Sources of Uncertainty in Calculating Mortality and Morbidity Attributable to Air Pollution
Bibliographic record
Abstract
Quantifying the burden of illness and mortality from air pollution exposure relies on statistical estimates and other assumptions that have inherent uncertainties. Through an intensive study in Hamilton, Canada, this study illustrates for policymakers the sensitivity of health effect estimates to a wide range of possible uncertainties. Dose-response relationships were derived based on pooled and averaged estimates published in the scientific literature from 1997 to 2001. These estimates were applied to local air pollution, mortality, and hospital admissions data for the years 1995-1999. The data were adjusted to reflect uncertainties in the current state of knowledge, including (1) baseline pollution, (2) single versus multipollutant effects, (3) local or pooled estimates, and (4) chronic effects. The estimates of mortality ranged from 96 to 374 annual deaths, while admissions ranged from 139 to 607 respiratory and from 479 to 2000 cardiovascular admissions. Chronic fine particle exposure resulted in 232 annual deaths. As conclusions, first, there should be an effort to reach a consensus on reporting scientific findings from air pollution studies using standardized study designs and reporting formats. Second, given the sensitivity of the estimates to underlying assumptions, an immediate need exists for widely accepted burden of illness and mortality estimation conventions. Third, many areas of air pollution research require considerable refinement before complete estimates can be ascribed.
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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 | 0.060 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".