The Role of Pollution in Heat Wave Mortality: Systematic Review
Bibliographic record
Abstract
SAB4-PD-12 Short-term heat wave effects on mortality as well as the short-term effects of specific air pollutants’ concentrations are well known. In situations of extreme heat, the concentration levels of pollutants and the pollutant mix are affected through various mechanisms. It is therefore necessary to control for the confounding effects of air pollution when investigating the heat wave effects, and it is of significant interest to study the combined, possibly synergistic, effects of heat and other meteorologic variables on the one hand and air pollutants’ concentrations on the other. A review of the above topics will be presented as part of a dedicated session to the social and environmental determinants of heat wave mortality. The pollutants of interest are those affected by photochemical procedures, mainly ozone and ambient particulate matter. The heat wave effect is often assessed using composite indices, which combine a measure of heat and humidity or more meteorologic variables into synoptic weather categories. Comparability and interpretation of the results are sometimes made more difficult by the use of indices. Papers addressing the issue of confounding are few but represent a diversity of locations, for example, Canada, Mexico, and central and southern Europe. The confounding effects of pollution in the heat wave or heat-related mortality appear to be small but go in either direction. Only a few studies have addressed the possible interactive effects of temperature or heat waves and specific air pollutants on health outcomes. The outcome most readily studied is mortality. Although the independent effects of heat waves and of pollution are established and generally accepted, the evidence for synergistic effects is sparse. From published studies, it appears that there is interaction only for very high temperatures or in heat wave situations. The study of interactive effects presents methodologic challenges, which will be discussed.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".