ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)
University of Liverpool · Academic
In term In term in the September 2026 edition: the latest version runs to 29 May 2027.
- Reference
- DARS-NIC-402963-P0Y5D
- Current version
- v3.8
- Term of current version
- 30 May 2025 to 29 May 2027
- Start date
- 28 September 2020
- Data controller
- Sole Data Controller
- Commercial purposes
- No
- Sublicensing
- No
- Files released to date
- 1,529
Data controllers
Why the data was released
Objective for processing
The University of Oxford requires access to NHS England data for the purpose of the following project: ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)
The following is a summary of the aims provided by the University of Oxford:
The aim of the ISARIC WHO Clinical Characterisation Protocol is to rapidly provide a deep clinical and phenotypical characterisation of any new infection, any outbreaks of infectious disease & threats of public health importance which may have novel pathogenesis, clinical presentation or non-resolving, serious consequences. ISARIC4C investigators have already enrolled hundreds of thousands of COVID-19 patients and wanted to enrich the dataset including routinely collected health data in England and Scotland for COVID-19 and other infectious diseases or threats of public health importance. The dataset was derived from 360 hospitals comprising of the covid clinical information network (CO-CIN), henceforth the dataset is known as CO-CIN.
The following NHS England Data will be accessed:
Hospital Episode Statistics
o Admitted Patient Care – necessary because it provides numerator and denominator data for the relevant population admitted to hospital.
o Accident and Emergency (A&E) and Emergency Care Data Set (ECDS)- necessary because it provides numerator and denominator data for the relevant population reviewed in A&E and not admitted.
Summary Hospital level mortality indicator – necessary because it provides follow-up on the impact of infectious diseases outbreaks in their initial phase and long-term impact of the episode(s) of infectious diseases on the population mortality
Emergency Care Data Set (ECDS) – necessary because it provides numerator and denominator data for the relevant population and additional details regarding initial assessment treatment and outcome
Mental Health Services Dataset (MHSDS) – necessary because mental health difficulties and cognitive problems are being described in survivors of severe or non-resolving infectious diseases. This dataset will facilitate description of what type of difficulties survivors are facing, and what treatment they are currently receiving, enabling planning of targeted resources and interventions.
Civil Registration Mortality – necessary to be able to report the longer-term all-cause and excess mortality for patients hospitalised with diagnosis of infectious disease or exposure to threat of public health importance.
Improving Access to Psychological Therapies (IAPT) (v1.5) - necessary to evaluate susceptibility and late effects of post infection syndromes.
National Diabetes Audit (NDA) – necessary because CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the wider relationship between diabetes, infectious diseases and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality.
• COVID-19
General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR) – necessary for numerator and denominator data for the relevant population and prescription history and comorbidities.
GPES GDPPR data will be used for COVID-19 purposes only.
Vaccination Status - necessary for numerator and denominator data for the relevant population enabling timely evaluation of real-world efficacy in preventing admissions and reducing duration of stay, levels of care, disability and death.
The level of the Data will be:
• Pseudonymised
The Data will be minimised as follow:
- Limited to a study cohort identified by the University of Oxford (Approximately 275,000)
-Limited to data between 2016 and 2022
The University of Oxford is the research sponsor and 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 the UK 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 UK 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, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
Regulation 3 (1) of the Health Service Control of Patient Information) Regulations 2002 is relied on by the University of Oxford to address Common Law Duty of Confidentiality and process confidential patient information without consent. The University of Oxfords reliance on Regulation 3(1) applies to current and future intended activity described in the strict limits of the protocol, and only where that activity matches the activities listed in Regulation 3 (1).
Communicable disease and other risks to public health
3.—(1) Subject to paragraphs (2) and (3) and regulation 7, confidential patient information may be processed with a view to—
(a)diagnosing communicable diseases and other risks to public health;
(b)recognising trends in such diseases and risks;
(c)controlling and preventing the spread of such diseases and risks;
(d)monitoring and managing—
(i)outbreaks of communicable disease;
(ii)incidents of exposure to communicable disease;
(iii)the delivery, efficacy and safety of immunisation programmes;
(iv)adverse reactions to vaccines and medicines;
(v)risks of infection acquired from food or the environment (including water supplies);
(vi)the giving of information to persons about the diagnosis of communicable disease and risks of acquiring such disease.
So far, ISARIC4C CCP-UK project has provided outcomes in the following areas:
- 3(1) a – diagnosing communicable diseases and other risks to public health:
• Prediction models: CCP-UK clinical characterisation data from hospitalised COVID-19 cases allowed to produce the most accurate risk prediction models for the UK population, published in the British Medical Journal in 2020 (doi: 10.1136/bmj.m1985)
• Disease diagnosis: ISARIC4C researchers found a link between adeno-associated virus 2 and acute hepatitis of unknown cause in children using data from one of the other CCP-UK cohorts (Nature 2023, https://doi.org/10.1038/s41586-023-05948-2)
- 3(1) c – controlling and preventing the spread of such diseases and risks:
• Laboratory assays development: Data from CCP-UK analysed samples helped to optimise laboratory assays for measuring neutralising antibodies’ levels, with the outcomes published in the Journal of Virological Methods in 2022 (https://doi.org/10.1016/j.jviromet.2022.114475)
• Pathogen diagnosis: CCP-UK samples enabled comparative analysis of different RNA sequences of viral variants to determine mutations and their effect on vaccines’ ability to prevent infection or illness. This research supports future vaccine strategies to prevent the rapid spread of SARS-CoV2 (Cell 2022, DOI: 10.1016/j.cell.2022.06.005)
- 3(1) d(ii) – monitoring and managing incidents of exposure to communicable disease
• Disease characterisation: CCP-UK sample data from deceased COVID-19 patients, using a technique called proteomics, identified differentially abundant proteins involved in the propagation of inflammatory cascades in affected tissues. This allowed better understanding of distinct disease stages, their severity, and duration, laying foundation for developing therapeutics to prevent or mitigate inflammation escalation (American Journal of Respiratory Cell and Molecular Biology 2021 https://doi.org/10.1165/rcmb.2021-0358OC).
• CCP UK data assessed disease severity in children, its implications, and identifying the viral variants with the highest risk of causing severe infections. (JAMA Paediatrics 2023, doi:10.1001/jamapediatrics.2023.3117)
• Co-Infections: CCP-UK data compared outcomes for people who tested positive for three viruses (influenza, adenovirus and RSV) finding that co-infection with the flu virus was associated with a higher chance of critical illness and death (SAGE minutes 2022), in anticipation of the flu ‘comeback’ in 2022
- 3(1) d(iv) - monitoring and managing adverse reactions to vaccines and medicines
• Vaccination policy: CCP-UK data from paediatric patients in the ISARIC4C study were used to help inform SAGE’s vaccination policy for children and young people in the context of SARS-CoV2 variant changes – the outcomes were published in the Nature (Paediatric Research, 2022 doi.org/10.1038/s41390-022-02052-5)
The planned COVID-19 analyses will build upon previous work conducted in the following areas:
- 3(1) b recognising trends in such diseases and risks:
• Long-term outcomes: In 2021, CCP-UK data analysis indicated that long-term symptoms are often present in people who had acute COVID-19, with over half of patients not fully recovered several months later, and some even longer (Lancet 2021 - DOI: 10.1016/j.lanepe.2021.100186). These findings highlighted the susceptibility of young, previously healthy, working-age adults to long-term consequences. Planned analyses will compare long-term COVID-19 symptom resolution, stratified by age, using data on the frequency of these individuals' visits to acute and primary care healthcare settings, as well as the reasons (i.e. fatigue, loss of smell, lung tissue scarring) for their attendance.
• CCP-UK data analysed hospital mortality in patients with cancer during the COVID-19 pandemic (2020-2022, Lancet Oncology 2024, doi: 10.1016/S1470-2045(24)00107-4). Future linked data will enrich these investigations for 2023-2027 period.
• Social care impact of infectious diseases. Policy Impact. In 2023, CCP-UK data investigated the severity of fatigue in COVID-19 versus non-COVID-19 critical illness survivors (Journal of Intensive Care Society 2023, DOI: 10.1177/17511437211052226). Planned analyses will broaden these investigations examining consequences of repetitive COVID-19 infections, with or without association with other viral infections like Influenza of RSV (Respiratory Syncytial Virus) or non-viral illnesses. Further analysis will explore whether the pandemic became a trigger for the survivors to suffer from the decline in work productivity and in more demand for social care or outpatient care provision, stratified by age and co-morbidities.
• CCP-UK data were fed to Public Health Scotland, Public Health England, SPI-M, NERVTAG, SAGE and cited in some key UK policy documents (e.g. The Green Book - COVID-19, chapter 14a, Remdesivir – national prescribing guidelines). Future work will aim to inform Care Quality Commission (CQC) and DoH to strategies to improve adult social care and reduce pressure on the NHS acute settings. This may involve raising awareness of the long-term consequences of the COVID-19 pandemic and associated co-infections, with a focus on transferring safety net provision for these patients into community. This could be achieved through expansion of ‘bridging solutions’ provided by the dedicated intermediate inpatient/outpatient rehabilitation centres and a trained workforce.
• Co-infections mitigation. The planned analyses will build on the work done for SAGE in 2022 regarding co-infections, correlating prediction models with outcomes in the period 2023-2027, including implementation of mitigation strategies such as vaccination and therapeutics
- 3(1) c - controlling and preventing the spread of such diseases and risks
• Hospital-acquired COVID-19. In 2022, CCP-UK data identified that 1 in 5 hospitalised patients in England during the first wave of the pandemic contracted COVID-19 while hospitalised for other causes (BMC Infectious Diseases 2022, https://doi.org/10.1186/s12879-022-07490-4). Planned analyses will update this data, taking into account public perceptions on threats posed by the SARS-CoV2, herd immunity, vaccines and therapeutics availability, as well as regional consistency in implementation of treatment guidelines in hospitals.
The above analyses will use both hospital and GP-COVID-19 data extracts.
The future non-COVID-19 analyses from other cohorts may provide more-in-depth investigations in the following areas:
- 3(1) a /b – diagnosing communicable diseases and other risks to public health and recognising trends in such diseases and risks
• Virus evolution prediction and preparedness. Data on hospital admissions for children affected by adeno-associated virus 2 (causing acute hepatitis) will provide better insight into viral evolutionary ecology. Adenoviral infections are common in young children and typically cause mild illnesses. However, there is a plausible risk of overlap between adenovirus niches in the host, which could drive mutations and increase susceptibility of this cohort to other adenoviral infections. Equally, the host susceptibility in this cohort may underlie more severe symptoms from otherwise mild illnesses. New analyses will provide further information about the risks of diagnosis and preventative measures (3(1) d(vi).
• The funding for the Clinical Characterisation of COVID-19 cohort admitted to hospitals in the United Kingdom was provided by NIHR Health Protection Research Unit Emerging and Zoonotic Infection. The clinical characterisation of other infectious diseases or public health threats may be funded by a variety of sources. The funder will have no authority to suppress or otherwise limit the publication of findings.
The funding for the Clinical Characterisation of COVID-19 cohort admitted to hospitals in the United Kingdom was provided by NIHR Health Protection Research Unit Emerging and Zoonotic Infection. The clinical characterisation of other infectious diseases or public health threats may be funded by a variety of sources. The funder will have no authority to suppress or otherwise limit the publication of findings.
The condition of the original funding was that data would be retained in perpetuity for future research use, given its likely historic value, and need for very long-term follow-up studies as yet to be determined. The perpetual data retention has remained in place after the completion of the CO-CIN project for the same scientific rationale.
The ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) was established in 2012 as an open, inclusive UK-wide collaboration of doctors and scientists committed to answering urgent questions about emerging infections and public health threats quickly and transparently. Worldwide, ISARIC Clinical Characterisation Protocol was used in the response to outbreaks of:
Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV) in 2012-2013
influenza A H7N9 in 2013
Ebola virus disease in 2014
MPox &MERS-CoV in 2018
tick-borne encephalitis virus (TBEV) in 2019
Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) in 2020
Lassa fever in 2022
Data received previously under this agreement has only been used for COVID-19 related analysis
As part of UK response to COVID-19, ISARIC4C formed a consortium (COVID-19 Clinical Information Network, CO-CIN) of 10 Higher Education Institutes (HEI’s) with the University of Oxford as the Lead Institution and Data Controller to carry out the Coronavirus Clinical Characterisation Study and collect data in accordance with the approved protocol for incorporation into the ISARIC4C database. The Consortium members were contractually bound under CO-CIN collaboration agreement during COVID-19 pandemic 2020-2023 with assigned roles in supervising patients’ recruitment, samples collection and/or processing and data analyses for COVID-19 and/or other outbreaks.
The core ISARIC4C Consortium Members include University of Oxford, University of Edinburgh and University of Liverpool.
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 University of Liverpool is a processor acting under the instructions of the University of Oxford. The University of Liverpool was a CO-CIN grant holder and is the primary employing institution for the Chief Investigator of the CCP-UK protocol. The University of Liverpool role is focused within its clinical trials unit which play vital role in establishing and supporting the hospital recruitment sites and supplementary research.,
The University of Edinburgh is a processor acting under the instructions of the University of Oxford. The University of Edinburgh (Pandemic Science Hub) primary role focused on the organisation of the research samples collection and processing. It was also involved in pathogen sequencing and linkage of this data to the clinical data. Additional role for the University of Edinburgh is acting as a hosting institution for Edinburgh Parallel Computing Centre (EPCC) which is responsible for providing secure environment where project data is uploaded, stored and accessed.
Public Health Scotland is a processor acting under the instructions of the University of Oxford. Its role is limited to providing research services to ISARIC4C Consortium through their Electronic Data Research and Innovation Service (eDRIS) which sub-contracts EPCC National Safe Haven (NSH) for the storage and access to the linked NHS-England – CCP-UK data.
Other CO-CIN members include
- Imperial College London
- University of Leicester
- University of Glasgow
- University of Cambridge
- University College London
- University of Birmingham
- University of Sheffield
- University of Lancaster
- London School of Hygiene and Tropical Medicine
These organisations are processors acting under the instruction of the University of Oxford. Their role is focused on providing scientific expertise, which includes optimisation of research questions, outlining the analysis framework, and contributing analytical tools to address these questions.
Only the organisations listed in this Data Sharing Agreement (DSA) will have access to the NHS England-linked data, acting as Data Processors under the instructions of the University of Oxford, the Data Controller.
This Agreement permits the processing of NHS England Data within secure storage environment of the Edinburgh Parallel Computing Centre (EPCC), University of Edinburgh under the governing framework of eDRIS, Public Health Scotland (PHS) or equivalent setting.
The Scientific Advisory Group for Emergencies (SAGE) have received findings from the CO-CIN dataset. CO-CIN provided near real-time epidemiological descriptions and analyses of hospitalised COVID-19 patients. These analyses included patient factors such as ethnicity, age, and comorbidities, and their associations with in-hospital mortality. This enabled SAGE to make decisions based on near real-time evidence. SAGE didn’t have access to the NHS England data shared under this Agreement.
Processing activities
The University of Edinburgh will transfer data to NHS England (these data are called the CCP-UK database). The data will consist of identifying details such as NHS Number, Date of Birth, Postcode, and a unique person ID for the cohort with NHS England data.
NHS England will provide the relevant records from the HES/ECDS, Civil Registration Deaths, COVID-19 data sets, SUS, NDA, IAPT, MHSDS datasets to the University of Edinburgh.
The Data will
• contain no direct identifying data items but will contain a unique person ID can be used to link the Data with other record-level data already held by the recipient
These pseudonymised data will be stored on servers at the University of Edinburgh in the NSH or equivalent setting.
The Data will not be transferred to any other location.
The identifiable CCP-UK database sent from University of Edinburgh to NHS England will never be stored in the same location as the pseudonymised NHS England data listed above.
Data will go through an anonymisation process in the NSH (or equivalent) before being made available for analysis by any of the named processors.
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).
Remote processing will be from secure locations within the UK. The data will not leave the UK at any time.
Access is restricted to substantive employees of the University of Oxford, the University of Edinburgh or the University of Liverpool, Imperial College London, University of Leicester, University of Glasgow, University of Cambridge, University College London, University of Birmingham, University of Sheffield, University of Lancaster, London School of Hygiene and Tropical Medicine who have authorisation from the Chief Investigator.
No honorary contracts are permitted under this agreement.
Employees from these organsations will access data via a specific folder that will contain only the data necessary for the analysis they are conducting, all analysis will be within the parameters of the objectives listed in this agreement.
All analyses under this agreement will use the linked anonymised dataset. There will be no requirement and no attempt to reidentify individuals when using the anonymised linked dataset.
The data controller is responsible for ensuring that all processors and sub processors are subject to NHS England audit in line with the requirements of the Data Sharing Framework Contract.
Expected output
• Reports of findings have already been made to SAGE and the UK Government on an ad-hoc basis
• Submissions to peer reviewed journals have already been made. It is expected that more submissions will be made.
The outputs will be communicated to relevant recipients through the following dissemination channels:
• Journals – peer-reviewed publications in Open Access Journals to widely distribute arising knowledge
• Webinars open to clinician, scientist, government and public health teams
• Social media
• Public reports
• Briefing documents provided to SAGE, NERVTAG, SPiM and Public Health Agencies including UKHSA.
• Frameworks for all the ISARIC data collection are based on open source codes.
• Patient Information leaflets available at ISARIC4C Website (https://isaric4c.net ).
• Press/media engagement completed via the science media centre
• Public promotion of the research via social media.
• Reports aimed at policy makers.
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.
Expected measurable benefits
The use of the data could:
• help the system to better understand the health and care needs of populations, with particular attention to sex, age, ethnicity, and deprivation.
• 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 hypertension, obesity, cancer and diabetes.
• inform planning health services and programmes, for example:
- to plan in advance annual vaccination strategies stratified by population groups
- 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 via follow-on QI projects and clinical audits.
• support knowledge creation or further investigations (and the innovations and developments that might result from that work).
• Efficacy of vaccination.
The following benefits are expected:
• Improved access to effective therapies.
• Appropriate prioritisation of care and vaccination.
• Predict population healthcare needs enabling planning of healthcare resources, e.g. need for mechanical ventilators
- better understanding of hospital / primary care to social care referral system pathways and services allocation
It is hoped that through publication of findings in appropriate media, the findings of this project 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.
Clients will need to take action based on the information provided to them in order to realise the potential improvement opportunities. For example…
1. Accurate clinical epidemiology has identified groups most at risk of severe disease and those at a low risk, enabling stratification of care,
2. Healthcare resource planning was made possible by this dataset.
3. Future benefits are likely to include understanding risk factors for very long-term complications.
Follow-on data on respiratory infections including COVID-19, other infectious diseases and co-infections will provide valuable feedback on already implemented measures, innovation progress and outstanding actions.
All reports to SAGE and publications have been put in the public domain. Making full use of pre-printing servers and open access publication. The investigators have been active in the media.
Benefits reported so far
ISARIC4C need to be able to report and respond to the longer-term impact infectious diseases & threats of public health importance outbreaks are having on hospitalised survivors. ISARIC4C have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission (https://isaric4c.net/outputs/characterisation/)
ISARIC4C have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality (https://isaric4c.net/outputs/4c_score/)
ISARIC4C have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). This had direct implications for the NHS, suggesting that ISARIC4C should test patients more often for flu. It also provides another reason to vaccinate people more widely against both flu and Covid, particularly as flu was expected to make a comeback in 2022. (https://isaric4c.net/outputs/coinfection_flu/).
As with much of ISARIC's work, this was discussed immediately at the UK government advisory group, SAGE, and has already influenced the thinking around policy
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
ISARIC4C has produced academic papers published in high impact journals (such as the Lancet) (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
ISARIC4C has supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV. External researchers will not have access to NHS England linked data.
Datasets on the current version
Legal basis for provision: Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Civil Registrations of Death | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR) | Anonymised - ICO Code Compliant | Non-Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| COVID-19 SGSS First Positives (Second Generation Surveillance System) | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2) | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| COVID-19 Vaccination Adverse Reactions | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| COVID-19 Vaccination Status | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Emergency Care Data Set (ECDS) | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| HES-ID to MPS-ID HES Accident and Emergency | Anonymised - ICO Code Compliant | Non-Sensitive | One-Off | Statutory exemption to flow confidential data without consent |
| Hospital Episode Statistics Accident and Emergency (HES A and E) | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Hospital Episode Statistics Admitted Patient Care (HES APC) | Identifiable | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Improving Access to Psychological Therapies (IAPT) v1.5 | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Improving Access to Psychological Therapies (IAPT) v2 | Anonymised - ICO Code Compliant | Non-Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Mental Health Services Data Set (MHSDS) | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| National Diabetes Audit | Anonymised - ICO Code Compliant | Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Secondary Uses Service Payment By Results Accident & Emergency | Anonymised - ICO Code Compliant | Non-Sensitive | One-Off | Statutory exemption to flow confidential data without consent |
| Secondary Uses Service Payment By Results Episodes | Anonymised - ICO Code Compliant | Non-Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
| Secondary Uses Service Payment By Results Outpatients | Anonymised - ICO Code Compliant | Non-Sensitive | Ongoing | Statutory exemption to flow confidential data without consent |
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.
Patient opt-outs were not applied to any of the 1,529 files released under this agreement, across every version. About opt-outs
No files recorded as released under the current version. 1,529 were released under earlier versions, shown in the version history.
Version history
The register lists each renewal of this agreement as a separate row. This site has 4 versions.
DARS-NIC-402963-P0Y5D-v3.8 30 May 2025 to 29 May 2027
- Title
- ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)
- Commercial
- No
- Sublicensing
- No
- Datasets
- 17
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR); 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); HES-ID to MPS-ID HES Accident and Emergency; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Improving Access to Psychological Therapies (IAPT) v1.5; Improving Access to Psychological Therapies (IAPT) v2; Mental Health Services Data Set (MHSDS); National Diabetes Audit; Secondary Uses Service Payment By Results Accident & Emergency; Secondary Uses Service Payment By Results Episodes; Secondary Uses Service Payment By Results Outpatients
What changed from DARS-NIC-402963-P0Y5D-v2.10
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2025-05-30 | |
| End date | 2027-05-29 | |
| COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 Vaccination Adverse Reactions: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 Vaccination Adverse Reactions: sensitivity | Sensitive | |
| COVID-19 Vaccination Status: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 Vaccination Status: sensitivity | Sensitive | |
| Emergency Care Data Set (ECDS): sensitivity | Sensitive | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): sensitivity | Sensitive | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): sensitivity | Sensitive | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): type of data | Identifiable | |
| Mental Health Services Data Set (MHSDS): sensitivity | Sensitive |
Datasets:
+ Improving Access to Psychological Therapies (IAPT) v2 · − COVID-19 Hospitalization in England Surveillance System; − Secondary Uses Service Payment By Results Spells
Objective for processing
The Coronavirus Clinical Information Network (CO-CIN) has collected data for the International Severe Acute Respiratory Infection Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium through a commission from the Chief Medical Officer to conduct Urgent Public Health Research to provide evidence that informs public health policy in response to the COVID-19 emergency.
The University of Oxford requires access to NHS England data for the purpose of the following project: ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)
ISARIC’s purpose is to prevent illness and deaths from infectious disease outbreaks. ISARIC is a global federation of clinical research networks, providing a proficient, coordinated and agile research response to outbreak-prone infectious diseases.
The following is a summary of the aims provided by the University of Oxford:
The ISARIC Coronavirus Clinical Characterisation Consortium is a UK-wide consortium of leading experts in outbreak medicine with a proficient, coordinated, and agile research response to COVID-19.
The aim of the ISARIC WHO Clinical Characterisation Protocol is to rapidly provide a deep clinical and phenotypical characterisation of any new infection, any outbreaks of infectious disease & threats of public health importance which may have novel pathogenesis, clinical presentation or non-resolving, serious consequences. ISARIC4C investigators have already enrolled hundreds of thousands of COVID-19 patients and wanted to enrich the dataset including routinely collected health data in England and Scotland for COVID-19 and other infectious diseases or threats of public health importance. The dataset was derived from 360 hospitals comprising of the covid clinical information network (CO-CIN), henceforth the dataset is known as CO-CIN.
In 2019 a new virus, SARS coronavirus-2 (SARS-Cov-2) emerged. It seems highly likely that SARS-CoV-2 and its associated disease COVID-19 will cause mortality unprecedented in modern times. This is a new disease. There is a high chance that clinical trials will fail to detect therapeutic effects, by enrolling at the wrong time, or missing key subgroups or endpoints. Concurrent biological phenotyping can mitigate these risks, providing rapid, efficient clinical evidence.
The following NHS England Data will be accessed:
CO-CIN response has been planned and tested over the past 8 years within the International Severe Acute Respiratory Infection Consortium (ISARIC).
Hospital Episode Statistics
CO-CIN informs the Department of Health and Social Care (DHSC) on a weekly basis about the clinical evolution of disease in the United Kingdom. To achieve this, clinical research nurses and administrators gather anonymised data from clinical notes and enter it into a simple online database. This allows the characterisation of the patients’ clinical features as well as risk factors associated with severity, risk of hospitalisation and death. The information gathered is essential to help health service planning and provision, and to rapidly evaluate the impact of interventions such as new therapeutics or vaccines.
o Admitted Patient Care – necessary because it provides numerator and denominator data for the relevant population admitted to hospital.
The legal basis for the processing and storage of personal data for COCIN is that it is a task in the public interest Article 6(1)(e) it is in the public interest to conduct public health research to provide evidence to inform public health policy in response to the COVID-19 emergency and to understand and report on the risk factors associated COVID-19 and that sensitive personal data is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes Article 9(2)(j).
o Accident and Emergency (A&E) and Emergency Care Data Set (ECDS)- necessary because it provides numerator and denominator data for the relevant population reviewed in A&E and not admitted.
All data processors as listed in this agreement comply with Regulation 7(2) of COPI.
Summary Hospital level mortality indicator – necessary because it provides follow-up on the impact of infectious diseases outbreaks in their initial phase and long-term impact of the episode(s) of infectious diseases on the population mortality
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February 2020, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS England for linkage.
Emergency Care Data Set (ECDS) – necessary because it provides numerator and denominator data for the relevant population and additional details regarding initial assessment treatment and outcome
Patients are recruited into one of three Tiers. Tier 0 sites are recruited for data collection only without consent, while Tiers 1 and 2 provide consent for sample collection in addition to data collection. The distinction of the study into three Tiers was made to allow for a resource appropriate implementation of the protocol, as it was understood that data and/or sample collection may be limited in some settings.
Mental Health Services Dataset (MHSDS) – necessary because mental health difficulties and cognitive problems are being described in survivors of severe or non-resolving infectious diseases. This dataset will facilitate description of what type of difficulties survivors are facing, and what treatment they are currently receiving, enabling planning of targeted resources and interventions.
For Tier 0 patients clinical data is collected but no additional biological samples are obtained for research purposes. The minimum clinical data set summarises the illness episode and outcome, with the option to collect additional detailed clinical data at frequent intervals, according to local resources/needs.
Civil Registration Mortality – necessary to be able to report the longer-term all-cause and excess mortality for patients hospitalised with diagnosis of infectious disease or exposure to threat of public health importance.
Given the scale of the current COVID-19 pandemic, and because initially data collection for Tier 0 participants was clinical data only from which the participant could not be identified, consent was not sought. The data is collected by a health care professional who has access to the patient's information by virtue of their clinical role. The addition of collection of NHS number, Date of Birth (DOB) and postcode for Tier 0 participants means they are now able to be identified from the dataset in order to support linkage to other NHS data sources and is currently being done under Control of Patient Information Regulations (COPI). The identifiable data is not made available to researchers. Tier 0 is being retrospectively and prospectively completed with identifying data relying on COPI.
Improving Access to Psychological Therapies (IAPT) (v1.5) - necessary to evaluate susceptibility and late effects of post infection syndromes.
The datasets are required primarily to enable CO-CIN to report early and accurate findings to the Scientific Advisory Group for Emergencies (SAGE). Since the early growth phase of COVID-19 in the UK, CO-CIN has presented near real-time epidemiological descriptions and analyses of hospitalised patients with COVID-19. CO-CIN have presented analyses of patient factors including ethnicity, age, comorbidity, and their association with in-hospital mortality, enabling SAGE to make decisions based on near real-time evidence. SAGE will have no access to the NHS England data shared under this Agreement.
National Diabetes Audit (NDA) – necessary because CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the wider relationship between diabetes, infectious diseases and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality.
Each of the datasets, including the GDPPR data (which will provide data on shielded patients), is essential to support the analysis for the use cases as these questions cannot be answered using just the available CO-CIN data. Specific questions about shielding, pre-existing patient co-morbidities, and the outcomes for patients are important to be able to understand the full impact of the disease and interventions. CO-CIN was set up as a pandemic Case Report Form (CRF), and comorbidity categories are broad. There is a need to understand the duration and severity of comorbidities in more granularity. CO-CIN need to be able to report and respond to the impact that multi-morbidity and frailty have on COVID. CO-CIN have detailed data regarding in-hospital sequelae of COVID-19, but require post-discharge follow-up data in. Follow up of contacts with primary and secondary care, and in particular cardio-respiratory and psychiatric sequelae will be imperative to understanding the long-term impact of severe COVID-19.
• COVID-19
CO-CIN need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. CO-CIN have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission. CO-CIN need to be able to report the longer-term all-cause and excess mortality for patients hospitalised with covid-19. The data will help to understand the longer-term mortality for patients admitted to hospital with COVID-19 (long-COVID).
General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR) – necessary for numerator and denominator data for the relevant population and prescription history and comorbidities.
CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality
GPES GDPPR data will be used for COVID-19 purposes only.
CO-CIN have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). CO-CIN have developed a secure, password protected dashboard where SAGE members are able to access aggregated data with small numbers suppressed. This data is accurate to the same day.
Vaccination Status - necessary for numerator and denominator data for the relevant population enabling timely evaluation of real-world efficacy in preventing admissions and reducing duration of stay, levels of care, disability and death.
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
The level of the Data will be:
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals. SPI-M will not have access to NHS England data.
• Pseudonymised
CO-CIN have supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV.
The Data will be minimised as follow:
It should be noted that non-ISARIC researchers, including external collaborators, will not have access to NHS England data. This data will be stored solely within the ISARIC project space within the safe haven and only accessible by approached ISARIC researchers. NHS England data will not be included or made available for access to external researchers via the standard ISARIC4C Data and Materials Access Committee (IDAMAC).
- Limited to a study cohort identified by the University of Oxford (Approximately 275,000)
In a subgroup of 2,500 patients, CO-CIN have linked with detailed biological and follow-up data.
-Limited to data between 2016 and 2022
Use cases for supporting SAGE reporting include the following research questions:
The University of Oxford is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
- What are the outcomes for patients on the shielding list?
The lawful basis for processing personal data under the UK GDPR is:
- How do patient comorbidities/multi-morbidity contribute to in-hospital mortality in patients admitted to hospital with covid-19?
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
- What is the physical and mental health impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
The lawful basis for processing special category data under the UK GDPR is:
- Does access to hospital affect outcomes?
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.
- What are the longer term sequelae for hospitalised survivors of covid-19, including changes in mental health?
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
- What is the longer term mortality in patients admitted to hospital with covid-19?
Regulation 3 (1) of the Health Service Control of Patient Information) Regulations 2002 is relied on by the University of Oxford to address Common Law Duty of Confidentiality and process confidential patient information without consent. The University of Oxfords reliance on Regulation 3(1) applies to current and future intended activity described in the strict limits of the protocol, and only where that activity matches the activities listed in Regulation 3 (1).
- What is the association between diabetes and in-hospital mortality?
Communicable disease and other risks to public health
The following organisations are involved in the study:
3.—(1) Subject to paragraphs (2) and (3) and regulation 7, confidential patient information may be processed with a view to—
DATA CONTROLLER
(a)diagnosing communicable diseases and other risks to public health;
University of Oxford are the lead organisation for the study and host the data collection, they are the data controller.
(b)recognising trends in such diseases and risks;
DATA PROCESSORS
(c)controlling and preventing the spread of such diseases and risks;
University of Edinburgh are hosting the research databases and have the data science team that will be analysing the linkage, they are a data processor.
(d)monitoring and managing—
University of Liverpool support the hospital recruitment sites and supplementary research. They are a data processor.
(i)outbreaks of communicable disease;
Public Health Scotland are listed data processors working as part of the covid research response.
(ii)incidents of exposure to communicable disease;
Only the University of Edinburgh will store the NHS data from England, within its data safe haven. Access to the safe haven is tightly controlled and audited.
(iii)the delivery, efficacy and safety of immunisation programmes;
OTHER ORGANISATIONS
(iv)adverse reactions to vaccines and medicines;
The collaborators to the study will not have access to the NHS England data and are not considered a joint data controller or data processor.
(v)risks of infection acquired from food or the environment (including water supplies);
Imperial University are solely analysing the collected sample data and no NHS England data. They are therefore not listed as a joint data controller or data processors.
(vi)the giving of information to persons about the diagnosis of communicable disease and risks of acquiring such disease.
AMENDMENT (version 1.8)
So far, ISARIC4C CCP-UK project has provided outcomes in the following areas:
COVID-19 Vaccination Status and COVID-19 Vaccine Adverse Reactions datasets have been requested to provide analysis and reporting on a number of questions relating to vaccines, including any changes in effectiveness relating to variants of concern and other related questions. This is at the request of the Chief Medical Officer for England to support research and reporting to SAGE.
- 3(1) a – diagnosing communicable diseases and other risks to public health:
• Prediction models: CCP-UK clinical characterisation data from hospitalised COVID-19 cases allowed to produce the most accurate risk prediction models for the UK population, published in the British Medical Journal in 2020 (doi: 10.1136/bmj.m1985)
• Disease diagnosis: ISARIC4C researchers found a link between adeno-associated virus 2 and acute hepatitis of unknown cause in children using data from one of the other CCP-UK cohorts (Nature 2023, https://doi.org/10.1038/s41586-023-05948-2)
- 3(1) c – controlling and preventing the spread of such diseases and risks:
• Laboratory assays development: Data from CCP-UK analysed samples helped to optimise laboratory assays for measuring neutralising antibodies’ levels, with the outcomes published in the Journal of Virological Methods in 2022 (https://doi.org/10.1016/j.jviromet.2022.114475)
• Pathogen diagnosis: CCP-UK samples enabled comparative analysis of different RNA sequences of viral variants to determine mutations and their effect on vaccines’ ability to prevent infection or illness. This research supports future vaccine strategies to prevent the rapid spread of SARS-CoV2 (Cell 2022, DOI: 10.1016/j.cell.2022.06.005)
- 3(1) d(ii) – monitoring and managing incidents of exposure to communicable disease
• Disease characterisation: CCP-UK sample data from deceased COVID-19 patients, using a technique called proteomics, identified differentially abundant proteins involved in the propagation of inflammatory cascades in affected tissues. This allowed better understanding of distinct disease stages, their severity, and duration, laying foundation for developing therapeutics to prevent or mitigate inflammation escalation (American Journal of Respiratory Cell and Molecular Biology 2021 https://doi.org/10.1165/rcmb.2021-0358OC).
• CCP UK data assessed disease severity in children, its implications, and identifying the viral variants with the highest risk of causing severe infections. (JAMA Paediatrics 2023, doi:10.1001/jamapediatrics.2023.3117)
• Co-Infections: CCP-UK data compared outcomes for people who tested positive for three viruses (influenza, adenovirus and RSV) finding that co-infection with the flu virus was associated with a higher chance of critical illness and death (SAGE minutes 2022), in anticipation of the flu ‘comeback’ in 2022
- 3(1) d(iv) - monitoring and managing adverse reactions to vaccines and medicines
• Vaccination policy: CCP-UK data from paediatric patients in the ISARIC4C study were used to help inform SAGE’s vaccination policy for children and young people in the context of SARS-CoV2 variant changes – the outcomes were published in the Nature (Paediatric Research, 2022 doi.org/10.1038/s41390-022-02052-5)
The planned COVID-19 analyses will build upon previous work conducted in the following areas:
- 3(1) b recognising trends in such diseases and risks:
• Long-term outcomes: In 2021, CCP-UK data analysis indicated that long-term symptoms are often present in people who had acute COVID-19, with over half of patients not fully recovered several months later, and some even longer (Lancet 2021 - DOI: 10.1016/j.lanepe.2021.100186). These findings highlighted the susceptibility of young, previously healthy, working-age adults to long-term consequences. Planned analyses will compare long-term COVID-19 symptom resolution, stratified by age, using data on the frequency of these individuals' visits to acute and primary care healthcare settings, as well as the reasons (i.e. fatigue, loss of smell, lung tissue scarring) for their attendance.
• CCP-UK data analysed hospital mortality in patients with cancer during the COVID-19 pandemic (2020-2022, Lancet Oncology 2024, doi: 10.1016/S1470-2045(24)00107-4). Future linked data will enrich these investigations for 2023-2027 period.
• Social care impact of infectious diseases. Policy Impact. In 2023, CCP-UK data investigated the severity of fatigue in COVID-19 versus non-COVID-19 critical illness survivors (Journal of Intensive Care Society 2023, DOI: 10.1177/17511437211052226). Planned analyses will broaden these investigations examining consequences of repetitive COVID-19 infections, with or without association with other viral infections like Influenza of RSV (Respiratory Syncytial Virus) or non-viral illnesses. Further analysis will explore whether the pandemic became a trigger for the survivors to suffer from the decline in work productivity and in more demand for social care or outpatient care provision, stratified by age and co-morbidities.
• CCP-UK data were fed to Public Health Scotland, Public Health England, SPI-M, NERVTAG, SAGE and cited in some key UK policy documents (e.g. The Green Book - COVID-19, chapter 14a, Remdesivir – national prescribing guidelines). Future work will aim to inform Care Quality Commission (CQC) and DoH to strategies to improve adult social care and reduce pressure on the NHS acute settings. This may involve raising awareness of the long-term consequences of the COVID-19 pandemic and associated co-infections, with a focus on transferring safety net provision for these patients into community. This could be achieved through expansion of ‘bridging solutions’ provided by the dedicated intermediate inpatient/outpatient rehabilitation centres and a trained workforce.
• Co-infections mitigation. The planned analyses will build on the work done for SAGE in 2022 regarding co-infections, correlating prediction models with outcomes in the period 2023-2027, including implementation of mitigation strategies such as vaccination and therapeutics
- 3(1) c - controlling and preventing the spread of such diseases and risks
• Hospital-acquired COVID-19. In 2022, CCP-UK data identified that 1 in 5 hospitalised patients in England during the first wave of the pandemic contracted COVID-19 while hospitalised for other causes (BMC Infectious Diseases 2022, https://doi.org/10.1186/s12879-022-07490-4). Planned analyses will update this data, taking into account public perceptions on threats posed by the SARS-CoV2, herd immunity, vaccines and therapeutics availability, as well as regional consistency in implementation of treatment guidelines in hospitals.
The above analyses will use both hospital and GP-COVID-19 data extracts.
The future non-COVID-19 analyses from other cohorts may provide more-in-depth investigations in the following areas:
- 3(1) a /b – diagnosing communicable diseases and other risks to public health and recognising trends in such diseases and risks
• Virus evolution prediction and preparedness. Data on hospital admissions for children affected by adeno-associated virus 2 (causing acute hepatitis) will provide better insight into viral evolutionary ecology. Adenoviral infections are common in young children and typically cause mild illnesses. However, there is a plausible risk of overlap between adenovirus niches in the host, which could drive mutations and increase susceptibility of this cohort to other adenoviral infections. Equally, the host susceptibility in this cohort may underlie more severe symptoms from otherwise mild illnesses. New analyses will provide further information about the risks of diagnosis and preventative measures (3(1) d(vi).
• The funding for the Clinical Characterisation of COVID-19 cohort admitted to hospitals in the United Kingdom was provided by NIHR Health Protection Research Unit Emerging and Zoonotic Infection. The clinical characterisation of other infectious diseases or public health threats may be funded by a variety of sources. The funder will have no authority to suppress or otherwise limit the publication of findings.
The funding for the Clinical Characterisation of COVID-19 cohort admitted to hospitals in the United Kingdom was provided by NIHR Health Protection Research Unit Emerging and Zoonotic Infection. The clinical characterisation of other infectious diseases or public health threats may be funded by a variety of sources. The funder will have no authority to suppress or otherwise limit the publication of findings.
The condition of the original funding was that data would be retained in perpetuity for future research use, given its likely historic value, and need for very long-term follow-up studies as yet to be determined. The perpetual data retention has remained in place after the completion of the CO-CIN project for the same scientific rationale.
The ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) was established in 2012 as an open, inclusive UK-wide collaboration of doctors and scientists committed to answering urgent questions about emerging infections and public health threats quickly and transparently. Worldwide, ISARIC Clinical Characterisation Protocol was used in the response to outbreaks of:
Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV) in 2012-2013
influenza A H7N9 in 2013
Ebola virus disease in 2014
MPox &MERS-CoV in 2018
tick-borne encephalitis virus (TBEV) in 2019
Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) in 2020
Lassa fever in 2022
Data received previously under this agreement has only been used for COVID-19 related analysis
As part of UK response to COVID-19, ISARIC4C formed a consortium (COVID-19 Clinical Information Network, CO-CIN) of 10 Higher Education Institutes (HEI’s) with the University of Oxford as the Lead Institution and Data Controller to carry out the Coronavirus Clinical Characterisation Study and collect data in accordance with the approved protocol for incorporation into the ISARIC4C database. The Consortium members were contractually bound under CO-CIN collaboration agreement during COVID-19 pandemic 2020-2023 with assigned roles in supervising patients’ recruitment, samples collection and/or processing and data analyses for COVID-19 and/or other outbreaks.
The core ISARIC4C Consortium Members include University of Oxford, University of Edinburgh and University of Liverpool.
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 University of Liverpool is a processor acting under the instructions of the University of Oxford. The University of Liverpool was a CO-CIN grant holder and is the primary employing institution for the Chief Investigator of the CCP-UK protocol. The University of Liverpool role is focused within its clinical trials unit which play vital role in establishing and supporting the hospital recruitment sites and supplementary research.,
The University of Edinburgh is a processor acting under the instructions of the University of Oxford. The University of Edinburgh (Pandemic Science Hub) primary role focused on the organisation of the research samples collection and processing. It was also involved in pathogen sequencing and linkage of this data to the clinical data. Additional role for the University of Edinburgh is acting as a hosting institution for Edinburgh Parallel Computing Centre (EPCC) which is responsible for providing secure environment where project data is uploaded, stored and accessed.
Public Health Scotland is a processor acting under the instructions of the University of Oxford. Its role is limited to providing research services to ISARIC4C Consortium through their Electronic Data Research and Innovation Service (eDRIS) which sub-contracts EPCC National Safe Haven (NSH) for the storage and access to the linked NHS-England – CCP-UK data.
Other CO-CIN members include
- Imperial College London
- University of Leicester
- University of Glasgow
- University of Cambridge
- University College London
- University of Birmingham
- University of Sheffield
- University of Lancaster
- London School of Hygiene and Tropical Medicine
These organisations are processors acting under the instruction of the University of Oxford. Their role is focused on providing scientific expertise, which includes optimisation of research questions, outlining the analysis framework, and contributing analytical tools to address these questions.
Only the organisations listed in this Data Sharing Agreement (DSA) will have access to the NHS England-linked data, acting as Data Processors under the instructions of the University of Oxford, the Data Controller.
This Agreement permits the processing of NHS England Data within secure storage environment of the Edinburgh Parallel Computing Centre (EPCC), University of Edinburgh under the governing framework of eDRIS, Public Health Scotland (PHS) or equivalent setting.
The Scientific Advisory Group for Emergencies (SAGE) have received findings from the CO-CIN dataset. CO-CIN provided near real-time epidemiological descriptions and analyses of hospitalised COVID-19 patients. These analyses included patient factors such as ethnicity, age, and comorbidities, and their associations with in-hospital mortality. This enabled SAGE to make decisions based on near real-time evidence. SAGE didn’t have access to the NHS England data shared under this Agreement.
Processing activities
All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)”
The University of Edinburgh will transfer data to NHS England (these data are called the CCP-UK database). The data will consist of identifying details such as NHS Number, Date of Birth, Postcode, and a unique person ID for the cohort with NHS England data.
Data will only be used for the purposes within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS England.
NHS England will provide the relevant records from the HES/ECDS, Civil Registration Deaths, COVID-19 data sets, SUS, NDA, IAPT, MHSDS datasets to the University of Edinburgh.
NHS England will be provided with a mapping file containing a list of NHS numbers of patients in England within the CO-CIN cohort, as well as the patient date of birth, postcode and the matched CO-CIN subject ID., this will flow from University of Edinburgh.
The Data will
NHS England will then create an extract , removing any identifiers from the datasets except for the subject ID. The linked pseudonymised data will then be sent securely to University of Edinburgh where it will be stored in the National Data Safe Haven (DSH) managed by the Edinburgh Parallel Computing Centre (EPCC) within an ISO27001 accredited data centre and processes. The EPCC manages its own data centre and services for the DSH.
• contain no direct identifying data items but will contain a unique person ID can be used to link the Data with other record-level data already held by the recipient
There is a flow of identifiable data into NHS England and the flow out of NHS England will be pseudonymised, with the identifying data removed and replaced with a subject study id.
These pseudonymised data will be stored on servers at the University of Edinburgh in the NSH or equivalent setting.
University of Oxford hold the CO-CIN database (the clinical data that is collected from the hospitals). The University of Edinburgh will extract the cohort from the CO-CIN database and submit to NHS England, NHS England then create the pseudonymised extracts and make available for University of Edinburgh to download to secure server where the files will be processed into the Data Safe Haven ISARIC data store. The CO-CIN data and the NHS England data are both held in a study specific folder within the National Safe Haven governed by PHS.
The Data will not be transferred to any other location.
Identifiable data
The identifiable CCP-UK database sent
from
CO-CIN and
University of Edinburgh to
NHS England will never be stored
within
in
the same location
and
as
the
linkage will be managed solely by
pseudonymised
NHS
England.
England data listed above.
NHS England data is not included in the resource overseen by IDAMAC. The data is not available to non-ISARIC researchers. It is stored solely within the ISARIC project space within the safe haven.
Data will go through an anonymisation process in the NSH (or equivalent) before being made available for analysis by any of the named processors.
PHS are named as a data processor due to their role supporting CO-CIN data flows and as the managers of the Serv-U gateway, which is used to transfer files in and out of the National Safe Haven. PHS eDRIS have access to all data once in the National Safe Haven, including pseudonymised and NHS England linked data files. The National Safe Haven is governed by PHS and operated by EPCC on behalf of PHS where EPCC is a data processor subcontracted by PHS.
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.
Access to the data within the DSH is strictly controlled, and the linked, de-identified data will only be available to named data scientists within the University of Edinburgh and, where necessary, system administrators, all of whom are substantive employees of University of Edinburgh. All access will be logged. Access to the de-identified dataset will be controlled by the CO-CIN data manager and, where data is made available for research principles of anonymisation and minimisation will be applied in line with the HES analysis guide and ICO best practice.
For remote access:
Data cannot be extracted from the DSH without going through a managed process, including statistical disclosure control checks, to ensure that only anonymised data will be extracted for the purposes of reporting to SAGE and the Department of Health and Social Care (DHSC), or covid-19 research. No direct identifiers will be used in these analyses.
- 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 stored in a pseudonymised form. Only anonymised data with small numbers suppressed will be used for reporting. The data will not be onwardly shared and accessed by other external partners without approval from NHS England and appropriate data sharing agreements being established.
- Access controls granting users the minimum level of access required are in place;
AUDIT:
- Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;
All processing and use of data provided is auditable by NHS England in accordance with the Data Sharing Framework Contract and NHS England terms.
- Multifactor authentication (MFA) is required for remote access;
Under the Local Audit and Accountability Act 2014, section 35, the Secretary of State has power to audit all data that has flowed, including under COPI.
- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
The DSH and EPCC processes ensure that all data flows and activity will be recorded and auditable in line with ISO27001.
- 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.
Data Minimisation:
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).
CO-CIN are conscious that, in accordance with GDPR, only data required to answer the research questions should be requested, and as such, CO-CIN have only selected the variables that are relevant. CO-CIN have not requested identifiable data including patient address, forename and surname. CO-CIN have not requested GPES data regarding declines, contraindications and other exceptions; or review and monitoring codes. The main priority of the work is to answer as yet unasked questions directed by SAGE, and as such it is difficult to be absolutely sure which variables may or may not be required to answer these questions.
Remote processing will be from secure locations within the UK. The data will not leave the UK at any time.
There will be no data linkage undertaken with NHS England data provided under this agreement that is not already noted in the agreement.
Access is restricted to substantive employees of the University of Oxford, the University of Edinburgh or the University of Liverpool, Imperial College London, University of Leicester, University of Glasgow, University of Cambridge, University College London, University of Birmingham, University of Sheffield, University of Lancaster, London School of Hygiene and Tropical Medicine who have authorisation from the Chief Investigator.
AMENDMENT (version 1.8)
No honorary contracts are permitted under this agreement.
The methodology applied above will be used for the amendment to add Vaccination data.
Employees from these organsations will access data via a specific folder that will contain only the data necessary for the analysis they are conducting, all analysis will be within the parameters of the objectives listed in this agreement.
All analyses under this agreement will use the linked anonymised dataset. There will be no requirement and no attempt to reidentify individuals when using the anonymised linked dataset.
The data controller is responsible for ensuring that all processors and sub processors are subject to NHS England audit in line with the requirements of the Data Sharing Framework Contract.
Expected output
The primary outputs will include regular and ad-hoc reports to SAGE and the UK Government with only aggregated data and small number suppression.
• Reports of findings have already been made to SAGE and the UK Government on an ad-hoc basis
These will be done on a regular basis for the duration of the project (October 2020 to November 2021), and the capacity to provide this output at short notice retained for future outbreaks. The study is a ‘Prepared Urgent Public Health Research Study’ which is kept read for future outbreaks.
• Submissions to peer reviewed journals have already been made. It is expected that more submissions will be made.
The project will also produce submissions to peer reviewed journals, again with only aggregated data and small number suppression. It is expected that at least one submission will be made prior to the end of the project.
The outputs will be communicated to relevant recipients through the following dissemination channels:
Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS England requirements).
• Journals – peer-reviewed publications in Open Access Journals to widely distribute arising knowledge
Within 1 week of CO-CIN receiving the data from NHS England, it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:
• Webinars open to clinician, scientist, government and public health teams
- Support the NHS and government response to COVID-19
• Social media
- Analyse the spread of patients hospitalised with covid-19 geographically and demographically, to identify any trends. Appointment activity will also be analysed to better understand use of non-face to face consultation trends and potential differences across geographical areas.
• Public reports
- Analyse potential hospital-acquired covid-19 geographically and demographically
• Briefing documents provided to SAGE, NERVTAG, SPiM and Public Health Agencies including UKHSA.
- Diagnosing and monitoring the effects of COVID-19 at a national level.
• Frameworks for all the ISARIC data collection are based on open source codes.
- Ensuring the Department of Health and Social Care and NHS England has adequate data to inform that interventions and measures put in place to reduce the transmission of COVID-19 are being effective and impactful.
• Patient Information leaflets available at ISARIC4C Website (https://isaric4c.net ).
- Analyse factors that result in increased service utilisation for COVID-19 patients.
• Press/media engagement completed via the science media centre
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of care for both physical and mental health. Learning from and predicting likely patient pathways in order to influence early interventions and other alternatives for patients and develop new predictive modelling and tools for use by care professionals and commissioners.
• Public promotion of the research via social media.
The ISARIC website, https://isaric4c.net has recently been updated, and is under continuous review with regards to ensuring that it is up to date and relevant to inform the public of the programme. The ISARIC programme is also working with HDRUK to ensure that the use of data is made available to the general public as part of the overall national strategy and work with them to ensure that feedback from the public is heard. Senior researchers have also been active in promoting the study on social media and mainstream news outlets, https://isaric4c.net/privacy/
• Reports aimed at policy makers.
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.
Expected measurable benefits
Expected benefits will include:
The use of the data could:
~ Reduce deaths associated with COVID-19
• help the system to better understand the health and care needs of populations, with particular attention to sex, age, ethnicity, and deprivation.
~ Assist commissioners in making decisions to better support patients
• lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
~ Identifying COVID-19 trends and risks to public health
• advance understanding of regional and national trends in health and social care needs.
~ Enables Department of Health and NHS England to provide guidance and develop policies to respond to the outbreak
• advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as hypertension, obesity, cancer and diabetes.
~ Controlling and helping to prevent the spread of the virus
• inform planning health services and programmes, for example:
~ The Department of Health and NHS England can share a common understanding of activity levels across the system in regard to COVID-19. Better activity data will also enable a more robust national planning process and improve the allocation of resources across the system.
- to plan in advance annual vaccination strategies stratified by population groups
This is hoped will support the response to the pandemic but also the recovery of services.
- to improve equity of access, experience and outcomes.
Expected benefits of the new data request (version 1.8):
• inform decisions on how to effectively allocate and evaluate funding according to health needs.
These data will be linked with the prospective ISARIC-4C CCP-UK study, which has recruited over 250,000 patients admitted to hospital with covid-19 in the UK, representing over 40% of all patients admitted with covid-19. This is the largest prospective cohort of covid-19 in the world.
• 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 via follow-on QI projects and clinical audits.
These data linkages will ensure maximum benefit from these data, and ensure that the analyses can continue to inform policy and improve care for individuals with covid-19 at a national and international level.
• support knowledge creation or further investigations (and the innovations and developments that might result from that work).
The linked data will provide the following specific benefits:
• Efficacy of vaccination.
1. Reduction of missing data, particularly for ethnicity, deprivation, duration of hospital stay and in-hospital mortality. This is of particular relevance to the first wave where research teams were overwhelmed, and unable to complete the case report forms. It will also allow for quality checks of the prospective data, including where we know data have been entered inaccurately.
The following benefits are expected:
2. More granular description of pre-existing health conditions (comorbidity), and the impact and severity of these before and after covid-19.
• Improved access to effective therapies.
3. Confirmation of vaccine status, vaccine type and date of first/second/third doses for patients along with variant of concern data will enable us to look at outcomes in the context of changing vaccination status and changing variants. This is key to interpretation of hospital events and interventions.
• Appropriate prioritisation of care and vaccination.
4. Post-hospital follow-up for all those who survived to hospital discharge. This longitudinal cohort will allow us to examine longer-term effects of hospitalisation with covid-19 in both primary and secondary care.
• Predict population healthcare needs enabling planning of healthcare resources, e.g. need for mechanical ventilators
5. Mental health difficulties and cognitive problems are being described in covid-19 survivors. These data will facilitate description of what type of difficulties survivors are facing, and what treatment they are currently receiving. This will enable planning of targeted resources and interventions.
- better understanding of hospital / primary care to social care referral system pathways and services allocation
It is hoped that through publication of findings in appropriate media, the findings of this project 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.
Clients will need to take action based on the information provided to them in order to realise the potential improvement opportunities. For example…
1. Accurate clinical epidemiology has identified groups most at risk of severe disease and those at a low risk, enabling stratification of care,
2. Healthcare resource planning was made possible by this dataset.
3. Future benefits are likely to include understanding risk factors for very long-term complications.
Follow-on data on respiratory infections including COVID-19, other infectious diseases and co-infections will provide valuable feedback on already implemented measures, innovation progress and outstanding actions.
All reports to SAGE and publications have been put in the public domain. Making full use of pre-printing servers and open access publication. The investigators have been active in the media.
Benefits reported
ISARIC4C need to be able to report and respond to the longer-term impact
COVID-19 is
infectious diseases & threats of public health importance outbreaks are
having on hospitalised survivors. ISARIC4C have outcomes for patients at hospital discharge
[7 words unchanged]
the majority of patients this is <28 days from hospital admission (https://isaric4c.net/outputs/characterisation/)
[1 paragraph unchanged]
ISARIC4C have presented data on hospital acquired infection, level of treatment (oxygen,
[35 words unchanged]
vaccinate people more widely against both flu and Covid, particularly as flu
is
was
expected to make a comeback in 2022.
(https://isaric4c.net/outputs/coinfection_flu/)
(https://isaric4c.net/outputs/coinfection_flu/).
[2 paragraphs unchanged]
ISARIC4C has produced academic papers published in high impact journals
(such as the Lancet)
(early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
[1 paragraph unchanged]
DARS-NIC-402963-P0Y5D-v2.10 10 May 2024 to 9 May 2027
- Title
- ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)
- Commercial
- No
- Sublicensing
- No
- Datasets
- 18
- Files released
- 0
Datasets: Civil Registrations of Death; COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR); 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); HES-ID to MPS-ID HES Accident and Emergency; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Improving Access to Psychological Therapies (IAPT) v1.5; Mental Health Services Data Set (MHSDS); National Diabetes Audit; Secondary Uses Service Payment By Results Accident & Emergency; Secondary Uses Service Payment By Results Episodes; Secondary Uses Service Payment By Results Outpatients; Secondary Uses Service Payment By Results Spells
What changed from DARS-NIC-402963-P0Y5D-v1.8
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Title | ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK) | |
| Start date | 2024-05-10 | |
| End date | 2027-05-09 | |
| COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): sensitivity | Sensitive | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): sensitivity | Sensitive | |
| COVID-19 Vaccination Adverse Reactions: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| COVID-19 Vaccination Status: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Para 8.3.2 of the Covid-19 Directions; Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Emergency Care Data Set (ECDS): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| HES-ID to MPS-ID HES Accident and Emergency: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Improving Access to Psychological Therapies Data Set_v1.5: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Mental Health Services Data Set (MHSDS): legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| National Diabetes Audit: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Secondary Uses Service Payment By Results Accident & Emergency: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Secondary Uses Service Payment By Results Episodes: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Secondary Uses Service Payment By Results Outpatients: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 | |
| Secondary Uses Service Payment By Results Spells: legal basis | Health and Social Care Act 2012 – s261(2)(a); Other-Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002 |
Datasets:
− NHS 111 Online Dataset
Objective for processing
[8 paragraphs unchanged]
The research is conducted with relevant Health Research Authority ethical approvals throughout
[48 words unchanged]
in the UK. Only data from England will be sent to NHS
Digital
England
for linkage.
[3 paragraphs unchanged]
The datasets are required primarily to enable CO-CIN to report early and
[57 words unchanged]
on near real-time evidence. SAGE will have no access to the NHS
Digital
England
data shared under this Agreement.
[5 paragraphs unchanged]
CO-CIN have produced academic papers published in high impact journals (early general
[10 words unchanged]
based practice in UK hospitals. SPI-M will not have access to NHS
Digital
England
data.
[1 paragraph unchanged]
It should be noted that non-ISARIC researchers, including external collaborators, will not have access to NHS
Digital
England
data. This data will be stored solely within the ISARIC project space within the safe haven and only accessible by approached ISARIC researchers. NHS
Digital
England
data will not be included or made available for access to external researchers via the standard ISARIC4C Data and Materials Access Committee (IDAMAC).
[18 paragraphs unchanged]
The collaborators to the study will not have access to the NHS
Digital
England
data and are not considered a joint data controller or data processor.
Imperial University are solely analysing the collected sample data and no NHS
Digital
England
data. They are therefore not listed as a joint data controller or data processors.
AMENDMENT
REQUEST
(version 1.8)
[1 paragraph unchanged]
Processing activities
[1 paragraph unchanged]
Data will only be used for the purposes within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS
Digital.
England.
NHS
Digital
England
will be provided with a mapping file containing a list of NHS
[19 words unchanged]
the matched CO-CIN subject ID., this will flow from University of Edinburgh.
NHS
digital
England
will then create an extract , removing any identifiers from the datasets
[46 words unchanged]
The EPCC manages its own data centre and services for the DSH.
There is a flow of identifiable data into NHS
Digital
England
and the flow out of NHS
Digital
England
will be pseudonymised, with the identifying data removed and replaced with a subject study id.
University of Oxford hold the CO-CIN database (the clinical data that is
[8 words unchanged]
will extract the cohort from the CO-CIN database and submit to NHS
Digital,
England,
NHS
Digital
England
then create the pseudonymised extracts and make available for University of Edinburgh
[7 words unchanged]
files will be processed into the Data Safe Haven ISARIC data store.
University of Edinburgh hold all the pseudonymised
The
CO-CIN data and
will also hold
the
linked
NHS
D and CO-CIN
England
data
(England only) separately.
are both held in a study specific folder within the National Safe Haven governed by PHS.
Identifiable data from CO-CIN and NHS
Digital
England
will never be stored within the same location and the linkage will be managed solely by NHS
Digital.
England.
NHS
Digital
England
data is not included in the resource overseen by IDAMAC. The data
[7 words unchanged]
is stored solely within the ISARIC project space within the safe haven.
PHS are named as a data processor due to their role supporting CO-CIN data flows and as the managers of the Serv-U
gateway. They do not
gateway, which is used to transfer files in and out of the National Safe Haven. PHS eDRIS
have access to
all data once in
the
National Safe Haven, including pseudonymised and NHS England linked
data
but do have responsibility for the security
files. The National Safe Haven is governed by PHS
and
processes around accessing the data. They provide services to extract CO-CIN
operated by EPCC on behalf of PHS where EPCC is a
data
but do not have any access to the DSH or any ability to access NHS Digital data.
processor subcontracted by PHS.
[2 paragraphs unchanged]
The data will be stored in a pseudonymised form. Only anonymised data
[14 words unchanged]
onwardly shared and accessed by other external partners without approval from NHS
Digital
England
and appropriate data sharing agreements being established.
[1 paragraph unchanged]
All processing and use of data provided is auditable by NHS
Digital
England
in accordance with the Data Sharing Framework Contract and NHS
Digital
England
terms.
[4 paragraphs unchanged]
There will be no data linkage undertaken with NHS
Digital
England
data provided under this agreement that is not already noted in the agreement.
AMENDMENT
REQUEST
(version 1.8)
[1 paragraph unchanged]
Expected output
[3 paragraphs unchanged]
Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS
Digital
England
requirements).
Within 1 week of CO-CIN receiving the data from NHS
Digital,
England,
it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:
[8 paragraphs unchanged]
Expected measurable benefits
[8 paragraphs unchanged]
Expected benefits of the new data
request:
request (version 1.8):
[8 paragraphs unchanged]
Benefits reported
The study has already reported to SAGE on early risk factors for mortality in hospital, including obesity and ethnicity; changes in mortality over the first wave; complications in hospital; new variants of concern in the hospitalised population; hospitalised children with covid-19; and vaccination status in hospitalised patients and ongoing high risk of immunocompromised patients.
ISARIC4C need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. ISARIC4C have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission (https://isaric4c.net/outputs/characterisation/)
ISARIC4C have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality (https://isaric4c.net/outputs/4c_score/)
ISARIC4C have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). This had direct implications for the NHS, suggesting that ISARIC4C should test patients more often for flu. It also provides another reason to vaccinate people more widely against both flu and Covid, particularly as flu is expected to make a comeback in 2022. (https://isaric4c.net/outputs/coinfection_flu/)
As with much of ISARIC's work, this was discussed immediately at the UK government advisory group, SAGE, and has already influenced the thinking around policy
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
ISARIC4C has produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
ISARIC4C has supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV. External researchers will not have access to NHS England linked data.
Objective for processing
The Coronavirus Clinical Information Network (CO-CIN) has collected data for the International Severe Acute Respiratory Infection Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium through a commission from the Chief Medical Officer to conduct Urgent Public Health Research to provide evidence that informs public health policy in response to the COVID-19 emergency.
ISARIC’s purpose is to prevent illness and deaths from infectious disease outbreaks. ISARIC is a global federation of clinical research networks, providing a proficient, coordinated and agile research response to outbreak-prone infectious diseases.
The ISARIC Coronavirus Clinical Characterisation Consortium is a UK-wide consortium of leading experts in outbreak medicine with a proficient, coordinated, and agile research response to COVID-19.
In 2019 a new virus, SARS coronavirus-2 (SARS-Cov-2) emerged. It seems highly likely that SARS-CoV-2 and its associated disease COVID-19 will cause mortality unprecedented in modern times. This is a new disease. There is a high chance that clinical trials will fail to detect therapeutic effects, by enrolling at the wrong time, or missing key subgroups or endpoints. Concurrent biological phenotyping can mitigate these risks, providing rapid, efficient clinical evidence.
CO-CIN response has been planned and tested over the past 8 years within the International Severe Acute Respiratory Infection Consortium (ISARIC).
CO-CIN informs the Department of Health and Social Care (DHSC) on a weekly basis about the clinical evolution of disease in the United Kingdom. To achieve this, clinical research nurses and administrators gather anonymised data from clinical notes and enter it into a simple online database. This allows the characterisation of the patients’ clinical features as well as risk factors associated with severity, risk of hospitalisation and death. The information gathered is essential to help health service planning and provision, and to rapidly evaluate the impact of interventions such as new therapeutics or vaccines.
The legal basis for the processing and storage of personal data for COCIN is that it is a task in the public interest Article 6(1)(e) it is in the public interest to conduct public health research to provide evidence to inform public health policy in response to the COVID-19 emergency and to understand and report on the risk factors associated COVID-19 and that sensitive personal data is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes Article 9(2)(j).
All data processors as listed in this agreement comply with Regulation 7(2) of COPI.
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February 2020, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS England for linkage.
Patients are recruited into one of three Tiers. Tier 0 sites are recruited for data collection only without consent, while Tiers 1 and 2 provide consent for sample collection in addition to data collection. The distinction of the study into three Tiers was made to allow for a resource appropriate implementation of the protocol, as it was understood that data and/or sample collection may be limited in some settings.
For Tier 0 patients clinical data is collected but no additional biological samples are obtained for research purposes. The minimum clinical data set summarises the illness episode and outcome, with the option to collect additional detailed clinical data at frequent intervals, according to local resources/needs.
Given the scale of the current COVID-19 pandemic, and because initially data collection for Tier 0 participants was clinical data only from which the participant could not be identified, consent was not sought. The data is collected by a health care professional who has access to the patient's information by virtue of their clinical role. The addition of collection of NHS number, Date of Birth (DOB) and postcode for Tier 0 participants means they are now able to be identified from the dataset in order to support linkage to other NHS data sources and is currently being done under Control of Patient Information Regulations (COPI). The identifiable data is not made available to researchers. Tier 0 is being retrospectively and prospectively completed with identifying data relying on COPI.
The datasets are required primarily to enable CO-CIN to report early and accurate findings to the Scientific Advisory Group for Emergencies (SAGE). Since the early growth phase of COVID-19 in the UK, CO-CIN has presented near real-time epidemiological descriptions and analyses of hospitalised patients with COVID-19. CO-CIN have presented analyses of patient factors including ethnicity, age, comorbidity, and their association with in-hospital mortality, enabling SAGE to make decisions based on near real-time evidence. SAGE will have no access to the NHS England data shared under this Agreement.
Each of the datasets, including the GDPPR data (which will provide data on shielded patients), is essential to support the analysis for the use cases as these questions cannot be answered using just the available CO-CIN data. Specific questions about shielding, pre-existing patient co-morbidities, and the outcomes for patients are important to be able to understand the full impact of the disease and interventions. CO-CIN was set up as a pandemic Case Report Form (CRF), and comorbidity categories are broad. There is a need to understand the duration and severity of comorbidities in more granularity. CO-CIN need to be able to report and respond to the impact that multi-morbidity and frailty have on COVID. CO-CIN have detailed data regarding in-hospital sequelae of COVID-19, but require post-discharge follow-up data in. Follow up of contacts with primary and secondary care, and in particular cardio-respiratory and psychiatric sequelae will be imperative to understanding the long-term impact of severe COVID-19.
CO-CIN need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. CO-CIN have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission. CO-CIN need to be able to report the longer-term all-cause and excess mortality for patients hospitalised with covid-19. The data will help to understand the longer-term mortality for patients admitted to hospital with COVID-19 (long-COVID).
CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality
CO-CIN have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). CO-CIN have developed a secure, password protected dashboard where SAGE members are able to access aggregated data with small numbers suppressed. This data is accurate to the same day.
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals. SPI-M will not have access to NHS England data.
CO-CIN have supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV.
It should be noted that non-ISARIC researchers, including external collaborators, will not have access to NHS England data. This data will be stored solely within the ISARIC project space within the safe haven and only accessible by approached ISARIC researchers. NHS England data will not be included or made available for access to external researchers via the standard ISARIC4C Data and Materials Access Committee (IDAMAC).
In a subgroup of 2,500 patients, CO-CIN have linked with detailed biological and follow-up data.
Use cases for supporting SAGE reporting include the following research questions:
- What are the outcomes for patients on the shielding list?
- How do patient comorbidities/multi-morbidity contribute to in-hospital mortality in patients admitted to hospital with covid-19?
- What is the physical and mental health impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
- Does access to hospital affect outcomes?
- What are the longer term sequelae for hospitalised survivors of covid-19, including changes in mental health?
- What is the longer term mortality in patients admitted to hospital with covid-19?
- What is the association between diabetes and in-hospital mortality?
The following organisations are involved in the study:
DATA CONTROLLER
University of Oxford are the lead organisation for the study and host the data collection, they are the data controller.
DATA PROCESSORS
University of Edinburgh are hosting the research databases and have the data science team that will be analysing the linkage, they are a data processor.
University of Liverpool support the hospital recruitment sites and supplementary research. They are a data processor.
Public Health Scotland are listed data processors working as part of the covid research response.
Only the University of Edinburgh will store the NHS data from England, within its data safe haven. Access to the safe haven is tightly controlled and audited.
OTHER ORGANISATIONS
The collaborators to the study will not have access to the NHS England data and are not considered a joint data controller or data processor.
Imperial University are solely analysing the collected sample data and no NHS England data. They are therefore not listed as a joint data controller or data processors.
AMENDMENT (version 1.8)
COVID-19 Vaccination Status and COVID-19 Vaccine Adverse Reactions datasets have been requested to provide analysis and reporting on a number of questions relating to vaccines, including any changes in effectiveness relating to variants of concern and other related questions. This is at the request of the Chief Medical Officer for England to support research and reporting to SAGE.
Expected output
The primary outputs will include regular and ad-hoc reports to SAGE and the UK Government with only aggregated data and small number suppression.
These will be done on a regular basis for the duration of the project (October 2020 to November 2021), and the capacity to provide this output at short notice retained for future outbreaks. The study is a ‘Prepared Urgent Public Health Research Study’ which is kept read for future outbreaks.
The project will also produce submissions to peer reviewed journals, again with only aggregated data and small number suppression. It is expected that at least one submission will be made prior to the end of the project.
Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS England requirements).
Within 1 week of CO-CIN receiving the data from NHS England, it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:
- Support the NHS and government response to COVID-19
- Analyse the spread of patients hospitalised with covid-19 geographically and demographically, to identify any trends. Appointment activity will also be analysed to better understand use of non-face to face consultation trends and potential differences across geographical areas.
- Analyse potential hospital-acquired covid-19 geographically and demographically
- Diagnosing and monitoring the effects of COVID-19 at a national level.
- Ensuring the Department of Health and Social Care and NHS England has adequate data to inform that interventions and measures put in place to reduce the transmission of COVID-19 are being effective and impactful.
- Analyse factors that result in increased service utilisation for COVID-19 patients.
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of care for both physical and mental health. Learning from and predicting likely patient pathways in order to influence early interventions and other alternatives for patients and develop new predictive modelling and tools for use by care professionals and commissioners.
The ISARIC website, https://isaric4c.net has recently been updated, and is under continuous review with regards to ensuring that it is up to date and relevant to inform the public of the programme. The ISARIC programme is also working with HDRUK to ensure that the use of data is made available to the general public as part of the overall national strategy and work with them to ensure that feedback from the public is heard. Senior researchers have also been active in promoting the study on social media and mainstream news outlets, https://isaric4c.net/privacy/
Benefits reported
ISARIC4C need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. ISARIC4C have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission (https://isaric4c.net/outputs/characterisation/)
ISARIC4C have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality (https://isaric4c.net/outputs/4c_score/)
ISARIC4C have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). This had direct implications for the NHS, suggesting that ISARIC4C should test patients more often for flu. It also provides another reason to vaccinate people more widely against both flu and Covid, particularly as flu is expected to make a comeback in 2022. (https://isaric4c.net/outputs/coinfection_flu/)
As with much of ISARIC's work, this was discussed immediately at the UK government advisory group, SAGE, and has already influenced the thinking around policy
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
ISARIC4C has produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
ISARIC4C has supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV. External researchers will not have access to NHS England linked data.
DARS-NIC-402963-P0Y5D-v1.8 15 February 2021 to 28 September 2023
- Title
- ISARIC4C Coronavirus Clinical Information Network (COCIN) GPES record linkage
- Commercial
- No
- Sublicensing
- No
- Datasets
- 19
- Files released
- 1,082
Datasets: Civil Registrations of Death; COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR); 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); HES-ID to MPS-ID HES Accident and Emergency; Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Improving Access to Psychological Therapies (IAPT) v1.5; Mental Health Services Data Set (MHSDS); National Diabetes Audit; NHS 111 Online Dataset; Secondary Uses Service Payment By Results Accident & Emergency; Secondary Uses Service Payment By Results Episodes; Secondary Uses Service Payment By Results Outpatients; Secondary Uses Service Payment By Results Spells
What changed from DARS-NIC-402963-P0Y5D-v0.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Applicant organisation | UNIVERSITY OF LIVERPOOL | |
| Start date | 2021-02-15 | |
| COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| COVID-19 Hospitalization in England Surveillance System: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| COVID-19 SGSS First Positives (Second Generation Surveillance System): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Civil Registrations of Death: legal basis | Other-(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Civil Registrations of Death: type of data | Anonymised - ICO Code Compliant | |
| Emergency Care Data Set (ECDS): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Improving Access to Psychological Therapies Data Set_v1.5: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Mental Health Services Data Set (MHSDS): legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| NHS 111 Online Dataset: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| National Diabetes Audit: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Secondary Uses Service Payment By Results Accident & Emergency: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Secondary Uses Service Payment By Results Episodes: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Secondary Uses Service Payment By Results Outpatients: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) | |
| Secondary Uses Service Payment By Results Spells: legal basis | Other-Other(CV19: Regulation 3 (1) of the Health Service (Control of Patient Information) Regulations 2002) |
Datasets: + COVID-19 Vaccination Adverse Reactions; + COVID-19 Vaccination Status; + HES-ID to MPS-ID HES Accident and Emergency; + Hospital Episode Statistics Admitted Patient Care (HES APC)
Objective for processing
[7 paragraphs unchanged]
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS Digital for linkage.
All data processors as listed in this agreement comply with Regulation 7(2) of COPI.
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February 2020, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS Digital for linkage.
[4 paragraphs unchanged]
Each of the datasets, including the GDPPR data (which will provide data
[31 words unchanged]
co-morbidities, and the outcomes for patients are important to be able to
understanding
understand
the full impact of the disease and interventions. CO-CIN was set up
[76 words unchanged]
sequelae will be imperative to understanding the long-term impact of severe COVID-19.
[4 paragraphs unchanged]
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
SPI-M will not have access to NHS Digital data.
[1 paragraph unchanged]
It should be noted that non-ISARIC researchers, including external collaborators, will not have access to NHS Digital data. This data will be stored solely within the ISARIC project space within the safe haven and only accessible by approached ISARIC researchers. NHS Digital data will not be included or made available for access to external researchers via the standard ISARIC4C Data and Materials Access Committee (IDAMAC).
[4 paragraphs unchanged]
- What is the
physical and mental health
impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
[1 paragraph unchanged]
- What are the longer term sequelae for hospitalised survivors of
covid-19?
covid-19, including changes in mental health?
[3 paragraphs unchanged]
DATA CONTROLLER
[1 paragraph unchanged]
DATA PROCESSORS
[1 paragraph unchanged]
University of Liverpool support the hospital recruitment
sites.
sites and supplementary research. They are a data processor.
Imperial University are solely analysing the collected sample data and no NHS Digital data.
Public Health Scotland are listed data processors working as part of the covid research response.
Only the University of Edinburgh will store
or have access to
the NHS data from England, within its data safe haven.
Access to the safe haven is tightly controlled and audited.
OTHER ORGANISATIONS
The collaborators to the study will not have access to the NHS Digital data and are not considered a joint data controller or data processor.
Imperial University are solely analysing the collected sample data and no NHS Digital data. They are therefore not listed as a joint data controller or data processors.
AMENDMENT REQUEST
COVID-19 Vaccination Status and COVID-19 Vaccine Adverse Reactions datasets have been requested to provide analysis and reporting on a number of questions relating to vaccines, including any changes in effectiveness relating to variants of concern and other related questions. This is at the request of the Chief Medical Officer for England to support research and reporting to SAGE.
Processing activities
All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)”
[1 paragraph unchanged]
NHS Digital will be provided with a mapping file containing a list
[13 words unchanged]
as the patient date of birth, postcode and the matched CO-CIN subject
ID.
ID., this will flow from University of Edinburgh.
[2 paragraphs unchanged]
University of Oxford hold the CO-CIN database (the clinical data that is collected from the
hospitals)
hospitals). The University of Edinburgh will extract the cohort from the CO-CIN database
and
Oxford will flow the England data
submit
to NHS Digital, NHS Digital then
send
create
the pseudonymised
extract to
extracts and make available for
University of
Edinburgh.
Edinburgh to download to secure server where the files will be processed into the Data Safe Haven ISARIC data store.
University of Edinburgh hold all the pseudonymised CO-CIN data and will also hold the linked NHS D and CO-CIN data (England only) separately.
[1 paragraph unchanged]
PHS are named as a data processor due to their role supporting CO-CIN data flows and as the managers of the Serv-U gateway. They provide services to extract CO-CIN data but do not have any access to the DSH or any ability to access NHS Digital data.
NHS Digital data is not included in the resource overseen by IDAMAC. The data is not available to non-ISARIC researchers. It is stored solely within the ISARIC project space within the safe haven.
PHS are named as a data processor due to their role supporting CO-CIN data flows and as the managers of the Serv-U gateway. They do not have access to the data but do have responsibility for the security and processes around accessing the data. They provide services to extract CO-CIN data but do not have any access to the DSH or any ability to access NHS Digital data.
[9 paragraphs unchanged]
There will be no data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the agreement.
AMENDMENT REQUEST
The methodology applied above will be used for the amendment to add Vaccination data.
Expected output
[1 paragraph unchanged]
These will be done on a regular basis for the duration of the project (October 2020 to
April 2021).
November 2021), and the capacity to provide this output at short notice retained for future outbreaks. The study is a ‘Prepared Urgent Public Health Research Study’ which is kept read for future outbreaks.
[9 paragraphs unchanged]
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of
care.
care for both physical and mental health.
Learning from and predicting likely patient pathways in order to influence early
[8 words unchanged]
new predictive modelling and tools for use by care professionals and commissioners.
The ISARIC
website
website, https://isaric4c.net
has recently been updated, and is under continuous review with regards to
[62 words unchanged]
been active in promoting the study on social media and mainstream news
outlets.
outlets, https://isaric4c.net/privacy/
Expected measurable benefits
Expected benefits
within the project timescales to April 2021
will include:
~
Reduce deaths associated with COVID-19
~
Assist commissioners in making decisions to better support patients
~
Identifying COVID-19 trends and risks to public health
~
Enables Department of Health and NHS England to provide guidance and develop policies to respond to the outbreak
~
Controlling and helping to prevent the spread of the virus
~
The Department of Health and NHS England can share a common understanding
[19 words unchanged]
national planning process and improve the allocation of resources across the system.
This
is hoped
will support the response to the pandemic but also the recovery of services.
Expected benefits of the new data request:
These data will be linked with the prospective ISARIC-4C CCP-UK study, which has recruited over 250,000 patients admitted to hospital with covid-19 in the UK, representing over 40% of all patients admitted with covid-19. This is the largest prospective cohort of covid-19 in the world.
These data linkages will ensure maximum benefit from these data, and ensure that the analyses can continue to inform policy and improve care for individuals with covid-19 at a national and international level.
The linked data will provide the following specific benefits:
1. Reduction of missing data, particularly for ethnicity, deprivation, duration of hospital stay and in-hospital mortality. This is of particular relevance to the first wave where research teams were overwhelmed, and unable to complete the case report forms. It will also allow for quality checks of the prospective data, including where we know data have been entered inaccurately.
2. More granular description of pre-existing health conditions (comorbidity), and the impact and severity of these before and after covid-19.
3. Confirmation of vaccine status, vaccine type and date of first/second/third doses for patients along with variant of concern data will enable us to look at outcomes in the context of changing vaccination status and changing variants. This is key to interpretation of hospital events and interventions.
4. Post-hospital follow-up for all those who survived to hospital discharge. This longitudinal cohort will allow us to examine longer-term effects of hospitalisation with covid-19 in both primary and secondary care.
5. Mental health difficulties and cognitive problems are being described in covid-19 survivors. These data will facilitate description of what type of difficulties survivors are facing, and what treatment they are currently receiving. This will enable planning of targeted resources and interventions.
Benefits reported
Yielded Benefits is not a requirement for new applications.
The study has already reported to SAGE on early risk factors for mortality in hospital, including obesity and ethnicity; changes in mortality over the first wave; complications in hospital; new variants of concern in the hospitalised population; hospitalised children with covid-19; and vaccination status in hospitalised patients and ongoing high risk of immunocompromised patients.
Objective for processing
The Coronavirus Clinical Information Network (CO-CIN) has collected data for the International Severe Acute Respiratory Infection Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium through a commission from the Chief Medical Officer to conduct Urgent Public Health Research to provide evidence that informs public health policy in response to the COVID-19 emergency.
ISARIC’s purpose is to prevent illness and deaths from infectious disease outbreaks. ISARIC is a global federation of clinical research networks, providing a proficient, coordinated and agile research response to outbreak-prone infectious diseases.
The ISARIC Coronavirus Clinical Characterisation Consortium is a UK-wide consortium of leading experts in outbreak medicine with a proficient, coordinated, and agile research response to COVID-19.
In 2019 a new virus, SARS coronavirus-2 (SARS-Cov-2) emerged. It seems highly likely that SARS-CoV-2 and its associated disease COVID-19 will cause mortality unprecedented in modern times. This is a new disease. There is a high chance that clinical trials will fail to detect therapeutic effects, by enrolling at the wrong time, or missing key subgroups or endpoints. Concurrent biological phenotyping can mitigate these risks, providing rapid, efficient clinical evidence.
CO-CIN response has been planned and tested over the past 8 years within the International Severe Acute Respiratory Infection Consortium (ISARIC).
CO-CIN informs the Department of Health and Social Care (DHSC) on a weekly basis about the clinical evolution of disease in the United Kingdom. To achieve this, clinical research nurses and administrators gather anonymised data from clinical notes and enter it into a simple online database. This allows the characterisation of the patients’ clinical features as well as risk factors associated with severity, risk of hospitalisation and death. The information gathered is essential to help health service planning and provision, and to rapidly evaluate the impact of interventions such as new therapeutics or vaccines.
The legal basis for the processing and storage of personal data for COCIN is that it is a task in the public interest Article 6(1)(e) it is in the public interest to conduct public health research to provide evidence to inform public health policy in response to the COVID-19 emergency and to understand and report on the risk factors associated COVID-19 and that sensitive personal data is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes Article 9(2)(j).
All data processors as listed in this agreement comply with Regulation 7(2) of COPI.
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February 2020, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS Digital for linkage.
Patients are recruited into one of three Tiers. Tier 0 sites are recruited for data collection only without consent, while Tiers 1 and 2 provide consent for sample collection in addition to data collection. The distinction of the study into three Tiers was made to allow for a resource appropriate implementation of the protocol, as it was understood that data and/or sample collection may be limited in some settings.
For Tier 0 patients clinical data is collected but no additional biological samples are obtained for research purposes. The minimum clinical data set summarises the illness episode and outcome, with the option to collect additional detailed clinical data at frequent intervals, according to local resources/needs.
Given the scale of the current COVID-19 pandemic, and because initially data collection for Tier 0 participants was clinical data only from which the participant could not be identified, consent was not sought. The data is collected by a health care professional who has access to the patient's information by virtue of their clinical role. The addition of collection of NHS number, Date of Birth (DOB) and postcode for Tier 0 participants means they are now able to be identified from the dataset in order to support linkage to other NHS data sources and is currently being done under Control of Patient Information Regulations (COPI). The identifiable data is not made available to researchers. Tier 0 is being retrospectively and prospectively completed with identifying data relying on COPI.
The datasets are required primarily to enable CO-CIN to report early and accurate findings to the Scientific Advisory Group for Emergencies (SAGE). Since the early growth phase of COVID-19 in the UK, CO-CIN has presented near real-time epidemiological descriptions and analyses of hospitalised patients with COVID-19. CO-CIN have presented analyses of patient factors including ethnicity, age, comorbidity, and their association with in-hospital mortality, enabling SAGE to make decisions based on near real-time evidence. SAGE will have no access to the NHS Digital data shared under this Agreement.
Each of the datasets, including the GDPPR data (which will provide data on shielded patients), is essential to support the analysis for the use cases as these questions cannot be answered using just the available CO-CIN data. Specific questions about shielding, pre-existing patient co-morbidities, and the outcomes for patients are important to be able to understand the full impact of the disease and interventions. CO-CIN was set up as a pandemic Case Report Form (CRF), and comorbidity categories are broad. There is a need to understand the duration and severity of comorbidities in more granularity. CO-CIN need to be able to report and respond to the impact that multi-morbidity and frailty have on COVID. CO-CIN have detailed data regarding in-hospital sequelae of COVID-19, but require post-discharge follow-up data in. Follow up of contacts with primary and secondary care, and in particular cardio-respiratory and psychiatric sequelae will be imperative to understanding the long-term impact of severe COVID-19.
CO-CIN need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. CO-CIN have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission. CO-CIN need to be able to report the longer-term all-cause and excess mortality for patients hospitalised with covid-19. The data will help to understand the longer-term mortality for patients admitted to hospital with COVID-19 (long-COVID).
CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality
CO-CIN have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). CO-CIN have developed a secure, password protected dashboard where SAGE members are able to access aggregated data with small numbers suppressed. This data is accurate to the same day.
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals. SPI-M will not have access to NHS Digital data.
CO-CIN have supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV.
It should be noted that non-ISARIC researchers, including external collaborators, will not have access to NHS Digital data. This data will be stored solely within the ISARIC project space within the safe haven and only accessible by approached ISARIC researchers. NHS Digital data will not be included or made available for access to external researchers via the standard ISARIC4C Data and Materials Access Committee (IDAMAC).
In a subgroup of 2,500 patients, CO-CIN have linked with detailed biological and follow-up data.
Use cases for supporting SAGE reporting include the following research questions:
- What are the outcomes for patients on the shielding list?
- How do patient comorbidities/multi-morbidity contribute to in-hospital mortality in patients admitted to hospital with covid-19?
- What is the physical and mental health impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
- Does access to hospital affect outcomes?
- What are the longer term sequelae for hospitalised survivors of covid-19, including changes in mental health?
- What is the longer term mortality in patients admitted to hospital with covid-19?
- What is the association between diabetes and in-hospital mortality?
The following organisations are involved in the study:
DATA CONTROLLER
University of Oxford are the lead organisation for the study and host the data collection, they are the data controller.
DATA PROCESSORS
University of Edinburgh are hosting the research databases and have the data science team that will be analysing the linkage, they are a data processor.
University of Liverpool support the hospital recruitment sites and supplementary research. They are a data processor.
Public Health Scotland are listed data processors working as part of the covid research response.
Only the University of Edinburgh will store the NHS data from England, within its data safe haven. Access to the safe haven is tightly controlled and audited.
OTHER ORGANISATIONS
The collaborators to the study will not have access to the NHS Digital data and are not considered a joint data controller or data processor.
Imperial University are solely analysing the collected sample data and no NHS Digital data. They are therefore not listed as a joint data controller or data processors.
AMENDMENT REQUEST
COVID-19 Vaccination Status and COVID-19 Vaccine Adverse Reactions datasets have been requested to provide analysis and reporting on a number of questions relating to vaccines, including any changes in effectiveness relating to variants of concern and other related questions. This is at the request of the Chief Medical Officer for England to support research and reporting to SAGE.
Expected output
The primary outputs will include regular and ad-hoc reports to SAGE and the UK Government with only aggregated data and small number suppression.
These will be done on a regular basis for the duration of the project (October 2020 to November 2021), and the capacity to provide this output at short notice retained for future outbreaks. The study is a ‘Prepared Urgent Public Health Research Study’ which is kept read for future outbreaks.
The project will also produce submissions to peer reviewed journals, again with only aggregated data and small number suppression. It is expected that at least one submission will be made prior to the end of the project.
Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS Digital requirements).
Within 1 week of CO-CIN receiving the data from NHS Digital, it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:
- Support the NHS and government response to COVID-19
- Analyse the spread of patients hospitalised with covid-19 geographically and demographically, to identify any trends. Appointment activity will also be analysed to better understand use of non-face to face consultation trends and potential differences across geographical areas.
- Analyse potential hospital-acquired covid-19 geographically and demographically
- Diagnosing and monitoring the effects of COVID-19 at a national level.
- Ensuring the Department of Health and Social Care and NHS England has adequate data to inform that interventions and measures put in place to reduce the transmission of COVID-19 are being effective and impactful.
- Analyse factors that result in increased service utilisation for COVID-19 patients.
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of care for both physical and mental health. Learning from and predicting likely patient pathways in order to influence early interventions and other alternatives for patients and develop new predictive modelling and tools for use by care professionals and commissioners.
The ISARIC website, https://isaric4c.net has recently been updated, and is under continuous review with regards to ensuring that it is up to date and relevant to inform the public of the programme. The ISARIC programme is also working with HDRUK to ensure that the use of data is made available to the general public as part of the overall national strategy and work with them to ensure that feedback from the public is heard. Senior researchers have also been active in promoting the study on social media and mainstream news outlets, https://isaric4c.net/privacy/
Benefits reported
The study has already reported to SAGE on early risk factors for mortality in hospital, including obesity and ethnicity; changes in mortality over the first wave; complications in hospital; new variants of concern in the hospitalised population; hospitalised children with covid-19; and vaccination status in hospitalised patients and ongoing high risk of immunocompromised patients.
DARS-NIC-402963-P0Y5D-v0.2 28 September 2020 to 28 September 2023
- Title
- ISARIC4C Coronavirus Clinical Information Network (COCIN) GPES record linkage
- Commercial
- No
- Sublicensing
- No
- Datasets
- 15
- Files released
- 447
Datasets: Civil Registrations of Death; COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR); 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); Emergency Care Data Set (ECDS); Hospital Episode Statistics Accident and Emergency (HES A and E); Improving Access to Psychological Therapies (IAPT) v1.5; Mental Health Services Data Set (MHSDS); National Diabetes Audit; NHS 111 Online Dataset; Secondary Uses Service Payment By Results Accident & Emergency; Secondary Uses Service Payment By Results Episodes; Secondary Uses Service Payment By Results Outpatients; Secondary Uses Service Payment By Results Spells
Objective for processing
The Coronavirus Clinical Information Network (CO-CIN) has collected data for the International Severe Acute Respiratory Infection Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium through a commission from the Chief Medical Officer to conduct Urgent Public Health Research to provide evidence that informs public health policy in response to the COVID-19 emergency.
ISARIC’s purpose is to prevent illness and deaths from infectious disease outbreaks. ISARIC is a global federation of clinical research networks, providing a proficient, coordinated and agile research response to outbreak-prone infectious diseases.
The ISARIC Coronavirus Clinical Characterisation Consortium is a UK-wide consortium of leading experts in outbreak medicine with a proficient, coordinated, and agile research response to COVID-19.
In 2019 a new virus, SARS coronavirus-2 (SARS-Cov-2) emerged. It seems highly likely that SARS-CoV-2 and its associated disease COVID-19 will cause mortality unprecedented in modern times. This is a new disease. There is a high chance that clinical trials will fail to detect therapeutic effects, by enrolling at the wrong time, or missing key subgroups or endpoints. Concurrent biological phenotyping can mitigate these risks, providing rapid, efficient clinical evidence.
CO-CIN response has been planned and tested over the past 8 years within the International Severe Acute Respiratory Infection Consortium (ISARIC).
CO-CIN informs the Department of Health and Social Care (DHSC) on a weekly basis about the clinical evolution of disease in the United Kingdom. To achieve this, clinical research nurses and administrators gather anonymised data from clinical notes and enter it into a simple online database. This allows the characterisation of the patients’ clinical features as well as risk factors associated with severity, risk of hospitalisation and death. The information gathered is essential to help health service planning and provision, and to rapidly evaluate the impact of interventions such as new therapeutics or vaccines.
The legal basis for the processing and storage of personal data for COCIN is that it is a task in the public interest Article 6(1)(e) it is in the public interest to conduct public health research to provide evidence to inform public health policy in response to the COVID-19 emergency and to understand and report on the risk factors associated COVID-19 and that sensitive personal data is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes Article 9(2)(j).
The research is conducted with relevant Health Research Authority ethical approvals throughout the UK. Since early February, CO-CIN has collected data on over 79,000 patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales, accounting for approximately 60% of all patients admitted to hospital with covid-19 in the UK. Only data from England will be sent to NHS Digital for linkage.
Patients are recruited into one of three Tiers. Tier 0 sites are recruited for data collection only without consent, while Tiers 1 and 2 provide consent for sample collection in addition to data collection. The distinction of the study into three Tiers was made to allow for a resource appropriate implementation of the protocol, as it was understood that data and/or sample collection may be limited in some settings.
For Tier 0 patients clinical data is collected but no additional biological samples are obtained for research purposes. The minimum clinical data set summarises the illness episode and outcome, with the option to collect additional detailed clinical data at frequent intervals, according to local resources/needs.
Given the scale of the current COVID-19 pandemic, and because initially data collection for Tier 0 participants was clinical data only from which the participant could not be identified, consent was not sought. The data is collected by a health care professional who has access to the patient's information by virtue of their clinical role. The addition of collection of NHS number, Date of Birth (DOB) and postcode for Tier 0 participants means they are now able to be identified from the dataset in order to support linkage to other NHS data sources and is currently being done under Control of Patient Information Regulations (COPI). The identifiable data is not made available to researchers. Tier 0 is being retrospectively and prospectively completed with identifying data relying on COPI.
The datasets are required primarily to enable CO-CIN to report early and accurate findings to the Scientific Advisory Group for Emergencies (SAGE). Since the early growth phase of COVID-19 in the UK, CO-CIN has presented near real-time epidemiological descriptions and analyses of hospitalised patients with COVID-19. CO-CIN have presented analyses of patient factors including ethnicity, age, comorbidity, and their association with in-hospital mortality, enabling SAGE to make decisions based on near real-time evidence. SAGE will have no access to the NHS Digital data shared under this Agreement.
Each of the datasets, including the GDPPR data (which will provide data on shielded patients), is essential to support the analysis for the use cases as these questions cannot be answered using just the available CO-CIN data. Specific questions about shielding, pre-existing patient co-morbidities, and the outcomes for patients are important to be able to understanding the full impact of the disease and interventions. CO-CIN was set up as a pandemic Case Report Form (CRF), and comorbidity categories are broad. There is a need to understand the duration and severity of comorbidities in more granularity. CO-CIN need to be able to report and respond to the impact that multi-morbidity and frailty have on COVID. CO-CIN have detailed data regarding in-hospital sequelae of COVID-19, but require post-discharge follow-up data in. Follow up of contacts with primary and secondary care, and in particular cardio-respiratory and psychiatric sequelae will be imperative to understanding the long-term impact of severe COVID-19.
CO-CIN need to be able to report and respond to the longer-term impact COVID-19 is having on hospitalised survivors. CO-CIN have outcomes for patients at hospital discharge (alive, dead, palliative discharge, ongoing rehab). For the majority of patients this is <28 days from hospital admission. CO-CIN need to be able to report the longer-term all-cause and excess mortality for patients hospitalised with covid-19. The data will help to understand the longer-term mortality for patients admitted to hospital with COVID-19 (long-COVID).
CO-CIN have shown that diabetes is independently associated with in-hospital mortality for patients with COVID-19, and this partially mediates the relationship between ethnicity and mortality. The data requested is essential to understanding the relationship between diabetes, COVID-19 and mortality, by increased granularity of diabetes comorbidity including duration since diagnosis, complications, comorbidity, ethnicity and longer-term mortality
CO-CIN have presented data on hospital acquired infection, level of treatment (oxygen, critical care, invasive ventilation), and specific treatments (including dexamethasone, remdesivir, convalescent plasma). CO-CIN have developed a secure, password protected dashboard where SAGE members are able to access aggregated data with small numbers suppressed. This data is accurate to the same day.
The data has been used for modelling by Scientific Pandemic Influenza Group on Modelling (SPI-M), and is the compulsory national registry for patients who receive remdesivir.
CO-CIN have produced academic papers published in high impact journals (early general description, paediatrics, risk prediction model, ethnicity) which have supported evidence based practice in UK hospitals.
CO-CIN have supported external collaborations with specialty academic groups to explore their patient groups, such as those with interstitial lung disease and HIV.
In a subgroup of 2,500 patients, CO-CIN have linked with detailed biological and follow-up data.
Use cases for supporting SAGE reporting include the following research questions:
- What are the outcomes for patients on the shielding list?
- How do patient comorbidities/multi-morbidity contribute to in-hospital mortality in patients admitted to hospital with covid-19?
- What is the impact of ethnicity and socio-economic deprivation on outcomes in patients admitted to hospital with covid-19?
- Does access to hospital affect outcomes?
- What are the longer term sequelae for hospitalised survivors of covid-19?
- What is the longer term mortality in patients admitted to hospital with covid-19?
- What is the association between diabetes and in-hospital mortality?
The following organisations are involved in the study:
University of Oxford are the lead organisation for the study and host the data collection, they are the data controller.
University of Edinburgh are hosting the research databases and have the data science team that will be analysing the linkage, they are a data processor.
University of Liverpool support the hospital recruitment sites.
Imperial University are solely analysing the collected sample data and no NHS Digital data.
Only the University of Edinburgh will store or have access to the NHS data from England, within its data safe haven.
Expected output
The primary outputs will include regular and ad-hoc reports to SAGE and the UK Government with only aggregated data and small number suppression.
These will be done on a regular basis for the duration of the project (October 2020 to April 2021).
The project will also produce submissions to peer reviewed journals, again with only aggregated data and small number suppression. It is expected that at least one submission will be made prior to the end of the project.
Any outputs to 3rd parties not included as a Data Controller/Processor in this agreement will be aggregated (with small number suppression applied in line with NHS Digital requirements).
Within 1 week of CO-CIN receiving the data from NHS Digital, it will be able to use the dataset to provide analysis for the described use cases that start to respond to the following:
- Support the NHS and government response to COVID-19
- Analyse the spread of patients hospitalised with covid-19 geographically and demographically, to identify any trends. Appointment activity will also be analysed to better understand use of non-face to face consultation trends and potential differences across geographical areas.
- Analyse potential hospital-acquired covid-19 geographically and demographically
- Diagnosing and monitoring the effects of COVID-19 at a national level.
- Ensuring the Department of Health and Social Care and NHS England has adequate data to inform that interventions and measures put in place to reduce the transmission of COVID-19 are being effective and impactful.
- Analyse factors that result in increased service utilisation for COVID-19 patients.
- Start building modelling and forecasting tools for COVID-19 from a linked Primary to Secondary pathway perspective to understand trajectories of care. Learning from and predicting likely patient pathways in order to influence early interventions and other alternatives for patients and develop new predictive modelling and tools for use by care professionals and commissioners.
The ISARIC website has recently been updated, and is under continuous review with regards to ensuring that it is up to date and relevant to inform the public of the programme. The ISARIC programme is also working with HDRUK to ensure that the use of data is made available to the general public as part of the overall national strategy and work with them to ensure that feedback from the public is heard. Senior researchers have also been active in promoting the study on social media and mainstream news outlets.
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.
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July 2021 —
already listed in the earliest edition this site holds, so it may be older. 1 version: DARS-NIC-402963-P0Y5D-v0.2
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February 2022
1 version added: DARS-NIC-402963-P0Y5D-v1.8
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January 2023
Amended DARS-NIC-402963-P0Y5D-v0.2
- Datasets:
+ COVID-19 SGSS First Positives (Second Generation Surveillance System) ·
− COVID-19 Second Generation Surveillance System (SGSS)
Amended DARS-NIC-402963-P0Y5D-v1.8- 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) ·
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June 2024
1 version added: DARS-NIC-402963-P0Y5D-v2.10
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February 2025
Amended DARS-NIC-402963-P0Y5D-v2.10
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis:
“
Other-Para 8.3.2 of the Covid-19 Directions;” taken out
- COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2): legal basis:
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August 2025
1 version added: DARS-NIC-402963-P0Y5D-v3.8
"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-402963-P0Y5D, “ISARIC4C (ISARIC Coronavirus Clinical Characterisation Consortium) - Clinical Characterisation Protocol (CCP-UK)”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-402963-p0y5d/ (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-402963-P0Y5D to see the original rows.