Long-term impacts of Atlantic hurricanes on asthma exacerbations among children with asthma in the eastern United States
Bibliographic record
Abstract
BACKGROUND AND AIM: Tropical cyclones (TCs) are associated with substantial, acute increases in mortality and morbidity. Relatively few studies have examined the longer-term health consequences of such storms. We assessed whether TCs increased the frequency of symptom exacerbation among children with a diagnosis of asthma in the 12 months following storms in counties in the eastern United States (US), 2000-2018. METHODS: We defined exposure to TCs as maximum sustained windspeed at the county center 21 meters/second, and matched each exposed county to one or more unexposed counties on sociodemographic variables, climate, and distance from the coast. Within each exposed and matched unexposed county, we used data from the OptumLabs Data Warehouse, a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data, to estimate monthly rates of asthma exacerbations requiring medical attention among children aged 5-17 with a prior diagnosis of asthma. Finally, we used a difference-in-differences approach implemented via a log-linear fixed effects model with an offset for eligible population size to compare the rate of asthma exacerbations occurring in exposed versus unexposed counties, in the 12 months before versus 12 months after each storm. RESULTS:Our analysis encompasses 43 TCs that affected at least one county during the study period. Overall, across these storms, we did not observe evidence of an increase in symptom exacerbation in the 12 months following the storm (random effects meta-analytic summary estimate: RR: 1.03 [95% CI: 0.96, 1.10], I2 = 19%). However, we did find evidence of an increase in symptom exacerbation following specific storms, such as Hurricane Sandy. CONCLUSIONS:These findings suggest that some TCs may be detrimental to the respiratory health of children, but that tropical cyclones are not in aggregate substantially associated with long-term exacerbation of asthma among a population of children with health insurance. KEYWORDS: Climate change, Tropical cyclones, Asthma
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".