Tropical Cyclone and Risk of Preterm Birth: A Retrospective Analysis of 20 Million Births across 378 US Counties
Bibliographic record
Abstract
BackgroundThe public health impact of tropical cyclones (TCs) is expected to increase due to the continued growth of coastal populations and the increasing severity of these events. We aimed to estimate the association between prenatal exposure to TC and risk of preterm birth (PTB) in the eastern United States (US).MethodsWe included data on 19,529,748 spontaneous singleton births from 1989 to 2002 across 378 US counties. In each county, we classified days as exposed to a TC when TC-associated peak sustained winds at the county’s population-weighted center were >17.2 m/s (gale-force winds or greater). We used distributed lag log-linear mixed-effects models to estimate the relative risk (RR) and absolute risk difference (ARD) for TC exposure by comparing PTBs occurring in TC-periods (from 2 days before to 30 days after) to matched non-TC periods. We conducted secondary analyses using different exposure metrics and thresholds.Results During the study period, there were 1,981,797 (10.1%) PTBs and 58 TCs that affected at least one studied US county. The risk of PTB was positively associated with TC exposure defined as peak sustained wind speed >17.2 m/s [RR: 1.01 (95% CI: 0.99, 1.03); ARD: 9 (95% CI: -7, 25) per 10,000 pregnancies], distance to storm track <60km [RR: 1.02 (95% CI: 1.01, 1.04); ARD: 23 (95% CI: 9, 38) per 10,000 pregnancies], and cumulative rainfall >100mm [RR: 1.04 (95% CI: 1.02, 1.06); ARD: 36 (95% CI: 16, 56) per 10,000 pregnancies]. Results were comparable when considering other wind, distance, or rain thresholds. The association was more pronounced among early PTBs and mothers living in more socially vulnerable counties but did not vary across strata of other hypothesized risk factors.ConclusionsPrenatal exposure to TC was associated with a higher risk of PTB. Our findings provide initial evidence that severe storms may trigger PTB.1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".