Severe Tornadoes and Infant Birth Weight in the United States
Bibliographic record
Abstract
Increasing evidence links exposure to extreme weather events in utero with adverse health outcomes at birth, including lower birth weight. This research, however, often faces data limitations because natural disasters may be localized, often affecting some neighborhoods but not others, whereas outcome data are often available only at higher geographic levels, such as counties. In this article, we introduce a novel strategy for estimating the effects of geographically bounded disasters when localized outcome data are unavailable. We employ this strategy to estimate the effect of exposure to severe tornadoes on infant birth weight in the United States from 1991 to 2017. We merge county-month data on singleton births with block-group-level monthly data on the paths of severe tornadoes and block-group data on the distribution of the population at risk of a birth. We then estimate difference-in-differences models in which the treatment variable is equal to the percentage of the population at risk of a birth affected by the tornado. This strategy results in an estimand that is both more interpretable and more policy-relevant than estimands from traditional models. Our findings demonstrate that exposure to a tornado during pregnancy reduced birth weight for Black mothers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".