Mortality Attributable to Ambient Fine Particulate Matter Exposure in a Changing Canadian Population, 2001 to 2021
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We aim to understand how changes in ambient fine particulate matter (PM 2.5 ) over the last two decades have influenced PM 2.5 -attributable mortality in a Canadian population experiencing both growth and changing baseline health status. We conducted a health impact analysis using dynamic estimates of population, baseline mortality rates, and satellite-based PM 2.5 concentrations to estimate mortality attributable to long-term PM 2.5 exposure every five years between 2001 and 2021, applying risk estimates from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) to the population aged 25 and older. We conducted a decomposition analysis to examine the influences of population exposure, size, and health status on trends in PM 2.5 -attributable mortality. Between 2001 and 2021, population-weighted exposure to PM 2.5 declined by 18% in Canada, with improvements occurring in most urban areas. In recent years, these changes have led to 4,400 (95% CI: 3,700–5,000) to 4,700 (95% CI: 4,100–5,400) fewer PM 2.5 -attributable deaths annually based on log–linear and log–log shapes of concentration–response. However, a growing population alongside higher baseline mortality risks in several regions, likely due to aging, has led to a small net increase in total PM 2.5 -attributable deaths between 2001 and 2021. These findings suggest that the Canadian population has benefitted broadly from air quality management strategies implemented in North America over recent decades.
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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 | 0.001 | 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 | 0.000 | 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) | 0.005 | 0.005 |
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; both teacher heads agree on what is shown here.
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".