Cited by 957SARS-CoV-2 and COVID-19 ResearchCOVID-19 Clinical Research StudiesCOVID-19 epidemiological studies

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index; 1 of the 9 sources its claims cite were checked for support.

Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7

Open access
CMMID COVID-19 Working Group, Davies, Nicholas G., Jarvis, Christopher I., Edmunds, W. John, Jewell, Nicholas P., Diaz-Ordaz, Karla, Keogh, Ruth H.·2021·Nature

Published version of a preprint

Abstract

SARS-CoV-2 lineage B.1.1.7, a variant that was first detected in the UK in September 2020 1, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than pre-existing variants, but have not identified whether it leads to any change in disease severity 2. Here we analyse a dataset that links 2,245,263 positive SARS-CoV-2 community tests and 17,452 deaths associated with COVID-19 in England from 1 November 2020 to 14 February 2021. For 1,146,534 (51%) of these tests, the presence or absence of B.1.1.7 can be identified because mutations in this lineage prevent PCR amplification of the spike (S) gene target (known as S gene target failure (SGTF)1). On the basis of 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% confidence interval, 39-72%) higher than in cases without SGTF after adjustment for age, sex, ethnicity, deprivation, residence in a care home, the local authority of residence and test date. This corresponds to the absolute risk of death for a 55-69-year-old man increasing from 0.6% to 0.9% (95% confidence interval, 0.8-1.0%) within 28 days of a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate that the hazard of death associated with B.1.1.7 is 61% (42-82%) higher than with pre-existing variants. Our analysis suggests that B.1.1.7 is not only more transmissible than pre-existing SARS-CoV-2 variants, but may also cause more severe illness.

Overview

Integrity signals

Registry

Crossref · 10.1038/s41586-021-03426-1

Cites

15 of 15 references verified0 citation errors · 0 not checkable

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Claims the paper makes

26 Own result · 14 Own method · 11 Own interpretation · 5 Not checked yet · 5 No basis given · 1 Supported

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Show all 25 claims

  • Probably not a substantial claim. 26
  • It may state two separate claims as one. 25
37 of 37
  • The authors state this as an assumption.

    In the complete-case analysis, death is independent of whether SGTF status is observed or missing, given the individual's other characteristics included in the survival model.

    “The analysis of the complete cases assumes that whether an individual dies is independent of whether their SGTF status is observed or missing, given the individual's other characteristics included in the survival model”
  • The authors state this as an assumption.

    In the IPW analysis, whether an individual has SGTF is independent of whether their SGTF status is observed or missing, given the individual's other characteristics included in the model used to derive weights.

    “whereas the IPW analysis assumes that whether an individual has SGTF is independent of whether their SGTF status is observed or missing, given the individual's other characteristics included in the model used to derive weights for IPW 5 .”
  • The authors state this as an assumption.

    The baseline risk of death among individuals tested in the community between August and October 2020 is illustrative of the case fatality ratio associated with pre-existing SARS-CoV-2 variants.

    “applying hazard ratios for SGTF to the baseline risk of death among individuals tested in the community between August and October 2020 (assumed to be illustrative of the case fatality ratio associated with pre-existing variants of SARS-CoV-2)”
  • The authors state this as an assumption.

    The complete-case analysis assumes that missing SGTF status is independent of the outcome given the variables in the models, a type of missing-not-at-random assumption that may depend on the underlying SGTF value.

    “This analysis of complete cases assumes that for each analysis, the missing data-in this case missing SGTF status-are independent of the outcome of interest given the variables included in the models.This is a specific type of 'missing not at random' assumption, as in particular it is allowed to depend on the underlying value of SGTF.”
  • The authors state this as an assumption.

    The IPW analysis assumes that SGTF status is missing at random.

    “We also performed an analysis of the complete cases using inverse probability weights 5 (IPW) to address the missing data on SGTF, under a 'missing at random' assumption.”
  • The authors state this as an assumption.

    The analysis assumes that censoring is uninformative.

    “The analysis assumes that censoring is uninformative, which is plausible as all censoring is administrative.”
  • The authors state this as an assumption.

    A negative SGTF test is assumed to indicate absence of B.1.1.7 infection.

    “A negative test of SGTF is assumed to be indicative of an absence of infection with B.1.1.7.”
  • The authors state this as an assumption.

    Poisson confidence intervals for mortality rates assume constant rates.

    “Exact Poisson confidence intervals are used for mortality rates, assuming constant rates.”
  • The authors state this as an assumption.

    The Cox model assumes proportional hazards for SGTF and the covariates.

    “We began by assuming the proportionality of the hazards for SGTF and the covariates included in the model.”
9 of 9

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Metadata

Authors
CMMID COVID-19 Working Group, Davies, N. G., Jarvis, C. I., Edmunds, W. J., Jewell, N. P., Diaz-Ordaz, K., & Keogh, R. H.
Affiliations
London School of Hygiene & Tropical Medicine (GB)
Corresponding author
Davies, Nicholas G.
Venue
Nature
Published
2021-03-15
Volume
593
Issue
7858
Pages
270-274
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W3137149656
Cited by
921
References
12
Citations by year
2026: 12 · 2025: 37 · 2024: 74 · 2023: 130 · 2022: 303 · 2021: 362
FWCI
68.81
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
Funders
  • Wellcome Trust (208812/Z/17/Z, 206471/Z/17/Z, 218554/Z/19/Z)
  • UK Research and Innovation (ES/P010873/1, MR/S017968/1, ES/P010873)
  • National Institute for Health Research Health Protection Research Unit (NIHR200908, NIHR200929)
  • National Institute for Health and Care Research ((NIHR200929), (NIHR200908), NIHR-INF-1846)
  • Research Councils UK (ES/P010873/1)
  • European Commission (101003688)
  • Public Health England
  • National Institutes of Health
  • Medical Research Council (NIHR200908, MR/S017968/1, MC_PC_19065)
  • Economic and Social Research Council (ES/P010873/1)
  • National Institute of Allergy and Infectious Diseases (R01AI148127)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
Keywords
COVID-19 mortality, S-gene target failure, spike gene mutations, disease severity, SARS-CoV-2 variants, England
MeSH terms
COVID-19, SARS-CoV-2, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, England, Ethnicity, Female, Homes for the Aged, Humans, Infant, Male, Middle Aged, Phylogeny, Time Factors, Proportional Hazards Models, Survival Analysis, Risk Assessment, Evolution, Molecular, Young Adult

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