ArticleFrontiers in stroke2026
Neighborhood economic and demographic landscape as predictors of 90-day outcomes post-stroke hospitalization.
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.
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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.
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Who cites it
1 citing paper in PubMed.
- Predicting 30-Day Readmission After Stroke: A Systematic Review and Meta-Analysis to Inform Predictor Selection.Diagnostics (Basel, Switzerland) · 2026Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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