Cancer Risks among Emergency Medical Services Workers in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Emergency medical services workers, such as paramedics, provide important emergency care and may be exposed to potential carcinogens while working. Few studies have examined the risk of cancer among paramedics demonstrating an important knowledge gap in existing literature. This study aimed to investigate cancer risks among paramedics in a large cohort of Ontario workers. METHODS: Paramedics were identified in the Occupational Disease Surveillance System (ODSS) from 1996 to 2019. The ODSS was established by linking lost-time worker's compensation claims to administrative health data, including the Ontario Cancer Registry to identify incident cases of cancer. Cox-proportional hazard models were used to calculate age and sex-adjusted hazard ratios and 95% confidence intervals to estimate the risk of cancer among paramedics compared to all other workers in the ODSS. RESULTS: A total of 7240 paramedics were identified, with just over half of the paramedics identifying as male similar to the overall ODSS cohort. Paramedics had a statistically significant elevated risk of any cancer (HR 1.19, 95% CI 1.06-1.34), and elevated risks for melanoma (HR 2.18, 95% CI 1.46-3.26) and prostate cancer (HR 1.73, 95% CI 1.34-2.22). Paramedics had a statistically significant reduced risk for lung cancer (HR 0.48, 95% CI 0.28-0.83). Findings were similar to cancer risks identified in firefighters and police in the same cohort. CONCLUSIONS: This study contributes valuable findings to understanding cancer risks among paramedics and further supports the existing evidence on the increased risk of cancer among emergency medical services workers. We have observed some similar results for firefighters and police, which may be explained by similar exposures, including vehicle exhaust, shiftwork, and intermittent solar radiation. This can lead to a better understanding of carcinogens and other exposures among paramedics and inform cancer prevention strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.038 | 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".