Long-term Impact of Tropical Cyclones on Disease Exacerbation Among Children with Asthma in the Eastern United States, 2000–2018
Bibliographic record
Abstract
BACKGROUND: Tropical cyclones are associated with acute increases in mortality and morbidity, but few studies have examined their longer-term health consequences. We assessed whether tropical cyclones are associated with a higher frequency of symptom exacerbation among children with asthma in the following 12 months in eastern United States counties, 2000-2018. METHODS: We defined exposure to tropical cyclones as a maximum sustained windspeed >21 meters/second at the county center and used coarsened exact matching to match each exposed county to one or more unexposed counties. We used longitudinal, de-identified administrative claims data to estimate the county-level, monthly risk of experiencing at least one asthma exacerbation requiring medical attention among commercially insured children aged 5-17 with prior diagnosis of asthma. We used a difference-in-differences approach implemented via a Poisson fixed effects model to compare the risk of asthma exacerbation in the 12 months before versus after each storm in exposed versus unexposed counties. RESULTS: Across 43 tropical cyclones impacting the eastern United States, we did not observe evidence of an increase in the risk of symptom exacerbation in the 12 months following the storm (random-effects meta-analytic summary estimate: risk ratio = 1.03 [95% confidence interval = 0.96, 1.10], I2 = 17%). However, certain storms, such as Hurricane Sandy, were associated with a higher risk of symptom exacerbation. CONCLUSIONS: These findings are consistent with the hypothesis that some tropical cyclones are detrimental to children's respiratory health. However, tropical cyclones were not associated in aggregate with long-term exacerbation of clinically apparent asthma symptoms among a population of children with commercial health insurance.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".