Your browser doesn't support javascript.
loading
Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales
Robert W Aldridge; Helen Pineo; Ellen Fragaszy; Max Eyre; Jana Kovar; Vincent Nguyen; Sarah Beale; Thomas Byrne; Anna Aryee; Colette Smith; Delanjathan Devakumar; Jonathon Taylor; Vittal Katikireddi; Wing Lam Erica Fong; Cyril Geismar; Parth Patel; Madhumita Shrotri; Isobel Braithwaite; Annalan M D Navaratnam; Anne M Johnson; Andrew Hayward.
Afiliação
  • Robert W Aldridge; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Helen Pineo; Institute for Environmental Design and Engineering, Bartlett School of Environment, Energy and Resources, University College London, London, UK.
  • Ellen Fragaszy; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK; Institute of Epidemiology and Health Care, University Col
  • Max Eyre; Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW, UK
  • Jana Kovar; Institute of Epidemiology and Health Care, University College London, London, UK
  • Vincent Nguyen; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Sarah Beale; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK; Institute of Epidemiology and Health Care, University Col
  • Thomas Byrne; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Anna Aryee; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Colette Smith; Institute for Global Health, University College London, London, UK.
  • Delanjathan Devakumar; Institute for Global Health, University College London, London, UK.
  • Jonathon Taylor; Department of Civil Engineering, Tampere University, Finland.
  • Vittal Katikireddi; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing, Glasgow, UK.
  • Wing Lam Erica Fong; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Cyril Geismar; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK; Institute of Epidemiology and Health Care, University Col
  • Parth Patel; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Madhumita Shrotri; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Isobel Braithwaite; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK.
  • Annalan M D Navaratnam; Centre for Public Health Data Science, Institute of Health Informatics, University College London, UK; Institute of Epidemiology and Health Care, University Col
  • Anne M Johnson; Institute for Global Health, University College London, London, UK.
  • Andrew Hayward; Institute of Epidemiology and Health Care, University College London, London, UK
Preprint em En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21256912
ABSTRACT
BackgroundHousehold 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. MethodsThe Virus Watch study is a household community cohort of acute respiratory infections in England & Wales that began recruitment in June 2020. We calculated the persons per room for each household and classified accommodation as overcrowded when the number of rooms{square}was fewer than the number of people. We considered two primary outcomes - PCR-confirmed positive SARS-CoV-2 antigen tests and laboratory confirmed SARS-CoV-2 antibodies (Roche Elecsys anti-N total immunoglobulin assay). We used mixed effects logistic regression models that accounted for household structure to estimate the association between household overcrowding and SARS-CoV-2 infection. ResultsThe proportion of participants with a positive SARS-CoV-2 PCR result was highest in the overcrowded group (6.6%; 73/1,102) and lowest in the under-occupied group (2.9%; 682/23,219). In a mixed effects logistic regression model that included age, sex, ethnicity, household income and geographical region, we found strong evidence of an increased odds of having a positive PCR SARS-CoV-2 antigen result (Odds Ratio 3.72; 95% CI 1.92, 7.13; p-value < 0.001) and increased odds of having a positive SARS-CoV-2 antibody result in individuals living in overcrowded houses (2.96; 95% CI 1.13, 7.74; p-value =0.027) compared to people living in under-occupied houses. The proportion of variation at the household level was 9.91% and 9.97% in the PCR and antibody models respectively. DiscussionPublic health interventions to prevent and stop the spread of SARS-CoV-2 should consider the much greater risk of infection for people living in overcrowded households and pay greater attention to reducing household transmission. There is an urgent need to better recognise housing as a leading determinant of health in the context of a pandemic and beyond.
Licença
cc_by
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Cohort_studies / Observational_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Cohort_studies / Observational_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Preprint