Examining lung cancer risks across different industries and occupations in Ontario, Canada: the establishment of the Occupational Disease Surveillance System
Bibliographic record
Abstract
BACKGROUND: The Occupational Disease Surveillance System (ODSS) was established in Ontario, Canada by linking a cohort of workers with data created from Workplace Safety and Insurance Board (WSIB) claims to administrative health databases. The aim of this study was to use ODSS to identify high-risk industry and occupation groups for lung cancer in Ontario. METHODS: Workers in the WSIB lost time claims database were linked to the Ontario Cancer Registry using subjects' health insurance numbers, name, sex, birthdate and death date (if applicable). Several occupations and industries known to be at increased risk were outlined a priori to examine whether ODSS could replicate these associations. Age-adjusted, sex-stratified Cox proportional hazard models compared the risk of lung cancer within one industry/occupation versus all other groups in the cohort. Workers with a lung cancer diagnosis prior to cohort entry were excluded for analysis, leaving 2 187 762 workers for analysis. RESULTS: During the 1983 to 2014 follow-up, 34 661 workers in the cohort were diagnosed with lung cancer. Among expected high-risk industries, elevated risks were observed among workers in quarries/sand pits and construction industries for both sexes, and among males in metal mines, iron foundries, non-metallic mineral products industries and transportation industries. Excess risk was also observed among occupations in drilling/blasting, other mining/quarrying, mineral ore treating, excavating/grading/paving, truck driving, painting, bus driving and construction. CONCLUSIONS: This current surveillance system identified several established high-risk groups for lung cancer and could be used for ongoing surveillance of occupational lung cancer in Ontario.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".