The association between fire smoke exposure and emergency department (ED) visits and hospital admissions (HA): A systematic review and meta-analysis
Bibliographic record
Abstract
The negative impact of fire smoke on human health is widely recognized. However, studies have not adequately compared subtle differences in rates of emergency department (ED) visits and hospital admissions (HA) for various diseases, nor have they grounded comparisons in differences in healthcare systems across countries. To bridge this gap, we conducted a systematic meta-analysis to assess global health risks associated with fire smoke exposure. We performed an up-to-date systematic review by searching PubMed and Web of Science databases, using ED visits and HA as the main hospital indicators, and meta-analyzed the estimated risks. Our findings revealed an overall odds ratio (OR) of 1.07 (95 % CI: 1.06, 1.07) for global health risks (7 % increase) post-exposure to fire smoke. Specifically, the OR increased by 9 % for ED visits (OR = 1.09; 95 % CI: 1.08, 1.10) and by 4 % for HA (OR = 1.04; 95 % CI: 1.03, 1.05). When analyzing data by country, we noted that fire smoke exposure was associated with heightened disease risk in Sweden (OR = 1.63; 95 % CI: 1.22, 2.17), Canada (OR = 1.27; 95 % CI: 1.10, 1.46), the United States (OR = 1.10; 95 % CI: 1.08, 1.11), Brazil (OR = 1.07; 95 % CI: 1.04, 1.10), and Australia (OR = 1.04; 95 % CI: 1.03, 1.04). By continent, Europe exhibited the highest risk increase at 63 %, followed by North America at 10 %, South America at 7 %, and Australia at 4 %. By disease type, the most prevalent conditions were respiratory diseases, with a 7 % increase for total respiratory diseases and chronic obstructive pulmonary disease (COPD), 5 % for other pulmonary diseases, and 11 % for asthma. Cardiovascular diseases also showed significant increases: 5 % for general cardiovascular issues, 18 % for heart failure, and 5 % for ischemic diseases. Additional health concerns included headaches (26 %), itching (28 %), and skin allergies and rashes (34 %). We also explored the toxicological mechanisms behind these disease risks, the impact of climate variations, and the disparities in healthcare systems across different countries. This study provides stakeholders such as academics and physicians with a systematic understanding of the quantifiable health risks associated with fire smoke exposure. Additionally, it innovatively compares the differences between ED visits and HA, and between environmental and health systems in each country. The results of this study provide a foundation for subsequent relevant academic research and offer easy-to-understand information for policymakers, healthcare organizations, and the public. Ultimately, this will contribute to a better understanding and mitigation of the health risks associated with fire smoke.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".