Risks of infectious disease hospitalisations in the aftermath of tropical cyclones: a multi-country time-series study
Notice bibliographique
Résumé
BACKGROUND: The proportion of intense tropical cyclones is expected to increase in a changing climate. However, there is currently no consistent and comprehensive assessment of infectious disease risk following tropical cyclone exposure across countries and over decades. We aimed to explore the tropical cyclone-associated hospitalisation risks and burden for cause-specific infectious diseases on a multi-country scale. METHODS: Hospitalisation records for infectious diseases were collected from six countries and territories (Canada, South Korea, New Zealand, Taiwan, Thailand, and Viet Nam) during various periods between 2000 and 2019. The days with tropical cyclone-associated maximum sustained windspeeds of 34 knots or higher derived from a parametric wind field model were considered as tropical cyclone exposure days. The association of monthly infectious diseases hospitalisations and tropical cyclone exposure days was first examined at location level using a distributed lag non-linear quasi-Poisson regression model, and then pooled using a random-effects meta-analysis. The tropical cyclone-attributable number and fraction of infectious disease hospitalisations were also calculated. FINDINGS: Overall, 2·2 million people who were hospitalised for infectious diseases in 179 locations that had at least one tropical cyclone exposure day in the six countries and territories were included in the analysis. The elevated hospitalisation risks for infectious diseases associated with tropical cyclones tended to dissipate 2 months after the tropical cyclone exposure. Overall, each additional tropical cyclone day was associated with a 9% (cumulative relative risk 1·09 [95% CI 1·05-1·14]) increase in hospitalisations for all-cause infectious diseases, 13% (1·13 [1·05-1·21]) for intestinal infectious diseases, 14% (1·14 [1·05-1·23]) for sepsis, and 22% (1·22 [1·03-1·46]) for dengue during the 2 months after a tropical cyclone. Associations of tropical cyclones with hospitalisations for tuberculosis and malaria were not significant. In total, 0·72% (95% CI 0·40-1·01) of the hospitalisations for all-cause infectious diseases, 0·33% (0·15-0·49) for intestinal infectious diseases, 1·31% (0·57-1·95) for sepsis, and 0·63% (0·10-1·04) for dengue were attributable to tropical cyclone exposures. The attributable burdens were higher among young populations (aged ≤19 years) and male individuals compared with their counterparts, especially for intestinal infectious diseases. The heterogeneous spatiotemporal pattern was further revealed at the country and territory level-tropical cyclone-attributable fractions showed a decreasing trend in South Korea during the study period but an increasing trend in Viet Nam, Taiwan, and New Zealand. INTERPRETATION: Tropical cyclones were associated with persistent elevated hospitalisation risks of infectious diseases (particularly sepsis and intestinal infectious diseases). Targeted interventions should be formulated for different populations, regions, and causes of infectious diseases based on evidence on tropical cyclone epidemiology to respond to the increasing risk and burden. FUNDING: Australian Research Council, Australian National Health, and Medical Research Council.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| 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 ».