ArticleCureus2026
Geospatial Patterning of Hypertension and High Cholesterol Prevalence in Inland Southern California: An Ecological ZIP-Code-Level Study.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background California's Inland Empire (IE) is a federally designated Health Professional Shortage Area (HPSA) with high burdens of cardiovascular disease (CVD). Given limited regional health surveillance, this study assesses spatial clustering of hypertension and high cholesterol and associations between these outcomes and neighborhood-level demographic, socioeconomic, and health characteristics in spatially adjusted models. Methods This cross-sectional ecological study uses data from the 2025 CDC Population Level Analysis and Community Estimates (PLACES) dataset and 2019-2023 American Community Survey (ACS), representing 132 ZIP Code Tabulation Areas (ZCTAs) in Riverside and San Bernardino counties. With respect to data analysis, a spatial econometric workflow, variance inflation factors (VIF), diagnostic ordinary least squares (OLS) regression, Moran's I for geospatial clustering, and spatial error models (SEM) were applied. All results in this study are presented as hypothesis-generating. Results The mean modeled prevalence across ZCTAs was 34.3% for hypertension and 36.5% for high cholesterol, with significant geospatial clustering being present for hypertension and cholesterol (I = 0.293, p < 0.001; I = 0.162, p = 0.002). Additionally, in SEM-adjusted models, both outcomes were associated with obesity (β = 0.584, p < 0.001; β = 0.308, p < 0.001) and recent checkups (β = 1.395, p < 0.001; β = 1.215, p < 0.001), and negatively associated with median income (β = -0.465, p < 0.001; β = -0.180, p < 0.001). Conclusions Modeled hypertension and high cholesterol prevalence in the IE varied by ZCTA and were spatially clustered, while obesity, income, and healthcare engagement were associated with neighborhood-level cardiovascular risk. These results support the use of spatial surveillance and may inform future public health research and practice in these medically underserved regions.
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