You are here

  • Home
  • Online First
  • Work-related and personal predictors of COVID-19 transmission: evidence from the UK and USA

Article Text

Article menu

other Versions

Download PDFPDF
Original research
Work-related and personal predictors of COVID-19 transmission: evidence from the UK and USA
  1. Paul Anand1,2,3,
  2. Heidi L Allen4,
  3. Robert L Ferrer5,
  4. Natalie Gold2,6,
  5. Rolando Manuel Gonzales Martinez7,
  6. Evangelos Kontopantelis8,
  7. Melanie Krause9,
  8. Francis Vergunst10
  1. 1Department of Economics, The Open University, Milton Keynes, UK
  2. 2CPNSS, The London School of Economics and Political Science, London, UK
  3. 3Social Policy and Intervention, Oxford University, Oxford, UK
  4. 4School of Social Work, Columbia University, New York, New York, USA
  5. 5Family and Community Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
  6. 6Public Health England, London, UK
  7. 7Agder University College, Kristiansand, Norway
  8. 8Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
  9. 9MRC Lab for Molecular Cell Biology, University College London, London, UK
  10. 10Public Health, University of Montreal, Montreal, Québec, Canada
  1. Correspondence to Professor Paul Anand, The Open University, Milton Keynes MK7 6AA, UK; pa68{at}open.ac.uk

Abstract

Objective To develop evidence of work-related and personal predictors of COVID-19 transmission.

Setting and respondents Data are drawn from a population survey of individuals in the USA and UK conducted in June 2020.

Background methods Regression models are estimated for 1467 individuals in which reported evidence of infection depends on work-related factors as well as a variety of personal controls.

Results The following themes emerge from the analysis. First, a range of work-related factors are significant sources of variation in COVID-19 infection as indicated by self-reports of medical diagnosis or symptoms. This includes evidence about workplace types, consultation about safety and union membership. The partial effect of transport-related employment in regression models makes the chance of infection over three times more likely while in univariate analyses, transport-related work increases the risk of infection by over 40 times in the USA. Second, there is evidence that some home-related factors are significant predictors of infection, most notably the sharing of accommodation or a kitchen. Third, there is some evidence that behavioural factors and personal traits (including risk preference, extraversion and height) are also important.

Conclusions The paper concludes that predictors of transmission relate to work, transport, home and personal factors. Transport-related work settings are by far the greatest source of risk and so should be a focus of prevention policies. In addition, surveys of the sort developed in this paper are an important source of information on transmission pathways within the community.

  • environmental epidemiology
  • health inequalities
  • multilevel modelling
  • policy
  • psychosocial factors

Data availability statement

Data are available in a public, open-access repository. Data are available from a link in the online supplemental materials at https://osf.io/v9t8a/?view_only=8531e8dd672f41e6bf532e280a2f31e6.

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

https://bmj.com/coronavirus/usage

https://doi.org/10.1136/jech-2020-215208

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Data availability statement

    Data are available in a public, open-access repository. Data are available from a link in the online supplemental materials at https://osf.io/v9t8a/?view_only=8531e8dd672f41e6bf532e280a2f31e6.

    View Full Text

    Footnotes

    • Twitter @dataevan

    • Contributors PA contributed to all aspects of the paper. HLA, RLF, NG, MK and FV contributed to the analysis and write-up of the paper. EK contributed to write-up and statistical design while RMGM was responsible for conducting the statistical analysis.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; internally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.