Incidence of severe COVID-19 among 1.2 million workers in Ontario, Canada

Certain groups of workers in this cohort demonstrated elevated risks of severe COVID-19. The findings align with previous studies and emphasize the need to include occupational surveillance methods in future pandemic preparedness in Canada.

Authors

Institutions

Publication Details

Journal
Occupational Medicine
Published
2025-04-01
DOI
https://doi.org/10.1093/occmed/kqaf017
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Incidence of severe COVID-19 among 1.2 million workers in Ontario, Canada

Victoria H Arrandale, Brendan T. Smith, Tracy L Kirkham, Chaojie Song et al.
Occupational Medicine
COVID-19 epidemiological studies
article

Incidence of severe COVID-19 among 1.2 million workers in Ontario, Canada

Victoria H Arrandale, Brendan T. Smith, Tracy L Kirkham, Chaojie Song, Marianne Harris, Jeavana Sritharan, Paul A. Demers, John Kim
article en

Abstract

Certain groups of workers in this cohort demonstrated elevated risks of severe COVID-19. The findings align with previous studies and emphasize the need to include occupational surveillance methods in future pandemic preparedness in Canada.

Occupational MedicineVol. 75(3-4)
3M (United States) (US), University of Toronto (CA), Occupational Cancer Research Centre (CA), Public Health Ontario (CA), Toronto Metropolitan University (CA)
Workplace Safety and Insurance Board
Good health and well-being
Openalex Percentile: Top 89%
COVID-19 epidemiological studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.