Respiratory risks from wildfire-specific PM2.5 across multiple countries and territories
Bibliographic record
Abstract
Abstract Under a warming climate, wildfires are becoming more frequent and severe. Multicountry studies evaluating associations between wildfire fine particulate matter (PM2.5) and respiratory hospitalizations are lacking. Here we evaluate the short-term effects of wildfire-specific PM2.5 on respiratory hospitalizations from 1,052 communities across Australia, Brazil, Canada, Chile, New Zealand, Vietnam, Thailand and Taiwan, during 2000–2019. A 1 µg m−3 increase in wildfire-specific PM2.5 was associated with increased hospitalization risks for all-cause respiratory, asthma, chronic obstructive pulmonary disease, acute upper respiratory infection, influenza and pneumonia by 0.36%, 0.48%, 0.38%, 0.42%, 0.79% and 0.36%, respectively. Higher risks were observed among populations ≤19 or ≥60 years old, from low-income or high non-wildfire PM2.5 communities, and residing in Brazil, Thailand, Taiwan and Vietnam. Australia and New Zealand exhibited a greater hospitalization risk for asthma associated with wildfire-specific PM2.5. Compared with non-wildfire PM2.5, wildfire-specific PM2.5 posed greater hospitalization risks for all respiratory diseases and a greater burden of asthma. Wildfire-specific PM2.5 contributed to 42.4% of PM2.5-linked respiratory hospitalizations, dominating in Thailand. Overall, the substantial contribution of wildfire-specific PM2.5 to respiratory hospitalizations demands continued mitigation and adaptation efforts across most countries. Intervention should be prioritized for influenza, children, adolescents, the elderly and populations in low-income or high-polluted communities.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".