Wildfire-related PM2.5 and cardiovascular mortality: A difference-in-differences analysis in Brazil
Bibliographic record
Abstract
Brazil has experienced unprecedented wildfires recently. We aimed to investigate the association of wildfire-related fine particulate matter (PM2.5) exposure with cause-specific cardiovascular mortality, and to estimate the attributable mortality burden. Exposure to wildfire-related PM2.5 was defined as exposure to annual mean wildfire-related PM2.5 concentrations in the 1-year prior to death. The variant difference-in-differences method was employed to explore the wildfire-related PM2.5-cardiovascular mortality association. We found that, in Brazil, compared with the population in the first quartile (Q1: ≤1.82 μg/m3) of wildfire-related PM2.5 exposure, those in the fourth quartile (Q4: 4.22–17.12 μg/m3) of wildfire-related PM2.5 exposure had a 2.2% (RR: 1.022, 95% CI: 1.013–1.032) higher risk for total cardiovascular mortality, 3.1% (RR: 1.031, 95% CI: 1.014–1.048) for ischaemic heart disease mortality, and 2.0% (RR: 1.020, 95% CI: 1.002–1.038) for stroke mortality. From 2010 to 2018, an estimation of 35,847 (95% CI: 22,424–49,177) cardiovascular deaths, representing 17.77 (95% CI: 11.12–24.38) per 100,000 population, were attributable to wildfire-related PM2.5 exposure. Targeted health promotion strategies should be developed for local governments to protect the public from the risk of wildfire-related cardiovascular premature deaths.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".