Evidence map›Paper›PMID 41415481›Full record

ArticleInformatics in medicine unlocked2025

Excess CMS morbidity and FEMA declarations: Phenome Wide Association Study of Generalized Linear Model interactions between CMS diagnostic code utilization and FEMA Incident types, 1999-2020.

Nick Williams

Abstract read
In one paragraph

Article in Informatics in medicine unlocked, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nick WilliamsLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.

Funding

Intramural NIH HHS Z99 LM999999
6 · The paper itself

Abstract

Introduction: Despite the availability of descriptive clinical and disaster data, the associations between disaster type and clinical morbidity remains unknown. The Federal Emergency Management Agency (FEMA) responds to disasters in the United States by mobilizing federal resources to support state and local emergency response efforts. The Centers for Medicare and Medicaid Services (CMS) provides payment services for covered individuals and activities in the United States. The association of CMS phenotypes of service utilization (i.e. Essential hypertension, Coronary atherosclerosis [Atherosclerotic heart disease], Herpes simplex) with incident types (i.e. Hurricane, Fire, Terrorism) remains under described. Methods: Historic Emergency Disaster Declarations were retrieved from FEMA API, and the resulting records were transformed to the study data standard. CMS claims were sourced from Chronic Conditions Warehouse and transformed to the study data standard. Records were then merged by county and month to create a Per-Member Per-Month Per-County phenotype model of CMS service utilization with FEMA county-month declaration detail. A generalized linear model was produced using H2o.ai predicting Per Member, Per Month, Per County attack rate within service phenotype, and interactions between phenotype and disaster type produced z-values, standardized coefficients and p-values to assess the association or disassociation of the interaction between phecodes and incident types. Results: Specific disaster declaration types have high coefficient (associated) and low coefficient (disassociated) relationships. These relationships for specific phenotypes show marked consistencies over time across named disaster declarations within disaster type. Conclusions: CMS claimants seek or avoid specific phenotypes of services statistically attributable to disaster incident types. FEMA disaster exposure is both an accelerant of care seeking and a barrier to seeking kinds of care (especially STD care). Future research should control lag, lead, distance, migrancy, impact and duration effects to improve association detection.

Indexed as

Covid-19Emergency Preparedness and ResponseMachine LearningMedicaidMedicarePheWASSexually Transmitted InfectionsWildfire Smoke

Identifiers

PMID41415481
PMCPMC12711138

What OpenQuestion holds

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Registered trials

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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.