Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales [version 1; peer review: 1 approved, 2 approved with reservations]

Vincent Nguyen; Max T Eyre; Robert W Aldridge; Anna Aryee; Parth Patel; Wing Lam Erica Fong; Colette Smith; Sarah Beale; Anne M. Johnson; Nicholas Patni; Helen Pineo; Jonathon Taylor; Isobel Braithwaite; Srinivasa Vittal Katikireddi; Cyril Geismar; Ellen Fragaszy; Madhumita Shrotri; Thomas Byrne; Delan Devakumar; Annalan M.D. Navaratnam; Jana Kovar; Andrew Hayward

Wellcome Open Research (Dec 2021)

Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales [version 1; peer review: 1 approved, 2 approved with reservations]

Vincent Nguyen,
Max T Eyre,
Robert W Aldridge,
Anna Aryee,
Parth Patel,
Wing Lam Erica Fong,
Colette Smith,
Sarah Beale,
Anne M. Johnson,
Nicholas Patni,
Helen Pineo,
Jonathon Taylor,
Isobel Braithwaite,
Srinivasa Vittal Katikireddi,
Cyril Geismar,
Ellen Fragaszy,
Madhumita Shrotri,
Thomas Byrne,
Delan Devakumar,
Annalan M.D. Navaratnam,
Jana Kovar,
Andrew Hayward

Affiliations

Vincent Nguyen: Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Max T Eyre: ORCiD; Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
Robert W Aldridge: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Anna Aryee: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Parth Patel: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Wing Lam Erica Fong: Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Colette Smith: Institute for Global Health, University College London, London, UK
Sarah Beale: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Anne M. Johnson: ORCiD; Institute for Global Health, University College London, London, UK
Nicholas Patni: University of Oxford Medical School, Medical Sciences Divisional Office, University of Oxford, Oxford, UK
Helen Pineo: ORCiD; Institute for Environmental Design and Engineering, Bartlett School of Environment, Energy and Resources, University College London, London, WC1H 0NN, UK
Jonathon Taylor: ORCiD; Department of Civil Engineering, Tampere University, Tampere, Finland
Isobel Braithwaite: Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Srinivasa Vittal Katikireddi: ORCiD; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing,, University of Glasgow, Glasgow, UK
Cyril Geismar: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Ellen Fragaszy: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Madhumita Shrotri: Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Thomas Byrne: Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Delan Devakumar: ORCiD; Institute for Global Health, University College London, London, UK
Annalan M.D. Navaratnam: ORCiD; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
Jana Kovar: ORCiD; Institute of Epidemiology and Health Care, University College London, London, UK
Andrew Hayward: ORCiD; Institute of Epidemiology and Health Care, University College London, London, UK

Journal volume & issue: Vol. 6

Abstract

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Background: Household overcrowding is associated with increased risk of infectious diseases across contexts and countries. Limited data exist linking household overcrowding and risk of COVID-19. We used data collected from the Virus Watch cohort to examine the association between overcrowded households and SARS-CoV-2. Methods: The Virus Watch study is a household community cohort of acute respiratory infections in England and Wales. We calculated overcrowding using the measure of persons per room for each household. We considered two primary outcomes: PCR-confirmed positive SARS-CoV-2 antigen tests and laboratory-confirmed SARS-CoV-2 antibodies. We used mixed-effects logistic regression models that accounted for household structure to estimate the association between household overcrowding and SARS-CoV-2 infection. Results: 26,367 participants were included in our analyses. The proportion of participants with a positive SARS-CoV-2 PCR result was highest in the overcrowded group (9.0%; 99/1,100) and lowest in the under-occupied group (4.2%; 980/23,196). In a mixed-effects logistic regression model, we found strong evidence of an increased odds of a positive PCR SARS-CoV-2 antigen result (odds ratio 2.45; 95% CI:1.43–4.19; p-value=0.001) and increased odds of a positive SARS-CoV-2 antibody result in individuals living in overcrowded houses (3.32; 95% CI:1.54–7.15; p-value<0.001) compared with people living in under-occupied houses. Conclusion: Public health interventions to prevent and stop the spread of SARS-CoV-2 should consider the risk of infection for people living in overcrowded households and pay greater attention to reducing household transmission.

Published in Wellcome Open Research

ISSN: 2398-502X (Online)
Publisher: Wellcome
Country of publisher: United Kingdom
LCC subjects: Medicine; Science
Website: https://wellcomeopenresearch.org/

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