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 (PM 2.5 ) and respiratory hospitalizations are lacking. Here we evaluate the short-term effects of wildfire-specific PM 2.5 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 PM 2.5 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 PM 2.5 communities, and residing in Brazil, Thailand, Taiwan and Vietnam. Australia and New Zealand exhibited a greater hospitalization risk for asthma associated with wildfire-specific PM 2.5 . Compared with non-wildfire PM 2.5 , wildfire-specific PM 2.5 posed greater hospitalization risks for all respiratory diseases and a greater burden of asthma. Wildfire-specific PM 2.5 contributed to 42.4% of PM 2.5 -linked respiratory hospitalizations, dominating in Thailand. Overall, the substantial contribution of wildfire-specific PM 2.5 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".