Risk of opioid-related harms by occupation within a large cohort of formerly injured workers in Ontario, Canada: findings from the Occupational Disease Surveillance System
Bibliographic record
Abstract
Objective Working-age individuals have been disproportionately affected by the opioid crisis, prompting interest in the potential role of occupation as a contributor. This study aimed to estimate the risk of opioid-related poisonings and mental and behavioural disorders by occupation and industry within a cohort of 1.7 million formerly injured workers. Methods Workers were identified in the Occupational Disease Surveillance System, a system linking workers’ compensation data (1983–2019) to emergency department and hospitalisation records (2006–2020) in Ontario, Canada. Cox proportional hazards models were used to estimate HRs and 95% CIs for hospital encounters for opioid-related poisonings and mental and behavioural disorders by occupation and industry compared with all other workers, adjusted for age, sex and birth year. Results In total, 13 702 opioid-related poisoning (p) events (n=10 064 workers) and 19 629 opioid-related mental and behavioural (mb) disorder events (n=11 755 workers) were observed. Elevated risks were identified among workers in forestry and logging (HR p =1.45, 95% CI 1.09 to 1.94; HR mb =1.70, 95% CI 1.34 to 2.16); processing (minerals, metals, clay, chemical) (HR p =1.27, 95% CI 1.14 to 1.42; HR mb =1.26, 95% CI 1.14 to 1.39); processing (food, wood, textile) (HR p =1.12, 95% CI 1.01 to 1.24; HR mb =1.19, 95% CI 1.09 to 1.31); machining (HR p =1.13, 95% CI 1.04 to 1.21; HR mb =1.17, 95% CI 1.09 to 1.25); construction trades (HR p =1.57, 95% CI 1.48 to 1.67; HR mb =1.59, 95% CI 1.51 to 1.68); materials handling (HR p =1.32, 95% CI 1.22 to 1.43; HR mb =1.22, 95% CI 1.13 to 1.31); mining and quarrying (HR mb =1.68, 95% CI 1.34 to 2.11); and transport equipment operating occupations (HR p =1.18, 95% CI 1.09 to 1.27). Elevated risks were observed among select workers in service, sales, clerical and health. Findings by industry were similar. Conclusions Results provide additional evidence that opioid-related harms cluster among certain occupational groups. Findings can be used to strategically target prevention and harm reduction activities in the workplace.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".