available exclusively within the QResearch Trusted Research Environment (TRE), where technical controls prevent any local download, storage, or copying of data, including disabling copy/paste functions. All outputs are subject to a strict airlock process whereby researchers must request release and each file is independently reviewed by the QResearch team to ensure it contains only non-disclosive aggregate results (e.g. summary tables or graphs) and no individual-level data or small cell sizes (≤5). Only approved outputs are released to researchers. No NHS England individual level data can be extracted or downloaded from the server. At project closure, data are securely archived, with any further use limited to the original approved purpose and named researchers; reuse for new research questions requires a separate application and governance approval from QResearch.
For remote access:
QMUL uses offsite back-up services provided by Dancing House Consulting.
- Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;
The Data will be accessed by authorised personnel via remote access.
- Access controls granting users the minimum level of access required are in place;
The Controller(s) must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.
- Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;
For remote access:
- Multifactor authentication (MFA) is required for remote access;
• Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA.
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
• Access controls granting users the minimum level of access required are in place.
- All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.
• Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data.
The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).
• Multifactor authentication (MFA) is required for remote access.
Remote processing will be from secure locations within the UK. The data will not leave the UK at any time.
• Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access.
The QResearch database linked to NHS England Data will only be accessed by a limited number of substantively employed, individuals within the QResearch unit. They will produce subsets of the data that will be accessed by the organisation or its sublicensee(s) as per the QResearch Application and Approvals process.
• All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.
The subsets of data are then used for undertaking research as described in this DSA. These staff will process and analyse the subset of data to address an approved research question(s).
The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).
For QMUL research studies, Data may be accessed by individuals with an honorary contract with QMUL. The individual(s) will act as an agent of QMUL at all times under supervision from employees of QMUL. Aside from these individuals, access is restricted to employees or agents of QMUL who have authorisation from the Principal Investigator.
EMIS Health (commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to QMUL. EMIS is not able to access or process any GP data once it is located at QMUL.
Optum (commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to QMUL. Optum is not able to access or process any GP data once it is located at QMUL.
EMIS Health is neither a processor nor a controller for the Data provided by NHS England under this DSA. EMIS Health is not permitted to access the NHS England Data under any circumstances. GP practices (controllers) have given permission for the GP data it supplies to be linked with the Data from NHS England for purposes determined by the Chief Investigator at QMUL and described in this DSA.
Optum is neither a processor nor a controller for the Data provided by NHS England under this DSA. Optum is not permitted to access the NHS England Data under any circumstances. GP practices (controllers) have given permission for the GP data it supplies to be linked with the Data from NHS England for purposes determined by the Chief Investigator at QMUL and described in this DSA.
The datasets are linked to ICNARC (intensive care data), congenital abnormalities and lung cancer screening data as well as COVID-19 medications used to prevent or treat covid including but not limited to vaccinations, antivirals and antibody treatments.
All personnel accessing the Data have been appropriately trained in data protection and confidentiality.
The Data will be linked at person record level with GP data and other datasets as described in this DSA. This is the QResearch linked database.
There will be no requirement and no attempt to reidentify individuals when using the Data.
Regular reviews will be undertaken to ensure that all appropriate controls are in place to minimise any risk of re-identification.
QMUL conducts an annual internal audit for auditing the technical controls in place. The scope of the internal audit applies to the review of the QResearch Systems Level Security Policy and QResearch Workstation Setup Requirements.
Expected output
[2 paragraphs unchanged]
- Presentation at conferences. Examples of conferences include the annual academic conference
[13 words unchanged]
(UKRI); international conferences such as the North American Primary Care Research Group;
the annual conferences of the Optum National User Group (a national education and research charity representing the GP practices who contribute data to QResearch);
annual conferences run by cancer charities
such as Macmillan
Cancer
Support and Pancreatic Cancer
Research
UK; local and regional
conferences run by the Nottingham Biomedical Research Centre.
conferences.
[2 paragraphs unchanged]
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
[1 paragraph unchanged]
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by Optum to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
[11 paragraphs unchanged]
CRUK have funded a DPhil student who has developed a risk prediction
[12 words unchanged]
to improve the efficiency of screening programmes. Results were published in late
2023.
2023.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
Expected measurable benefits
The
The
findings of research studies using the QResearch linked database are expected to
[24 words unchanged]
of health care users relevant to the subject matter of the study.
[8 paragraphs unchanged]
- support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory
work).
work)
- understanding the safety of medicines in pregnancy, both for the mother and her baby/child over time.
[12 paragraphs unchanged]
Benefits reported
There
There
have been many yielded benefits arising from the work. Below are examples of some benefits realised;
[13 paragraphs unchanged]
Several other studies are reported in the QResearch News Update
Spring 2023
Winter 2024
newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
https://www.qresearch.org/media/3rllbkhx/qresearch-newsletter-10-2024.pdf
DARS-NIC-382794-T3L3M-v11.4 28 November 2025 to 27 November 2026
- Title
- QResearch Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 19
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Demographics; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v10.6
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2025-11-28 | |
| End date | 2026-11-27 | |
| Demographics: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Demographics: type of data | Anonymised - ICO Code Compliant |
Objective for processing
[5 paragraphs unchanged]
The QResearch database is a dynamic database of over 40 million patients
[12 words unchanged]
NHS England Data is for all citizens and is not minimised to
Egton Medical Information Systems’ (EMIS)
Optum
patients or patients of a practice using the
EMIS
Optum
electronic health record system. The unmatched Data at a point in time
[8 words unchanged]
practices when it is then linked. Data which is unmatched with the
EMIS
Optum
data cannot flow under sub-licence.
[90 paragraphs unchanged]
Processing activities
[21 paragraphs unchanged]
EMIS Health
Optum
(commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to QMUL.
EMIS
Optum
is not able to access or process any GP data once it is located at QMUL.
EMIS Health
Optum
is neither a processor nor a controller for the Data provided by NHS England under this DSA.
EMIS Health
Optum
is not permitted to access the NHS England Data under any circumstances.
[23 words unchanged]
determined by the Chief Investigator at QMUL and described in this DSA.
[5 paragraphs unchanged]
Expected output
[2 paragraphs unchanged]
- Presentation at conferences. Examples of conferences include the annual academic conference
[18 words unchanged]
the North American Primary Care Research Group; the annual conferences of the
EMIS
Optum
National User Group (a national education and research charity representing the GP
[20 words unchanged]
UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
[10 paragraphs unchanged]
- Industry newsletters including those distributed by
EMIS
Optum
to practices who contribute data to QResearch
[17 paragraphs unchanged]
Unchanged: Expected measurable benefits, Benefits reported.
Objective for processing
QMUL requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by QMUL for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
The QResearch database is a dynamic database of over 40 million patients with new patients registering with the practices all of the time. The NHS England Data is for all citizens and is not minimised to Optum patients or patients of a practice using the Optum electronic health record system. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the Optum data cannot flow under sub-licence.
QMUL is the controller for the QResearch database. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
QMUL is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the QMUL’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by QMUL for specific research purposes:
QMUL will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via QMUL QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the QMUL QResearch governance and approvals route.
Although it is acknowledged that COVID-19 has been ongoing for over three years, QMUL is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Example of a general medical research project:
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
This is just one example of projects which can only be done using the linked HES and mortality Data. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within QMUL and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the QMUL (as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from QMUL)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 23/EM/0166. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of QMUL substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on QMUL servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via QMUL servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). Type 1 opt-outs have not been upheld for this Data. This Data will only be used to support projects (a) and (e) detailed above in the Covid-19 specific projects and will only be analysed by QMUL researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
QMUL is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the QMUL to generate an indicative quote for QResearch data access and service costs.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
QMUL may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for QMUL based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for QMUL research projects, individuals substantively employed by QMUL, students registered with QMUL and individuals from other universities that have an honorary contract or secondment agreement with QMUL which NHS England has confirmed are acceptable before access is granted.
- for other UK Universities research projects this will be under the sublicence data sharing model.
Data for QMUL research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the QMUL. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the QMUL’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of QMUL. QMUL would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with QMUL.
QMUL consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the Optum National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by Optum to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results were published in late 2023.
Benefits reported
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
DARS-NIC-382794-T3L3M-v10.6 31 January 2025 to 30 November 2025
- Title
- QResearch Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 19
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Demographics; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v9.4
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Title | QResearch Data Linkage Project | |
| Applicant organisation | QUEEN MARY UNIVERSITY OF LONDON | |
| Start date | 2025-01-31 |
Data controllers:
+ QUEEN MARY UNIVERSITY OF LONDON · − UNIVERSITY OF OXFORD
Objective for processing
The University of Oxford
QMUL
requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by
the University of Oxford
QMUL
for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) Onward sharing to UK universities via a sublicensing agreement (subject to the
University of Oxford’s
QMUL’s
governance approvals described in this DSA).
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QResearch is a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
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The University of Oxford
QMUL
is the controller for the
NHS England datasets (deaths, cancer, Covid-19 Data, and hospital Data) which are linked to the GP data in the
QResearch
database via a pseudonymised unique key.
database.
The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford
QMUL
is the single point of access for UK universities to apply to
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All UK universities can apply to access the GP data via the
University of Oxford’s
QMUL’s
governance/approval route. This DSA will also allow UK universities access to NHS
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same governance/approval route and will be via a sublicense data sharing model.
1) Use by
the University of Oxford
QMUL
for specific research purposes:
The University of Oxford
QMUL
will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via
the University of Oxford
QMUL
QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the
University of Oxford
QMUL
QResearch governance and approvals route.
A sub-licence agreement will not be required by the University of Oxford since they are the Controller and are also permitted to process the Data.
Although it is acknowledged that
Covid-19
COVID-19
has been ongoing for over three years,
the University of Oxford
QMUL
is still being commissioned for rapid results research from funders, including the
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of monoclonal antibodies which are targeted to those with a positive test.
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2) Onward sharing to UK universities via a sublicensing agreement (subject to the
University of Oxford’s
QMUL’s
governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within
the University of Oxford
QMUL
and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the
University of Oxford
QMUL
(as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
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Researchers are charged for the work in generating the outputs for research
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also licensed for a fee as closed source software from third parties
such as Oxford University Innovations,
for organisations unable to use the open source.
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12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from
the University of Oxford)
QMUL)
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The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC
18/EM/0400.
23/EM/0166.
The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
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The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of
University of Oxford
QMUL
substantive employees). Once an application has been approved (and a sub-licence agreement
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data (as approved for the study) will be extracted and stored on
University of Oxford
QMUL
servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via
University of Oxford
QMUL
servers to conduct their analysis. All remote access must take place within
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data can be downloaded by researchers as the IT system restricts this.
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To support two specific Covid-19 studies the following NHS England Data was
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above in the Covid-19 specific projects and will only be analysed by
University of Oxford
QMUL
researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
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The University of Oxford
QMUL
is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
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QResearch use a cost recovery-based costing model approved by the
University of Oxford
QMUL
to generate an indicative quote for QResearch data access and service costs.
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The University of Oxford
QMUL
may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for
Oxford
QMUL
based studies. They will have no access to patient level data.
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- for
University of Oxford
QMUL
research projects, individuals substantively employed by
the University of Oxford,
QMUL,
students registered with
the University of Oxford
QMUL
and individuals from other universities that have an honorary contract or secondment agreement with
the University of Oxford
QMUL
which NHS England has confirmed are acceptable before access is granted.
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Data for
University of Oxford
QMUL
research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the
University of Oxford.
QMUL.
Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the
University of Oxford’s
QMUL’s
policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of
University of Oxford. University of Oxford
QMUL. QMUL
would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with
the University of Oxford.
QMUL.
The University of Oxford
QMUL
consider Public and Patient Involvement (PPI) as part of the QResearch Application
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on the QResearch Advisory Board and PPI representatives on individual research projects.
Processing activities
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NHS England provides the relevant records from the Covid-19, SUS, HES, ECDS, MSDS and Mortality datasets to
the University of Oxford.
QMUL.
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Before providing Data to
the University of Oxford,
QMUL,
NHS England use the Open Pseudonymiser software (www.openpseudonymiser.org) to pseudonymise the NHS
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project specific ‘salt’ key to ensure that the identifiers are specific to
the University of Oxford.
QMUL.
NHS England retains the salt key, meaning that
the University of Oxford
QMUL
are unable to re-identify the Data but as described below they are
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other data suppliers using the same Open Pseudonymiser software and salt key.
The University of Oxford
QMUL
will not be provided with a copy of the pseudonymisation salt key.
NHS England provide pseudonymised Data to
the University of Oxford
QMUL
via Secure Electronic File Transfer (SEFT) which is then linked to the
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England Data. The data linkage is undertaken by a substantive employee of
the University of Oxford.
QMUL.
The NHS England Data and GP data are linked to health care
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Date of birth is rounded to year of birth before receipt by
the University of Oxford.
QMUL.
The Data will be stored on servers at
the University of Oxford.
QMUL.
The University of Oxford
QMUL
uses offsite back-up services provided by Dancing House Consulting.
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For
the University of Oxford
QMUL
research studies, Data may be accessed by individuals with an honorary contract with
the University of Oxford.
QMUL.
The individual(s) will act as an agent of
the University of Oxford
QMUL
at all times under supervision from employees of
the University of Oxford.
QMUL.
Aside from these individuals, access is restricted to employees or agents of
the University of Oxford
QMUL
who have authorisation from the Principal Investigator.
EMIS Health (commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to
the University of Oxford.
QMUL.
EMIS is not able to access or process any GP data once it is located at
the University of Oxford.
QMUL.
EMIS Health is neither a processor nor a controller for the Data
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Data from NHS England for purposes determined by the Chief Investigator at
the University of Oxford
QMUL
and described in this DSA.
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The University of Oxford
QMUL
conducts an annual internal audit for auditing the technical controls in place.
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of the QResearch Systems Level Security Policy and QResearch Workstation Setup Requirements.
Expected output
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CRUK have funded a DPhil student who has developed a risk prediction
[8 words unchanged]
dying from breast cancer to improve the efficiency of screening programmes. Results
due to be
were
published in late 2023.
Unchanged: Expected measurable benefits, Benefits reported.
Objective for processing
QMUL requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by QMUL for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
The QResearch database is a dynamic database of over 40 million patients with new patients registering with the practices all of the time. The NHS England Data is for all citizens and is not minimised to Egton Medical Information Systems’ (EMIS) patients or patients of a practice using the EMIS electronic health record system. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
QMUL is the controller for the QResearch database. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
QMUL is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the QMUL’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by QMUL for specific research purposes:
QMUL will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via QMUL QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the QMUL QResearch governance and approvals route.
Although it is acknowledged that COVID-19 has been ongoing for over three years, QMUL is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Example of a general medical research project:
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
This is just one example of projects which can only be done using the linked HES and mortality Data. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the QMUL’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within QMUL and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the QMUL (as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from QMUL)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 23/EM/0166. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of QMUL substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on QMUL servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via QMUL servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). Type 1 opt-outs have not been upheld for this Data. This Data will only be used to support projects (a) and (e) detailed above in the Covid-19 specific projects and will only be analysed by QMUL researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
QMUL is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the QMUL to generate an indicative quote for QResearch data access and service costs.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
QMUL may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for QMUL based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for QMUL research projects, individuals substantively employed by QMUL, students registered with QMUL and individuals from other universities that have an honorary contract or secondment agreement with QMUL which NHS England has confirmed are acceptable before access is granted.
- for other UK Universities research projects this will be under the sublicence data sharing model.
Data for QMUL research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the QMUL. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the QMUL’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of QMUL. QMUL would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with QMUL.
QMUL consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results were published in late 2023.
Benefits reported
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
DARS-NIC-382794-T3L3M-v9.4 10 October 2024 to 30 November 2025
- Title
- QResearch-Oxford Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 19
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Demographics; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v8.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2024-10-10 | |
| End date | 2025-11-30 |
Datasets: + Demographics
Objective for processing
[2 paragraphs unchanged]
2) for use by the University of Nottingham for ongoing research studies, as described in this DSA.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA).
[21 paragraphs unchanged]
2) For use by the University of Nottingham for ongoing research studies:
2) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA):
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous DSA (DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a processor with an appropriate data processing agreement in place between the respective organisations. As of December 2023, there is currently 1 ongoing research project with the University of Nottingham as a processor. This project is expected to be completed by June 2024.
Requests to use the linked NHS England Data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
The above only allows access to NHS England Datasets for deaths, cancer, and hospital Data (linked to GP data). It does not allow access to NHS England Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS England Data must be via a sublicence agreement.
[62 paragraphs unchanged]
The University of Nottingham is a processor acting under the instructions of the University of Oxford. The University of Nottingham’s role is limited to processing the Data for the ongoing University of Nottingham research studies as described in point 2 above.
[4 paragraphs unchanged]
- for existing University of Nottingham research projects, individuals substantively employed by the University of Nottingham.
- for other UK Universities research projects this will be under the sublicence data sharing model.
- for other UK Universities research projects this will be under the sublicence data sharing model. This includes future University of Nottingham research projects.
[4 paragraphs unchanged]
Processing activities
[1 paragraph unchanged]
NHS England
will provide
provides
the relevant records from the Covid-19, SUS, HES, ECDS, MSDS and Mortality datasets to the University of Oxford.
[26 paragraphs unchanged]
Unchanged: Expected output, Expected measurable benefits, Benefits reported.
Objective for processing
The University of Oxford requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by the University of Oxford for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
QResearch is a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
The QResearch database is a dynamic database of over 40 million patients with new patients registering with the practices all of the time. The NHS England Data is for all citizens and is not minimised to Egton Medical Information Systems’ (EMIS) patients or patients of a practice using the EMIS electronic health record system. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
The University of Oxford is the controller for the NHS England datasets (deaths, cancer, Covid-19 Data, and hospital Data) which are linked to the GP data in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the University of Oxford’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by the University of Oxford for specific research purposes:
The University of Oxford will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via the University of Oxford QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the University of Oxford QResearch governance and approvals route. A sub-licence agreement will not be required by the University of Oxford since they are the Controller and are also permitted to process the Data.
Although it is acknowledged that Covid-19 has been ongoing for over three years, the University of Oxford is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Example of a general medical research project:
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
This is just one example of projects which can only be done using the linked HES and mortality Data. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Controller who also process data), all other approvals to access NHS England Data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties such as Oxford University Innovations, for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from the University of Oxford)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of University of Oxford substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). Type 1 opt-outs have not been upheld for this Data. This Data will only be used to support projects (a) and (e) detailed above in the Covid-19 specific projects and will only be analysed by University of Oxford researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
The University of Oxford is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the University of Oxford to generate an indicative quote for QResearch data access and service costs.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
The University of Oxford may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for Oxford based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS England has confirmed are acceptable before access is granted.
- for other UK Universities research projects this will be under the sublicence data sharing model.
Data for University of Oxford research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the University of Oxford. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the University of Oxford’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with the University of Oxford.
The University of Oxford consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results due to be published in late 2023.
Benefits reported
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
DARS-NIC-382794-T3L3M-v8.2 1 December 2023 to 30 November 2024
- Title
- QResearch-Oxford Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 18
- Files released
- 116
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v7.4
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2023-12-01 | |
| End date | 2024-11-30 |
Objective for processing
[7 paragraphs unchanged]
The QResearch database is a dynamic database of over
35
40
million patients with new patients registering with the practices all of the time.
The NHS England Data is for all citizens and is not minimised to Egton Medical Information Systems’ (EMIS) patients or patients of a practice using the EMIS electronic health record system.
The unmatched Data at a point in time is required for when
[10 words unchanged]
Data which is unmatched with the EMIS data cannot flow under sub-licence.
[14 paragraphs unchanged]
Examples
Example
of
a
general medical research
projects:
project:
(a) QResearch data linked to HES Data is being used to undertake
[51 words unchanged]
cause and date of death which is needed for undertaking survival analysis.
.
The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
These are
This is
just
two examples
one example
of projects which can only be done using the linked HES and
mortalityData.
mortality Data.
The results help identify patients at high risk of cancer suitable for
[37 words unchanged]
decisions and increase the evidence base to inform guideline development and policy.
[1 paragraph unchanged]
Due to QResearch originally being under the University of Nottingham, when roles
[41 words unchanged]
with an appropriate data processing agreement in place between the respective organisations.
There are
As of December 2023, there is
currently
6
1
ongoing research
projects
project
with the University of Nottingham as a processor. This
will
project is expected to
be
in place until those projects have been completed.
completed by June 2024.
Examples of ongoing Nottingham Projects using QResearch as of July 2023:
(a) Quantification of the risks of different types of antidepressants to help personalise treatment decisions
(b) Analysis of use of antipsychotic medication in children
(C) Hormone replacement therapy and survival from cancer.
[37 paragraphs unchanged]
To support two specific Covid-19 studies the following NHS England Data was
[6 words unchanged]
available) under an Information Governance (IG) Letter of Release (due to urgency).
Type 1 opt-outs have not been upheld for this Data.
This Data will only be used to support projects (a) and (e) detailed above in the
covid-19
Covid-19
specific projects and will only be analysed by University of Oxford researchers
[7 words unchanged]
purpose. The Data will not be included in the sublicence data-sharing model.
[11 paragraphs unchanged]
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data,
,
and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
[24 paragraphs unchanged]
- Individuals with an honorary contract with the University of Oxford.
For these individuals to access the Data the University of Oxford must provide to NHS England the following details of the individual(s) to be given access under an honorary contract:
• Their substantive employer
• Their role in respect of the purpose for processing specified in the DSA
• The necessity for the data to be accessed by the person(s) holding an honorary contract instead of by a substantive employee of an organisation named as a Data Controller or Data Processor in the DSA
• Confirmation that an appropriate contract is in place which follows the relevant guidance and that is countersigned by the substantive employer of the honorary contract holder.
[1 paragraph unchanged]
Processing activities
[7 paragraphs unchanged]
The Data will be accessed by authorised personnel via remote access.
The Data will remain on the servers at the University of Oxford at all times.
Remote processing will only be through a secure electronic network and technical/organisational controls prevent personnel from downloading or copying data to local devices.
The Controller(s) must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.
Remote processing will be subject to the following being in place:
For remote access:
• Multifactor authentication (MFA);
- Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;
•
-
Access controls granting users the minimum level of access
required;
required are in place;
• Secure
- Remote access is only via secure
connections (e.g., VPNs or secure protocols) to protect
data during remote access;
data;
• Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls.
- Multifactor authentication (MFA) is required for remote access;
All remote access is undertaken within the scope of the relevant organisations’ DSPT (or other security arrangements as per this DSA.
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
- All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.
The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).
[8 paragraphs unchanged]
There
is
will be
no requirement
to re-identify individuals from the Data
and no
attempts will ever be made
attempt
to
do this.
reidentify individuals when using the Data.
[2 paragraphs unchanged]
Expected output
[30 paragraphs unchanged]
CRUK have funded a DPhil student who has developed a risk prediction
[12 words unchanged]
to improve the efficiency of screening programmes. Results due to be published
Sept
in late
2023.
Unchanged: Expected measurable benefits, Benefits reported.
Objective for processing
The University of Oxford requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by the University of Oxford for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) for use by the University of Nottingham for ongoing research studies, as described in this DSA.
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
QResearch is a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
The QResearch database is a dynamic database of over 40 million patients with new patients registering with the practices all of the time. The NHS England Data is for all citizens and is not minimised to Egton Medical Information Systems’ (EMIS) patients or patients of a practice using the EMIS electronic health record system. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
The University of Oxford is the controller for the NHS England datasets (deaths, cancer, Covid-19 Data, and hospital Data) which are linked to the GP data in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the University of Oxford’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by the University of Oxford for specific research purposes:
The University of Oxford will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via the University of Oxford QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the University of Oxford QResearch governance and approvals route. A sub-licence agreement will not be required by the University of Oxford since they are the Controller and are also permitted to process the Data.
Although it is acknowledged that Covid-19 has been ongoing for over three years, the University of Oxford is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Example of a general medical research project:
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
This is just one example of projects which can only be done using the linked HES and mortality Data. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) For use by the University of Nottingham for ongoing research studies:
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous DSA (DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a processor with an appropriate data processing agreement in place between the respective organisations. As of December 2023, there is currently 1 ongoing research project with the University of Nottingham as a processor. This project is expected to be completed by June 2024.
The above only allows access to NHS England Datasets for deaths, cancer, and hospital Data (linked to GP data). It does not allow access to NHS England Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS England Data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties such as Oxford University Innovations, for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from the University of Oxford)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of University of Oxford substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). Type 1 opt-outs have not been upheld for this Data. This Data will only be used to support projects (a) and (e) detailed above in the Covid-19 specific projects and will only be analysed by University of Oxford researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
The University of Oxford is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the University of Oxford to generate an indicative quote for QResearch data access and service costs.
The University of Nottingham is a processor acting under the instructions of the University of Oxford. The University of Nottingham’s role is limited to processing the Data for the ongoing University of Nottingham research studies as described in point 2 above.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
The University of Oxford may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for Oxford based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS England has confirmed are acceptable before access is granted.
- for existing University of Nottingham research projects, individuals substantively employed by the University of Nottingham.
- for other UK Universities research projects this will be under the sublicence data sharing model. This includes future University of Nottingham research projects.
Data for University of Oxford research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the University of Oxford. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the University of Oxford’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with the University of Oxford.
The University of Oxford consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results due to be published in late 2023.
Benefits reported
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
DARS-NIC-382794-T3L3M-v7.4 1 September 2023 to 30 November 2023
- Title
- QResearch-Oxford Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 18
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v6.7
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2023-09-01 | |
| End date | 2023-11-30 | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| COVID-19 Therapeutics Programme Data Set’: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| COVID-19 Vaccination Adverse Reactions: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| COVID-19 Vaccination Status: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Emergency Care Data Set (ECDS): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| HES-ID to MPS-ID HES Accident and Emergency: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| HES-ID to MPS-ID HES Admitted Patient Care: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| HES-ID to MPS-ID HES Outpatients: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Hospital Episode Statistics Critical Care (HES Critical Care): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| Hospital Episode Statistics Outpatients (HES OP): legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| MSDS (Maternity Services Data Set) v1.5: legal basis | Health and Social Care Act 2012 – s261(2)(a) | |
| SUS plus - Admitted Patient Care (beta version): legal basis | Health and Social Care Act 2012 – s261(2)(a) |
Datasets: + MSDS (Maternity Services Data Set) v2.0
Objective for processing
The University of Oxford requires access to NHS
Digital data
England Data
for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by the University of Oxford for specific research purposes, as described in this
agreement
Data Sharing Agreement (DSA).
2) for use by the University of Nottingham for ongoing research studies, as described in this
agreement.
DSA.
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this
agreement).
DSA).
[2 paragraphs unchanged]
QResearch
was originally
is
a not for profit collaboration, originally between the University of Nottingham and
[34 words unchanged]
Management Board representing the interests of EMIS and the University of Oxford.
[1 paragraph unchanged]
The University of Oxford is the
sole data
controller for the NHS
Digital
England
datasets (deaths, cancer, Covid-19
data,
Data,
and hospital
data)
Data)
which are linked to the GP
Data
data
in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK
[21 words unchanged]
access the GP data via the University of Oxford’s governance/approval route. This
agreement
DSA
will also allow UK universities access to NHS
Digital data
England Data
(linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
[1 paragraph unchanged]
The University of Oxford will access
data
Data
from the QResearch linked database for both COVID-19 specific research projects and
[43 words unchanged]
not be required by the University of Oxford since they are the
Data
Controller and are also permitted to process the
data.
Data.
Although it is acknowledged that Covid-19 has been ongoing for over
two
three
years, the University of Oxford is still being commissioned for
urgent and
rapid results research from funders, including the Department of Health and Social
[8 words unchanged]
and NIHR Health Technology Assessment Programme (HTA). The team have considered the
recent
changes to the Testing policy by the UK government and confirm they still require
monthly data
regular Data
on testing to continue to monitor vaccine safety, effectiveness and uptake as
[7 words unchanged]
of monoclonal antibodies which are targeted to those with a positive test.
[1 paragraph unchanged]
Examples of
current
COVID-19 specific research projects:
[7 paragraphs unchanged]
Examples of
current
general medical research projects:
(a) QResearch data linked to HES
data
Data
is being used to undertake an assessment of
different types of direct anticoagulant medication which is prescribed in primary care to reduce
the risk of
stroke
oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking
and
manage thrombosis. Adverse effects from anticoagulants include major haemorrhage
ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death
which
can be life threatening or life ending. Haemorrhage can affect the brain, gastrointestinal tract, urinary tract or other parts of the body.
is needed for undertaking survival analysis. .
The primary care data
provides
provide
information on
exposure to
the
prescriptions issued
medication
and the linked HES
data
Data
provides information on
haemorrhage which is serious enough to require hospital admission.
thrombosis.
(b) Study looking at the safety of the oral contraceptive pill. The primary care data provide information on exposure to the medication and the linked HES data provides information on thrombosis.
These are just two examples of projects which can only be done using the linked HES and mortalityData. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
These are just two examples of projects which can only be done using the linked HES data. The results help identify and quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
[1 paragraph unchanged]
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous
data sharing agreement
DSA
(DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a
data
processor with an appropriate data processing agreement in place between the respective organisations. There are currently
19
6
ongoing research projects with the University of Nottingham as a
data
processor. This will be in place until those projects have been completed.
Examples of ongoing Nottingham Projects using QResearch as of July
2022:
2023:
(a) Validating a postpartum venous thromboembolism risk prediction model.
(a) Quantification of the risks of different types of antidepressants to help personalise treatment decisions
(b) Prostate specific antigen (PSA) testing in the UK population and its implications.
(b) Analysis of use of antipsychotic medication in children
(C) Hormone replacement therapy and
risk of breast cancer: case-control study using QResearch.
survival from cancer.
The above only allows access to NHS
Digital datasets
England Datasets
for deaths, cancer, and hospital
data
Data
(linked to GP data). It does not allow access to NHS
Digital
England
Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this
agreement):
DSA):
Requests to use the linked NHS
Digital data
England Data
come from researchers within the University of Oxford and/or other UK Universities.
[11 words unchanged]
With the exception of the University of Oxford (as they are the
Data
Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS
Digital data
England Data
must be via a sublicence agreement.
[14 paragraphs unchanged]
12) Approve within one month (with associated sublicence agreement in place before NHS
Digital
England
Data is released, if the application is not from the University of Oxford)
[11 paragraphs unchanged]
Current projects with four UK Universities where data is being accessed as a data processor under the previous version of this agreement will be transferred to sublicence agreements, namely;
The following NHS England Data will be accessed:
- Intensive Care National Audit & Research Centre (ICNARC)
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- London School of Hygiene and Tropical Medicine
- University of Liverpool
- University of Leicester
To support Covid-19 related research the following NHS Digital data is disseminated on a monthly basis (latest available) unless indicated otherwise. The Covid-19 and SUS Plus data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
[1 paragraph unchanged]
- COVID-19 Vaccination Adverse Reactions
(annual dissemination only)
[1 paragraph unchanged]
- COVID-19 Hospitalization in England Surveillance System (CHESS)
(annual dissemination only)
[2 paragraphs unchanged]
To support two specific Covid-19 studies the following NHS
Digital
England
Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). This
data
Data
will only be used to support projects (a) and (e) detailed above
[12 words unchanged]
of Oxford researchers and cannot be used for any other purpose. The
data
Data
will not be included in the sublicence data-sharing model.
[1 paragraph unchanged]
To support general medical research projects the following NHS
Digital data
England Data
is
disseminated on a monthly basis (latest available) unless indicated.
disseminated.
The
data
Data
can also be used to support COVID-19 risk stratification work.
[6 paragraphs unchanged]
- Maternity Services Data Set (MSDS)
(One off – April
(April
2015
to March 2019)
onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality
data
Data
are used to support the project where historical comparisons are made (for
example
example,
risk associated with COVID-19 compared with risks associated with influenza in previous
[7 words unchanged]
information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This
agreement
DSA
absorbs DARS-NIC-240279-Y2V2N HES and Mortality
data
Data
and its research purposes. Therefore, DARS-NIC-240279-Y2V2N
is
was
superseded by this
DSA in
version
of this agreement,
6,
DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications
data, pseudonymised MSDS data from April 2019 to latest available,
Data, ,
and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS
Digital
England
Data Access Request Service in the future.
All data disseminated under this agreement is pseudonymised.
The level of the Data will be pseudonymised.
The
data is
Data will be
minimised by limiting to pseudonymised
data
Data
only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT)
data,
Data,
radiotherapy
data (RTDS)
Data (RTDS),
data originally supplied directly by Public Health England (PHE), which is now being released through NHS
Digital,
England,
the original Office of Data Release (ODR) reference was ODR 1819_247 and
provisionally has a
now under the
DARS reference of
DARS-NIC-656839-K5V9L-v0 whilst the transition takes place.
DARS-NIC-656839-K5V9L.
[1 paragraph unchanged]
The University of Oxford is the
sole Data Controller who also process data. The University of Oxford is the data
controller
for QResearch (and therefore, the data disseminated under this agreement)
as
it is
the organisation responsible for ensuring that the
data
Data
will only be processed for the
purposes
purpose
described above.
The lawful basis for processing personal data under GDPR is Article 6
[17 words unchanged]
interest or in the exercise of official authority vested in the controller.
As a higher education establishment, the University of Oxford conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest.
The lawful basis for processing special category data under the GDPR is
[57 words unchanged]
to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
[1 paragraph unchanged]
- National Institute for Health Research (NIHR).
- Blood cancer UK
- Wellcome Trust.
- Cancer Research UK
-
National Institute for
Health
Data
Research
UK (HDRUK).
(NIHR)
[1 paragraph unchanged]
The funders will have no ability to suppress or otherwise limit the publication of findings.
[1 paragraph unchanged]
The University of Nottingham is a processor acting under the instructions of the University of Oxford. The University of Nottingham’s role is limited to processing the Data for the ongoing University of Nottingham research studies as described in point 2 above.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
[1 paragraph unchanged]
Dancing House Consulting is a data processor as it undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
The patient level data linked to QResearch (QResearch linked database) is only accessed by either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS England has confirmed are acceptable before access is granted.
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS Digital has confirmed are acceptable before access is granted.
[2 paragraphs unchanged]
Data for University of Oxford research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the University of Oxford. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the University of Oxford’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with the University of Oxford. For these individuals to access the Data the University of Oxford must provide to NHS England the following details of the individual(s) to be given access under an honorary contract:
• Their substantive employer
• Their role in respect of the purpose for processing specified in the DSA
• The necessity for the data to be accessed by the person(s) holding an honorary contract instead of by a substantive employee of an organisation named as a Data Controller or Data Processor in the DSA
• Confirmation that an appropriate contract is in place which follows the relevant guidance and that is countersigned by the substantive employer of the honorary contract holder.
The University of Oxford consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Processing activities
EMIS Health (commercial supplier of GP computer systems) process the GP data from the original data controllers (GP practices) and sends it to the University of Oxford. EMIS is not able to access or process any GP data once it is located at the University of Oxford.
No data will flow to NHS England for the purposes of this DSA.
EMIS Health is neither a data processor nor a data controller for the data provided by NHS Digital under this Agreement. EMIS Health is not able to access the NHS Digital data under any circumstances. GP practices (data controllers) have given permission for the GP data it supplies to be linked with the data from NHS Digital for purposes determined by the Principal Investigator at the University of Oxford and described in this agreement.
NHS England will provide the relevant records from the Covid-19, SUS, HES, ECDS, MSDS and Mortality datasets to the University of Oxford.
No data will flow to NHS Digital for the purposes of this Agreement.
NHS Digital will provide the data from the Covid-19, SUS, HES, ECDS, MSDS and Mortality datasets to the University of Oxford.
[1 paragraph unchanged]
Before providing
data
Data
to the University of Oxford, NHS
Digital
England
use the Open Pseudonymiser software (www.openpseudonymiser.org) to pseudonymise the NHS
Digital data
England Data
at source. NHS
Digital
England
use a project specific ‘salt’ key to ensure that the identifiers are specific to the University of Oxford. NHS
Digital
England
retains the salt key, meaning that the University of Oxford are unable to re-identify the
data
Data
but as described below they are able to link with GP data
[20 words unchanged]
will not be provided with a copy of the pseudonymisation salt key.
NHS
Digital
England
provide pseudonymised
data
Data
to the University of Oxford via Secure Electronic File Transfer (SEFT) which
[18 words unchanged]
number which has been supplied in both GP data and the NHS
Digital data.
England Data.
The data linkage is undertaken by a substantive employee of the University of Oxford. The NHS
Digital data
England Data
and GP data are linked to health care data such as hospital
[90 words unchanged]
which would identify the data subjects are received by QResearch as the
data
Data
is pseudonymised-at-source by NHS
Digital.
England.
Date of birth is rounded to year of birth before receipt by the University of Oxford.
The Data will be stored on servers at the University of Oxford.
[2 paragraphs unchanged]
Personnel are prohibited
Remote processing will only be through a secure electronic network and technical/organisational controls prevent personnel
from
and not technically capable of
downloading or copying data to local devices.
The data will not leave the UK at any time. All remote access must take place within the UK.
Remote processing will be subject to the following being in place:
The QResearch database linked to NHS digital data will only be accessed by a limited number of substantively employed, individuals within the QResearch unit. They will produce subsets of the data that will be accessed by the organisation or its sublicensee(s) as per the QResearch Application and Approvals process.
• Multifactor authentication (MFA);
The subsets of data are then used for undertaking research as described in this agreement. These staff will process and analyse the subset of data to address an approved research question(s).
• Access controls granting users the minimum level of access required;
• Secure connections (e.g., VPNs or secure protocols) to protect data during remote access;
• Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls.
All remote access is undertaken within the scope of the relevant organisations’ DSPT (or other security arrangements as per this DSA.
Remote processing will be from secure locations within the UK. The data will not leave the UK at any time.
The QResearch database linked to NHS England Data will only be accessed by a limited number of substantively employed, individuals within the QResearch unit. They will produce subsets of the data that will be accessed by the organisation or its sublicensee(s) as per the QResearch Application and Approvals process.
The subsets of data are then used for undertaking research as described in this DSA. These staff will process and analyse the subset of data to address an approved research question(s).
For the University of Oxford research studies, Data may be accessed by individuals with an honorary contract with the University of Oxford. The individual(s) will act as an agent of the University of Oxford at all times under supervision from employees of the University of Oxford. Aside from these individuals, access is restricted to employees or agents of the University of Oxford who have authorisation from the Principal Investigator.
EMIS Health (commercial supplier of GP computer systems) process the GP data from the original controllers (GP practices) and sends it to the University of Oxford. EMIS is not able to access or process any GP data once it is located at the University of Oxford.
EMIS Health is neither a processor nor a controller for the Data provided by NHS England under this DSA. EMIS Health is not permitted to access the NHS England Data under any circumstances. GP practices (controllers) have given permission for the GP data it supplies to be linked with the Data from NHS England for purposes determined by the Chief Investigator at the University of Oxford and described in this DSA.
[1 paragraph unchanged]
The Data will be linked at person record level with GP data and other datasets as described in this DSA. This is the QResearch linked database.
[3 paragraphs unchanged]
Expected output
The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
The expected outputs of the processing include:
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences.
Examples of conferences include the annual academic conference for the Society of
[59 words unchanged]
UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
-
Results are also shared with policy makers, including
Cheif
Chief
Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee
[29 words unchanged]
their stakeholder consultations in order to support the development of relevant guidelines.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
[1 paragraph unchanged]
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
[3 paragraphs unchanged]
All research is published in academic journals with a link from the QResearch website on an ongoing basis.
A list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
[5 paragraphs unchanged]
Research to identify early symptoms of Motor Neurone Disease, funded by the Motor Neurone Association, has identified a set of symptoms which could be used to increase referrals and improve early recognition and diagnosis. This is expected to lead to the development of a decision support tools for use in primary care (excepted 2022)
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results expected 2022/2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results expected 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results due to be published Sept 2023.
Expected measurable benefits
The QResearch database is widely used by researchers to help understand patterns of disease, safety or medicines, development of prediction tools, research into health inequalities and other similar research questions where the results are likely to have benefits for patients, the NHS or to improve understanding of disease. The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice. The research is ongoing with target dates for individual projects rather than one overall target date.
The findings of research studies using the QResearch linked database are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment and experience of health care users relevant to the subject matter of the study.
The use of the data could:
- help the system to better understand the health and care needs of populations.
- lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
- advance understanding of regional and national trends in health and social care needs.
- advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as obesity and diabetes.
- inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
- inform decisions on how to effectively allocate and evaluate funding according to health needs.
- provide a mechanism for checking the quality of care. This could include identifying areas of good practice to learn from, or areas of poorer practice which need to be addressed.
- support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).
The QResearch database is widely used by researchers to help understand patterns of disease, safety or medicines, development of prediction tools, research into health inequalities and other similar research questions where the results are likely to have benefits for patients, the NHS or to improve understanding of disease. The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice.
[3 paragraphs unchanged]
CV19
Covid-19
Work:
[1 paragraph unchanged]
Specifically, it
will
may
help research to understand whether drugs commonly taken for chronic conditions such
[33 words unchanged]
possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
[1 paragraph unchanged]
There are also immune-suppressive therapies that may either increase the risk of severe illness by preventing the body’s response to
infection,
infection
or attenuate the hyperinflammation syndrome associated with COVID-19 disease, so preventing severe disease.
[1 paragraph unchanged]
ICNARC is already providing up-to-date information on the admission characteristics and outcomes
[7 words unchanged]
treated on an Intensive Care Unit (ICU) in England, Wales and Northern
Ireland
Ireland.
It is hoped that through publication of findings in appropriate media, the findings of this research will add to the body of evidence that is considered by the bodies, organisations and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.
Benefits reported
There have been many yielded benefits arising from the work.
As requested, the team
Below
are
limiting it to three examples;
examples of some benefits realised;
[2 paragraphs unchanged]
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
[3 paragraphs unchanged]
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
[5 paragraphs unchanged]
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
Objective for processing
The University of Oxford requires access to NHS England Data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by the University of Oxford for specific research purposes, as described in this Data Sharing Agreement (DSA).
2) for use by the University of Nottingham for ongoing research studies, as described in this DSA.
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
QResearch is a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
The QResearch database is a dynamic database of over 35 million patients with new patients registering with the practices all of the time. The unmatched Data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
The University of Oxford is the controller for the NHS England datasets (deaths, cancer, Covid-19 Data, and hospital Data) which are linked to the GP data in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the University of Oxford’s governance/approval route. This DSA will also allow UK universities access to NHS England Data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by the University of Oxford for specific research purposes:
The University of Oxford will access Data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via the University of Oxford QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the University of Oxford QResearch governance and approvals route. A sub-licence agreement will not be required by the University of Oxford since they are the Controller and are also permitted to process the Data.
Although it is acknowledged that Covid-19 has been ongoing for over three years, the University of Oxford is still being commissioned for rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the changes to the Testing policy by the UK government and confirm they still require regular Data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed Data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These Data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES Data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Examples of general medical research projects:
(a) QResearch data linked to HES Data is being used to undertake an assessment of the risk of oesophageal cancer to determine factors associated with increased risk of cancer who may be suitable for targeted screening. The primary data gives information on co-morbidities, prescriptions, smoking and ethnicity whilst the linked HES information on cancer diagnoses and hospital treatment. The linked death information provides cause and date of death which is needed for undertaking survival analysis. . The primary care data provide information on exposure to the medication and the linked HES Data provides information on thrombosis.
These are just two examples of projects which can only be done using the linked HES and mortalityData. The results help identify patients at high risk of cancer suitable for screening programs as well as quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) For use by the University of Nottingham for ongoing research studies:
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous DSA (DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a processor with an appropriate data processing agreement in place between the respective organisations. There are currently 6 ongoing research projects with the University of Nottingham as a processor. This will be in place until those projects have been completed.
Examples of ongoing Nottingham Projects using QResearch as of July 2023:
(a) Quantification of the risks of different types of antidepressants to help personalise treatment decisions
(b) Analysis of use of antipsychotic medication in children
(C) Hormone replacement therapy and survival from cancer.
The above only allows access to NHS England Datasets for deaths, cancer, and hospital Data (linked to GP data). It does not allow access to NHS England Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this DSA):
Requests to use the linked NHS England Data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS England Data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties such as Oxford University Innovations, for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS England Data is released, if the application is not from the University of Oxford)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of University of Oxford substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The following NHS England Data will be accessed:
To support Covid-19 related research the following NHS England Data is disseminated. The Covid-19 and SUS Plus Data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS England Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). This Data will only be used to support projects (a) and (e) detailed above in the covid-19 specific projects and will only be analysed by University of Oxford researchers and cannot be used for any other purpose. The Data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS England Data is disseminated. The Data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (Data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (April 2015 onwards)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality Data are used to support the project where historical comparisons are made (for example, risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This DSA absorbs DARS-NIC-240279-Y2V2N HES and Mortality Data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N was superseded by this DSA in version 6, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications Data, , and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS England Data Access Request Service in the future.
The level of the Data will be pseudonymised.
The Data will be minimised by limiting to pseudonymised Data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) Data, radiotherapy Data (RTDS), data originally supplied directly by Public Health England (PHE), which is now being released through NHS England, the original Office of Data Release (ODR) reference was ODR 1819_247 and now under the DARS reference of DARS-NIC-656839-K5V9L.
The Q-Research linked database also holds data from the UK Teratology Service for congenital abnormalities and maternal confidential enquiry.
The University of Oxford is the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under GDPR is 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.
The lawful basis for processing special category data under the GDPR is 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.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- Blood cancer UK
- Cancer Research UK
- National Institute for Health Research (NIHR)
Funding to continue the work described will be sought on an ongoing basis as required.
The funders will have no ability to suppress or otherwise limit the publication of findings.
QResearch use a cost recovery-based costing model approved by the University of Oxford to generate an indicative quote for QResearch data access and service costs.
The University of Nottingham is a processor acting under the instructions of the University of Oxford. The University of Nottingham’s role is limited to processing the Data for the ongoing University of Nottingham research studies as described in point 2 above.
Dancing House Consulting is a processor as it undertakes IT consultancy on behalf of the controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the Data.
The University of Oxford may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for Oxford based studies. They will have no access to patient level data.
The patient level Data linked to QResearch (QResearch linked database) is only accessed either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS England has confirmed are acceptable before access is granted.
- for existing University of Nottingham research projects, individuals substantively employed by the University of Nottingham.
- for other UK Universities research projects this will be under the sublicence data sharing model. This includes future University of Nottingham research projects.
Data for University of Oxford research projects may be accessed by:
- Undergraduate, Masters or PhD students affiliated with the University of Oxford. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to the University of Oxford’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA.
- Individuals with an honorary contract with the University of Oxford. For these individuals to access the Data the University of Oxford must provide to NHS England the following details of the individual(s) to be given access under an honorary contract:
• Their substantive employer
• Their role in respect of the purpose for processing specified in the DSA
• The necessity for the data to be accessed by the person(s) holding an honorary contract instead of by a substantive employee of an organisation named as a Data Controller or Data Processor in the DSA
• Confirmation that an appropriate contract is in place which follows the relevant guidance and that is countersigned by the substantive employer of the honorary contract holder.
The University of Oxford consider Public and Patient Involvement (PPI) as part of the QResearch Application Governance Process. There is also PPI membership on the QResearch Advisory Board. Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Expected output
The expected outputs of the processing include:
- Research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
- Presentation at conferences. Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
- Results are also shared with policy makers, including Chief Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
- Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
The outputs will be communicated to relevant recipients through the following dissemination channels:
- Journals
- Workshops involving funding bodies
- Webinars open to policy makers
- Social media, including a list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
- Public reports
- Industry newsletters including those distributed by EMIS to practices who contribute data to QResearch
- Open source frameworks such as github https://github.com/qresearchcode
- Posters displayed at scientific conferences such as the Society for Academic Primary Care
- Press/media engagement https://www.qresearch.org/publications/press/
- Participant newsletters to GP practices contributing data to Qresearch
- Reports aimed at patients
The research is ongoing with target dates for individual projects rather than one overall target date.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results published August 2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results published May 2023).
CRUK have funded a DPhil student who has developed a risk prediction model to identify women at high risk of dying from breast cancer to improve the efficiency of screening programmes. Results due to be published Sept 2023.
Benefits reported
There have been many yielded benefits arising from the work. Below are examples of some benefits realised;
HES and Mortality Linked Data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
The team developed a new tool, called ‘CanPredict’, which is able to identify the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.
https://www.ox.ac.uk/news/2023-04-06-new-tool-uses-existing-health-records-predict-people-s-risk-developing-lung-cancer
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
The team found that People with haematological malignancies are at increased risk of severe outcomes from COVID-19 including hospitalisation and death Non-cancerous blood disorders, such as sickle cell disease, may also be linked to poor outcomes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Several other studies are reported in the QResearch News Update Spring 2023 newsletter
https://www.qresearch.org/media/yh5jffr5/qresearch-newsletter-8-2023.pdf
DARS-NIC-382794-T3L3M-v6.7 1 September 2022 to 31 August 2023
- Title
- QResearch-Oxford Data Linkage Project
- Commercial
- Yes
- Sublicensing
- Yes
- Datasets
- 17
- Files released
- 144
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 Therapeutics Programme Data Set; COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Emergency Care Data Set (ECDS); HES-ID to MPS-ID HES Accident and Emergency; HES-ID to MPS-ID HES Admitted Patient Care; HES-ID to MPS-ID HES Outpatients; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); Maternity Services Data Set (MSDS) v1.5; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v5.4
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Title | QResearch-Oxford Data Linkage Project | |
| Start date | 2022-09-01 | |
| End date | 2023-08-31 | |
| Sublicensing | Yes | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Vaccination Adverse Reactions: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| COVID-19 Vaccination Status: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Emergency Care Data Set (ECDS): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Critical Care (HES Critical Care): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| Hospital Episode Statistics Outpatients (HES OP): legal basis | Health and Social Care Act 2012 - s261(5)(d) | |
| SUS plus - Admitted Patient Care (beta version): legal basis | Health and Social Care Act 2012 - s261(5)(d) |
Datasets: + COVID-19 Therapeutics Programme Data Set’; + HES-ID to MPS-ID HES Accident and Emergency; + HES-ID to MPS-ID HES Admitted Patient Care; + HES-ID to MPS-ID HES Outpatients; + MSDS (Maternity Services Data Set) v1.5
Objective for processing
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The University of Oxford requires access to NHS Digital data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
QResearch was originally a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer, Covid-19 data, and hospital data) and the single point of access to the data.
1) for use by the University of Oxford for specific research purposes, as described in this agreement
All UK universities can apply to access the GP data via the governance/approval route, however only University of Oxford and its data processors listed in this agreement can access NHS Digital data contained within the QResearch database.
2) for use by the University of Nottingham for ongoing research studies, as described in this agreement.
This agreement specifically relates to QResearch's urgent COVID-19 research projects to support the COVID-19 pandemic, for example;
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this agreement).
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by National Institute for Health Research (NIHR).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
QResearch was originally a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
The QResearch database is a dynamic database of over 35 million patients with new patients registering with the practices all of the time. The unmatched data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
The University of Oxford is the sole data controller for the NHS Digital datasets (deaths, cancer, Covid-19 data, and hospital data) which are linked to the GP Data in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the University of Oxford’s governance/approval route. This agreement will also allow UK universities access to NHS Digital data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by the University of Oxford for specific research purposes:
The University of Oxford will access data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via the University of Oxford QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the University of Oxford QResearch governance and approvals route. A sub-licence agreement will not be required by the University of Oxford since they are the Data Controller and are also permitted to process the data.
Although it is acknowledged that Covid-19 has been ongoing for over two years, the University of Oxford is still being commissioned for urgent and rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the recent changes to the Testing policy by the UK government and confirm they still require monthly data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of current COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
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e)
other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest such as the
urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
Although it is acknowledged that Covid-19 has been ongoing for over two years, University of Oxford are still being commissioned for urgent and rapid results research from funders, including Department of Health and Social Care (DHSC), NIHR and HTA.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available pseudonymised data for COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, COVID-19 UK Non-hospital Antigen Testing Results (pillar 2), Civil Registration Deaths (Mortality), COVID-19 Hospitalization in England Surveillance System (CHESS), COVID-19 Second Generation Surveillance System (SGSS). This monthly release of data will be used to support COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N to support COVID-19 risk stratification work but only while the data sharing agreement DARS-NIC-240279-Y2V2N remains active. Data released under DARS-NIC-240279-Y2V2N are used to support COVID-19 where historical comparisons are made (for example risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The linked HES data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed data at scale in order identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care. The data under this agreement will only be used for COVID-19 research as outlined in this agreement.
Examples of current general medical research projects:
The University of Oxford (Data Controller) processes the data under 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. As a higher education establishment, the University conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest. Additionally, under GDPR Article 9(2)(j) processing of Special Category Personal Data is necessary for archiving for research purposes. Data minimisation process is being followed and only data that is required specifically for the study purposes outlined within this agreement has been requested, to protect the rights of the data subjects.
(a) QResearch data linked to HES data is being used to undertake an assessment of different types of direct anticoagulant medication which is prescribed in primary care to reduce the risk of stroke and manage thrombosis. Adverse effects from anticoagulants include major haemorrhage which can be life threatening or life ending. Haemorrhage can affect the brain, gastrointestinal tract, urinary tract or other parts of the body. The primary care data provides information on the prescriptions issued and the linked HES data provides information on haemorrhage which is serious enough to require hospital admission.
The University of Oxford is the sole Data Controller who also process data. The University of Oxford are solely responsibility for determining the purpose for which, or the manner in which NHS Digital data will be processed.
(b) Study looking at the safety of the oral contraceptive pill. The primary care data provide information on exposure to the medication and the linked HES data provides information on thrombosis.
The data processors are:
These are just two examples of projects which can only be done using the linked HES data. The results help identify and quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
- Intensive Care National Audit & Research Centre (ICNARC)
2) For use by the University of Nottingham for ongoing research studies:
- London School of Hygiene and Tropical Medicine
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous data sharing agreement (DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a data processor with an appropriate data processing agreement in place between the respective organisations. There are currently 19 ongoing research projects with the University of Nottingham as a data processor. This will be in place until those projects have been completed.
- University of Liverpool
Examples of ongoing Nottingham Projects using QResearch as of July 2022:
- University of Leicester
(a) Validating a postpartum venous thromboembolism risk prediction model.
- Dancing House Consulting. This data processor undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake analysis of the data.
(b) Prostate specific antigen (PSA) testing in the UK population and its implications.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stays onsite, stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford.
(C) Hormone replacement therapy and risk of breast cancer: case-control study using QResearch.
Data will not be used for any solely commercial purposes and all applications for the use of Covid-19 datasets, HES and/or mortality linked data are subject to a governance process explained below.
The above only allows access to NHS Digital datasets for deaths, cancer, and hospital data (linked to GP data). It does not allow access to NHS Digital Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent Medical Research and Ethics Committee (MREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this agreement):
Application Governance Process
Requests to use the linked NHS Digital data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Data Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS Digital data must be via a sublicence agreement.
Requests to use the linked NHS Digital data come from researchers within University of Oxford and/or its Data Processing organisations. The DHSC, NIHR and other organisations also commission research.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
A summary of the application process is detailed below:
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties such as Oxford University Innovations, for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
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12) Approve within one month
12) Approve within one month (with associated sublicence agreement in place before NHS Digital Data is released, if the application is not from the University of Oxford)
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The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC
18/EM/0400
18/EM/0400. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research
[81 words unchanged]
and advising on research applications where data is requested from the QResearch
database or QResearch linked
database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following
the
advice of the advisory and scientific committees.
The researchers do not have access to the
full
QResearch
linked
database (access is restricted to
two
a limited number of
University of Oxford
substantive
employees). Once an application has been
approved,
approved (and a sub-licence agreement is in place where required),
a subset of the pseudonymised record level data (as approved for the
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on University of Oxford servers (as described in more detail in section
b)
5b)
and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis.
All remote access must take place within the UK.
No record level data can be downloaded by
researchers.
researchers as the IT system restricts this.
[5 paragraphs unchanged]
The team have considered the recent changes to the Testing policy by the UK government and confirm they still require monthly data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Current projects with four UK Universities where data is being accessed as a data processor under the previous version of this agreement will be transferred to sublicence agreements, namely;
- Intensive Care National Audit & Research Centre (ICNARC)
- London School of Hygiene and Tropical Medicine
- University of Liverpool
- University of Leicester
To support Covid-19 related research the following NHS Digital data is disseminated on a monthly basis (latest available) unless indicated otherwise. The Covid-19 and SUS Plus data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions (annual dissemination only)
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS) (annual dissemination only)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS Digital Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). This data will only be used to support projects (a) and (e) detailed above in the covid-19 specific projects and will only be analysed by University of Oxford researchers and cannot be used for any other purpose. The data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS Digital data is disseminated on a monthly basis (latest available) unless indicated. The data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (One off – April 2015 to March 2019)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality data are used to support the project where historical comparisons are made (for example risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This agreement absorbs DARS-NIC-240279-Y2V2N HES and Mortality data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N is superseded by this version of this agreement, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications data, pseudonymised MSDS data from April 2019 to latest available, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS Digital Data Access Request Service in the future.
All data disseminated under this agreement is pseudonymised.
The data is minimised by limiting to pseudonymised data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) data, radiotherapy data (RTDS) data originally supplied directly by Public Health England (PHE), which is now being released through NHS Digital, the original Office of Data Release (ODR) reference was ODR 1819_247 and provisionally has a DARS reference of DARS-NIC-656839-K5V9L-v0 whilst the transition takes place.
The Q-Research linked database also holds data from the Uk Teratology Service for congenital abnormalities and maternal confidential enquiry.
The University of Oxford is the sole Data Controller who also process data. The University of Oxford is the data controller for QResearch (and therefore, the data disseminated under this agreement) as it is the organisation responsible for ensuring that the data will only be processed for the purposes described above.
The lawful basis for processing personal data under GDPR is 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. As a higher education establishment, the University of Oxford conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest.
The lawful basis for processing special category data under the GDPR is 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. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- National Institute for Health Research (NIHR).
- Wellcome Trust.
- Health Data Research UK (HDRUK).
Funding to continue the work described will be sought on an ongoing basis as required.
QResearch use a cost recovery-based costing model approved by the University of Oxford to generate an indicative quote for QResearch data access and service costs.
The University of Oxford may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for Oxford based studies. They will have no access to patient level data.
Dancing House Consulting is a data processor as it undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the data.
The patient level data linked to QResearch (QResearch linked database) is only accessed by either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS Digital has confirmed are acceptable before access is granted.
- for existing University of Nottingham research projects, individuals substantively employed by the University of Nottingham.
- for other UK Universities research projects this will be under the sublicence data sharing model. This includes future University of Nottingham research projects.
Processing activities
EMIS
and TPP
Health (commercial supplier of GP computer systems)
process the GP data from the original data controllers (GP practices) and sends it to the University of Oxford. EMIS
and TPP are
is
not able to access or process any GP data once it is located at the University of Oxford.
EMIS
and TPP are
Health is
neither a data processor nor a data controller for the data provided by NHS Digital under this Agreement. EMIS
and TPP are
Health is
not able to access the NHS Digital data under any circumstances.
EMIS and TPP
GP practices (data controllers)
have given permission for the GP data it supplies to be linked
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Principal Investigator at the University of Oxford and described in this agreement.
Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the NHS Digital data at source. NHS Digital retains the salt key for this pseudonymisation, meaning that the University of Oxford are unable to re-identify the data but as described below they are able to link with GP data that was pseudonymised using the same Open Pseudonymiser tool. The University of Oxford will not be provided with a copy of the pseudonymisation salt key.
No data will flow to NHS Digital for the purposes of this Agreement.
NHS Digital provide the pseudonymised-at-source data to the University of Oxford via Secure Electronic File Transfer (SEFT) which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the NHS Digital data. The data linkage is undertaken by a substantive employee of the University of Oxford. The GP data is linked to health care data such as hospital admissions and attendances, mortality data, COVID-19 data, cancer data and occupational data from Office for National Statistics (ONS). No data items which would identify the data subjects are received by QResearch as the data is pseudonymised-at-source and at NHS Digital. Date of birth is rounded to year of birth before receipt by the University of Oxford.
NHS Digital will provide the data from the Covid-19, SUS, HES, ECDS, MSDS and Mortality datasets to the University of Oxford.
The data will contain no direct identifying data items. The data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.
Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser software (www.openpseudonymiser.org) to pseudonymise the NHS Digital data at source. NHS Digital use a project specific ‘salt’ key to ensure that the identifiers are specific to the University of Oxford. NHS Digital retains the salt key, meaning that the University of Oxford are unable to re-identify the data but as described below they are able to link with GP data that was processed by other data suppliers using the same Open Pseudonymiser software and salt key. The University of Oxford will not be provided with a copy of the pseudonymisation salt key.
NHS Digital provide pseudonymised data to the University of Oxford via Secure Electronic File Transfer (SEFT) which is then linked to the QResearch database at individual patient level using a pseudonymised version of the NHS number which has been supplied in both GP data and the NHS Digital data. The data linkage is undertaken by a substantive employee of the University of Oxford. The NHS Digital data and GP data are linked to health care data such as hospital admissions and attendances, mortality data, COVID-19 data and cancer, SACT, and RTDS data. The datasets are also linked to ICNARC (intensive care data), congenital abnormalities and lung cancer screening data as well as COVID-19 medications used to prevent or treat covid including but not limited to vaccinations, antivirals and antibody treatments. In order to develop risk stratification models (see section 5a, project (a)) and assess uptake, effectiveness and safety of COVID-19 (see section 5a, project e), the data will also be linked to the COVID-19 therapeutics cohort. No data items which would identify the data subjects are received by QResearch as the data is pseudonymised-at-source by NHS Digital. Date of birth is rounded to year of birth before receipt by the University of Oxford.
The University of Oxford uses offsite back-up services provided by Dancing House Consulting.
The data will be accessed by authorised personnel via remote access. The data will remain on the servers at the University of Oxford at all times.
Personnel are prohibited from and not technically capable of downloading or copying data to local devices.
The data will not leave the UK at any time. All remote access must take place within the UK.
The QResearch database linked to NHS digital data will only be accessed by a limited number of substantively employed, individuals within the QResearch unit. They will produce subsets of the data that will be accessed by the organisation or its sublicensee(s) as per the QResearch Application and Approvals process.
The subsets of data are then used for undertaking research as described in this agreement. These staff will process and analyse the subset of data to address an approved research question(s).
All personnel accessing the data have been appropriately trained in data protection and confidentiality.
[1 paragraph unchanged]
The resulting data are then used for undertaking primary research relating to COVID-19. The linked data are only accessed by approved research staff with substantive or honorary contracts employed by University of Oxford or its data processors. These organisations' staff will remotely access the data stored by the University of Oxford and will not store any additional copies of the data. These staff will undertake data processing tasks, data manipulation, cleaning and data analysis to address COVID 19 research questions determined solely by University of Oxford. These organisations will not have responsibility for determining the purpose for which, or the manner in which data will be processed, and they are only permitted to process data for the purpose of supporting COVID 19 research. Data is only processed on site on secure servers at the University of Oxford. Data may be processed by individuals not employed by organisations listed as data processors and this will be under an honorary contract with University of Oxford and this covers all legal responsibilities.
No individual record level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement as a data processor.
[1 paragraph unchanged]
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).
The University of Oxford conducts an annual internal audit for auditing the technical controls in place. The scope of the internal audit applies to the review of the QResearch Systems Level Security Policy and QResearch Workstation Setup Requirements.
Expected output
The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
Results are also shared with policy makers, including Cheif Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Examples of Covid-19 related outputs:
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Other outputs include analysis of
the
safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
[3 paragraphs unchanged]
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool-for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
[1 paragraph unchanged]
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new-covid-19-therapeutics/
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
Examples of General Medical research outputs:
Results are also shared with policy makers including CMO’s office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Research to identify early symptoms of Motor Neurone Disease, funded by the Motor Neurone Association, has identified a set of symptoms which could be used to increase referrals and improve early recognition and diagnosis. This is expected to lead to the development of a decision support tools for use in primary care (excepted 2022)
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results expected 2022/2023).
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results expected 2023).
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Expected measurable benefits
The aim is to provide useful knowledge that patients, GPs and intensive care doctors can use to identify patients at high risk of severe outcomes from COVID-19 and reduce the risk of severe COVID-19 infection within this pandemic and assess the uptake safety and effectiveness of COVID-19 treatments such as COVID-19 vaccine, antivirals and monoclonal antibodies. The results of the analyses may help inform policy regarding which patients are likely to benefit most from these treatments.
The QResearch database is widely used by researchers to help understand patterns of disease, safety or medicines, development of prediction tools, research into health inequalities and other similar research questions where the results are likely to have benefits for patients, the NHS or to improve understanding of disease. The results of research undertaken continues to result in new knowledge and understanding regarding disease epidemiology, health inequalities, drug safety, methods of identifying patients at high risk of serious illnesses. Every year new research is published in high impact international research journals such as the British Medical Journal and the British Journal of General Practice. The research is ongoing with target dates for individual projects rather than one overall target date.
In addition it may help research to understand whether drugs commonly taken for chronic conditions such as hypertension or diabetes may exacerbate or reduce the severity of COVID-19 disease. It is hoped this study will be able to identify alternative drugs for patients with chronic conditions, as well as possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
A complete list of research papers using QResearch database is published at http://www.qresearch.org/SitePages/publications.aspx
Research arising from the QResearch database including the linked data has been used to inform national policy. For example, research findings have been included in NICE guidelines on fragility fracture, diabetes, suspected cancer and lipid modification. Research findings have informed the NHS Health Checks programme and Department of Health guidelines on health checks.
Examples of research include assessment of the safety of antidepressant drugs and novel anticoagulants; investigation of potential links between diabetes drugs and cancer; quantification of the risk of thrombosis associated with various types of the oral contraceptive pill.
CV19 Work:
The aim for the Covid research is to provide useful knowledge that patients, GPs and intensive care doctors can use to identify patients at high risk of severe outcomes from COVID-19 and reduce the risk of severe COVID-19 infection within this pandemic and assess the uptake safety and effectiveness of COVID-19 treatments such as COVID-19 vaccine, antivirals and monoclonal antibodies. The results of the analyses may help inform policy regarding which patients are likely to benefit most from these treatments.
Specifically, it will help research to understand whether drugs commonly taken for chronic conditions such as hypertension or diabetes may exacerbate or reduce the severity of COVID-19 disease. It is hoped this study will be able to identify alternative drugs for patients with chronic conditions, as well as possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
[3 paragraphs unchanged]
ICNARC is already providing up-to-date information on the admission characteristics and outcomes
[7 words unchanged]
treated on an Intensive Care Unit (ICU) in England, Wales and Northern
Ireland.
Ireland
Benefits reported
The first paper was published in Heart (an international peer reviewed journal) as a fast track submission and showed that Angiotensin-converting enzyme (ACE) inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs. ACE inhibitors are medications that help relax the veins and arteries to lower blood pressure.
There have been many yielded benefits arising from the work. As requested, the team are limiting it to three examples;
Other papers have been published including
HES and Mortality Linked data:
- three papers in the British Medical Journal (BMJ) describing the first, second and third version of the risk stratification tool and vaccine safety showing that although there are increased risks of thrombosis with the Oxford vaccine, these are much smaller than the risks associated with the virus;
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
- two papers published in Nature Medicine Journal on Vaccine Safety looking at neurological and cardiological side effects;
Covid 19 work:
- two papers in the Lancet Journals on COVID-19 and respiratory conditions;
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
- one paper in Annals Journal regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
In addition, three reports have been produced for Scientific Advisory Group for Emergencies (SAGE) including (a) risk of COVID-19 associated with variations in household size; (b) differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
In summary, the benefits yielded so far include for the COVID-19 work;
The COVID risk assessment work led to a new tool which NHS Digital host that allows clinicians to assess their patient’s individualised risk of severe outcomes from Covid-19 which then inform actions to take and appropriate patient care.
https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool
The COVID risk assessment work also led to a risk stratification tool which NHS Digital used to identify 1.5M to add to the shielded patient list and prioritise for vaccination.
https://digital.nhs.uk/coronavirus/risk-assessment/population
The work also was the first to highlight ethnic differences in covid-19 which directly informed government policy to mitigate differential risk in occupational and clinical settings.
NIHR have published a case impact report on the NIHR covid risk stratification work. For example, an additional 1.8 million patients were added to the shielded patient list in Feb 2021 and prioritised for early vaccination. A further 1 million patients were prioritised for monoclonal antibodies and received eligibility letters sent by NHS Digital in Dec 2021 as these new treatments became available.
https://www.nihr.ac.uk/documents/case-studies/innovative-model-identifies-high-risk-people-for-priority-covid-19-vaccination/29995
In summary the benefits yielded so far include;
[4 paragraphs unchanged]
Objective for processing
The University of Oxford requires access to NHS Digital data for the purpose of providing a linked research database (QResearch linked database) for the following reasons:
1) for use by the University of Oxford for specific research purposes, as described in this agreement
2) for use by the University of Nottingham for ongoing research studies, as described in this agreement.
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this agreement).
QResearch is a database of linked medical (GP) records that have been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
The database is widely used for medical research into the causes of disease, its natural history, treatment and outcomes. QResearch was started in 2003 in order to improve access for research to primary care data and will continue for the foreseeable future.
QResearch was originally a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford.
The QResearch database is a dynamic database of over 35 million patients with new patients registering with the practices all of the time. The unmatched data at a point in time is required for when new patients register with practices when it is then linked. Data which is unmatched with the EMIS data cannot flow under sub-licence.
The University of Oxford is the sole data controller for the NHS Digital datasets (deaths, cancer, Covid-19 data, and hospital data) which are linked to the GP Data in the QResearch database via a pseudonymised unique key. The QResearch database (GP data only) is distinct from the linked asset (QResearch linked database).
The University of Oxford is the single point of access for UK universities to apply to use the data in the QResearch database and QResearch linked database. All UK universities can apply to access the GP data via the University of Oxford’s governance/approval route. This agreement will also allow UK universities access to NHS Digital data (linked to the GP data) contained within the QResearch linked database via the same governance/approval route and will be via a sublicense data sharing model.
1) Use by the University of Oxford for specific research purposes:
The University of Oxford will access data from the QResearch linked database for both COVID-19 specific research projects and general research projects. Current projects have been approved via the University of Oxford QResearch governance and approvals route. All future studies that require access to QResearch data will apply via the University of Oxford QResearch governance and approvals route. A sub-licence agreement will not be required by the University of Oxford since they are the Data Controller and are also permitted to process the data.
Although it is acknowledged that Covid-19 has been ongoing for over two years, the University of Oxford is still being commissioned for urgent and rapid results research from funders, including the Department of Health and Social Care (DHSC), National Institute for Health Research (NIHR) and NIHR Health Technology Assessment Programme (HTA). The team have considered the recent changes to the Testing policy by the UK government and confirm they still require monthly data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed data at scale in order undertake the epidemiology of COVID-19, identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines as well as research the indirect effects of COVID-19 on other conditions and the delivery of the health service. These data are only used for research purposes to generate new knowledge to inform policy and clinical care.
Examples of current COVID-19 specific research projects:
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by the National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
f) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest.
The linked HES data enables researchers to analyse additional information on patient characteristics, treatment and outcomes which will improve the epidemiological analyses of studies since the data will be more complete. Without the data linkage, research studies may under-estimate the risk and benefits associated with interventions such as prescribed medicines.
Examples of current general medical research projects:
(a) QResearch data linked to HES data is being used to undertake an assessment of different types of direct anticoagulant medication which is prescribed in primary care to reduce the risk of stroke and manage thrombosis. Adverse effects from anticoagulants include major haemorrhage which can be life threatening or life ending. Haemorrhage can affect the brain, gastrointestinal tract, urinary tract or other parts of the body. The primary care data provides information on the prescriptions issued and the linked HES data provides information on haemorrhage which is serious enough to require hospital admission.
(b) Study looking at the safety of the oral contraceptive pill. The primary care data provide information on exposure to the medication and the linked HES data provides information on thrombosis.
These are just two examples of projects which can only be done using the linked HES data. The results help identify and quantify the risks and benefits associated with different types of medication, used in different patients, at different doses over time because of the outcomes. The results help doctors and patients make better decisions and increase the evidence base to inform guideline development and policy.
2) For use by the University of Nottingham for ongoing research studies:
Due to QResearch originally being under the University of Nottingham, when roles and responsibilities transferred to the University of Oxford it was agreed in a previous data sharing agreement (DARS-NIC-240279-Y2V2N) that projects at the University of Nottingham, that were already in progress using QResearch linked data could continue to process the data as a data processor with an appropriate data processing agreement in place between the respective organisations. There are currently 19 ongoing research projects with the University of Nottingham as a data processor. This will be in place until those projects have been completed.
Examples of ongoing Nottingham Projects using QResearch as of July 2022:
(a) Validating a postpartum venous thromboembolism risk prediction model.
(b) Prostate specific antigen (PSA) testing in the UK population and its implications.
(C) Hormone replacement therapy and risk of breast cancer: case-control study using QResearch.
The above only allows access to NHS Digital datasets for deaths, cancer, and hospital data (linked to GP data). It does not allow access to NHS Digital Covid-19 datasets. All future project requests from the University of Nottingham will be processed as a sublicence agreement (subject to the University of Oxford’s governance approvals).
3) Onward sharing to UK universities via a sublicensing agreement (subject to the University of Oxford’s governance approvals described in this agreement):
Requests to use the linked NHS Digital data come from researchers within the University of Oxford and/or other UK Universities. All requests must follow the QResearch Application Governance process described below. With the exception of the University of Oxford (as they are the Data Controller who also process data) and ongoing University of Nottingham projects, all other approvals to access NHS Digital data must be via a sublicence agreement.
Research undertaken using the data from the QResearch linked database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Derby Research and Ethics Committee (DREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing and intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Researchers are charged for the work in generating the outputs for research projects and a contribution to the running costs. All subsequent research outputs from QResearch are made publicly and freely available. Risk prediction algorithms derived from QResearch linked data such as QRISK2 are published as free open-source software and also licensed for a fee as closed source software from third parties such as Oxford University Innovations, for organisations unable to use the open source.
A summary of the QResearch Application Governance Process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month (with associated sublicence agreement in place before NHS Digital Data is released, if the application is not from the University of Oxford)
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400. The minutes of the Science Committee are published here https://www.qresearch.org/about/scientific-committee/committee-minutes/
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database or QResearch linked database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following the advice of the advisory and scientific committees.
The researchers do not have access to the full QResearch linked database (access is restricted to a limited number of University of Oxford substantive employees). Once an application has been approved (and a sub-licence agreement is in place where required), a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section 5b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. All remote access must take place within the UK. No record level data can be downloaded by researchers as the IT system restricts this.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
Current projects with four UK Universities where data is being accessed as a data processor under the previous version of this agreement will be transferred to sublicence agreements, namely;
- Intensive Care National Audit & Research Centre (ICNARC)
- London School of Hygiene and Tropical Medicine
- University of Liverpool
- University of Leicester
To support Covid-19 related research the following NHS Digital data is disseminated on a monthly basis (latest available) unless indicated otherwise. The Covid-19 and SUS Plus data will only be used for Covid-19 related research and not for any additional purpose (whether under sublicence or not).
- COVID-19 Vaccination Status
- COVID-19 Vaccination Adverse Reactions (annual dissemination only)
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
- COVID-19 Hospitalization in England Surveillance System (CHESS) (annual dissemination only)
- COVID-19 Second Generation Surveillance System (SGSS).
- Secondary User Services (SUS) Plus (Hospital Episodes Statistics (HES) Admitted Patient Care (APC)
To support two specific Covid-19 studies the following NHS Digital Data was disseminated on a one-off basis (latest available) under an Information Governance (IG) Letter of Release (due to urgency). This data will only be used to support projects (a) and (e) detailed above in the covid-19 specific projects and will only be analysed by University of Oxford researchers and cannot be used for any other purpose. The data will not be included in the sublicence data-sharing model.
- COVID-19 Therapeutics Programme Data Set
To support general medical research projects the following NHS Digital data is disseminated on a monthly basis (latest available) unless indicated. The data can also be used to support COVID-19 risk stratification work.
- Civil Registration Deaths (Mortality)
- Hospital Episode Statistics (HES) Admitted Patient Care (APC) (April 1997 onwards)
- HES Critical Care (CC) (April 2008 onwards)
- HES Outpatients (OP) (April 2003 onwards)
- HES Accident and Emergency (A&E) (data is already held as this dataset has been superseded by ECDS) (April 2017 to March 2020)
- Emergency Care Dataset (ECDS) (April 2020 onwards)
- Maternity Services Data Set (MSDS) (One off – April 2015 to March 2019)
For the COVID-19 risk stratification work HES, ECDS, MSDS and Mortality data are used to support the project where historical comparisons are made (for example risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
The HES and ECDS were previously disseminated to QResearch under DARS-NIC-240279-Y2V2N and prior to that under DARS-NIC-375354-G8V1H (which was superseded by DARS-NIC-240279-Y2V2N). This agreement absorbs DARS-NIC-240279-Y2V2N HES and Mortality data and its research purposes. Therefore, DARS-NIC-240279-Y2V2N is superseded by this version of this agreement, DARS-NIC-382794-T3L3M-v6.
The QResearch team would also like to request, for inclusion in the linked database, pseudonymised Birth Registrations / Notifications data, pseudonymised MSDS data from April 2019 to latest available, and the S-Gene Target Failure (SGTF) field within the SGSS dataset, if these become available for dissemination through NHS Digital Data Access Request Service in the future.
All data disseminated under this agreement is pseudonymised.
The data is minimised by limiting to pseudonymised data only.
The QResearch linked database also holds Cancer registration, systemic anticancer treatment (SACT) data, radiotherapy data (RTDS) data originally supplied directly by Public Health England (PHE), which is now being released through NHS Digital, the original Office of Data Release (ODR) reference was ODR 1819_247 and provisionally has a DARS reference of DARS-NIC-656839-K5V9L-v0 whilst the transition takes place.
The Q-Research linked database also holds data from the Uk Teratology Service for congenital abnormalities and maternal confidential enquiry.
The University of Oxford is the sole Data Controller who also process data. The University of Oxford is the data controller for QResearch (and therefore, the data disseminated under this agreement) as it is the organisation responsible for ensuring that the data will only be processed for the purposes described above.
The lawful basis for processing personal data under GDPR is 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. As a higher education establishment, the University of Oxford conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest.
The lawful basis for processing special category data under the GDPR is 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. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding comes from multiple sources. Current funders include:
- National Institute for Health Research (NIHR).
- Wellcome Trust.
- Health Data Research UK (HDRUK).
Funding to continue the work described will be sought on an ongoing basis as required.
QResearch use a cost recovery-based costing model approved by the University of Oxford to generate an indicative quote for QResearch data access and service costs.
The University of Oxford may have a collaborator at another university on the project team acting in an advisory capacity on clinical aspects or interpretation of findings for Oxford based studies. They will have no access to patient level data.
Dancing House Consulting is a data processor as it undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data thus has access to the QResearch database and the QResearch linked database. Dancing House Consulting do not undertake analysis of the data.
The patient level data linked to QResearch (QResearch linked database) is only accessed by either:
- for University of Oxford research projects, individuals substantively employed by the University of Oxford, students registered with the University of Oxford and individuals from other universities that have an honorary contract or secondment agreement with the University of Oxford which NHS Digital has confirmed are acceptable before access is granted.
- for existing University of Nottingham research projects, individuals substantively employed by the University of Nottingham.
- for other UK Universities research projects this will be under the sublicence data sharing model. This includes future University of Nottingham research projects.
Expected output
The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Examples of conferences include the annual academic conference for the Society of Academic Primary Care and the UK Research and Innovation (UKRI); international conferences such as the North American Primary Care Research Group; the annual conferences of the EMIS National User Group (a national education and research charity representing the GP practices who contribute data to QResearch); annual conferences run by cancer charities such as Macmillan Cancer Support and Pancreatic Cancer UK; local and regional conferences run by the Nottingham Biomedical Research Centre.
Results are also shared with policy makers, including Cheif Medical Officer's (CMO’s) office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support the development of relevant guidelines.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced that show the performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Examples of Covid-19 related outputs:
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of the safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
All research is published in academic journals with a link from the QResearch website on an ongoing basis.
A list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool- for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new- covid-19-therapeutics/
Examples of General Medical research outputs:
Research to identify early symptoms of Motor Neurone Disease, funded by the Motor Neurone Association, has identified a set of symptoms which could be used to increase referrals and improve early recognition and diagnosis. This is expected to lead to the development of a decision support tools for use in primary care (excepted 2022)
Research funded by INNOVATE UK to develop a risk stratification tool to identify those at high risk of oesophageal cancer is likely to lead to a more efficient way of identifying patients who might be eligible for the Cytosponge device (which is an alternative to endoscopy). This is especially important given the limitations on use of endoscopy arising from the COVID-19 pandemic (results expected 2022/2023).
Another project funded by INNOVATE UK investigates the risks and benefits and health economic consequences the pilot lung cancer screening program is expected to inform the development of the national screening program (results expected 2023).
Benefits reported
There have been many yielded benefits arising from the work. As requested, the team are limiting it to three examples;
HES and Mortality Linked data:
The database was used to develop the QRisk tool – www.qrisk.org is a 10-year risk of cardiovascular disease. It replaced Framingham in the NICE lipid guideline [CG181]. It is central to the NHS Health checks and the GP Quality and Outcomes Framework. The QRISK lifetime version of the tool is used on NHS Choices website to estimate heart age; It was updated to QRISK3 (https://qrisk.org/three/) making it the first CVD risk algorithm to include major risk factors such as mental illness, antipsychotics and migraine. A microsimulation study(Mytton 2018) estimated that QRISK helps reduce health inequalities and prevent approximately 300 deaths (before 80 years) and resulting in an additional 1,000 people being free of cardiovascular diseases, dementia, and lung cancer at age 80 each year in England. QRISK outperformed the American Cardiovascular risk Assessment tool in external validation.
Covid 19 work:
QResearch linked database was used to develop the NIHR-funded QCOVID tool https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool which identifies patients at risk of COVID-19 death and hospital admission. It is used to risk stratify the entire population to add patients to the shielded patient list and prioritise them for vaccination. It is also used as a clinical tool in a consultation between patients and clinicians to personalise risk improve decision making and guide interventions.
The QResearch linked database was used to undertake the UK’s largest COVID-19 vaccine safety studies, covering > 38M people including 2 papers in Nature Medicine & 1 in the British Medical Journal (BMJ). This provided UK’s 1st peer reviewed evidence of association between COVID-19 vaccination (a) myocarditis (b) Guillain Barre syndrome (c) thrombosis though these risks were lower than the risk following SARS-CoV-2 infection. The results were used immediately by SAGE, UK, EU & US drug regulators to quantify risks & benefits of COVID-19 vaccinations
In summary, the benefits yielded so far include for the COVID-19 work;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
DARS-NIC-382794-T3L3M-v5.4 1 May 2022 to 31 August 2022
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 12
- Files released
- 55
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; Emergency Care Data Set (ECDS); Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Hospital Episode Statistics Critical Care (HES Critical Care); Hospital Episode Statistics Outpatients (HES OP); SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v4.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2022-05-01 | |
| End date | 2022-08-31 | |
| COVID-19 Vaccination Status: sensitivity | Sensitive |
Datasets: + Emergency Care Data Set (ECDS); + Hospital Episode Statistics Accident and Emergency (HES A and E); + Hospital Episode Statistics Admitted Patient Care (HES APC); + Hospital Episode Statistics Critical Care (HES Critical Care); + Hospital Episode Statistics Outpatients (HES OP)
Objective for processing
This agreement specifically relates to QResearch's urgent COVID-19 research projects
to support the current pandemic for example
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the CMO via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by NIHR
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by HDRUK and
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the MRC
e) other urgent COVID research in response to the emerging pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system and covid-19 SGSS and HES data This monthly release of data will be used to support urgent COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active. Given that COVID-19 is still a novel disease which is mutating and that we have new treatments and vaccines being used at scale and at pace and changing levels of immunity, we need detailed data at scale in order identify new risk factor, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care as rapidly as possible. The data under this agreement will only be used for COVID-19 research outlined in this agreement.
[1 paragraph unchanged]
QResearch was originally a not for profit
collaboration
collaboration,
originally between the University of Nottingham and Egton Medical Information Systems (EMIS)
[9 words unchanged]
since been transferred to the University of Oxford. Strategic decisions about the
GP
General Practitioner (GP)
data are taken by a Management Board representing the interests of EMIS
[11 words unchanged]
sole data controller for the datasets which are linked to QResearch (deaths,
cancer
cancer, Covid-19 data,
and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stay on site stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
All UK universities can apply to access the GP data via the governance/approval route, however only University of Oxford and its data processors listed in this agreement can access NHS Digital data contained within the QResearch database.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
This agreement specifically relates to QResearch's urgent COVID-19 research projects to support the COVID-19 pandemic, for example;
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest such as the urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
Although it is acknowledged that Covid-19 has been ongoing for over two years, University of Oxford are still being commissioned for urgent and rapid results research from funders, including Department of Health and Social Care (DHSC), NIHR and HTA.
To support this work, NHS Digital will provide a monthly release of the latest available pseudonymised data for COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, COVID-19 UK Non-hospital Antigen Testing Results (pillar 2), Civil Registration Deaths (Mortality), COVID-19 Hospitalization in England Surveillance System (CHESS), COVID-19 Second Generation Surveillance System (SGSS). This monthly release of data will be used to support COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N to support COVID-19 risk stratification work but only while the data sharing agreement DARS-NIC-240279-Y2V2N remains active. Data released under DARS-NIC-240279-Y2V2N are used to support COVID-19 where historical comparisons are made (for example risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed data at scale in order identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care. The data under this agreement will only be used for COVID-19 research as outlined in this agreement.
The University of Oxford (Data Controller) processes the data under 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. As a higher education establishment, the University conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest. Additionally, under GDPR Article 9(2)(j) processing of Special Category Personal Data is necessary for archiving for research purposes. Data minimisation process is being followed and only data that is required specifically for the study purposes outlined within this agreement has been requested, to protect the rights of the data subjects.
The University of Oxford is the sole Data Controller who also process data. The University of Oxford are solely responsibility for determining the purpose for which, or the manner in which NHS Digital data will be processed.
The data processors are:
- Intensive Care National Audit & Research Centre (ICNARC)
- London School of Hygiene and Tropical Medicine
- University of Liverpool
- University of Leicester
- Dancing House Consulting. This data processor undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake analysis of the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stays onsite, stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford.
Data will not be used for any solely commercial purposes and all applications for the use of Covid-19 datasets, HES and/or mortality linked data are subject to a governance process explained below.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent Medical Research and Ethics Committee (MREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Application Governance Process
Requests to use the linked NHS Digital data come from researchers within University of Oxford and/or its Data Processing organisations. The DHSC, NIHR and other organisations also commission research.
A summary of the application process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following advice of the advisory and scientific committees.
The researchers do not have access to the QResearch database (access is restricted to two University of Oxford employees). Once an application has been approved, a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. No record level data can be downloaded by researchers.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The team have considered the recent changes to the Testing policy by the UK government and confirm they still require monthly data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Processing activities
[1 paragraph unchanged]
EMIS and TPP are neither a data processor nor a data controller
[7 words unchanged]
under this Agreement. EMIS and TPP are not able to access the
HES
NHS Digital
data under any circumstances. EMIS and TPP have given permission for the
[12 words unchanged]
Digital for purposes determined by the Principal Investigator at the University of
Oxford.
Oxford and described in this agreement.
Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the
HES
NHS Digital
data at source. NHS Digital retains the salt key for this pseudonymisation,
[37 words unchanged]
will not be provided with a copy of the pseudonymisation salt key.
NHS Digital provide the pseudonymised-at-source data to the University of Oxford
via Secure Electronic File Transfer (SEFT)
which is then linked to the QResearch database at individual patient level
[6 words unchanged]
NHS number which has been supplied in both GP data and the
SUS
NHS Digital
data. The data linkage is undertaken by
an
a substantive
employee of the University of Oxford. The GP data is linked to
other
health care data such as hospital admissions and attendances,
mortality, ICNARC,
mortality data,
COVID-19 data, cancer data and occupational data from
ONS.
Office for National Statistics (ONS).
No data items which would identify the data subjects are received by
[13 words unchanged]
is rounded to year of birth before receipt by the University of
Oxford .
Oxford.
[1 paragraph unchanged]
The resulting data are then used for undertaking primary research relating to
[13 words unchanged]
or honorary contracts employed by University of Oxford or its data processors.
In order to support the urgent COVID-19 risk stratification work, a small number of employees from the University of Cambridge, University of Leicester, University College London, Kings College London, Guys and St Thomas's Trust, ICNARC, NHSBT, London School of Hygiene and Tropical Medicine, and University of Liverpool may act as additional data processors.
These organisations' staff will remotely access the data stored by the University
[6 words unchanged]
any additional copies of the data. These staff will undertake data processing
tasks
tasks,
data manipulation,
cleaning,
cleaning and
data analysis to address
urgent
COVID
19
research questions
as
determined solely by
University of
Oxford. These organisations will not have responsibility for determining the purpose for
[10 words unchanged]
and they are only permitted to process data for the purpose of
supportin urgent COVID-19
supporting COVID 19
research. Data is only processed on site on secure servers at the
[14 words unchanged]
as data processors and this will be under an honorary contract with
oxford
University of Oxford
and this covers all legal responsibilities.
No individual level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement.
The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake analysis of the data.
No individual record level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement as a data processor.
All outputs are restricted to aggregate data with small numbers suppressed in line with the HES Analysis Guide.
[1 paragraph unchanged]
All organisations party to this agreement must comply with the
Data Sharing Framework Contract
data sharing framework contract
requirements, including those regarding the use (and purposes of that use) by
“Personnel”
“personnel”
(as defined within the
Data Sharing Framework Contract i.e.:
data sharing framework contract i.e.
employees, agents and contractors of the
Data Recipient
data recipient
who may have access to that data).
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at
[12 words unchanged]
reports, research papers which are published in peer reviewer academic scientific journals
(for example, British Medical Journal (BMJ) and Lancet Journals)
and presented at academic
conferences. All research is published in academic journals with a link from
conferences (for example, Annual Scientific Meeting of
the
QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Society for Academic Primary Care (SAPC).
Results are also shared with policy makers including CMO.s office, MHRA, JCVI, DHSC, Scottish Office and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Other outputs include analysis of safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
All research is published in academic journals with a link from the QResearch website on an ongoing basis.
A list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool-for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new-covid-19-therapeutics/
The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers including CMO’s office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
[1 paragraph unchanged]
The results tables within the papers will only contain statistical information with cell counts of > 5.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
[1 paragraph unchanged]
Expected measurable benefits
The aim is to provide useful knowledge that patients, GPs and intensive
[14 words unchanged]
COVID-19 and reduce the risk of severe COVID-19 infection within this pandemic
and
assess the uptake safety and effectiveness of COVID-19 treatments such as COVID-19 vaccine, antivirals and monoclonal antibodies.
The results of the analyses may help inform policy regarding which patients are likely to benefit most from these treatments.
In addition
will
it may
help research to understand whether drugs commonly taken for chronic conditions such
[33 words unchanged]
possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
[3 paragraphs unchanged]
ICNARC is already providing up-to-date information on the admission characteristics and outcomes of all patients with severe COVID-19 infection treated on an
ICU
Intensive Care Unit (ICU)
in England, Wales and Northern Ireland.
Benefits reported
The first paper was published in Heart
(an international peer reviewed journal)
as a fast track submission and showed that
ACE
Angiotensin-converting enzyme (ACE)
inhibitors were not associated with an increased risk of poor outcomes from
[7 words unchanged]
reassurance to public and professionals on the safety aspect of these drugs.
ACE inhibitors are medications that help relax the veins and arteries to lower blood pressure.
[1 paragraph unchanged]
- three papers in the
BMJ
British Medical Journal (BMJ)
describing the first, second and third version of the risk stratification tool
[15 words unchanged]
vaccine, these are much smaller than the risks associated with the virus;
- two papers published in Nature
medicine
Medicine Journal
on Vaccine Safety looking at neurological and cardiological side effects;
[1 paragraph unchanged]
- one paper in Annals
Journal
regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
In addition, three reports have been produced
already
for
SAGE
Scientific Advisory Group for Emergencies (SAGE)
including (a) risk of COVID-19 associated with variations in household size;
(b)
differences in COVID-19 risk between the first and second pandemic waves by
[7 words unchanged]
study of COVID-19 outcomes in children including the differential by ethnic group.
The COVID risk assessment work led to a new tool which NHS Digital host that allows clinicians to assess their patient’s individualised risk of severe outcomes from Covid-19 which then inform actions to take and appropriate patient care.
https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool
The COVID risk assessment work also led to a risk stratification tool which NHS Digital used to identify 1.5M to add to the shielded patient list and prioritise for vaccination.
https://digital.nhs.uk/coronavirus/risk-assessment/population
The work also was the first to highlight ethnic differences in covid-19 which directly informed government policy to mitigate differential risk in occupational and clinical settings.
NIHR have published a case impact report on the NIHR covid risk stratification work. For example, an additional 1.8 million patients were added to the shielded patient list in Feb 2021 and prioritised for early vaccination. A further 1 million patients were prioritised for monoclonal antibodies and received eligibility letters sent by NHS Digital in Dec 2021 as these new treatments became available.
https://www.nihr.ac.uk/documents/case-studies/innovative-model-identifies-high-risk-people-for-priority-covid-19-vaccination/29995
In summary the benefits yielded so far include;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
Objective for processing
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration, originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the General Practitioner (GP) data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer, Covid-19 data, and hospital data) and the single point of access to the data.
All UK universities can apply to access the GP data via the governance/approval route, however only University of Oxford and its data processors listed in this agreement can access NHS Digital data contained within the QResearch database.
This agreement specifically relates to QResearch's urgent COVID-19 research projects to support the COVID-19 pandemic, for example;
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the Chief Medical Officer (CMO) via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by National Institute for Health Research (NIHR).
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust.
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by Health Data Research UK (HDRUK).
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the Medical Research Council (MRC).
e) other urgent COVID research in response to the pandemic which needs to be undertaken rapidly in the national interest such as the urgent commission by NIHR Health Technology Assessment Programme (HTA) for the evaluation of uptake, safety and effectiveness of novel therapeutics such as the monoclonal antibodies for COVID-19.
Although it is acknowledged that Covid-19 has been ongoing for over two years, University of Oxford are still being commissioned for urgent and rapid results research from funders, including Department of Health and Social Care (DHSC), NIHR and HTA.
To support this work, NHS Digital will provide a monthly release of the latest available pseudonymised data for COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, COVID-19 UK Non-hospital Antigen Testing Results (pillar 2), Civil Registration Deaths (Mortality), COVID-19 Hospitalization in England Surveillance System (CHESS), COVID-19 Second Generation Surveillance System (SGSS). This monthly release of data will be used to support COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N to support COVID-19 risk stratification work but only while the data sharing agreement DARS-NIC-240279-Y2V2N remains active. Data released under DARS-NIC-240279-Y2V2N are used to support COVID-19 where historical comparisons are made (for example risk associated with COVID-19 compared with risks associated with influenza in previous years or where vaccine safety analyses require information about HES admissions from pre-pandemic periods as part of the analysis).
Given that COVID-19 is still a relatively novel disease (there are still many unknowns compared with other diseases which have been affecting people for many decades), which is mutating and that there are new treatments and vaccines being used at scale and at pace and changing levels of immunity, there is a need for detailed data at scale in order identify new risk factors, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care. The data under this agreement will only be used for COVID-19 research as outlined in this agreement.
The University of Oxford (Data Controller) processes the data under 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. As a higher education establishment, the University conduct research to improve health care and service and the linkage requested is necessary for the performance of a task carried out in the public interest. Additionally, under GDPR Article 9(2)(j) processing of Special Category Personal Data is necessary for archiving for research purposes. Data minimisation process is being followed and only data that is required specifically for the study purposes outlined within this agreement has been requested, to protect the rights of the data subjects.
The University of Oxford is the sole Data Controller who also process data. The University of Oxford are solely responsibility for determining the purpose for which, or the manner in which NHS Digital data will be processed.
The data processors are:
- Intensive Care National Audit & Research Centre (ICNARC)
- London School of Hygiene and Tropical Medicine
- University of Liverpool
- University of Leicester
- Dancing House Consulting. This data processor undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake analysis of the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stays onsite, stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford.
Data will not be used for any solely commercial purposes and all applications for the use of Covid-19 datasets, HES and/or mortality linked data are subject to a governance process explained below.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent Medical Research and Ethics Committee (MREC). Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Application Governance Process
Requests to use the linked NHS Digital data come from researchers within University of Oxford and/or its Data Processing organisations. The DHSC, NIHR and other organisations also commission research.
A summary of the application process is detailed below:
1) Researchers originate a research question and write an outline protocol.
2) Pre-submission enquiry
3) IF feasible, cost estimate & letter of support
4) Researchers secure funding
5) A detailed data specification is produced
6) Co produce a lay summary with Patient and Public Involvement (PPI) groups
7) Submit Application
8) Review by Scientific Committee & feedback is given
9) Revisions if needed
10) Obtain approval
11) Timeline agreed for extract
12) Approve within one month
Initial enquiries are to qresearch@phc.ox.ac.uk. Applications are made to the QResearch Science Committee as described below.
Requests are submitted and then reviewed at the monthly QResearch Science Committee. Fast Track requests are reviewed by the Chair between meetings as necessary.
The QResearch Scientific Committee undertakes scientific reviews of research applications to QResearch and approval, if given by the committee, constitutes Research Ethics Approval under REC 18/EM/0400
The committee advise the QResearch team on scientific issues relating to research applications, ensuring that each application has a clear research question or hypothesis which is likely to lead to generalisable findings capable of publication in a peer reviewed medical journal. They advise if the research meets a minimum scientific standard and if not, amendments that will be required. They also assess the risks involved and will seek advice from QResearch Advisory Group if required. The committee ensure they follow the criteria and principles set out by the QResearch Advisory Board in assessing and advising on research applications where data is requested from the QResearch database.
The Chief Investigator for QResearch is responsible for ensuring that data access is provided in accordance with the protocol, ethics approval for the research database and following advice of the advisory and scientific committees.
The researchers do not have access to the QResearch database (access is restricted to two University of Oxford employees). Once an application has been approved, a subset of the pseudonymised record level data (as approved for the study) will be extracted and stored on University of Oxford servers (as described in more detail in section b) and researchers will be given a login to remotely access the specific extract via University of Oxford servers to conduct their analysis. No record level data can be downloaded by researchers.
For further information on the application governance process please see the links to the QResearch website below.
QResearch Home Page: https://www.qresearch.org/
Scientific Committee – QResearch: https://www.qresearch.org/about/scientific-committee/
Approved Research Programs and Projects – QResearch: https://www.qresearch.org/research/approved-research-programs-and-projects/
Data – QResearch: https://www.qresearch.org/data/
The team have considered the recent changes to the Testing policy by the UK government and confirm they still require monthly data on testing to continue to monitor vaccine safety, effectiveness and uptake as well as evaluation of safety and uptake of monoclonal antibodies which are targeted to those with a positive test.
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals (for example, British Medical Journal (BMJ) and Lancet Journals) and presented at academic conferences (for example, Annual Scientific Meeting of the Society for Academic Primary Care (SAPC).
Other outputs include analysis of safety of COVID-19 vaccinations and of the uptake, safety and effectiveness of monoclonal antibodies.
All research is published in academic journals with a link from the QResearch website on an ongoing basis.
A list of all publications arising from the QResearch database https://www.qresearch.org/publications/research-papers/
Collected resources for the NIHR funded covid-19 risk stratification work
https://www.qresearch.org/research/approved-research-programs-and-projects/development-and-evaluation-of-a-tool-for-predicting-risk-of-short-term-adverse-outcomes-due-to-covid-19-in-the-general-uk-population/
Collected resources arising from the evaluation of covid therapeutics including covid vaccination and including infographics intended to communicate results to the public and press releases.
https://www.qresearch.org/research/approved-research-programs-and-projects/uptake-and-comparative-safety-of-new-covid-19-therapeutics/
The publications are accompanied by press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers including CMO’s office, Medicines and Healthcare Products Agency (MHRA), Joint Committee on Vaccination and Immunisation (JCVI), Department of Health and Social Care (DHSC), Scottish Office and National Institute for Health Care Excellence (NICE) guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
The first paper was published in Heart (an international peer reviewed journal) as a fast track submission and showed that Angiotensin-converting enzyme (ACE) inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs. ACE inhibitors are medications that help relax the veins and arteries to lower blood pressure.
Other papers have been published including
- three papers in the British Medical Journal (BMJ) describing the first, second and third version of the risk stratification tool and vaccine safety showing that although there are increased risks of thrombosis with the Oxford vaccine, these are much smaller than the risks associated with the virus;
- two papers published in Nature Medicine Journal on Vaccine Safety looking at neurological and cardiological side effects;
- two papers in the Lancet Journals on COVID-19 and respiratory conditions;
- one paper in Annals Journal regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
In addition, three reports have been produced for Scientific Advisory Group for Emergencies (SAGE) including (a) risk of COVID-19 associated with variations in household size; (b) differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
The COVID risk assessment work led to a new tool which NHS Digital host that allows clinicians to assess their patient’s individualised risk of severe outcomes from Covid-19 which then inform actions to take and appropriate patient care.
https://digital.nhs.uk/coronavirus/risk-assessment/clinical-tool
The COVID risk assessment work also led to a risk stratification tool which NHS Digital used to identify 1.5M to add to the shielded patient list and prioritise for vaccination.
https://digital.nhs.uk/coronavirus/risk-assessment/population
The work also was the first to highlight ethnic differences in covid-19 which directly informed government policy to mitigate differential risk in occupational and clinical settings.
NIHR have published a case impact report on the NIHR covid risk stratification work. For example, an additional 1.8 million patients were added to the shielded patient list in Feb 2021 and prioritised for early vaccination. A further 1 million patients were prioritised for monoclonal antibodies and received eligibility letters sent by NHS Digital in Dec 2021 as these new treatments became available.
https://www.nihr.ac.uk/documents/case-studies/innovative-model-identifies-high-risk-people-for-priority-covid-19-vaccination/29995
In summary the benefits yielded so far include;
Benefits for individuals – personalised risk estimates to improve decision making; prioritisation for vaccination and other measures including workplace adjustments.
Benefits for clinicians – more reliable objective information on risks associated with Covid-19 and the effects of therapeutics to support decision making; automated calculation to supplement decision making (avoids the need for every clinician to assess risk factors individually).
Benefits for researchers- tools to stratify patients for clinical trial entry, which may help make trials run more efficiently and report more quickly,
Benefits for policy makers – better evidence base to inform development of policy (e.g. distribution of vaccination, prioritisation of novel therapeutics; targeted information by UKSHA in relation to obesity), ensuing equity (e.g adjusting risk by ethnicity to avoid widening health inequalities), cost-effectiveness of use of resources and appropriate defendable prioritisation; planning of services (e.g. quantification of mental health outcomes following covid admission).
DARS-NIC-382794-T3L3M-v4.2 1 March 2022 to 30 April 2022
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 7
- Files released
- 22
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v3.1
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2022-03-01 | |
| End date | 2022-04-30 |
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from
COVID-19,
COVID-19 and multiple COVID-19 related
research reports, research papers which are published in peer reviewer academic scientific
[29 words unchanged]
with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers
including CMO.s office, MHRA, JCVI, DHSC, Scottish Office
and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
[3 paragraphs unchanged]
Expected measurable benefits
The aim is to provide useful knowledge that patients, GPs and intensive
[21 words unchanged]
COVID-19 infection within this pandemic assess the uptake safety and effectiveness of
the
COVID-19
vaccine.
treatments such as COVID-19 vaccine, antivirals and monoclonal antibodies.
[5 paragraphs unchanged]
Benefits reported
[1 paragraph unchanged]
Two other papers have been published - one in the BMJ describing the first version of the risk stratification tool and another in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
Other papers have been published including
Three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
- three papers in the BMJ describing the first, second and third version of the risk stratification tool and vaccine safety showing that although there are increased risks of thrombosis with the Oxford vaccine, these are much smaller than the risks associated with the virus;
- two papers published in Nature medicine on Vaccine Safety looking at neurological and cardiological side effects;
- two papers in the Lancet Journals on COVID-19 and respiratory conditions;
- one paper in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
In addition, three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
Unchanged: Objective for processing, Processing activities.
Objective for processing
This agreement specifically relates to QResearch's urgent COVID-19 research projects
to support the current pandemic for example
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the CMO via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by NIHR
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by HDRUK and
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the MRC
e) other urgent COVID research in response to the emerging pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system and covid-19 SGSS and HES data This monthly release of data will be used to support urgent COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active. Given that COVID-19 is still a novel disease which is mutating and that we have new treatments and vaccines being used at scale and at pace and changing levels of immunity, we need detailed data at scale in order identify new risk factor, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care as rapidly as possible. The data under this agreement will only be used for COVID-19 research outlined in this agreement.
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stay on site stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19 and multiple COVID-19 related research reports, research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers including CMO.s office, MHRA, JCVI, DHSC, Scottish Office and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The results tables within the papers will only contain statistical information with cell counts of > 5. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs.
Other papers have been published including
- three papers in the BMJ describing the first, second and third version of the risk stratification tool and vaccine safety showing that although there are increased risks of thrombosis with the Oxford vaccine, these are much smaller than the risks associated with the virus;
- two papers published in Nature medicine on Vaccine Safety looking at neurological and cardiological side effects;
- two papers in the Lancet Journals on COVID-19 and respiratory conditions;
- one paper in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
In addition, three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
DARS-NIC-382794-T3L3M-v3.1 5 March 2021 to 4 March 2022
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 7
- Files released
- 133
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); COVID-19 Vaccination Adverse Reactions; COVID-19 Vaccination Status; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v2.5
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2021-03-05 | |
| End date | 2022-03-04 | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Civil Registrations of Death: type of data | Anonymised - ICO Code Compliant | |
| SUS plus - Admitted Patient Care (beta version): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' |
Datasets: + COVID-19 Vaccination Adverse Reactions; + COVID-19 Vaccination Status
Objective for processing
[7 paragraphs unchanged]
To support this work, NHS Digital will provide a monthly release of the latest available
COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status,
covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system
[97 words unchanged]
immunity, we need detailed data at scale in order identify new risk
facto,
factor,
potential treatments and risks and benefits of the new COVID-19 vaccines. These
[25 words unchanged]
agreement will only be used for COVID-19 research outlined in this agreement.
[4 paragraphs unchanged]
Unchanged: Processing activities, Expected output, Expected measurable benefits, Benefits reported.
Objective for processing
This agreement specifically relates to QResearch's urgent COVID-19 research projects
to support the current pandemic for example
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the CMO via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by NIHR
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by HDRUK and
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the MRC
e) other urgent COVID research in response to the emerging pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available COVID-19 Vaccination Adverse Reactions, COVID-19 Vaccination Status, covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system and covid-19 SGSS and HES data This monthly release of data will be used to support urgent COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active. Given that COVID-19 is still a novel disease which is mutating and that we have new treatments and vaccines being used at scale and at pace and changing levels of immunity, we need detailed data at scale in order identify new risk factor, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care as rapidly as possible. The data under this agreement will only be used for COVID-19 research outlined in this agreement.
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stay on site stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19, research reports, research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The results tables within the papers will only contain statistical information with cell counts of > 5. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs.
Two other papers have been published - one in the BMJ describing the first version of the risk stratification tool and another in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
Three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
DARS-NIC-382794-T3L3M-v2.5 17 December 2020 to 17 December 2021
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 5
- Files released
- 4
Datasets: Civil Registrations of Death; COVID-19 Hospitalization in England Surveillance System; COVID-19 SGSS First Positives (Second Generation Surveillance System); COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2); SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v1.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2020-12-17 | |
| End date | 2021-12-17 | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 – s261(2)(c) |
Datasets: + COVID-19 Hospitalization in England Surveillance System; + COVID-19 SGSS First Positives (Second Generation Surveillance System); + COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2)
Objective for processing
This agreement specifically relates to QResearch's urgent piece of COVID-19 work commissioned by the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) to prepare a COVID-19 risk stratification tool. To support this work, NHS Digital will provide a one-off release of the latest available SUS+ Admitted Patient Care (APC) and mortality data. This one-off release of data will be used to support the urgent risk stratification work, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active.
This agreement specifically relates to QResearch's urgent COVID-19 research projects
to support the current pandemic for example
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the CMO via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by NIHR
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by HDRUK and
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the MRC
e) other urgent COVID research in response to the emerging pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system and covid-19 SGSS and HES data This monthly release of data will be used to support urgent COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active. Given that COVID-19 is still a novel disease which is mutating and that we have new treatments and vaccines being used at scale and at pace and changing levels of immunity, we need detailed data at scale in order identify new risk facto, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care as rapidly as possible. The data under this agreement will only be used for COVID-19 research outlined in this agreement.
[2 paragraphs unchanged]
The patient level data linked to QResearch is only accessed by academics
[12 words unchanged]
this data sharing agreement. In all cases, data can only be accessed
by remote login to data stored
on
site
servers hosted
at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for
publication but not handling the data)
publication)
may be employed by other UK universities. The NHS Digital data stay on site
stored on servers based
at the University of Oxford and are only handled by University of
[22 words unchanged]
clinical aspects or interpretation of findings, but they will not receive any
data.
data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford.
Data will not be used for any solely commercial purposes and all
[10 words unchanged]
are subject to a governance process explained in the Processing Activities section.
Only University of Oxford staff and the named data processors will have access to SUS and Civil Registration - Deaths record level data. External researchers will only have access to tabular outputs that are aggregate with small numbers suppressed in line with the HES Analysis Guide. Record level data are not shared with researchers outside of the University of Oxford.
[1 paragraph unchanged]
Processing activities
[2 paragraphs unchanged]
Before providing data to the University of Oxford, NHS Digital use the Open Pseudonymiser tool to pseudonymise the HES
data.
data at source.
NHS Digital retains the salt key for this pseudonymisation, meaning that the
[34 words unchanged]
will not be provided with a copy of the pseudonymisation salt key.
NHS Digital provide the
pseudonymised
pseudonymised-at-source
data to the University of Oxford which is then linked to the
[27 words unchanged]
data linkage is undertaken by an employee of the University of Oxford.
The GP data is linked to other health care data such as hospital admissions and attendances, mortality, ICNARC, COVID-19 data, cancer data and occupational data from ONS.
No data items which would identify the data subjects are received by
[13 words unchanged]
is rounded to year of birth before receipt by the University of
Oxford. No other data linkage is permitted without further amendment to the data sharing agreement with NHS Digital. There is no requirement to re-identify individuals from the data and no attempts will ever be made to do this.
Oxford .
The resulting data are then used for undertaking primary research relating to COVID-19. The linked data are only accessed by approved research staff with substantive contracts employed by University of Oxford or its data processors. In order to support the urgent COVID-19 risk stratification work, a small number of employees from the University of Cambridge, University College London, London School of Hygiene and Tropical Medicine, and University of Liverpool may act as additional data processors. These organisations' staff will remotely access the data stored by the University of Oxford and will not store any additional copies of the data. These organisations will not have responsibility for determining the purpose for which, or the manner in which data will be processed, and they are only permitted to process data for the purpose of supporting this urgent COVID-19 risk stratification work. Data is only processed on site on secure servers at the University of Oxford. No individual level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement.
There is no requirement to re-identify individuals from the data and no attempts will ever be made to do this.
The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake data linkage or analysis of the data.
The resulting data are then used for undertaking primary research relating to COVID-19. The linked data are only accessed by approved research staff with substantive or honorary contracts employed by University of Oxford or its data processors. In order to support the urgent COVID-19 risk stratification work, a small number of employees from the University of Cambridge, University of Leicester, University College London, Kings College London, Guys and St Thomas's Trust, ICNARC, NHSBT, London School of Hygiene and Tropical Medicine, and University of Liverpool may act as additional data processors. These organisations' staff will remotely access the data stored by the University of Oxford and will not store any additional copies of the data. These staff will undertake data processing tasks data manipulation, cleaning, data analysis to address urgent COVID research questions as determined solely by Oxford. These organisations will not have responsibility for determining the purpose for which, or the manner in which data will be processed, and they are only permitted to process data for the purpose of supportin urgent COVID-19 research. Data is only processed on site on secure servers at the University of Oxford. Data may be processed by individuals not employed by organisations listed as data processors and this will be under an honorary contract with oxford and this covers all legal responsibilities. No individual level data will be shared or stored outside the University of Oxford or supplied to any third party not named in this data sharing agreement.
The data processor Dancing House Consulting undertakes IT consultancy on behalf of the data controller, including administration of data backups, database administration, and secure destruction of data. Dancing House Consulting do not undertake analysis of the data.
[1 paragraph unchanged]
Regular reviews
against the ICO code on anonymisation (2012)
will be undertaken to ensure that
the data remain anonymised and
all appropriate controls are in place to minimise any risk of re-identification.
[1 paragraph unchanged]
Expected output
The outputs
are
include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19, research reports,
research papers which are published in peer reviewer academic scientific journals and
[27 words unchanged]
with press releases from the relevant organisations and highlighted on social media.
[2 paragraphs unchanged]
The results tables within the papers will only contain statistical information with cell counts of >
5, being suppressed in line with the ICO code on anonymisation.
5.
Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
[1 paragraph unchanged]
Expected measurable benefits
The aim is to provide useful knowledge that patients, GPs and intensive care doctors can use to
identify patients at high risk of severe outcomes from COVID-19 and
reduce the risk of severe COVID-19 infection within this
pandemic.
pandemic assess the uptake safety and effectiveness of the COVID-19 vaccine.
Specifically it
In addition
will help research to understand whether drugs commonly taken for chronic conditions
[34 words unchanged]
possible drugs to treat COVID-19; and recognise high-risk patients in primary care.
[4 paragraphs unchanged]
Benefits reported
[1 paragraph unchanged] Two other papers have been published - one in the BMJ describing the first version of the risk stratification tool and another in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome. Three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
Objective for processing
This agreement specifically relates to QResearch's urgent COVID-19 research projects
to support the current pandemic for example
(a) development and maintenance of a COVID-19 risk stratification tool commissioned by the CMO via New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and funded by NIHR
(b) assessment of risk factors and outcomes of patients associated with admission to intensive care funded by the Wellcome Trust
(c) assessment of the safety, uptake and assessment of the COVID-19 vaccinations funded by HDRUK and
(d) research to improve understanding of the associations between ethnicity and risk of poor outcomes from COVID-19 funded by the MRC
e) other urgent COVID research in response to the emerging pandemic which needs to be undertaken rapidly in the national interest.
To support this work, NHS Digital will provide a monthly release of the latest available covid-19 pilar 2; civil registration deaths; covid-19 hospitalisation in England surveillance system and covid-19 SGSS and HES data This monthly release of data will be used to support urgent COVID research, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active. Given that COVID-19 is still a novel disease which is mutating and that we have new treatments and vaccines being used at scale and at pace and changing levels of immunity, we need detailed data at scale in order identify new risk facto, potential treatments and risks and benefits of the new COVID-19 vaccines. These data are only used for research purposes to generate new knowledge to inform policy and clinical care as rapidly as possible. The data under this agreement will only be used for COVID-19 research outlined in this agreement.
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed by remote login to data stored on servers hosted at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication) may be employed by other UK universities. The NHS Digital data stay on site stored on servers based at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data unless they are employed by one of the organisations listed as a data processor or have an honorary contract with the University of Oxford. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Expected output
The outputs include a risk prediction tool (QCovid) to identify those at high risk of severe outcomes from COVID-19, research reports, research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The results tables within the papers will only contain statistical information with cell counts of > 5. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs.
Two other papers have been published - one in the BMJ describing the first version of the risk stratification tool and another in Annals regarding the particularly high risk of poor outcomes for those people with Down's syndrome.
Three reports have been produced already for SAGE including (a) risk of COVID-19 associated with variations in household size; differences in COVID-19 risk between the first and second pandemic waves by ethnic group and (c) the first population-based study of COVID-19 outcomes in children including the differential by ethnic group.
DARS-NIC-382794-T3L3M-v1.2 26 September 2020 to 25 March 2021
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 2
- Files released
- 0
Datasets: Civil Registrations of Death; SUS plus - Admitted Patient Care (beta version)
What changed from DARS-NIC-382794-T3L3M-v0.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2020-09-26 | |
| End date | 2021-03-25 |
Benefits reported
Yielded Benefits is not a requirement for new applications.
The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs.
Unchanged: Objective for processing, Processing activities, Expected output, Expected measurable benefits.
Objective for processing
This agreement specifically relates to QResearch's urgent piece of COVID-19 work commissioned by the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) to prepare a COVID-19 risk stratification tool. To support this work, NHS Digital will provide a one-off release of the latest available SUS+ Admitted Patient Care (APC) and mortality data. This one-off release of data will be used to support the urgent risk stratification work, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active.
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed on site at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication but not handling the data) may be employed by other UK universities. The NHS Digital data stay on site at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
Only University of Oxford staff and the named data processors will have access to SUS and Civil Registration - Deaths record level data. External researchers will only have access to tabular outputs that are aggregate with small numbers suppressed in line with the HES Analysis Guide. Record level data are not shared with researchers outside of the University of Oxford.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Expected output
The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The results tables within the papers will only contain statistical information with cell counts of > 5, being suppressed in line with the ICO code on anonymisation. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
The first paper was published in Heart as a fast track submission and showed that ACE inhibitors were not associated with an increased risk of poor outcomes from COVID (as had been feared) so provided reassurance to public and professionals on the safety aspect of these drugs.
DARS-NIC-382794-T3L3M-v0.2 1 June 2020 to 25 September 2020
- Title
- QResearch - COVID-19 Risk Stratification project
- Commercial
- Yes
- Sublicensing
- No
- Datasets
- 2
- Files released
- 6
Datasets: Civil Registrations of Death; SUS plus - Admitted Patient Care (beta version)
Objective for processing
This agreement specifically relates to QResearch's urgent piece of COVID-19 work commissioned by the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) to prepare a COVID-19 risk stratification tool. To support this work, NHS Digital will provide a one-off release of the latest available SUS+ Admitted Patient Care (APC) and mortality data. This one-off release of data will be used to support the urgent risk stratification work, and not for any additional purpose. In addition to this, the University of Oxford and its data processors are permitted to use the HES and mortality data released under DARS-NIC-240279-Y2V2N and DARS-NIC-375354-G8V1H to support this urgent COVID-19 risk stratification work while those two data sharing agreements remain active.
QResearch is a database of linked medical records that has been used and continues to be used by a variety of research projects undertaken by UK universities, from reviewing the safety of antidepressant medicines to studying factors to predict variations in survival rates for cancer patients. The QResearch database consists of the coded pseudonymised electronic health records from primary care patients registered with approximately 1,500 general practices spread throughout the UK.
QResearch was originally a not for profit collaboration originally between the University of Nottingham and Egton Medical Information Systems (EMIS) but the University of Nottingham’s roles and responsibilities have since been transferred to the University of Oxford. Strategic decisions about the GP data are taken by a Management Board representing the interests of EMIS and the University of Oxford. The University of Oxford is the sole data controller for the datasets which are linked to QResearch (deaths, cancer and hospital data) and the single point of access to the data.
The patient level data linked to QResearch is only accessed by academics employed by University of Oxford or its data processors as named in this data sharing agreement. In all cases, data can only be accessed on site at the University of Oxford. However, the researchers involved in a given project (contributing to the research question, design, interpretation and writing of the paper for publication but not handling the data) may be employed by other UK universities. The NHS Digital data stay on site at the University of Oxford and are only handled by University of Oxford and its data processors. The University of Oxford may have a collaborator at another university on the project team advising on clinical aspects or interpretation of findings, but they will not receive any data. Data will not be used for any solely commercial purposes and all applications for the use of HES and/or mortality linked data are subject to a governance process explained in the Processing Activities section.
Only University of Oxford staff and the named data processors will have access to SUS and Civil Registration - Deaths record level data. External researchers will only have access to tabular outputs that are aggregate with small numbers suppressed in line with the HES Analysis Guide. Record level data are not shared with researchers outside of the University of Oxford.
Research undertaken using the extended database continues to be processed using the existing arrangements with respect to scientific review and annual reports to Trent MREC. Research has to be peer reviewed, original, hypothesis driven or hypothesis testing, intended for publication in an academic peer reviewed journal. All research undertaken using the QResearch database and linked data are subject to independent peer review and the results of all research are published.
Expected output
The outputs are research papers which are published in peer reviewer academic scientific journals and presented at academic conferences. All research is published in academic journals with a link from the QResearch website on an ongoing basis. The publications are accompanied by with press releases from the relevant organisations and highlighted on social media.
Results are also shared with policy makers and NICE guideline committees on a regular basis via their stakeholder consultations in order to support development of relevant guidelines.
Results are also regularly shared with patient participants on the QResearch Advisory Board and PPI representatives on individual research projects.
The results tables within the papers will only contain statistical information with cell counts of > 5, being suppressed in line with the ICO code on anonymisation. Outputs will only contain aggregate level data with small numbers suppressed in line with the HES analysis guide.
No indicators are produced which show performance of an organisation – indeed the identity of the GP practices contributing to QResearch are not shared with any third party.
Benefits reported
Yielded Benefits is not a requirement for new applications.
Register history
When this agreement appeared in, or was edited in, each monthly edition of the register. Built by comparing every edition this site holds, the earliest of which is July 2021.
-
July 2021 —
already listed in the earliest edition this site holds, so it may be older. 4 versions: DARS-NIC-382794-T3L3M-v0.2, DARS-NIC-382794-T3L3M-v1.2, DARS-NIC-382794-T3L3M-v2.5, DARS-NIC-382794-T3L3M-v3.1
-
April 2022
1 version added: DARS-NIC-382794-T3L3M-v4.2
-
June 2022
1 version added: DARS-NIC-382794-T3L3M-v5.4
-
September 2022
1 version added: DARS-NIC-382794-T3L3M-v6.7
-
December 2022
Register-wide edit DARS-NIC-382794-T3L3M-v0.2, DARS-NIC-382794-T3L3M-v1.2, DARS-NIC-382794-T3L3M-v2.5 — Datasets: legal basis: “
s261(1) and” taken out. Made to 639 agreements in this edition, so it is reported once, on the changes page, and not counted as an amendment of this agreement. -
January 2023
Amended DARS-NIC-382794-T3L3M-v2.5
- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
Amended DARS-NIC-382794-T3L3M-v3.1- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
Amended DARS-NIC-382794-T3L3M-v4.2- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
Amended DARS-NIC-382794-T3L3M-v5.4- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
Amended DARS-NIC-382794-T3L3M-v6.7- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
-
October 2023
1 version added: DARS-NIC-382794-T3L3M-v7.4
-
January 2024
1 version added: DARS-NIC-382794-T3L3M-v8.2
-
December 2024
1 version added: DARS-NIC-382794-T3L3M-v9.4
-
February 2025
1 version added: DARS-NIC-382794-T3L3M-v10.6
-
January 2026
1 version added: DARS-NIC-382794-T3L3M-v11.4
-
July 2026
1 version added: DARS-NIC-382794-T3L3M-v12.5
"Amended in place" means NHS England changed the record without issuing a new version number. The register publishes no changelog for those edits; this site infers them by comparing editions. An edit is attributed to the edition it first appears in, not to the date it was made.
Cite this page
NHS England (2026) Data Uses Register, September 2026 edition, agreement DARS-NIC-382794-T3L3M, “QResearch Data Linkage Project”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-382794-t3l3m/ (accessed [date]).
This address stays the same, but the page is rebuilt with each monthly edition, so the citation names the edition it shows. Every edition's data is kept in the facts store.
Source: datausesregister_september2026.xlsx, September 2026 edition of the NHS England Data Uses Register. Search that workbook for DARS-NIC-382794-T3L3M to see the original rows.