Breast Cancer Risk by Occupation in Females and Males in Ontario, Canada: Results from the Occupational Disease Surveillance System (ODSS), 1983-2016
Bibliographic record
Abstract
Background: While breast cancer is one of the most commonly diagnosed cancers among women, it accounts for fewer than 1% of cancer cases in men worldwide. Few prior studies have been able to study breast cancer in working men. This study uses data from the recently established Occupational Disease Surveillance System (ODSS) to examine risk of breast cancer in both women and men across different occupation groups.Methods: The ODSS was established through the linkage of existing administrative data and contains information on 2,190,246 Ontario workers (1983-2016). Workers were followed up for breast cancer diagnosis in the Ontario Cancer Registry (OCR). Cox-proportional hazard models were used to calculate age-adjusted hazard ratios (HR) and 95% confidence intervals (CI).Results: A total of 17, 865 and 492 breast cancer cases were identified in working women and men, respectively. Across both sexes, statistically significant (p<0.05) elevated risks were observed in management (w: HR 1.57, 95% CI 1.42-1.73; m: HR 2.41, 95% CI 1.24-4.66), administrative and clerical (w: HR 1.16, 95% CI 1.11-1.21; m: HR 1.56, 95% CI 1.13-2.13), and teaching occupations (w: HR 1.49, 95% CI 1.41-1.59; m: HR 2.82, 95% CI 1.40-5.66). Other statistically significant elevated risks were observed in social sciences, nursing and other health, transport and equipment operating, and sales commodity occupations for both sexes.Conclusions: Similar findings were found in women and men that warrant further investigation into job-related factors, such as sedentary behaviour, stress, shift work, and for some occupations, radiation exposure. The findings from this study, if validated in other study samples, may help focus breast cancer prevention and education efforts for both females and males.
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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 | 0.003 |
| 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".