Risks of infectious disease hospitalisations in the aftermath of tropical cyclones: a multi-country time-series study
Bibliographic record
Abstract
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".