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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".