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

## TL;DR

This study shows that living in a densely populated, low-income urban area increases the risk of death or readmission after a stroke, independent of personal health factors.

## Contribution

The study identifies specific neighborhood characteristics that independently predict poor stroke outcomes, offering new insights for community-level interventions.

## Key findings

- Neighborhoods with lower socioeconomic status and higher commercial density were linked to a 20% increased risk of death or readmission.
- Four factors explained 59% of the variance in neighborhood characteristics affecting stroke outcomes.
- Urbanization and population density were significant predictors of poor outcomes after stroke hospitalization.

## Abstract

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.

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®, and social or economic conditions such as insurance, support, and living arrangements.

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.

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.