An approach to estimating the environmental burden of cancer from known and probable carcinogens: application to Ontario, Canada
Bibliographic record
Abstract
Background: Quantifying the potential cancer cases associated with environmental carcinogen exposure can help inform efforts to improve population health. This study developed an approach to estimate the environmental burden of cancer and applied it to Ontario, Canada. The purpose was to identify environmental carcinogens with the greatest impact on cancer burden to support evidence-based decision making. Methods: We conducted a probabilistic assessment of the environmental burden of cancer in Ontario. We selected 23 carcinogens that we defined as “environmental” (e.g., pollutants) and were relevant to the province, based on select classifications provided by the International Agency for Research on Cancer. We evaluated population exposure to the carcinogens through inhalation of indoor/outdoor air; ingestion of food, water, and dust; and exposure to radiation. We obtained or calculated concentration-response functions relating carcinogen exposure and the risk of developing cancer. Using both human health risk assessment and population attributable fraction models in a Monte Carlo simulation, we estimated the annual cancer cases associated with each environmental carcinogen, reporting the simulation summary (e.g., mean and percentiles). Results: We estimated between 3540 and 6510 annual cancer cases attributable to exposure to 23 environmental carcinogens in Ontario. Three carcinogens were responsible for over 90% of the environmental burden of cancer: solar ultraviolet (UV) radiation, radon in homes, and fine particulate matter (PM2.5) in outdoor air. Eight other carcinogens had an estimated mean burden of at least 10 annual cancer cases: acrylamide, arsenic, asbestos, chromium, diesel engine exhaust particulate matter, dioxins, formaldehyde, and second-hand smoke. The remaining 12 carcinogens had an estimated mean burden of less than 10 annual cancer cases in Ontario. Conclusions: We found the environmental burden of cancer in Ontario to fall between previously estimated burdens of alcohol and tobacco use. These results allow for a comparative assessment across carcinogens and offer insights into strategies to reduce the environmental burden of cancer. Our analysis could be adopted by other jurisdictions and repeated in the future for Ontario to track progress in reducing cancer burden, assess newly classified environmental carcinogens, and identify top burden contributors.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".