Evidence map›Paper›PMID 41561558›Full record

ArticleBMJ public health2026

Evaluating administrative compliance as a predictor of nursing home postdisaster outcomes in the USA.

Natalia Festa, Kaitlin F Throgmorton, Manali A Phadke, John R O'Leary, Kendra Davis-Plourde, Emma Zang, Kai Chen, Jill Kelly, David M Dosa, Alexandra M Hajduk and 2 more

Abstract read
In one paragraph

Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Natalia FestaDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0003-4487-6832
Kaitlin F ThrogmortonYale University, New Haven, Connecticut, USA.
Manali A PhadkeDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
John R O'LearyDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Kendra Davis-PlourdeDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Emma ZangDepartment of Sociology, Yale University, New Haven, Connecticut, USA.
Kai ChenDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-0164-1112
Jill KellyDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.
David M DosaDepartment of Medicine, UMass Chan Medical School TH Chan School of Medicine, Worcester, Massachusetts, USA.
Alexandra M HajdukDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Andrew B CohenDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Thomas M GillDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Albert C Shaw · 2002 to 2026
$37.9M
Yale Training Program in Geriatric Clinical Epidemiology and Aging-Related ResearchT32AG019134 · NIA · YALE UNIVERSITY · PI Terri R. Fried · 2001 to 2026
$7.5M
Investigating Nursing Home Emergency Preparedness for Environmental and Climatological Determinants of Resident HealthR03AG088893 · NIA · YALE UNIVERSITY · PI FESTA, NATALIA · 2024 to 2024
$335k
NIA NIH HHS L70 AG089284NIA NIH HHS P30 AG021342NIA NIH HHS R03 AG088893NIA NIH HHS T32 AG019134
6 · The paper itself

Abstract

Introduction: Determining whether compliance with the US Centers for Medicare & Medicaid Services (CMS) emergency preparedness standards for nursing homes is a protective factor that improves residents' postdisaster outcomes is vital to informed regulatory oversight. Methods: This retrospective cohort study included 294 CMS-certified nursing homes exposed to Hurricane Michael (October 2018). The exposure was non-compliance with emergency preparedness (E-tag) and/or building code (K-tag) standards from the CMS Life Safety Code survey; we operationalised over 200 unique deficiencies as separate dichotomous indicator variables. We fit generalised linear models and applied elastic net regularisation to identify which regulatory deficiencies were most predictive of adverse postdisaster outcomes (30-day mortality (primary), 30-day hospitalisation and functional decline within 120 days). We selected the best fitting model for each outcome based on the lowest Bayesian Information Criterion and reported incidence rate ratios (IRRs) for retained variables. We performed 10-fold cross-validation and evaluated the predictive accuracy of the best-fitting models using root mean squared error (RMSE), compared with a null (intercept-only) model. Results: Across 294 nursing homes with 21 945 residents with an average age of 81 years, there were 697 deaths, 1316 hospitalisations and 1274 instances of functional decline in the postdisaster period. No emergency preparedness deficiency predicted adverse postdisaster outcomes. In contrast, two building code deficiencies predicted postdisaster functional decline (IRRs 1.21 and 1.51). The best-fitting models demonstrated modest improvements in predictive accuracy compared with the null model for postdisaster mortality (RMSE 1.76 vs 1.79) and functional decline (RMSE 3.35 vs 3.44), although these differences were not statistically significant. Conclusions: Measures of compliance with federal emergency-preparedness standards did not predict postdisaster mortality, hospitalisation or functional decline. These findings indicate a need to better align the measurement and oversight of nursing home emergency preparedness with the complexities of real-world disaster response.

Indexed as

Health PersonnelPatient HarmPublic Health

Identifiers

PMID41561558
PMCPMC12815042

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.