Incidence of severe COVID-19 among 1.2 million workers in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The disproportionate impact of coronavirus disease (COVID-19) on healthcare workers has been highlighted; however, there is a lack of evidence regarding other high-risk occupations and industries. AIMS: This study estimated the risk of severe COVID-19 among a large cohort of workers in Ontario, Canada. METHODS: This study used a cohort of 1.2 million workers identified using workers' compensation claims records (1983-2019). Identified workers were linked with emergency department (ED) visits and hospitalizations (2020-2021). Cases coded as U0.71 (virus detected, confirmed case) were identified from ED visits and hospitalizations. Hazard ratios (HRs) and 95% confidence intervals (CI95%) for COVID-19 for each occupational group compared to all other workers in the cohort were calculated, adjusting for age and birth year. Standardized incidence ratios and 95% CI, comparing workers to the general population of Ontario were also calculated, adjusting for age, sex, year and region. RESULTS: A total of 10 322 severe COVID-19 cases among workers were identified through ED visits and hospitalizations. Workers in material handling (HR=1.32, CI95%=1.21-1.43), medicine and health (HR=1.27, CI95%=1.18-1.37), processing (food, water, textile) (HR=1.23, CI95%=1.12-1.36) and machining occupations (HR=1.11, CI95%=1.02-1.20) had some of the highest risks of COVID-19 when compared to all other workers in the cohort. Findings were somewhat consistent when comparing workers to the general population of Ontario. CONCLUSIONS: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".