Firefighting, other protective service occupations and prostate cancer risk: a pooled analysis of three case-control studies
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) is the most frequent incident cancer among males in industrialized countries, but little is known about its aetiology. A role for occupational exposures is suggested. Occupational exposure as a firefighter, a protective service occupation (PSO), is classified as carcinogenic to humans by the International Agency for Research on Cancer, with limited evidence in humans for PCa. We studied the association between PSO and PCa risk considering tumour aggressiveness and screening practices. METHODS: The EPIdemiological study of Prostate Cancer (EPICAP), the Prostate cancer & Environment Study (PROtEuS) and the MultiCase-Control study in common tumours in Spain (MCC-Spain) are population-based case-control studies, conducted respectively in France, Canada, Spain, in 2005–2014 in men ≤ 85 years old, including overall 3,859 incident cases and 4,359 controls frequency-matched on age. Participants were interviewed face-to-face using general and occupational questionnaires covering all jobs held in career, coded according to the 1988 International Standard Classification of Occupations. Unconditional logistic regressions estimated associations between PSO and PCa, after adjusting for potential confounders. Two sets of analyses were conducted, without and with consideration of screening. The latter is believed to yield the main findings since less subject to detection bias. RESULTS: When restricting controls to those recently screened, men employed as firefighters ≥ 10 years had increased risk (OR (Odds ratio) = 2.01 [95% confidence interval] [1.02; 3.97]) of non-aggressive PCa. Positive associations for non-aggressive PCa among men employed < 10 years as police officers (OR = 2.53 [1.07; 5.96]) and police inspectors and detectives (OR = 6.75 [1.47; 30.96]) were observed. Very few cases in PSO were characterized by aggressive tumours. CONCLUSIONS: Findings from this large population-based study corroborate the higher PCa risk previously reported among firefighters, but only for non-aggressive tumours. Screening practices had a substantial impact on risk estimates. Future studies should investigate specific exposures, and account for PCa aggressiveness and individual screening patterns.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".