Abstract
Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY—a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53–1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29–1.69) and 1.45 (1.32–1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.
| Original language | English |
|---|---|
| Pages (from-to) | 430-436 |
| Number of pages | 7 |
| Journal | Nature |
| Volume | 584 |
| Issue number | 7821 |
| DOIs | |
| Publication status | Published - 20 Aug 2020 |
| Externally published | Yes |
Bibliographical note
Funding Information:Acknowledgements All authors are from The OpenSAFELY Collaborative. We are grateful for all the support received from the TPP Technical Operations team throughout this work; for assistance from the information governance and database teams at NHS England and NHSX; and for additional discussions on disease characterization, codelists and methodology with H. Drysdale, B. Nicholson, N. DeVito, W. Hulme, I. Lipska, J. Morley, J. Quint and T. Pham. No dedicated funding has yet been obtained for this work. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. The work of B.G. on better use of data in healthcare more broadly is currently funded in part by: the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation, NHS England and the Health Foundation; all DataLab staff are supported by the grants of B.G. for this work. L.S. reports grants from Wellcome, MRC, NIHR, UKRI, British Council, GSK, British Heart Foundation and Diabetes UK outside this work; K.B. holds a Sir Henry Dale fellowship jointly funded by Wellcome and the Royal Society; H.I.M. is funded by the NIHR Health Protection Research Unit in Immunisation (a partnership between Public Health England and LSHTM); A.Y.S.W. holds a fellowship from BHF; R.M. holds a Sir Henry Wellcome fellowship funded by the Wellcome Trust; E.J.W. holds grants from MRC; R.G. holds grants from NIHR and MRC; I.J.D. holds grants from NIHR and GSK; and H.F. holds a UKRI fellowship. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, Public Health England or the Department of Health and Social Care. The funders had no role in the study design; the collection, analysis and interpretation of data; the writing of the report; and the decision to submit the article for publication.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.
Funding
Acknowledgements All authors are from The OpenSAFELY Collaborative. We are grateful for all the support received from the TPP Technical Operations team throughout this work; for assistance from the information governance and database teams at NHS England and NHSX; and for additional discussions on disease characterization, codelists and methodology with H. Drysdale, B. Nicholson, N. DeVito, W. Hulme, I. Lipska, J. Morley, J. Quint and T. Pham. No dedicated funding has yet been obtained for this work. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. The work of B.G. on better use of data in healthcare more broadly is currently funded in part by: the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation, NHS England and the Health Foundation; all DataLab staff are supported by the grants of B.G. for this work. L.S. reports grants from Wellcome, MRC, NIHR, UKRI, British Council, GSK, British Heart Foundation and Diabetes UK outside this work; K.B. holds a Sir Henry Dale fellowship jointly funded by Wellcome and the Royal Society; H.I.M. is funded by the NIHR Health Protection Research Unit in Immunisation (a partnership between Public Health England and LSHTM); A.Y.S.W. holds a fellowship from BHF; R.M. holds a Sir Henry Wellcome fellowship funded by the Wellcome Trust; E.J.W. holds grants from MRC; R.G. holds grants from NIHR and MRC; I.J.D. holds grants from NIHR and GSK; and H.F. holds a UKRI fellowship. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, Public Health England or the Department of Health and Social Care. The funders had no role in the study design; the collection, analysis and interpretation of data; the writing of the report; and the decision to submit the article for publication.
ASJC Scopus subject areas
- General
