Davies, Nicholas G; Jarvis, Christopher I; CMMID COVID-19 Working Group; Edmunds, W John; Jewell, Nicholas P; Diaz-Ordaz, Karla; Keogh, Ruth H; (2021) Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7. Nature, 593 (7858). pp. 270-274. ISSN 0028-0836 DOI: https://doi.org/10.1038/s41586-021-03426-1
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Abstract
SARS-CoV-2 lineage B.1.1.7, a variant that was first detected in the UK in September 20201, 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 severity2. 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.
Item Type | Article |
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Faculty and Department |
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology & Dynamics (2023-) Faculty of Epidemiology and Population Health > Dept of Medical Statistics |
Research Centre |
Covid-19 Research Centre for the Mathematical Modelling of Infectious Diseases ?? 181801 ?? |
PubMed ID | 33723411 |
Elements ID | 158137 |
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