Impact of Particulate Air Pollution on Quality-Adjusted Life Expectancy in Canada
Bibliographic record
Abstract
Air pollution and premature death are important public health concerns. Analyses have repeatedly demonstrated that airborne particles are associated with increased mortality and estimates have been used to forecast the impact on life expectancy. In this analysis, we draw upon data from the American Cancer Society (ACS) cohort and literature on utility-based measures of quality of life in relation to health status to more fully quantify the effects of air pollution on mortality in terms of quality-adjusted life expectancy. The analysis was conducted within a decision analytic model using Monte Carlo simulation techniques. Outcomes were estimated based on projections of the Canadian population. A one-unit reduction in sulfate air pollution would yield a mean annual increase in Quality-Adjusted Life Years (QALYs) of 20,960, with gains being greater for individuals with lower educational status and for males compared to females. This suggests that the impact of reductions in sulfate air pollution on quality-adjusted life expectancy is substantial. Interpretation of the results is unclear. However, the potential gains in QALYs from reduced air pollutants can be contrasted to the costs of policies to bring about such reductions. Based on a tentative threshold for the value of health benefits, analysis suggests that an investment in Canada of over 1 billion dollars per annum would be an efficient use of resources if it could be demonstrated that this would reduce sulfate concentrations in ambient air by 1 microg/m(3). Further analysis can assess the efficiency of targeting such initiatives to communities that are most likely to benefit.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".