Wildfire-Related Air Pollution and Infectious Diseases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Amid the global rise in wildfire events, the health impacts of wildfire-related air pollution are increasingly scrutinized. While numerous reviews have examined the link between air pollution and infectious diseases, reviews specifically focusing on wildfire-related air pollution and infectious diseases remain scarce. To address this gap, we conducted a comprehensive search in MEDLINE, EMBASE, Scopus and Web of Science databases up to December 31, 2023, using PRISMA (Preferred Reporting Items for Systematic Reviews & Meta-Analyses) guidelines. Search terms included synonyms of wildfire and infectious diseases. Peer-reviewed epidemiological studies that reported any association or trend between wildfire air pollution and infectious diseases were selected against eligibility criteria. Risk of bias and quality of included studies were assessed using modified risk of bias and quality assessment tools. Our review included 30 studies, predominantly from developed countries including the United States (USA), Australia, and Canada. Most focused on respiratory infectious diseases ( n = 29), including 9 specifically on the coronavirus disease 2019 (COVID-19). The majority examined short-term wildfire air pollution ( n = 27) (exposure of one month or less). Twenty-three studies reported effect estimates for the meta-analysis. We found that a 10 μg/m 3 increase in short-term wildfire PM 2.5 (particulate matter with a diameter of 2.5 micrometer of less) exposure was associated with a 15% increase in COVID-19 infections (relative risk [RR] = 1.15; 95% confidence interval [CI]: 1.09–1.21; heterogeneity ( I 2 ): 83%), a 3% increase in respiratory diseases (RR = 1.03; 95% CI: 1.01–1.05; I 2: 0%) and a 3% increase in acute upper respiratory infection combined with acute bronchitis (RR = 1.03; 95% CI: 1.02–1.05; I 2: 62%). Medium-term exposure (more than a month but less than a year) to wildfire smoke was associated with 20% rising hospitalization for systemic fungal infections like coccidioidomycosis (95% CI: 5–38%). The current research exclusively examines respiratory infections in developed countries. Future high-quality primary studies should prioritize understanding the impact of wildfire-related air pollution on various infectious diseases.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".