8292418 Lung cancer risk among 2.2 million Ontario workers: joint effects of co-exposure to five occupational carcinogens by sex
Bibliographic record
Abstract
Objectives Several occupational exposures cause lung cancer, but few studies have examined co-exposure effects. We aim to investigate the joint effects of exposure to five carcinogens— asbestos, chromium VI, nickel, polycyclic aromatic hydrocarbons (PAHs) and benzo-a-pyrene (BaP) as a surrogate of PAH exposure, and silica— on lung cancer risk. Methods Lung cancer cases were identified from the Ontario Cancer Registry between 1983 to 2019 from a large cohort of Ontario workers. Exposure status (exposed/non-exposed) was assigned to workers based on their occupation using the Canadian job-exposure matrix for each carcinogen. Cox proportional hazards models estimated associations of carcinogen exposure on lung cancer risk overall, by sex, and by subtype, controlling for age, sex, and birth-year. Joint effects were analyzed using pairwise interaction models assessed on multiplicative and additive scales using the relative excess risk due to interaction (RERI). Results Overall, 36,125 lung cancer cases were identified among 2,223,408 workers. In preliminary analysis, joint effects of co-exposure to asbestos/silica (RERI= 0.27, 95% CI: 0.15, 0.39), BaP/silica (RERI=0.28, 95% CI: 0.17, 0.39), PAHS/silica (RERI=0.14, 95% CI: 0.05, 0.23), and chromium/BaP (RERI= 0.20, 95% CI: 0.01, 0.42) were more than additive. Conversely, joint effects of asbestos/PAHs (RERI: -0.44, 95% CI: -0.68, -0.24), BaP/nickel (RERI= -0.11, 95% CI: -0.21, -0.01), and chromium/PAHs (RERI=-0.38, 95% CI: -0.60, -0.18) were less than additive. The remaining pairwise joint effects did not significantly depart from additivity. The pattern of results was similar among male workers, but no pairwise joint effects significantly departed from additivity for females. Differences by lung cancer sub-type and additional analyses to follow. Conclusion Significant interaction effects were observed. Ongoing joint effects analysis may help identify high-risk occupations/industries by discovering the most impactful exposure combinations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".