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

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Factors associated with COVID-19-related death using OpenSAFELY

Open access
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

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-020-2521-4

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Figures

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.
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 platform
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.
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 sex
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.
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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Research works printed but not linked: 1

  1. 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).

Cited by

Sorted by citations

Items 301–400 of 1,015

Related

See all

Metadata

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
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
Licence
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review