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From food deserts to nutritional equity: exposing socioeconomic drivers of hypertension

Published online by Cambridge University Press:  13 January 2026

Zihao Yi
Affiliation:
Joseph J. Zilber College of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Masoud Khani
Affiliation:
Joseph J. Zilber College of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Mohammad Assadi Shalmani
Affiliation:
Joseph J. Zilber College of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Amirsajjad Taleban
Affiliation:
Joseph J. Zilber College of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Jennifer T. Fink
Affiliation:
School of Biomedical Science and Health Care Administration, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Robert F. Frediani
Affiliation:
School of Biomedical Science and Health Care Administration, University of Wisconsin Milwaukee, Milwaukee, WI, USA
Jake Luo*
Affiliation:
Joseph J. Zilber College of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
*
Corresponding author: Jake Luo; Email: jakeluo@uwm.edu

Abstract

This study investigates the associations between social determinants of health (SDOH) and hypertension prevalence across Wisconsin communities, with particular attention to food environments, economic factors, and transportation patterns. Using data from the 2019–2020 Wisconsin State Inpatient Database (387,047 patients) and the 2020 AHRQ SDOH database, we employed spatial analysis and logistic regression models to examine relationships between hypertension prevalence and neighbourhood characteristics across 597 ZIP codes. Lower-income areas exhibited significantly higher hypertension prevalence (EE = 1.233, 95% CI: 1.128–1.347 for incomes under $14,999), neighbourhoods with greater food resource density showed protective associations (EE = 0.549, 95% CI: 0.474–0.636 for supermarket access). Active transportation patterns were associated with lower hypertension rates (EE = 0.879, 95% CI: 0.829–0.933 for walking). We observed a ‘Hispanic paradox’ in Milwaukee County, where Hispanic populations demonstrated lower hypertension prevalence despite socioeconomic disadvantages, whereas African American populations with similar disadvantages exhibited higher prevalence. Our proposed ‘Food Environment Synergy Model’ helps frame these findings by conceptualising food environments through three interacting dimensions: physical access, economic accessibility, and cultural dietary patterns. This integrated approach highlights how these dimensions collectively relate to unique risk and resilience profiles within communities, challenging conventional binary classifications of ‘food deserts’ versus ‘food secure’ areas. These findings indicate that addressing food access disparities, promoting walkable neighbourhoods, and preserving beneficial cultural dietary traditions may be related to lower hypertension prevalence and advance health equity in diverse communities. However, the analysis is cross-sectional, causality cannot be inferred; further longitudinal studies are needed to establish causal relationships.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of The Nutrition Society
Figure 0

Table 1. Demographic characteristics, hypertension prevalence rates, and odds ratios among patients (n = 387,047)

Figure 1

Table 2. Correlation and effect estimates for social determinants of health variables

Figure 2

Figure 1. Relationship between insurance coverage types and patient prevalence rate.Scatter plots illustrate the association between the percentage of various insurance coverage types and patient prevalence rates. Each plot represents linear regression analysis with 95% confidence intervals (shaded areas). Panels depict insurance types individually: (a) Medicaid, (b) Private Insurance, (c) Medicare, (d) Self-pay, and (e) Other Insurance. Patient prevalence rates reflect the percentage of patients diagnosed with hypertension.

Figure 3

Figure 2. Demographic and socioeconomic characteristics across Milwaukee County by ZIP code region.Maps illustrate variations in racial composition, socioeconomic status, and patient demographic distribution within Milwaukee County. Panel (a) shows racial composition, indicating majority populations categorised as White, Hispanic, or Black across ZIP codes. Panel (b) displays the ZIP-code-level median household income quantiles, ranging from low (green, quantile 1) to high (red, quantile 4). Panel (c) illustrates patient demographic distribution by racial majority, categorised similarly to panel (a).

Figure 4

Figure 3. Hypertension prevalence rate (%) across Milwaukee County by ZIP code region.The choropleth map depicts the geographic distribution of hypertension prevalence rates across ZIP code regions within Milwaukee County. Prevalence rates are colour-coded, ranging from low (green) to high (red), with specific numerical breakpoints indicated in the colour legend. Hypertension prevalence rates represent the percentage of patients diagnosed with hypertension within each ZIP code region.

Figure 5

Figure 4. Geographic distribution of food resources across Milwaukee County by ZIP code region.Maps depicting the distribution of various food resources normalised per 1000 people and log transformed. Panel (a) illustrates the distribution of convenience stores, panel (b) shows supermarkets and grocery stores, panel (c) represents fast food restaurants, and panel (d) illustrates full-service restaurants. Colour gradients represent resource density from low (green) to high (red).