Health effects of repeated exposures during wildland firefighting: a data-linkage cohort study from Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Very little is known about the effects on the health of work as a wildland firefighter over repeated fire seasons. In Alberta, where the fire season runs from 1 March to 31 October, the great majority of firefighters are hired seasonally. We examined whether there was a dose-response relationship between hours of firefighting and ill-health. METHODS: A cohort was established linking employment records from Alberta Wildfire to administrative health data and cancer records. The employment records contained information on each deployment for all firefighters with employment from 1998 to 2022. Health records had details of diagnoses recorded at all physician consultations for the same period. Cancer records included diagnostic information for all confirmed cancers in the province. Exposure indices (hours worked) were related to health outcomes, with relative risk estimated by multilevel Poisson regression, using data lagged by 10 years for cancer outcomes. RESULTS: Of 16,816 firefighters with employment records, 12,731 were matched on name, age, and sex in health records and were living in Alberta at the end of at least one fiscal year. One in three had only been employed for one fire season with 10% employed in 10 or more years. The overall mean cumulative exposure was 795 h with 568 h of sustained attack (SA). In multivariable regression, adjusted for age, sex, and inferred First Nation origin, the risk of chronic obstructive pulmonary disease (COPD) and pneumonia increased with hours of firefighting on foot and decreased with fighting less complex fires or holding a permanent appointment. Hours of firefighting in the year of health report were protective for cardiovascular disease (CVD) and mental ill-health but the risk of injury from external causes increased with firefighting hours. The risk of COPD, pneumonia, and asthma increased with cumulative hours over multiple fire seasons of SA firefighting and decreased with cumulative hours fighting less complex fires. Risks of CVD and mental ill-health were also positively related to cumulative hours of SA. No increase in risk was found with cancer incidence (all cancers, bladder cancer, lung cancer, skin: melanoma or nonmelanoma), with exposures unlagged or lagged by 10 years. CONCLUSION: Wildland firefighters were found to be at increased risk of lung conditions, both acutely in the year of firefighting and in subsequent years. No increased risk was found for cancer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".