Evidence map›Paper›PMID 41909026›Full record

ArticleFrontiers in stroke2026

Neighborhood economic and demographic landscape as predictors of 90-day outcomes post-stroke hospitalization.

Farya Fakoori, Karlon H Johnson, Hannah Gardener, Carolina M Gutierrez, Negar Asdaghi, Lauri Bishop, Scott C Brown, Iszet Campo-Bustillo, Gillian Gordon Perue, Emir Veledar and 5 more

Abstract read
In one paragraph

Article in Frontiers in stroke, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Review
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

15 authors.

Farya FakooriUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Karlon H JohnsonUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Hannah GardenerUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Carolina M GutierrezUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Negar AsdaghiUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Lauri BishopUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Scott C BrownUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Iszet Campo-BustilloUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Gillian Gordon PerueUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Emir VeledarUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Hao YingUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Lili ZhouUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Jose G RomanoUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Tatjana RundekUniversity of Miami Miller School of Medicine, Miami, FL, United States.
Erika MarulandaUniversity of Miami Miller School of Medicine, Miami, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: An in-depth exploration of neighborhood environmental impact on post-discharge stroke outcomes is lacking yet essential for identifying populations at high risk. We assess neighborhood economic and demographic characteristics associated with 90-day death or readmission post-stroke hospitalization. Methods: We prospectively analyzed 1,329 acute stroke survivors in the Florida Stroke Registry's Transition of Care Stroke Disparities Study (91% ischemic, 56% male, 52% non-Hispanic White, 23% non-Hispanic Black, 22% Hispanic, median age 64). Neighborhood characteristics at the ZIP+4 level, including socioeconomic status (NSES), racial/ethnic composition, and business densities (food, tobacco/alcohol, gyms, medical services), were analyzed using factor analysis to generate four factors with eigenvalues greater than 1. Outcomes (death or readmission) were assessed through structured telephone interviews 90 days post-discharge. Logistic regression evaluated associations between neighborhood characteristics and outcomes, adjusting for demographics (race/ethnicity, sex, age), vascular risk factors, stroke severity from Get With The Guidelines-Stroke Results: Within 90 days, 208 patients experienced death or readmission. Four factors explained 59% of the variance in 24 neighborhood characteristics. Factor 1, defined by lower NSES, higher population density, and urbanization (RUCA code 1, greater densities of tobacco/alcohol outlets, restaurants, grocery stores, gyms, and pharmacies), was associated with a 20% increased risk. Conclusions: Living in densely populated, highly urbanized neighborhoods with lower SES and greater commercial density predicted poor stroke outcomes independent of individual health or SES. These findings can guide community interventions to reduce stroke mortality and readmission.

Indexed as

community healthneighborhood SESstroke outcomesstroke readmissiontransitions of care

Identifiers

PMID41909026
PMCPMC13017339

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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.