Abstract
The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.
| Original language | English |
|---|---|
| Pages (from-to) | 135-140 |
| Number of pages | 6 |
| Journal | Annals of Family Medicine |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 8 Mar 2021 |
Bibliographical note
Funding Information:Conflicts of interests: P.T. has consulted for Astra-Zeneca and Duke-NUS. J.H. reports the Clinical Trial Service Unit receives research grants from the pharmaceutical industry. In addition to University of Oxford affilitation, J.H-C. is founder and director of QResearch database, co-owner of ClinRisk Ltd, and was a paid director there until June 2019. No other authors have any competing interests to declare. The authors declare that no support from any organization and no financial relationships have influenced the submitted work.
Funding Information:
S.J.G. is supported by an MRC Epidemiology Unit program: MC_UU_12015/4. The University of Cambridge has received salary support for S.J.G. from the NHS in the East of England through the Clinical Academic Reserve. H.D-M. is a NIHR funded Academic Clinical Lecturer. R.M.M. is supported in part by the NIHR Bristol Bio-medical Research Centre and by a Cancer Research UK (C18281/A19169) program grant (the Integrative Cancer Epidemiology Programme). P.W. is supported in part by the NIHR Oxford Biomedical Research Centre. J.C.H. acknowledges personal support from the British Heart Foundation (FS/14/55/30806) and Cancer Research UK (C5255/A18085) through the Cancer Research UK Oxford Centre. J.H-C. also receives support from the NHS and NIHR. J.M. is an NIHR Senior Investigator.
Publisher Copyright:
© 2021, Annals of Family Medicine, Inc. All rights reserved.
Research Groups and Themes
- Covid19
Keywords
- big data
- coronavirus
- epidemiology
- primary health care
Access to Document
- Full-text PDF (author accepted manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Annals of Family Medicine at https://doi.org/10.1370/afm.2658. Please refer to any applicable terms of use of the publisher.