Evidence map›Paper›PMID 39286314›Full record

ArticleNIHR open research2023

The changing contributory role to infections of work, public transport, shopping, hospitality and leisure activities throughout the SARS-CoV-2 pandemic in England and Wales.

Susan Hoskins, Sarah Beale, Vincent G Nguyen, Thomas Byrne, Alexei Yavlinsky, Jana Kovar, Erica Wing Lam Fong, Cyril Geismar, Annalan M D Navaratnam, Martie van Tongeren and 3 more

Abstract read
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In one paragraph

Article in NIHR open research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Susan HoskinsInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0002-0418-309X
Sarah BealeInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Vincent G NguyenInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0002-9776-6242
Thomas ByrneInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Alexei YavlinskyInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Jana KovarInstitute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK.
Erica Wing Lam FongInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Cyril GeismarInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0002-8486-5890
Annalan M D NavaratnamInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0002-8141-5923
Martie van TongerenCentre for Occupational and Environmental Health, The University of Manchester, Manchester, England, UK.
Anne M JohnsonInstitute for Global Health, University College London, London, England, WC1E 6BT, UK.
Robert W AldridgeInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Andrew HaywardInstitute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: Understanding how non-household activities contributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections under different levels of national health restrictions is vital. Methods: Among adult Virus Watch participants in England and Wales, we used multivariable logistic regressions and adjusted-weighted population attributable fractions (aPAF) assessing the contribution of work, public transport, shopping, and hospitality and leisure activities to infections. Results: Under restrictions, among 17,256 participants (502 infections), work [adjusted odds ratio (aOR) 2.01 (1.65-2.44), (aPAF) 30% (22-38%)] and transport [(aOR 1.15 (0.94-1.40), aPAF 5% (-3-12%)], were risk factors for SARS-CoV-2 but shopping, hospitality and leisure were not. Following the lifting of restrictions, among 11,413 participants (493 infections), work [(aOR 1.35 (1.11-1.64), aPAF 17% (6-26%)] and transport [(aOR 1.27 (1.04-1.57), aPAF 12% (2-22%)] contributed most, with indoor hospitality [(aOR 1.21 (0.98-1.48), aPAF 7% (-1-15%)] and leisure [(aOR 1.24 (1.02-1.51), aPAF 10% (1-18%)] increasing. During the Omicron variant, with individuals more socially engaged, among 11,964 participants (2335 infections), work [(aOR 1.28 (1.16-1.41), aPAF (11% (7-15%)] and transport [(aOR 1.16 (1.04-1.28), aPAF 6% (2-9%)] remained important but indoor hospitality [(aOR 1.43 (1.26-1.62), aPAF 20% (13-26%)] and leisure [(aOR 1.35 (1.22-1.48), aPAF 10% (7-14%)] dominated. Conclusions: Work and public transport were important to transmissions throughout the pandemic with hospitality and leisure's contribution increasing as restrictions were lifted, highlighting the importance of restricting leisure and hospitality alongside advising working from home, when facing a highly infectious and virulent respiratory infection.

Indexed as

hospitalityleisureSARS-CoV-2social-activitiestransportwork

Identifiers

PMID39286314
PMCPMC11403290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.