Abstract 4370961: Neighborhood Level of Incarceration and Prevalence of Cardiometabolic Diseases: Evidence from Path Analysis

Background: Incarceration is associated with worse cardiometabolic health at the individual level; however little has been done to examine pathways accounting for structural racism at the neighborhood level. This study examined the pathway between structural racism, incarceration, and cardiometabolic disease across US census tracks, using historic residential redlining as the measure for structural racism. Methods: Direct and indirect relationships between redlining and each cardiometabolic disease via incarceration was investigated using structural equation modeling run in Stata v17. The final analytic sample included 11,457 observations across 51 states, 104 counties, and 6,797 census tracts created by combining census tract level data from multiple sources. Neighborhood level incarceration was defined as the % of a census tract incarcerated. Historic residential redlining was defined based on Home Owners’ Loan Corporation (HOLC) residential security maps. Prevalence of disease was based on CDC PLACES data - coronary heart disease (CHD), diabetes (DM), obesity, high cholesterol (HC) and high blood pressure (HBP). Results: Mean prevalence of CHD 6.0%, DM 11.8%, stroke 3.6%, obesity 31.8%, HC 31.3%, HBP 31.9%. Redlining (0.22, p<0.001) and lower food access (-0.11, p<0.001) were directly associated with higher prevalence of DM. Redlining was significantly indirectly associated with CHD, DM, stroke, obesity, HC and HBP via incarceration (0.29, p<0.001). Incarceration was directly associated with higher prevalence CHD (0.41, p<0.001), DM (0.54, p<0.001), stroke (0.59, p<0.001), obesity (0.67, p<0.001), HC (0.19, p<0.001), and HBP (0.59, p<0.001). In addition, redlining was directly associated with higher prevalence of CHD (0.02, p<0.05), DM (0.09, p<0.001), stroke (0.04, p<0.001), and obesity (0.02, p<0.01); and lower prevalence of HC (-0.08, p<0.001) and HBP (-0.04, p<0.001). Conclusions: Historic residential redlining is associated with higher prevalence of multiple cardiometabolic diseases via increased prevalence of incarceration within a census tract. Based on these results, high rates of incarceration within a neighborhood may be an underappreciated marker for risk of cardiometabolic disease. Interventions to address resource deprivation within a census tract and disproportionate rates of incarceration across communities may help address the impact of structural racism on the health of individuals at risk for cardiometabolic diseases.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-11-03
DOI
https://doi.org/10.1161/circ.152.suppl_3.4370961
Primary Topic
Cardiovascular Health and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4370961: Neighborhood Level of Incarceration and Prevalence of Cardiometabolic Diseases: Evidence from Path Analysis

Rebekah J. Walker, J. Campbell, Leonard E. Egede
Circulation
Cardiovascular Health and Risk Factors
article

Abstract 4370961: Neighborhood Level of Incarceration and Prevalence of Cardiometabolic Diseases: Evidence from Path Analysis

Rebekah J. Walker, J. Campbell, Leonard E. Egede
article en

Abstract

Background: Incarceration is associated with worse cardiometabolic health at the individual level; however little has been done to examine pathways accounting for structural racism at the neighborhood level. This study examined the pathway between structural racism, incarceration, and cardiometabolic disease across US census tracks, using historic residential redlining as the measure for structural racism. Methods: Direct and indirect relationships between redlining and each cardiometabolic disease via incarceration was investigated using structural equation modeling run in Stata v17. The final analytic sample included 11,457 observations across 51 states, 104 counties, and 6,797 census tracts created by combining census tract level data from multiple sources. Neighborhood level incarceration was defined as the % of a census tract incarcerated. Historic residential redlining was defined based on Home Owners’ Loan Corporation (HOLC) residential security maps. Prevalence of disease was based on CDC PLACES data - coronary heart disease (CHD), diabetes (DM), obesity, high cholesterol (HC) and high blood pressure (HBP). Results: Mean prevalence of CHD 6.0%, DM 11.8%, stroke 3.6%, obesity 31.8%, HC 31.3%, HBP 31.9%. Redlining (0.22, p<0.001) and lower food access (-0.11, p<0.001) were directly associated with higher prevalence of DM. Redlining was significantly indirectly associated with CHD, DM, stroke, obesity, HC and HBP via incarceration (0.29, p<0.001). Incarceration was directly associated with higher prevalence CHD (0.41, p<0.001), DM (0.54, p<0.001), stroke (0.59, p<0.001), obesity (0.67, p<0.001), HC (0.19, p<0.001), and HBP (0.59, p<0.001). In addition, redlining was directly associated with higher prevalence of CHD (0.02, p<0.05), DM (0.09, p<0.001), stroke (0.04, p<0.001), and obesity (0.02, p<0.01); and lower prevalence of HC (-0.08, p<0.001) and HBP (-0.04, p<0.001). Conclusions: Historic residential redlining is associated with higher prevalence of multiple cardiometabolic diseases via increased prevalence of incarceration within a census tract. Based on these results, high rates of incarceration within a neighborhood may be an underappreciated marker for risk of cardiometabolic disease. Interventions to address resource deprivation within a census tract and disproportionate rates of incarceration across communities may help address the impact of structural racism on the health of individuals at risk for cardiometabolic diseases.

CirculationVol. 152(Suppl_3)
University at Buffalo, State University of New York (US)
Openalex Percentile: Top 50%
Cardiovascular Health and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.