Cited by 6,674COVID-19 Clinical Research StudiesCOVID-19 and healthcare impactsCOVID-19 epidemiological studies

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Factors associated with COVID-19-related death using OpenSAFELY

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Williamson, Elizabeth J., Walker, Alex J., Bhaskaran, Krishnan, Bacon, Seb, Bates, Chris, Morton, Caroline E., Curtis, Helen J., Mehrkar, Amir, Evans, David, Inglesby, Peter, Cockburn, Jonathan, McDonald, Helen I., MacKenna, Brian, Tomlinson, Laurie, Douglas, Ian J., Rentsch, Christopher T., Mathur, Rohini, Wong, Angel Y. S., Grieve, Richard, Harrison, David, Forbes, Harriet, Schultze, Anna, Croker, Richard, Parry, John, Hester, Frank, Harper, Sam, Perera, Rafael, Evans, Stephen J. W., Smeeth, Liam, Goldacre, Ben·2020·Nature

DOI 10.1038/s41586-020-2521-4PMC PMC7611074

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Abstract

Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide 1. 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.

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Figure 1 Estimated log hazard ratio by age in years Footnote: From the primary fully adjusted model containing a 4-knot cubic spline for age, and adjusted for all covariates listed in Table 2 except for ethnicity.CC BY · PubMed Central

Figure 2 Illustration of data flows in the OpenSAFELY platformCC BY · PubMed Central

Figure 1 Flow diagram of cohort with numbers excluded at different stages and identification of cases for the main endpoints.CC BY · PubMed Central

Figure 2 Kaplan-Meier plots for COVID-19 related death over time by age and sexCC BY · PubMed Central

Figure 3 Estimated Hazard Ratios (shown on a log scale) for each potential risk factor from a multivariable Cox model Footnote: Error bars represent limits of the 95% confidence interval for the hazard ratio. Obese class I: 30-34.9kg/m2, class II: 35-39.9kg/m2, class III: >=40kg/m2. OCS = oral corticosteroid. All HRs are adjusted for all other factors listed other than ethnicity. Ethnicity estimates are from a separate model among those with complete ethnicity data, and are fully adjusted for other covariates. Total n = 17,278,392 for non-ethnicity models, and 12,718,279 for ethnicity model.CC BY · PubMed Central

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Institute for Fiscal Studies. Are some ethnic groups more vulnerable to COVID-19 than others? https://web.archive.org/web/20200502130148/https://www.ifs.org.uk/inequality/chapter/are-some-ethnic-groups-more-vulnerable-to-covid-19-than-others/ (2020).

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Authors: Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., Cockburn, J., McDonald, H. I., MacKenna, B., Tomlinson, L., Douglas, I. J., Rentsch, C. T., Mathur, R., Wong, A. Y. S., Grieve, R., Harrison, D., Forbes, H., Schultze, A., Croker, R., Parry, J., Hester, F., Harper, S., Perera, R., Evans, S. J. W., Smeeth, L., & Goldacre, B.
Affiliations: University of Oxford (GB)
London School of Hygiene & Tropical Medicine (GB)
National Institute for Health and Care Research (GB)
Corresponding author: Goldacre, Ben
ORCID: Williamson, Elizabeth J.
Walker, Alex J.
Bhaskaran, Krishnan
Bacon, Seb
Bates, Chris
Morton, Caroline E.
Curtis, Helen J.
Evans, David
Inglesby, Peter
Cockburn, Jonathan
McDonald, Helen I.
MacKenna, Brian
Tomlinson, Laurie
Douglas, Ian J.
Rentsch, Christopher T.
Mathur, Rohini
Wong, Angel Y. S.
Grieve, Richard
Harrison, David
Schultze, Anna
Croker, Richard
Hester, Frank
Perera, Rafael
Evans, Stephen J. W.
Smeeth, Liam
Goldacre, Ben
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2020-07-08
Volume: 584
Issue: 7821
Pages: 430-436
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-020-2521-4
PMC: PMC7611074
OpenAlex: W3047144258
Cited by: 6,674
References: 33
Citations by year: 2026: 176 · 2025: 303 · 2024: 698 · 2023: 904 · 2022: 1505 · 2021: 2173 · 2020: 908
FWCI: 179.94
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature (publishedVersion)
medRxiv (acceptedVersion, cc-by)
PubMed (publishedVersion)
LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
PubMed Central (acceptedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
Funders: London School of Hygiene and Tropical Medicine
Wellcome Trust (107731/Z/15/Z, 201375/Z/16/Z, 201375, 107731)
UK Research and Innovation
National Institute for Health Research Health Protection Research Unit
National Institute for Health and Care Research
British Heart Foundation (FS/19/19/34175)
Department of Health and Social Care
Royal Society
Diabetes UK
Public Health England
Medical Research Council (MR/S003940/1)
NIHR Oxford Biomedical Research Centre
National Institute for Health and Care Research Applied Research Collaboration Oxford and Thames Valley
Topics: Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
Keywords: COVID-19 pandemic, COVID-19 mortality, risk factors, OpenSAFELY, primary care electronic health records, sex differences, age, socioeconomic deprivation, diabetes mellitus, severe asthma, ethnicity, NHS England, cohort studies
MeSH terms: Betacoronavirus, COVID-19, SARS-CoV-2, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Asthma, Diabetes Mellitus, Female, Humans, Hypertension, Male, Middle Aged, Pneumonia, Viral, Sex Characteristics, Smoking, State Medicine, Cohort Studies, Proportional Hazards Models, Age Distribution, Coronavirus Infections, Risk Assessment, Black People, Asian People, Young Adult, Pandemics

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