Multi-pollutant case-crossover models of all-cause and cause-specific mortality and hospital admissions by age group in 47 Canadian cities
Bibliographic record
Abstract
Most of the existing epidemiological studies have investigated adverse health effects of multiple air pollutants for a limited number of cities, thus the evidence of the health impacts is limited and it is challenging to compare these results because of different modeling approaches and potential publication bias. In this paper, we expand the number of Canadian cities, with the use of the most recent available health data. A multi-pollutant model in a case-crossover design is used to investigate the short-term impacts of air pollution on various health outcomes in 47 Canadian main cities, comparing three age groups (all-age, senior (age 66+), non-senior). The main findings are that a 14 ppb increase of O3 was associated with a 0.17%–2.78% (0.62%–1.46%) increase in the odds of all-age respiratory mortality (hospitalization). A 12.8 ppb increase of NO2 was associated with a 0.57%–1.47% (0.68%–1.86%) increase in the odds of all-age (non-senior) respiratory hospitalization. A 7.6 μgm−3 increase of PM2.5 was associated with a 0.19%–0.69% (0.33%–1.1%) increase in the odds of all-age (non-senior) respiratory hospitalization.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".