Workplace contact patterns in England during the COVID-19 pandemic: Analysis of the Virus Watch prospective cohort study.

COVID-19 Epidemiology Occupation Public health

Journal

The Lancet regional health. Europe
ISSN: 2666-7762
Titre abrégé: Lancet Reg Health Eur
Pays: England
ID NLM: 101777707

Informations de publication

Date de publication:
May 2022
Historique:
pubmed: 28 4 2022
medline: 28 4 2022
entrez: 27 4 2022
Statut: ppublish

Résumé

Workplaces are an important potential source of SARS-CoV-2 exposure; however, investigation into workplace contact patterns is lacking. This study aimed to investigate how workplace attendance and features of contact varied between occupations across the COVID-19 pandemic in England. Data were obtained from electronic contact diaries (November 2020-November 2021) submitted by employed/self-employed prospective cohort study participants ( Workplace attendance and contact patterns varied across occupations and time. The predicted probability of intense space sharing during the day was highest for healthcare (78% [95% CI: 75-81%]) and education workers (64% [59%-69%]), who also had the highest probabilities for larger numbers of close contacts (36% [32%-40%] and 38% [33%-43%] respectively). Education workers also demonstrated relatively low predicted probability (51% [44%-57%]) of wearing a face covering during close contact. Across all occupational groups, workspace sharing and close contact increased and usage of face coverings decreased during phases of less stringent restrictions. Major variations in workplace contact patterns and mask use likely contribute to differential COVID-19 risk. Patterns of variation by occupation and restriction phase may inform interventions for future waves of COVID-19 or other respiratory epidemics. Across occupations, increasing workplace contact and reduced face covering usage is concerning given ongoing high levels of community transmission and emergence of variants. Medical Research Council; HM Government; Wellcome Trust.

Sections du résumé

Background UNASSIGNED
Workplaces are an important potential source of SARS-CoV-2 exposure; however, investigation into workplace contact patterns is lacking. This study aimed to investigate how workplace attendance and features of contact varied between occupations across the COVID-19 pandemic in England.
Methods UNASSIGNED
Data were obtained from electronic contact diaries (November 2020-November 2021) submitted by employed/self-employed prospective cohort study participants (
Findings UNASSIGNED
Workplace attendance and contact patterns varied across occupations and time. The predicted probability of intense space sharing during the day was highest for healthcare (78% [95% CI: 75-81%]) and education workers (64% [59%-69%]), who also had the highest probabilities for larger numbers of close contacts (36% [32%-40%] and 38% [33%-43%] respectively). Education workers also demonstrated relatively low predicted probability (51% [44%-57%]) of wearing a face covering during close contact. Across all occupational groups, workspace sharing and close contact increased and usage of face coverings decreased during phases of less stringent restrictions.
Interpretation UNASSIGNED
Major variations in workplace contact patterns and mask use likely contribute to differential COVID-19 risk. Patterns of variation by occupation and restriction phase may inform interventions for future waves of COVID-19 or other respiratory epidemics. Across occupations, increasing workplace contact and reduced face covering usage is concerning given ongoing high levels of community transmission and emergence of variants.
Funding UNASSIGNED
Medical Research Council; HM Government; Wellcome Trust.

Identifiants

pubmed: 35475035
doi: 10.1016/j.lanepe.2022.100352
pii: S2666-7762(22)00045-X
pmc: PMC9023315
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100352

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V028375/1
Pays : United Kingdom

Informations de copyright

© 2022 The Authors.

Déclaration de conflit d'intérêts

AH serves on the UK New and Emerging Respiratory Virus Threats Advisory Group. AJ and AH are members of the COVID-19 transmission sub-group of the Scientific Advisory Group for Emergencies (SAGE). AJ is Chair of the UK Strategic Coordination of Health of the Public Research board and is a member of COVID National Core studies oversight group.

Références

Nat Commun. 2021 Feb 9;12(1):893
pubmed: 33563992
BMJ Open. 2021 Jun 23;11(6):e048042
pubmed: 34162651
Prev Med. 2021 Dec;153:106833
pubmed: 34624386
Euro Surveill. 2020 Dec;25(50):
pubmed: 33334396
Occup Environ Med. 2022 Apr 21;:
pubmed: 35450951
Occup Med (Lond). 2020 Oct 27;70(7):470-472
pubmed: 32924061
Ann Intern Med. 2021 Jan;174(1):69-79
pubmed: 32941052
Eur J Epidemiol. 2019 Mar;34(3):211-219
pubmed: 30840181
Microbiol Spectr. 2021 Dec 22;9(3):e0133021
pubmed: 34908473
Euro Surveill. 2021 Oct;26(40):
pubmed: 34622761
PLoS One. 2020 Aug 6;15(8):e0237128
pubmed: 32760114
Occup Environ Med. 2022 Jul;79(7):433-441
pubmed: 34965981
BMJ. 2021 Dec 1;375:e065312
pubmed: 34853080

Auteurs

Sarah Beale (S)

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

Wing Lam Erica Fong (WLE)

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

Ellen Fragaszy (E)

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

Alexei Yavlinsky (A)

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

Anne M Johnson (AM)

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

Martie Van Tongeren (M)

Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK.

Robert W Aldridge (RW)

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

Andrew Hayward (A)

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

Classifications MeSH