Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales

  1. Robert W Aldridge
  2. Helen Pineo
  3. Ellen Fragaszy
  4. Max Eyre
  5. Jana Kovar
  6. Vincent Nguyen
  7. Sarah Beale
  8. Thomas Byrne
  9. Anna Aryee
  10. Colette Smith
  11. Delanjathan Devakumar
  12. Jonathon Taylor
  13. Srinivasa Vittal Katikireddi
  14. Wing Lam Erica Fong
  15. Cyril Geismar
  16. Parth Patel
  17. Madhumita Shrotri
  18. Isobel Braithwaite
  19. Annalan M D Navaratnam
  20. Anne M Johnson
  21. Andrew Hayward

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Abstract

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 & 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□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.

Results

The 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 75.1% and 74.0% in the PCR and antibody models respectively.

Discussion

Public 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.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.10.21256912: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Covariates: Our analysis strategy was informed by conceptual models17,18 that have previously described the possible pathways between ethnicity and socio-economic status, overcrowding and risk of SARS-CoV-2 infection and we developed a Directed Acyclic Graph to inform covariate selection (Supplementary Appendix Figure S1).
    Covariates
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    An important additional limitation is that only households of up to six people were eligible for inclusion. By not including households of over six people we are likely underestimating the risk associated with overcrowding. Conversely, we cannot rule out residual confounding as a possible alternative explanation for some of the excess risk in the associations we describe. Access to SARS-CoV-2 antigen testing is socio-economically patterned, with those in more deprived areas having less ability to access tests and less likely to be contact traced.20 Our data may therefore under-estimate the true infection risk associated with overcrowded housing. To mitigate this bias in access to antigen testing, we analysed antibody data from the laboratory cohort where home test kits were provided to all adult participants. Our findings highlight the importance of public health interventions to prevent and stop the spread of SARS-CoV-2 considering the much greater risk of being infected for people living in overcrowded households. The pathways between overcrowding and increased risk of infection are not fully understood.10 Overcrowded households are more likely to be found in socioeconomically deprived and urban areas, and higher levels of COVID-19 in these communities include higher likelihood of working in public-facing and essential occupations, lower likelihood of working from home during lockdowns, constraints to self-isolation when ill or in contact with a case (both financial and due...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.05.10.21256912 on medRxiv