Association of short-term exposure to fine particulate air pollution and mortality: effect modification by oxidant gases
Bibliographic record
Abstract
Abstract Short term changes in exposure to outdoor fine particulate matter (PM2.5) concentrations are associated with an increased risk of mortality. However, less is known about how oxidant gases may modify the acute health effects of PM2.5. Our objective was to investigate whether associations between acute exposure to PM2.5 and mortality were modified by the oxidant gases O3 and NO2 using their redox-weighted average (Ox). We conducted a multi-city case-crossover study in 24 cities across Canada between 1998–2011 including 1,179,491 nonaccidental mortality events. Interquartile increases in lag-0 and 3-day mean PM2.5 and Ox concentrations were each associated with small increases in nonaccidental and cardiovascular mortality. In stratified analyses, associations between PM2.5 and nonaccidental and cardiovascular mortality tended to be greatest in the highest tertile of Ox with a significant interaction observed between lag 0 PM2.5 and 3-day mean Ox (interaction p-value = 0.04). There was no evidence of effect modification by Ox in the relationship between PM2.5 and respiratory mortality. Overall, the relationship between short-term changes in outdoor PM2.5 and nonaccidental mortality may be greater when oxidant gas concentrations are also elevated. In some regions, reductions in oxidant gas concentrations may also reduce the acute health impacts of PM2.5.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".