Differential Risk of SARS-CoV-2 Infection by Occupation: Evidence from the Virus Watch prospective cohort study in England and Wales.

Infection Occupational health Pandemic SARS-CoV-2

Journal

Journal of occupational medicine and toxicology (London, England)
ISSN: 1745-6673
Titre abrégé: J Occup Med Toxicol
Pays: England
ID NLM: 101245790

Informations de publication

Date de publication:
03 Apr 2023
Historique:
received: 04 01 2023
accepted: 21 03 2023
medline: 5 4 2023
entrez: 4 4 2023
pubmed: 5 4 2023
Statut: epublish

Résumé

Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase. Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR). Increased risk was seen in nurses (aRR = 1.44, 1.25-1.65; AF = 30%, 20-39%), doctors (aRR = 1.33, 1.08-1.65; AF = 25%, 7-39%), carers (1.45, 1.19-1.76; AF = 31%, 16-43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30-49%), secondary school teachers (aRR = 1.48, 1.26-1.72; AF = 32%, 21-42%), and teaching support occupations (aRR = 1.42, 1.23-1.64; AF = 29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020-May 2021) and attenuated later (June-October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves. Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.

Sections du résumé

BACKGROUND BACKGROUND
Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase.
METHODS METHODS
Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR).
RESULTS RESULTS
Increased risk was seen in nurses (aRR = 1.44, 1.25-1.65; AF = 30%, 20-39%), doctors (aRR = 1.33, 1.08-1.65; AF = 25%, 7-39%), carers (1.45, 1.19-1.76; AF = 31%, 16-43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30-49%), secondary school teachers (aRR = 1.48, 1.26-1.72; AF = 32%, 21-42%), and teaching support occupations (aRR = 1.42, 1.23-1.64; AF = 29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020-May 2021) and attenuated later (June-October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves.
CONCLUSIONS CONCLUSIONS
Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.

Identifiants

pubmed: 37013634
doi: 10.1186/s12995-023-00371-9
pii: 10.1186/s12995-023-00371-9
pmc: PMC10068189
doi:

Types de publication

Journal Article

Langues

eng

Pagination

5

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 206602
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC 19070
Pays : United Kingdom

Investigateurs

Susan Michie (S)
Pia Hardelid (P)
Linda Wijlaars (L)
Eleni Nastouli (E)
Moira Spyer (M)
Ben Killingley (B)
Ingemar Cox (I)
Rachel A McKendry (RA)
Tao Cheng (T)
Yunzhe Liu (Y)
Jo Gibbs (J)
Richard Gilson (R)
Alison Rodger (A)

Informations de copyright

© 2023. The Author(s).

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Auteurs

Sarah Beale (S)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK. sarah.beale.19@ucl.ac.uk.
Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK. sarah.beale.19@ucl.ac.uk.

Susan Hoskins (S)

Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Thomas Byrne (T)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.

Wing Lam Erica Fong (WLE)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.

Ellen Fragaszy (E)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
Department of Infectious Disease Epidemiology, London, School of Hygiene and Tropical Medicine , Keppel Street, London, WC1E 7HT, UK.

Cyril Geismar (C)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Jana Kovar (J)

Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Annalan M D Navaratnam (AMD)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Vincent Nguyen (V)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Parth Patel (P)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.

Alexei Yavlinsky (A)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.

Anne M Johnson (AM)

Institute for Global Health, University College London, London, WC1N 1EH, UK.

Martie Van Tongeren (M)

Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, M13 9NT, UK.

Robert W Aldridge (RW)

Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.

Andrew Hayward (A)

Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.

Classifications MeSH