O-144 Incidence of opioid-related harms by occupation in Ontario, Canada: findings from the occupational disease surveillance system
Bibliographic record
Abstract
Introduction The opioid crisis continues unabated in Canada, yet current health surveillance systems that monitor opioid-related harms have limited or no employment information. The limited opioid overdose fatality data available suggest certain occupational groups have been disproportionately affected among those with known employment, namely those in construction and trades occupations, but little is known beyond these data. The Occupational Disease Surveillance System (ODSS), designed to detect work-related disease in a large cohort of workers in Ontario (Canada), was recently expanded to identify opioid-related hospitalizations and emergency department visits. We sought to estimate associations between occupation and risk of opioid-related harms in the Ontario, Canada workforce. Materials and Methods The ODSS was established through linkage of Workplace Safety and Insurance Board accepted workers’ compensation lost-time claims data to hospitalization and emergency department data. Workers aged 18–65 were followed from 2006 to 2020 to identify incident opioid-related poisonings (p) and mental and behavioural disorders (mb). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each of the opioid-related harms by occupation, adjusted for sex, age, and birth year. Results We identified 10,066 poisoning cases and 11,762 mental and behavioural disorder cases during follow-up among 1.7 million workers. Preliminary findings demonstrate consistent elevated risks for occupations in construction and trades (p: HR=1.57, 95% CI=1.48–1.67, mb: HR=1.59, 95% CI=1.51–1.68), forestry and logging (p: HR=1.45, 95% CI=1.09–1.94, mb: HR=1.70, 95% CI=1.34–2.16), materials handling and related (p: HR=1.32, 95% CI=1.22–1.43, mb: HR=1.22, 95% CI=1.13–1.31), processing (mineral, metal, chemical) (p: HR=1.27, 95% CI=1.14–1.42, mb: HR=1.26, 95% CI=1.14–1.39), among other occupations. Conclusions Results suggest opioid-related harms cluster among certain occupational groups in the Ontario workforce, some of which are consistent with fatality data. Identification of high-risk subgroups by occupation will help inform targeted prevention and harm reduction activities.
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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 | 0.002 | 0.006 |
| 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) | 0.002 | 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".