Measuring the Health Co-Benefits of Air Pollution Interventions on Premature Deaths in Canadian Cities
Bibliographic record
Abstract
Background: Climate change has significant consequences on human health. Cities are especially vulnerable, where air pollution is a major environmental health risk. Premature mortality (i.e., deaths before age 75) is a robust population health outcome amenable to targeted policy and programmatic interventions. We used the Premature Mortality Population Risk Tool augmented with environmental data (PreMPoRT-ENV) to predict the 5-year incidence of premature deaths under air pollution reduction policies. Methods: PreMPoRT-ENV is a sex-specific Weibull accelerated failure time survival model that uses the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Death Database and environmental data. We applied PreMPoRT-ENV to the 2016–2017 CCHS cycles and simulated Canadian Ambient Air Quality Standards targets to predict their impact on premature mortality across Canadian census metropolitan areas. We simulated capping annual mean particulate matter 2.5 microns or less in diameter (PM2.5) and nitrogen dioxide (NO2), as well as reducing air pollutants by 10% and 25% plus capping. Results: The weighted sample included 9,240,000 females and 9,260,000 males. Capping PM2.5 to 8.8 μg/m3 and NO2 to 12.0 ppb resulted in 12 per 100,000 fewer predicted premature deaths than observed exposures over 5 years (1,110 fewer absolute premature deaths). Reducing air pollutants by 10% and 25% plus capping resulted in even fewer predicted premature deaths. Conclusion: Our study highlights how to use a model that predicts premature mortality to provide estimates of the health impacts of environmental vulnerabilities. Results suggest that more aggressive targets may be needed to further realize population health benefits.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".