Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London

Snell, Luke B, Wang, Wenjuan, Alcolea-Medina, Adela, Charalampous, Themoula, Batra, Rahul, de Jongh, Leonardo, Higgins, Finola, Nebbia, Gaia, Wang, Yanzhong, Edgeworth, Jonathan, Curcin, Vasa, The COVID-19 Genomics UK (COG-UK) Consortium, , Bashton, Matthew, Smith, Darren and Crown, Matthew (2022) Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London. BMJ Open, 12 (2). e055474. ISSN 2044-6055

[img]
Preview
Text
e055474.full.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (898kB) | Preview
Official URL: https://doi.org/10.1136/bmjopen-2021-055474

Abstract

Background The Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 emerged and became the dominant circulating variant in the UK in late 2020. Current literature is unclear on whether the Alpha variant is associated with increased severity. We linked clinical data with viral genome sequence data to compare admitted cases between SARS-CoV-2 waves in London and to investigate the association between the Alpha variant and the severity of disease. Methods Clinical, demographic, laboratory and viral sequence data from electronic health record systems were collected for all cases with a positive SARS-CoV-2 RNA test between 13 March 2020 and 17 February 2021 in a multisite London healthcare institution. Multivariate analysis using logistic regression assessed risk factors for severity as defined by hypoxia at admission. Results There were 5810 SARS-CoV-2 RNA-positive cases of which 2341 were admitted (838 in wave 1 and 1503 in wave 2). Both waves had a temporally aligned rise in nosocomial cases (96 in wave 1 and 137 in wave 2). The Alpha variant was first identified on 15 November 2020 and increased rapidly to comprise 400/472 (85%) of sequenced isolates from admitted cases in wave 2. A multivariate analysis identified risk factors for severity on admission, such as age (OR 1.02, 95% CI 1.01 to 1.03, for every year older; p<0.001), obesity (OR 1.70, 95% CI 1.28 to 2.26; p<0.001) and infection with the Alpha variant (OR 1.68, 95% CI 1.26 to 2.24; p<0.001). Conclusions Our analysis is the first in hospitalised cohorts to show increased severity of disease associated with the Alpha variant. The number of nosocomial cases was similar in both waves despite the introduction of many infection control interventions before wave 2.

Item Type: Article
Additional Information: Funding information: Funding FH, LBS, YW and VC are supported by the National Institute for Health Research (NIHR) Biomedical Research Centre programme of Infection and Immunity (RJ112/N027) based at Guy’s and St Thomas’ National Health Service NHS) Foundation Trust and King’s College London. This work was also supported by The Health Foundation and the Guy’s and St Thomas’ Charity. COG-UK is supported by funding from the Medical Research Council part of UK Research & Innovation, the NIHR and Genome Research Limited, operating as the Wellcome Sanger Institute. VC is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The authors acknowledge use of the research computing facility at King's College London, Rosalind (https://rosalind.kcl.ac.uk), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy’s and St Thomas’ NHS Foundation Trusts, and partly funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s and St Thomas’ Charity (TR130505). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, King’s College London or the Department of Health and Social Care. Matthew Bashton, Darren Smith and Matthew Crown are members of the COVID-19 Genomics UK consortium.
Uncontrolled Keywords: epidemiology, public health, virology
Subjects: A300 Clinical Medicine
B100 Anatomy, Physiology and Pathology
B900 Others in Subjects allied to Medicine
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 22 Jul 2022 13:30
Last Modified: 22 Jul 2022 13:30
URI: http://nrl.northumbria.ac.uk/id/eprint/49597

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics