The association between fire smoke exposure and emergency department (ED) visits and hospital admissions (HA): A systematic review and meta-analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».