QResearch Logo

OX79 Coronavirus Record Linkage Project




OX79 Coronavirus Record Linkage Project

What were the objectives of the study?

The majority of UK residents will likely have been infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, the virus that causes Covid-19) by the end of the current pandemic. Large numbers of people will receive hospital treatment, some on an intensive care unit (ICU); and a substantial proportion of the latter will die.

Some commonly used drugs for conditions such as hypertension and diabetes may affect the severity of Covid-19. Other drugs or therapies, used for different diseases, may have activity on Covid-19. However, we don’t know, and so need a study to gather more evidence. Of particular interest in this study are drugs used to treat high blood pressure called angiotensin converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARBs).

Three databases, the Intensive Care National Audit and Research Centre’s (ICNARC) Case Mix Programme (CMP), Public Health England’s records of Covid-19 tests, and QResearch contain different but complementary data about the same patients. By linking them, we have created a resource that allows us to analyse data from before and after critical illness to better understand Covid-19 drugs and therapies and health resource use. This pooled data resource is of particular use in the current pandemic to study the illness pathway for patients with Covid-19 from primary to critical care.

Initially, some things we want to know are:
• what proportion of patients in ICU have chronic conditions?
• are some medications associated with a higher/lower chance of being admitted to ICU, and/or recovering?
• are some patients at higher risk of needing care in ICU, and/or at lower risk of recovering?
• if some drugs increase risk of needing care in an ICU, are there safer drugs for treatment of patients’ chronic conditions?
• are there drugs that might warrant further evaluation to treat Covid-19?

How was the research done?

The study is a record linkage cohort study combining routinely collected health care data and data collected for clinical audit. We will link the two databases, ICNARC CMP and QResearch, to connect primary care with critical care data. This linked database is a resource that will be used to conduct studies of all patients registered with practices contributing to QResearch, including those with a recorded, suspected or confirmed diagnosis of Covid-19, and those who were admitted to ICU with Covid-19, to try to answer important questions like those posed above.

Chief Investigator

Professor Julia Hippisley-Cox Professor Peter Watkinson Professor Kathy Rowan

Lead Applicant Organisation Name


University of Oxford

Location of research

University of Oxford

Date on which research approved


Project reference ID


Generic ethics approval reference


Are all data accessed are in anonymised form?


Brief summary of the dataset to be released (including any sensitive data)

Variables includes GP data demographics, diagnoses, medication, HES emergency admissions, civil registration mortality data, ICNARC linked ITU data, linked SGSS Covid-19 test results

Implications and Impact

The study will:
• identify the prevalence of patients in ICU with chronic conditions
• identify which, if any, medications (focusing on specific interventions initially) are associated with an altered chance of being admitted to ICU and/or recovery
• identify risk factors for patients at higher risk of needing care in ICU, or at lower risk of recovery
• try to identify likely safer drugs for patients to treat their chronic conditions
• try to identify possible candidate drugs for evaluation to treat Covid-19

Funding Source

Oxford NIHR BRC, Wellcome ISSF, John Fell Fund

Public Benefit Statement

Research Team

Professor Julia Hippisley-Cox, Nuffield Department of Primary Health Care Health Sciences, University of Oxford

Professor Peter Watkinson, Nuffield Department of Neuroscences Sciences, University of Oxford

Professor Duncan Young, Professor of Intensive Care Medicine, University of Oxford

Professor Carol Coupland, Professor of Medical Statistics in Primary Care, University of Nottingham

Mr Stephen Gerry, Senior Medical Statistician, Centre for Statistics in Medicine, University of Oxford

Professor David Clifton, Professor of Clinical Machine Learning, University of Oxford

Professor Keith Channon, Field Marshal Earl Alexander Professor of Cardiovascular Medicine, University of Oxford

Dr Pui San Tan, Data Scientist, Nuffield Department of Primary Health Care Health Sciences, University of Oxford

Dr Martina Patone, Statistician, Nuffield Department of Primary Health Care Health Sciences, University of Oxford



Press Releases

Access Type

Trusted Research Environment (TRE)

Share this