Health effects of wildfire PM2.5 in Latin American cities: A rapid systematic review and comparative synthesis
Bibliographic record
Abstract
INTRODUCTION: Wildfire activity is intensifying in Latin America due to climate and land-use changes, but the health impacts of wildfire-derived PM2.5 in urban areas remain poorly quantified and recognized. OBJECTIVE: To assess the evidence on wildfire-related PM2.5 and its association with mortality and morbidity in Latin American cities. MATERIALS AND METHODS: We conducted a rapid systematic review and meta-analysis following PRISMA guidelines, using data from PubMed, Scopus, and Bireme. One reviewer independently screened 163 articles and extracted data from 14 eligible studies. A risk of bias assessment was conducted using the Newcastle-Ottawa Scale. RESULTS: Most studies were conducted in Brazil (n = 12) and used time-series or modelling designs to estimate health risks. Wildfire-specific PM2.5 exposure was associated with allcause, cardiovascular, and respiratory mortality. Reported effect estimates ranged from 1.7 to 7.7% increases in risk per 10 μg/m³ of exposure. Other studies assessed preterm birth, COVID-19 outcomes, and site-specific cancers. While two studies provided harmonized RR estimates for all-cause mortality, high heterogeneity and methodological differences prevented formal meta-analysis. CONCLUSION: Wildfire smoke contributes measurably to premature mortality in Latin America, but current evidence is unevenly distributed across regions, time periods, and population subgroups. Studies rarely capture the disproportionate risks faced by indigenous and rural communities or the intraurban disparities linked to poverty and geography. Future research should focus on the health burden of morbidity linked to wildfire 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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".