Evidence map›Paper›PMID 39036785›Full record

ArticleNeurology. Clinical practice2024

Racial, Ethnic, and Regional Disparities of Post-Acute Service Utilization After Stroke in the United States.

Shumei Man, David Bruckman, Ken Uchino, Jesse D Schold, Jarrod Dalton

Abstract read
In one paragraph

Article in Neurology. Clinical practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
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  5. Article
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  7. Observational
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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

5 authors.

Shumei ManDepartment of Neurology and Cerebrovascular Center (SM, KU), Neurological Institute, Cleveland Clinic, OH; Center for Population Health Research (DB, JDS, JD), Department of Quantitative Health Sciences, Cleveland Clinic, OH.ORCID https://orcid.org/0000-0002-8345-6470
David BruckmanDepartment of Neurology and Cerebrovascular Center (SM, KU), Neurological Institute, Cleveland Clinic, OH; Center for Population Health Research (DB, JDS, JD), Department of Quantitative Health Sciences, Cleveland Clinic, OH.ORCID https://orcid.org/0000-0001-6542-4048
Ken UchinoDepartment of Neurology and Cerebrovascular Center (SM, KU), Neurological Institute, Cleveland Clinic, OH; Center for Population Health Research (DB, JDS, JD), Department of Quantitative Health Sciences, Cleveland Clinic, OH.ORCID https://orcid.org/0000-0001-9468-4172
Jesse D ScholdDepartment of Neurology and Cerebrovascular Center (SM, KU), Neurological Institute, Cleveland Clinic, OH; Center for Population Health Research (DB, JDS, JD), Department of Quantitative Health Sciences, Cleveland Clinic, OH.ORCID https://orcid.org/0000-0002-5341-7286
Jarrod DaltonDepartment of Neurology and Cerebrovascular Center (SM, KU), Neurological Institute, Cleveland Clinic, OH; Center for Population Health Research (DB, JDS, JD), Department of Quantitative Health Sciences, Cleveland Clinic, OH.ORCID https://orcid.org/0000-0001-8336-1212

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Post-acute care is critical for patient functional recovery and successful community transition. This study aimed to understand the current racial, ethnic, and regional disparities in post-acute service utilization after stroke. Methods: This retrospective cross-sectional study included patients hospitalized for ischemic stroke and intracerebral hemorrhage in 2017-2018 using the National Inpatient Sample. Discharge destinations were classified as follows: (1) facility including inpatient rehabilitation, skilled nursing facility, and facility hospice; (2) home health care (HHC), including home health and home hospice; and (3) home without HHC. Multinomial logistic regression was used to study the odds of discharge to a facility over home and HHC over home without HHC by race, ethnicity, insurance, and census division, adjusting for clinical factors and survey design. Results: Among the 1,000,980 weighted ischemic stroke admissions, 66.9% were White, 17.6% Black, 9.5% Hispanic, 3.1% Asian American/Pacific Islander, and 0.4% Native American. Relative to private insurance, uninsured patients had the lowest adjusted odds of facility over home discharge (0.44; 95% CI 0.40-0.48) and HHC discharge over home without HHC (0.79; 95% CI 0.71-0.88). Compared with White patients, only Hispanic patients with Medicare/Medicaid insurance or self-pay had lower odds of facility over home discharge (adjusted OR 0.80 and 0.75, respectively; 95% CI 0.76-0.84 and 0.63-0.93). Uninsured Hispanic patients also had lower odds of HHC discharge over home without HHC than White patients (0.74; 95% CI 0.57-0.97). Facility discharge rate was the highest in East North Central (39.2%) and lowest in Pacific (31.2%). HHC discharge rate was the highest in New England (20.2%) and lowest in West North Central (10.3%), which had the highest home without HHC discharge (46.1%). Compared with New England, other census divisions had lower odds of facility over any home discharge with Pacific being the lowest (adjusted OR, 0.66; 95% CI 0.60-0.71) and HHC over home without HHC discharge with West North Central being the lowest (adjusted OR, 0.33; 95% CI 0.29-0.38). Similar patterns were observed in intracerebral hemorrhage. Discussion: Significant insurance-dependent racial and ethnic disparities and regional variations were evident in post-acute service utilization after stroke. Targeted efforts are needed to improve post-acute service access for uninsured patients especially Hispanic patients and people in certain regions.

Identifiers

PMID39036785
PMCPMC11259533

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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.