Association Between Hurricane Sandy and Emergency Department Visits in New York City by Age and Cause
Bibliographic record
Abstract
The magnitude, timing, and etiology of morbidity associated with tropical cyclones remains incompletely quantified. We examined the relative change in cause-specific emergency department (ED) visits among residents of New York City during and after Hurricane Sandy, a tropical cyclone that affected the northeastern United States in October 2012. We used quasi-Poisson constrained distributed lag models to compare the number of ED visits on and after Hurricane Sandy with all other days, 2005-2014, adjusting for temporal trends. Among residents aged ≥65 years, Hurricane Sandy was associated with a higher rate of ED visits due to injuries and poisoning (relative risk (RR) = 1.19, 95% confidence interval (CI): 1.10, 1.28), respiratory disease (RR = 1.35, 95% CI: 1.21, 1.49), cardiovascular disease (RR = 1.10, 95% CI: 1.02, 1.19), renal disease (RR = 1.44, 95% CI: 1.22, 1.72), and skin and soft tissue infections (RR = 1.20, 95% CI: 1.03, 1.39) in the first week following the storm. Among adults aged 18-64 years, Hurricane Sandy was associated with a higher rate of ED visits for renal disease (RR = 2.15, 95% CI: 1.79, 2.59). Among those aged 0-17 years, the storm was associated with lower rates of ED visits for up to 3 weeks. These results suggest that tropical cyclones might result in increased health-care utilization due to a wide range of causes, particularly among older adults.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".