Urban Context, Constraint, and Cardiometabolic Health: A Comparative Spatial Analysis of Six U.S. Cities

Cardiometabolic conditions, including obesity, diabetes, hypertension, and high cholesterol, exhibit persistent geographic disparities across U.S. cities. These disparities are frequently attributed to unequal access to healthcare, healthy food, education, and socioeconomic opportunities. While relationships are well documented, less is understood about why identical health indicators manifest in markedly different spatial forms across cities, even when measured using consistent data sources and analytic methods. This study employs a comparative, place-based framework to examine census tract-level patterns of cardiometabolic health in six U.S. cities: Atlanta, Baltimore, San Antonio, Columbus (Ohio), Phoenix, and Portland. Using modeled prevalence estimates from the Centers for Disease Control and Prevention (CDC) PLACES dataset, the analysis integrates median household income, dominant racial or ethnic composition, educational attainment, health insurance coverage, and spatial measures of food and healthcare access. By maintaining constant indicators across cities, the study isolates urban context as a central factor shaping health geographies. Results reveal distinct spatial typologies of cardiometabolic burden, including polarized segregation, diffuse majority, corridor-based, growth, and climate related stress patterns. These typologies do not align consistently with access-based metrics alone. In several cities, elevated cardiometabolic prevalence persists in areas with relatively favorable access to food retailers or healthcare facilities, while some low access areas do not exhibit equivalent health burdens. The findings support a constraint-based framework emphasizing lived experiences, chronic stress, environmental exposure, and institutional context as pathways between structural conditions and health outcomes. The paper concludes by discussing implications for urban health policy, arguing for place specific interventions that reduce structural friction rather than relying solely on access expansion or information-based strategies.

Cardiometabolic diseases represent one of the most significant public health challenges in the United States, both in terms of population prevalence and long-term healthcare costs. Conditions such as obesity, diabetes, hypertension, and high cholesterol are strongly associated with morbidity, mortality, and quality of life, and they exhibit pronounced geographic disparities across and within cities (Marmot, 2005; Krieger, 2011). These disparities are commonly framed through the lens of access: to healthcare, to healthy food, to education, and to socioeconomic opportunity. Access based explanations have shaped much of contemporary urban health policy, motivating interventions such as expanding healthcare facilities, incentivizing grocery store development, and implementing community-based health education programs (Thornton et al., 2019).

While these interventions are necessary and often beneficial, their effectiveness has been uneven. In many urban contexts, the burden of cardiometabolic disease remains spatially persistent despite improvements in access to care (Cummins et al., 2014; Conrey et al., 2022). This persistence raises a central question: why do similar cardiometabolic health indicators produce different spatial patterns across cities, even when measured using consistent data sources and methods? Addressing this question requires moving beyond single city analyses and beyond access as a singular explanatory framework. This study adopts a comparative, multi-city approach to examine cardiometabolic health geographies across six U.S. cities with diverse demographic compositions, urban forms, and historical development trajectories. By holding health indicators and analytic methods constant across cities, the analysis employs urban context as a key factor shaping how disease burden is distributed spatially (Diez Roux, 2001; Macintyre et al., 2002).

1. Cardiometabolic Health and Neighborhood Context
A substantial body of research demonstrates that cardiometabolic health outcomes are spatially patterned and strongly associated with neighborhood level socioeconomic conditions (Diez Roux, 2001; Krieger, 2011). Income, education, and employment status influence access to resources, exposure to stressors, and the feasibility of healthy behaviors, operating at both the individual and neighborhood levels. Health geography emphasizes that disease prevalence is not randomly distributed but reflects the spatial organization of social, economic, and environmental conditions (Macintyre et al., 2002). From this perspective, place is not simply a container of individuals but an active determinant of health outcomes.

2. Access-Based Frameworks and Their Limits
Access to healthcare and healthy food have been widely studied as determinants of cardiometabolic health. Research on food deserts has linked limited proximity to healthy food retailers with higher rates of obesity and diabetes (Walker et al., 2010). Similarly, proximity to primary care services is often assumed to reduce chronic disease burden through improved prevention and disease management. However, empirical findings on access are mixed. Several studies report weak or inconsistent associations between food access and obesity once broader socioeconomic context is considered (Cummins et al., 2014; Conrey et al., 2022). Longitudinal and quasi-experimental research has shown that improved food access alone does not reliably lead to sustained dietary change or reductions in obesity prevalence (Cummins et al., 2014).

Similarly, healthcare access alone does not consistently account for persistent health disparities, especially in communities experiencing chronic stress, economic insecurity, or historical disinvestment. Evidence suggests that while access is important, it is not always sufficient for better cardiometabolic health.

3. Structural Constraint and Lived Feasibility
Medical anthropology and social epidemiology highlight that lived feasibility and constraint link broader structures to health outcomes. Health behaviors are shaped by time scarcity, financial stress, environmental exposure, and institutional trust, all of which influence whether access leads to use. Urban sociology also draws attention to segregation, disinvestment, and spatial polarization as structures that shape risk and opportunity. Recent research connects spatial income and racial-ethnic segregation directly to cardiometabolic burden, emphasizing the critical role of structural context.

1. Study Cities
The analysis focuses on six U.S. cities: Atlanta (Georgia), Baltimore (Maryland), San Antonio (Texas), Columbus (Ohio), Phoenix (Arizona), and Portland (Oregon). These cities were selected to capture variation in regional context, racial and ethnic composition, urban form, and historical development trajectories.

2. Health Outcomes
Census tract–level prevalence estimates for adult obesity, diabetes, hypertension, and high cholesterol were obtained from the CDC PLACES dataset (Centers for Disease Control and Prevention, 2023). These modeled estimates enable consistent comparison across cities while emphasizing relative spatial patterns rather than precise local prevalence values.

3. Socioeconomic and Demographic Context
Median household income, educational attainment, and health insurance coverage were derived from the American Community Survey via NHGIS (United States Census Bureau, 2019–2023). Dominant racial or ethnic composition was identified using a ≥40% threshold to characterize neighborhood context.

4. Food and Healthcare Access
Food access indicators were drawn from the USDA Food Access Research Atlas, focusing on proximity-based measures of access to healthy food retailers (United States Department of Agriculture, 2023). Healthcare access was measured by the distance from census tract centroids to Health Resources and Services Administration (HRSA)–designated health centers and look-alike clinics, capturing the spatial availability of safety-net primary care services.

5. Analytical Approach
Spatial patterns were analyzed comparatively across cities using identical classification schemes and visualization parameters. The analysis emphasizes pattern recognition and typology development rather than causal modeling. Interpretation draws on interdisciplinary literature to contextualize observed alignments and mismatches between health outcomes and structural indicators

1. Cross-City Patterns of Cardiometabolic Health
Across all four cardiometabolic indicators, substantial variation was observed in both the magnitude and the spatial organization of disease burden. Some cities exhibited large, contiguous areas of elevated prevalence, while others displayed fragmented, corridor based, or diffuse patterns. These differences were consistent across indicators, suggesting underlying structural influences rather than indicator-specific anomalies.

2. Alignment With Structural Indicators
Income gradients aligned strongly with cardiometabolic burden in some cities, particularly Atlanta and Baltimore, where patterns of segregation and economic polarization were pronounced (Massey & Denton, 1993; Sharkey, 2013). In other cities, such as San Antonio, elevated prevalence was more diffuse and extended across the majority-population neighborhoods. Food access and healthcare proximity showed variable and inconsistent alignment with health outcomes. In several cities, areas with relatively favorable access conditions nonetheless exhibited elevated cardiometabolic prevalence, while some low access areas did not display equivalent burden.

3. City Case Studies

San Antonio
In San Antonio, elevated cardiometabolic prevalence spanned large portions of the city and aligned closely with limited food access within a predominantly Hispanic urban context. Healthcare access showed weaker correspondence, suggesting that environmental exposure and structural conditions played a larger role than proximity to care.

Atlanta and Baltimore
Atlanta and Baltimore exhibited sharply polarized patterns of cardiometabolic burden aligned with income inequality and racialized disinvestment. These patterns were consistent across indicators and closely mirrored historical segregation.

Phoenix, Columbus, and Portland
Phoenix’s sprawling urban form produced distance based access challenges, while Columbus and Portland displayed more moderate and diffuse patterns shaped by suburbanization and gradual socioeconomic gradients.

The results demonstrate that cardiometabolic health disparities differ not only in magnitude but in spatial form across cities. These differences cannot be explained consistently by access-based metrics alone. Instead, they reflect city specific configurations of structural constraint, environmental exposure, and lived feasibility. A constraint based framework helps reconcile these findings by emphasizing how urban context shapes the translation of access into health outcomes. In this framework, access is necessary but insufficient; health behaviors and outcomes are mediated by chronic stress, time scarcity, environmental conditions, and institutional trust (Farmer, 2004; Williams & Mohammed, 2009; Rajagopalan et al., 2024).

The findings caution against one-size-fits-all public health interventions. Policies that focus exclusively on expanding access risk overlooking the structural conditions that limit the effectiveness of such interventions. Place specific strategies that reduce structural friction and align with everyday constraints are more likely to produce durable health improvements (Marmot, 2005; Thornton et al., 2019).

This study relies on modeled prevalence estimates and descriptive spatial analysis rather than causal inference. Future research could integrate longitudinal data, qualitative fieldwork, or policy evaluation to further test the constraint-based framework (Cheung et al., 2025).

Comparative spatial analysis reveals that cardiometabolic health disparities are deeply shaped by urban context and structural constraint. Access alone cannot explain persistent health burdens. Effective urban health policy must engage with the lived realities of place.

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