Exploring sex differences in lung cancer risk among workers in Ontario, Canada's Occupational Disease Surveillance System
Bibliographic record
Abstract
Background While it is well-established that occupational exposures significantly contribute to the risk of developing lung cancer, there remains a notable gap in understanding the specific sex differences in lung cancer risk, particularly among female workers. This study aimed to examine sex differences in lung cancer risk across various occupations, with an emphasis on the female workforce. Methods A cohort of approximately 2.37 million workers with lost-time compensation claims were linked to the Ontario Cancer Registry and followed until lung cancer diagnosis, age 85, emigration, death, or end of follow-up (Dec 31, 2020). Cox proportional hazards models were used to estimate sex-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for lung cancer by occupational group (division, major and minor levels), adjusted for birth-year and age and indirectly adjusted for cigarette smoking. Results A total of 12,216 and 30,291 incident lung cancer cases were identified among females and males, respectively. Several occupations demonstrated stronger associations for lung cancer in females, with at least a 20% increased risk compared to males. These occupations at the major level include food and beverage preparation services (HR = 1.19, 95% CI = 1.13–1.26); materials processing (chemical, petroleum, rubber, plastic) (HR = 1.35, 95% CI = 1.19–1.52); wood processing (HR = 1.87, 95% CI = 1.22–2.87); metal machining (HR = 1.56, 95% CI = 1.21–2.00); metal shaping and forming (HR = 1.46, 95% CI = 1.32–1.62); fabricating and assembling metal products (HR = 1.37, 95% CI = 1.25–1.51), other construction trades (HR = 1.54, 95% CI = 1.16–2.05), motor transport operating (HR = 1.69, 95% CI = 1.48–1.94), mechanic and repair work (HR = 1.39, 95% CI = 1.04–1.85); and printing (HR = 1.51, 95% CI = 1.30–1.75). These patterns were similar across minor level occupations. Conclusions This study identified sex differences across various occupations, with some occupational groups demonstrating stronger associations among female workers. However, these findings should be interpreted with caution. The observed differences may be attributed to various factors that influence risk, such as occupational exposures, use and effectiveness of personal protective equipment, and other biological or lifestyle factors.
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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 | 0.004 |
| 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) | 0.001 | 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".