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. 2021 Jul:6:100109.
doi: 10.1016/j.lanepe.2021.100109. Epub 2021 May 8.

Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform

Krishnan Bhaskaran  1 , Sebastian Bacon  2 , Stephen Jw Evans  1 , Chris J Bates  3 , Christopher T Rentsch  1 , Brian MacKenna  2 , Laurie Tomlinson  1 , Alex J Walker  2 , Anna Schultze  1 , Caroline E Morton  2 , Daniel Grint  1 , Amir Mehrkar  2 , Rosalind M Eggo  1 , Peter Inglesby  2 , Ian J Douglas  1 , Helen I McDonald  1 , Jonathan Cockburn  3 , Elizabeth J Williamson  1 , David Evans  2 , Helen J Curtis  2 , William J Hulme  2 , John Parry  3 , Frank Hester  3 , Sam Harper  3 , David Spiegelhalter  4 , Liam Smeeth  1 , Ben Goldacre  2
Affiliations

Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform

Krishnan Bhaskaran et al. Lancet Reg Health Eur. 2021 Jul.

Abstract

Background: Mortality from COVID-19 shows a strong relationship with age and pre-existing medical conditions, as does mortality from other causes. We aimed to investigate how specific factors are differentially associated with COVID-19 mortality as compared to mortality from causes other than COVID-19.

Methods: Working on behalf of NHS England, we carried out a cohort study within the OpenSAFELY platform. Primary care data from England were linked to national death registrations. We included all adults (aged ≥18 years) in the database on 1st February 2020 and with >1 year of continuous prior registration; the cut-off date for deaths was 9th November 2020. Associations between individual-level characteristics and COVID-19 and non-COVID deaths, classified according to the presence of a COVID-19 code as the underlying cause of death on the death certificate, were estimated by fitting age- and sex-adjusted logistic models for these two outcomes.

Findings: 17,456,515 individuals were included. 17,063 died from COVID-19 and 134,316 from other causes. Most factors associated with COVID-19 death were similarly associated with non-COVID death, but the magnitudes of association differed. Older age was more strongly associated with COVID-19 death than non-COVID death (e.g. ORs 40.7 [95% CI 37.7-43.8] and 29.6 [28.9-30.3] respectively for ≥80 vs 50-59 years), as was male sex, deprivation, obesity, and some comorbidities. Smoking, history of cancer and chronic liver disease had stronger associations with non-COVID than COVID-19 death. All non-white ethnic groups had higher odds than white of COVID-19 death (OR for Black: 2.20 [1.96-2.47], South Asian: 2.33 [2.16-2.52]), but lower odds than white of non-COVID death (Black: 0.88 [0.83-0.94], South Asian: 0.78 [0.75-0.81]).

Interpretation: Similar associations of most individual-level factors with COVID-19 and non-COVID death suggest that COVID-19 largely multiplies existing risks faced by patients, with some notable exceptions. Identifying the unique factors contributing to the excess COVID-19 mortality risk among non-white groups is a priority to inform efforts to reduce deaths from COVID-19.

Funding: Wellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, Health Data Research UK.

Keywords: COVID-19; Epidemiology; Mortality.

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Conflict of interest statement

TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. BG's work on better use of data in healthcare more broadly is currently funded in part by: 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 BG's grants on this work. LS reports grants from Wellcome, MRC, NIHR, UKRI, British Council, GSK, British Heart Foundation, and Diabetes UK outside this work. KB held a Sir Henry Dale fellowship (grant: 107731/Z/15/Z) jointly funded by Wellcome and the Royal Society and a Wellcome Senior Research Fellowship (grant: 220283/Z/20/Z) during this work. HIM is funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Immunisation, a partnership between Public Health England and LSHTM. EW holds grants from MRC. ID golds grants from NIHR and GSK. HF holds a UKRI fellowship. RME is funded by HDR UK (grant: MR/S003975/1) and MRC (grant: MC_PC 19065). 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. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Figures

Fig 1
Fig. 1
Flow chart of participants in the primary study cohort.
Fig. 2
Fig. 2
Estimated probability of death from different causes over the period between 1st February and 9th November 2020, by age group FOOTNOTES: Data from 1st February 2020 to 9th November 2020. Probabilities estimated from a multinomial logistic regression model with alive versus died from specific causes as outcomes, and with age group and sex fitted as covariates; estimates are standardised to a 50% male/female gender balance within each age group. Dementia includes Alzheimer's. CVD = cardiovascular diseases. For numerical estimates and 95% CIs please see appendix Table A1.
Fig. 3
Fig. 3
Odds ratios for the association between demographic and lifestyle-related factors and COVID-19 and non-COVID mortality, adjusted for age, sex and STP FOOTNOTES: Estimates for each covariate were produced by fitting two age, sex and STP-adjusted logistic models with outcomes of COVID-19 death and death from other causes respectively. Data from 1st February 2020 to 9th November 2020.
Fig. 4
Fig. 4
Odds ratios for the association between comorbidities and COVID-19 and non-COVID mortality, adjusted for age, sex and STP FOOTNOTES: Estimates for each covariate were produced by fitting two age, sex and STP-adjusted logistic models with outcomes of COVID-19 death and death from other causes respectively. Data from 1st February 2020 to 9th November 2020.
Fig. 5:
Fig. 5
Odds ratios for the association between ethnicity and COVID-19 death and death from specific other causes, adjusted for age, sex, and STP FOOTNOTES:From separate logistic regression models for each cause-specific mortality outcome, with age (spline), sex, STP and ethnicity as covariates. Note: the dementia outcome included Alzheimer's and the model was restricted to those aged ≥40y due to non-convergence when younger people were included. Data from 1st February 2020 to 9th November 2020.
Fig. 6:
Fig. 6
Odds ratio for COVID-19 cause of death (versus non-COVID causes) among those who died FOOTNOTES: Note that the odds ratio presented here are modelling the association between individual factors and the odds of a COVID-19 cause of death, among those who died. They cannot be interpreted as showing how factors are associated with the odds of death occurring (for this, see Fig. 3, Fig. 4). Estimates are from individual age, sex and STP-adjusted logistic regression models for each factor of interest, including only individuals that died, and with an outcome of COVID-19 cause of death. Age was parameterised as a 4-knot restricted cubic spline in all models, except to estimate the effect of age itself, where a linear age term was used for ease of presentation and interpretation. Data from 1st February 2020 to 9th November 2020.
See this image and copyright information in PMC

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