Redistribution of Emergency Department Patients After Disaster-Related Closures of a Public Versus Private Hospital in New York City
Bibliographic record
Abstract
Sudden hospital closures displace patients from usual sources of care and force them to access facilities that lack their prior medical records. For patients with complex needs and for nearby hospitals already strained by high volume, disaster-related hospital closures induce a public health emergency. Our objective was to analyze responses of patients from public versus private emergency departments after closure of their usual hospital after Hurricane Sandy. Using a statewide database of emergency visits, we followed patients with an established pattern of accessing 1 of 2 hospitals that closed after Hurricane Sandy: Bellevue Hospital Center and NYU Langone Medical Center. We determined how these patients redistributed for emergency care after the storm. We found that proximity strongly predicted patient redistribution to nearby open hospitals. However, for patients from the closed public hospital, this redistribution was also influenced by hospital ownership, because patients redistributed to other public hospitals at rates higher than expected by proximity alone. This differential response to hospital closures demonstrates significant differences in how public and private patients respond to changes in health care access during disasters. Public health response must consider these differences to meet the needs of all patients affected by disasters and other public health emergencies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".