Cause specific mortality risks associated with tropical cyclones in multiple countries and territories: two stage, time series study
Bibliographic record
Abstract
OBJECTIVE: To characterise and quantify the mortality risks for a range of causes after tropical cyclones in nine countries and territories. DESIGN: Two stage, time series study. SETTING: Nine countries or territories (Australia, Brazil, Canada, South Korea, Mexico, New Zealand, the Philippines, Taiwan, and Thailand), covering tropical, subtropical, and extra-tropical regions. PARTICIPANTS: General populations living in regions with tropical cyclones in the nine countries or territories, 2000-19. MAIN OUTCOMES MEASURES: Excess mortality risk of cardiovascular diseases, respiratory diseases, infectious diseases, injuries, neuropsychiatric disorders, renal diseases, digestive diseases, diabetes, and neoplasms as the leading cause of death. Wind speed and rainfall profiles were quantified with a physics based tropical cyclone field model. RESULTS: 14.8 million deaths and 217 tropical cyclone events in communities from the nine countries or territories were included in the analysis. Mortality risks from various causes consistently increased after tropical cyclones, with peaks occurring within the first two weeks after the cyclone, followed by a rapid decline. During the first two weeks after a tropical cyclone, the highest increases were seen in mortality from renal diseases and injuries, with a cumulative relative risk of 1.92 (95% confidence interval (CI) 1.63 to 2.26) and 1.21 (1.12 to 1.30), respectively, for each additional tropical cyclone day. Relatively more modest risks were found for mortality from diabetes (cumulative relative risk 1.15, 95% CI 1.08 to 1.21), neuropsychiatric disorders (1.12, 1.05 to 1.19), infectious diseases (1.11, 1.05 to 1.17), digestive diseases (1.06, 1.02 to 1.09), respiratory diseases (1.04, 1.00 to 1.08), cardiovascular diseases (1.02, 1.01 to 1.04), and neoplasms (1.02, 1.00 to 1.04). Mortality risks were substantially higher in communities with greater levels of deprivation and in those with historically fewer tropical cyclones, especially for renal, infectious, and digestive diseases, as well as for diabetes. Rainfall related to tropical cyclones had a more consistent increasing exposure-response relation with mortality risks, particularly for deaths related to respiratory, cardiovascular, and infectious diseases. CONCLUSIONS: After tropical cyclones, mortality risk increased variably for different causes, populations, and regions. Integrating epidemiological evidence into the development of management systems for climate extremes is urgently needed, particularly in regions with higher levels of deprivation and in those with historically fewer tropical cyclones. These measures are necessary to improve the adaptive capacity in responding to the growing risks and shifting activity of tropical cyclones in a warming climate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".