Estimating the Public Health Burden Attributable to Air Pollution: An Illustration Using the Development of an Alternative Air Quality Index
Bibliographic record
Abstract
The example of the development of an alternative air quality index (AQI) is used to illustrate issues related to quantifying the public health burden attributable to air pollution. These issues include: (1) appropriately representing the weight of evidence; (2) extrapolation of risk measures over time and space; (3) attribution of health effects to air pollution versus other risk factors and to individual pollutants versus the rest of the mix; (4) application of complementary approaches from health economics; and (5) effective risk communication. A no-threshold, multipollutant AQI was developed, based on the relationship of CO, NO(2), O(3), SO(2) and PM(2.5) with mortality in Canadian cities in a daily time-series study. Risk coefficients were applied to daily air pollution concentrations to calculate multipollutant percent excess mortality, and results were scaled to a 0 to 10 range. The observed distribution of values was used to characterize days as low, medium, high, or extreme risk. Considerable day-to-day variability in the index value was observed, and the percent of days falling in the high or extreme risk categories ranged from 0.3 to 33.2 among the cities considered. The new index was moderately correlated with conventionally derived AQIs. Results did not appear to be sensitive to an alternative choice of risk coefficients based on a worldwide meta-analysis. Additional efforts will be required to validate this AQI formulation against one based on the association between air pollution and other health outcomes, and to most effectively utilize the AQI as a communication tool regarding acute health risks associated with air pollution.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".