Obesity, Urbanization, and Food Access in Wisconsin: The Intersection of Healthcare Provider Access and Population Health Outcomes

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Fink, Robert Frediani, Christopher Weber, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8025002/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background. Social determinants of health (SDOH) are widely linked to obesity, but effects may depend on local structural conditions. We examined whether SDOH act uniformly or vary by urbanization and community profile in Wisconsin. Methods. We conducted a cross-sectional ecological study of 833 ZIP codes (72 counties) from 2019–2023. Obesity prevalence was derived from electronic health records. Predictors covered education, transportation, housing, household composition, insurance, income and poverty, and demographic specific food access. Analyses used a multi-level urbanization classification, bivariate associations to identify universal versus context dependent patterns, and UMAP with clustering to derive community archetypes. Results. Obesity prevalence increased in all cohorts. Urban Underserved communities rose by just over eight percentage points, Urban Advantaged by about three, and rural cohorts formed an intermediate band. Education was the only consistently protective factor across all strata, with stronger inverse associations in urban settings, whereas most other SDOH appeared context dependent. Food access associations are concentrated in specific subgroups, children in low access areas (r = 0.39), low-income populations (r = 0.39), and households without a vehicle (r = 0.36), with asymmetric racial patterns. UMAP revealed eight community archetypes spanning 42.6% to 53.7% obesity prevalence, indicating that structural profiles aligned more closely with burden than geography alone. Conclusions. Relationships between SDOH and obesity were not uniform. Education behaved as a protective indicator, while most determinants varied by urbanization and community structure, supporting context matched strategies that prioritize high risk subgroups and tailor interventions to community archetypes. Health sciences/Health care/Public health/Epidemiology Health sciences/Health care/Health policy Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 1. Introduction 1.1 Obesity as a Public Health Challenge Obesity affects over 890 million adults globally, contributing to cardiovascular disease, type 2 diabetes, certain cancers, and premature mortality [1]. The United States faces particularly concerning rates, with obesity prevalence reaching 41.9% among adults and generating healthcare expenditures exceeding $173 billion annually [2]. Prevention approaches have traditionally emphasized individual behavioral modification, focusing on dietary choices and physical activity patterns, while often overlooking the structural environments that may constrain or enable healthy behaviors [3].This approach appears to have yielded limited population-level impact, suggesting that effective obesity prevention likely requires addressing social determinants of health (SDOH) and understanding how contextual factors shape their influence [4]. 1.2 Social Determinants and Food Access Social determinants of health are the conditions in which people are born, grow, live, work, and age, including educational attainment, economic stability, housing quality, transportation access, and healthcare availability [5]. Research suggests these factors may influence obesity risk through multiple pathways [6]. Educational attainment appears to shape health literacy and employment opportunities, creating cascading effects on food purchasing power and healthcare access [7]. Economic stability may determine access to healthy foods and healthcare services while potentially influencing chronic stress exposure [8]. Housing quality seems to affect environmental exposures and proximity to health-promoting resources [9], while transportation systems govern accessibility to employment, healthcare, and food retail environments [10]. Food access social determinants of health factors include both physical accessibility to outlets providing affordable, nutritious foods and functional accessibility determined by transportation, economic resources, and cultural preferences [11]. Conventional measures focus primarily on distance to food retailers, yet emerging evidence suggests these indicators may obscure important variations among vulnerable populations [12]. Children, low-income households, households without vehicle access, and racial/ethnic minority groups often face distinct barriers inadequately captured by proximity measures [13]. These populations may rely on alternative procurement strategies, ethnic groceries, farmers markets, food assistance programs which conventional metrics fail to quantify [14]. 1.3 Urbanization and Context Effects Urbanization creates distinct structural environments that may fundamentally alter how social determinants influence health outcomes [15], [16]. In urban settings, higher population density, more varied transportation networks, and a broader mix of destinations are common; these features can support active travel and improve access to food retailers [17]. By contrast, many rural places have lower density and fewer transportation options, and social cohesion often appears stronger than in cities [18], [19]. However, urbanization effects are rarely uniform across communities. Urban areas include both advantaged neighborhoods and places of concentrated disadvantage, while rural areas range from economically stable communities to isolated locales with limited infrastructure [20], [21]. This heterogeneity suggests that identical SDOH factors may operate differently across contexts, yielding different health outcomes depending on surrounding structural conditions[22]. 1.4 Research Gaps Despite growing recognition of SDOH importance in obesity prevention, current research exhibits critical limitations that may constrain effective intervention development [23]. Many studies assess single determinants rather than their interactions or cumulative burden; as a result, compounding disadvantage and possible buffering effects may be missed [24]. Pooled national analyses and cumulative indices often assume uniform effects and can obscure place-specific variation [23], [25]. While studies demonstrate associations between selected social determinants and obesity outcomes [26], systematic tests that separate consistent effects from context-dependent patterns remain limited. Food access measurement approaches rely heavily on aggregate distance-based indicators that may inadequately capture the complexity of food procurement behaviors across different demographic groups [27]. Current metrics often appear to miss population-specific barriers, alternative food sources, and cultural preferences that significantly influence actual food acquisition patterns [28]. Evidence suggests that food access effects may vary substantially across different population subgroups [29], yet few studies have systematically examined these variations. These evidence gaps collectively limit the development of targeted, context-appropriate obesity prevention strategies and highlight the need for comprehensive analytical approaches that can identify both universal and context-dependent determinant effects. 1.5 Study Objectives This study examines how social determinants relate to obesity in context, drawing on comprehensive SDOH data, stratified analyses by urbanization level, and clustering to summarize community profiles. Wisconsin is an appropriate setting given its pronounced urban–rural variation, socioeconomic diversity, and surveillance infrastructure that supports population-level analysis. Research Question 1 : Do conventional food access measures demonstrate universal associations with obesity across all demographic groups, or do they exhibit selective effectiveness among specific vulnerable populations? Research Question 2 : Which social determinants of health exhibit universal protective or harmful associate with obesity across all urbanization contexts, versus those whose associations with obesity outcomes are modified by community structural characteristics? Research Question 3 : Do comprehensive social determinant profiles reveal distinct community archetypes with unique obesity patterns that transcend traditional administrative and urban-rural classification boundaries? Central Hypothesis : Social determinants operate through both universal and context-dependent mechanisms, with community structural characteristics modifying the strength and direction of SDOH-obesity associations, necessitating differentiated intervention approaches across community types. By addressing these questions systematically, our study may provide evidence-based guidance for developing context-appropriate obesity prevention strategies and establishing methodological frameworks for precision population health implementation. 1.6 Expected Contributions Our aim is to move beyond “one-size-fits-all” interventions toward strategies calibrated to community structure. We classify social determinants as universal, context-dependent, or setting-specific to guide resource allocation: universal determinants may justify broad policies, whereas context-dependent determinants likely merit selective deployment where enabling conditions exist. The framework is portable and may be adapted to other regions and outcomes; population-level data and vulnerability indices can be used to target geographically localized groups, while integrating SDOH information into care workflows and EHRs can connect measurement to action and has been associated with improvements, even though community-level data integration remains uncommon.[30], [31] 2. Methods 2.1 Study Context and Data Sources We conducted a cross-sectional ecological study of Wisconsin ZIP codes from 2019 to 2023. Analyses were stratified with a multi-level urbanization classification rather than a simple urban–rural split, and the work was descriptive rather than causal or longitudinal. Prevalence among the participants patients was obtained from the Wisconsin Collaborative for Healthcare Quality at the ZIP level using EHR data. Food environment indicators came from the 2019 USDA Food Access Research Atlas, which reports demographic-specific access constraints, and additional social determinants were assembled from Agency for Healthcare Research and Quality resources. All sources provided de-identified aggregates. After preprocessing, the analytic sample included 833 ZIP codes across 72 counties; exclusions were mainly special-purpose areas such as government facilities and airports. Urbanization classification was assigned before missingness screening to reduce potential selection bias. The primary outcome, prevalence among the participants patients, was defined as the share of patients with BMI ≥ 30 kg/m². Predictors covered established domains including education, transportation, housing, household composition, insurance, income and poverty, and demographic-specific food access. Variables were retained on native scales except where standardization was necessary for multivariate analyses. 2.2 Data Processing and Quality Control Census-tract food-access indicators were translated to ZIP codes using area-weighted correspondence, with population weighting when available. Social-determinant files were merged by standardized ZIP codes with checks for join integrity, and special-purpose or unstable ZIP codes (for example, PO boxes, universities) were excluded, consistent with prior ZIP-level population studies [ 23 ]Continuous variables were screened for outliers, and missing predictor values were imputed with k-nearest neighbors (k = 5) within urbanization strata after standardization; outcomes were not imputed, in line with evidence that k-NN can accurately recover missing values in health datasets when appropriately tuned [ 24 ]. We summarized distributions and estimated bivariate associations within strata at α = 0.05, classifying determinants as universal when direction and significance were consistent across strata and context dependent when confined to, or reversed in, particular strata. Community structure was characterized by UMAP applied to standardized features after collinearity reduction (|r| > 0.90); clusters were identified using density- and centroid-based algorithms with a minimum of 20 ZIP codes, and UMAP was selected for its ability to sharpen cluster separation while retaining broader gradients [ 25 ]. Analyses were implemented in Python with standard scientific libraries, using scripted pipelines and fixed random seeds for reproducibility. 2.3 Ethical Considerations This study used exclusively public or aggregated datasets with no individually identifiable information. All analyses operated at ZIP code/county levels representing population aggregates. Under established guidelines for secondary analysis of de-identified community health indicators, institutional review board oversight was not required. 3. Results Obesity prevalence across 833 Wisconsin ZIP codes from 2019 through 2023 were analyzed in relation to the social and structural conditions that shape them. We begin with classification and demographic gradients in burden, then evaluate food access correlations, map community structure with a UMAP embedding, and conclude with a matrix that distinguishes universally protective factors from those whose influence depends on urbanization context. 3.1 Demographic & Urban Level Urban Classification Trends Prevalence among the participants patients increased across all cohorts from 2019 to 2023, with magnitudes ordered by the classification. Urban Underserved communities showed the largest rise (just over eight percentage points). Urban Advantaged communities increased by about three points, while Rural Underserved, Rural, and Rural Advantaged formed an intermediate band, each gaining roughly three to three and a half points. This gradient aligns with accumulated educational, economic, and service advantages, and the divergent growth rates, on top of baseline gaps, suggest widening separation in burden without targeted mitigation[35]. Table 1. Distribution of Wisconsin ZIP codes across urban/rural classification cohorts, showing obesity among the participants patients, recent trends, and defining characteristics. Classification Count Prevalence distribution percentage among the participants patients Key Characteristics Rural Advantaged 171 50.57% Areas with fewer health care providers and higher rates of poverty, uninsured status, and Medicaid enrollment. They also show lower educational attainment and poorer health status. Rural 254 51.93% Standard rural areas with typical socioeconomic indicators and a moderate supply of health care providers. They experience moderate rates of poverty, uninsured status, Medicaid, educational attainment, and health status. Rural Underserved 110 54.88% Areas with fewer health care providers but lower rates of poverty, uninsured status, and Medicaid enrollment. They show moderate educational attainment and better health status. Urban Advantaged 63 40.93% Areas with many health care providers and lower rates of poverty, uninsured status, and Medicaid enrollment. They have higher educational attainment and better health status. Urban 116 48.19% Standard urban areas with typical socioeconomic indicators and fewer health care providers relative to advantaged urban areas. They show lower poverty, uninsured status, and Medicaid reliance along with moderate educational attainment and health status. Urban Underserved 20 55.17% Areas with a moderate number of health care providers and higher poverty, unemployment, uninsured status, and Medicaid enrollment. They have lower educational attainment and poorer health status. Unclassified ZIP 99 49.08% ZIP codes without a specific urban or rural classification. Demographic Burden Table 2 shows a stratified pattern shaped chiefly by race, age, and place. By race and ethnicity, Prevalence among the participants patients was highest among Black or African American patients (58.7%) and American Indian or Alaska Native patients (55.5%) and lowest among Asian or Pacific Islander patients (27.24%). Severe obesity showed a parallel contrast (18.65% vs 3.79%). Across age, prevalence rose from 34.76% in adults 18–29 to a peak of 54.34% at 50–59, then declined. Geography also differentiates risk. Urban Underserved and Rural Underserved both exceeded 54%, whereas Urban Advantaged communities were lower at 40.93%. Hispanic or Latino and White groups fell between these extremes, and sex differences were modest (48.61% in females vs 47.37% in males). Overall, the pattern suggests a multifactorial burden shaped by economic and insurance insecurity, constrained educational opportunity, mobility limits, and uneven access to health-supporting resources. Table 2. Demographic characteristics and obesity prevalence among the participants patients by race/ethnicity, age, gender, and urban/rural classification for Wisconsin ZIP codes in 2023. Demographics Total Obesity Class 1 Obesity Class 2 Obesity Class 3 Obesity Race/Ethnicity American Indian/Alaska Native 55.46% 23.38% 15.63% 16.45% Black/African American 58.66% 23.99% 16.02% 18.65% Hispanic/Latino 52.68% 26.75% 14.09% 11.84% White 47.48% 23.61% 12.95% 10.92% Asian/Pacific Islander 27.24% 17.22% 6.23% 3.79% Unknown/Multi-Racial 48.73% 23.76% 13.33% 11.64% Urban Classification Rural Underserved 54.88% 25.44% 15.49% 13.95% Rural 51.93% 25.04% 14.32% 12.57% Rural Advantaged 50.57% 25.03% 14.00% 11.54% Urban Underserved 55.17% 24.81% 15.14% 15.22% Urban 48.19% 23.49% 13.12% 11.58% Urban Advantaged 40.93% 21.81% 10.76% 8.36% Unknown/Outside WI 49.08% 24.36% 13.47% 11.25% Age Group 18-29 34.76% 15.50% 9.39% 9.87% 30-39 49.15% 21.28% 13.16% 14.71% 40-49 53.80% 23.80% 14.65% 15.35% 50-59 54.34% 25.89% 15.02% 13.43% 60-69 49.40% 25.45% 13.54% 10.41% 70-79 45.17% 24.99% 12.33% 7.85% 80+ 36.01% 23.02% 8.92% 4.07% Gender Female 47.37% 21.07% 13.20% 13.10% Male 48.61% 26.95% 12.81% 8.85% Unknown 34.61% 11.54% 15.38% 7.69% Overall All 49.80% 23.74% 13.47% Spatial Distribution Mapped Prevalence among the participants patients clustered in diffuse Rural Underserved areas and in compact Urban Underserved pockets, while lower values concentrated in Urban Advantaged corridors. Rural and Rural Advantaged areas formed a transitional mosaic of intermediate values rather than a uniform band, which may suggest partial buffering where supportive conditions are mixed. Sharp boundaries between higher- and lower-burden zones highlight interface areas where improvements in access, transportation connectivity, or food retail placement could be most consequential. The correspondence between mapped clusters and cohort characteristics supports the interpretive utility of the classification schema. 3.2 Food Access and Obesity Prevalence Correlations At the ZIP-code level, conventional food-access indicators showed their largest positive correlations with Prevalence among the participants patients for children with low access (r = 0.3944, p < 0.001), low-income populations (r = 0.3861, p < 0.001), and households without a vehicle (r = 0.3644, p < 0.01). SNAP participation had a smaller positive association (r = 0.2864, p = 0.0147). Racial and ethnic patterns were asymmetric: the White low-access share correlated positively (r = 0.3793, p = 0.0010), whereas the Asian measure correlated inversely (r = −0.3454, p = 0.0030). Several other subgroup indicators, including the senior low-access measure, were not significant. The pattern suggests that measured access constraints align most closely with economic and mobility vulnerabilities and that current indicators may not fully capture culturally specific or alternative provisioning channels. Table 3. Pearson correlations between low-access indicators and Prevalence among the participants patients at the ZIP-code level, sorted by absolute correlation. Statistically significant correlations are indicated by p-value thresholds. Food Access Variable Pearson Correlation P-value Interpretation Percentage of Total Low Food Access Population 0.3590 0.0020 Very significant Income Group Percentage of Total Low-Income Population 0.3861 0.0008 Highly significant Percentage of SNAP Recipients 0.2864 0.0147 Significant Percentage of Low Food Access Households without Vehicle 0.3644 0.0017 Very significant Age Group Percentage of Total Children with Low Food Access 0.3944 0.0006 Highly significant Percentage of Total Seniors with Low Food Access 0.2317 0.0502 Not significant Race/Ethnicity Percentage of Total Asian Population with Low Food Access -0.3454 0.0030 Very significant Percentage of White Population with Low Food Access 0.3793 0.0010 Very significant Percentage of Native Hawaiian/Pacific Islander with Low Food Access 0.1473 0.2168 Not significant Percentage of Black Population with Low Food Access 0.1190 0.3195 Not significant Percentage of American Indian/Alaska Native with Low Food Access -0.0699 0.5596 Not significant Percentage of Hispanic Population with Low Food Access 0.0321 0.7887 Not significant Percentage of Multiple Race Population with Low Food Access 0.0269 0.8228 Not significant 3.3 Context-Dependent Associations Between Social Determinants of Health and Obesity Prevalence Across Urbanization Levels The food access analysis reveals significant but limited associations, with effects concentrated among specific vulnerable groups rather than providing broad explanatory power. We therefore broadened the scope to the full set of SDOH and tested whether associations were universal or varied by urbanization level. A data-driven screen retained education indicators as the only consistently protective measures across strata, while for transportation and housing we selected variables whose significance or direction differed by setting to capture context-dependent. operation. Education emerges as the sole universal protective factor, with bachelor's and graduate degree attainment showing consistent negative associations across all urbanization levels, though magnitude varies dramatically from modest rural effects (-0.16 to -0.28) to powerful urban associations reaching -0.81. In sharp contrast, three-quarters of other significant determinants operate only within specific contexts. Transportation illustrates this strikingly: walking and transit use protect against obesity exclusively in urban areas (correlations reaching -0.57), while commute duration matters only in rural settings where time constraints rather than activity opportunities drive obesity risk. Housing wealth indicators demonstrate protective associations only in urban environments where they proxy for neighborhood amenities yet show no rural significance. Most intriguingly, associate degrees prove harmful in urban environments (+0.82) yet protective in Rural Underserved Communities (-0.29), reflecting how credential value shifts with local educational saturation. The matrix reveals that effective interventions must account for how identical social determinant profiles produce markedly different health outcomes depending on the urbanization context. 3.4 UMAP-Derived Community Structure and Outcomes With 328 social determinant variables creating a high-dimensional space, we employ dimensional reduction to identify natural community groupings that synthesize context-dependent effects into coherent community archetypes, examining how social determinants combine real-world settings to create distinct obesity patterns. Cluster 2 includes 40 affluent, well-educated communities and shows the lowest Prevalence among the participants patients at 42.6 percent. Indicators of concentrated educational and economic capital are prominent: bachelor’s attainment is nearly twice the state average (effect size 2.00), per-capita income is elevated (2.23), mortgage costs are higher (1.99), and the share above 400 percent of the poverty threshold is larger (1.99). Together, these features are consistent with an integrated protective profile in which educational credentials, economic resources, and housing investments cluster. Across community types, burden rises in ordered fashion. Transitional zones in Clusters 1 and 5 (103 ZIP codes) show intermediate Prevalence among the participants patients of 45.3 and 47.8 percent, respectively, where protective factors appear present but less concentrated. Different structural configurations are associated with similar outcomes through distinct pathways. Cluster 4’s urban food-desert communities register 48.0 percent, combining density with access barriers and markedly reduced homeownership (effect size −2.21). Cluster 3’s rural resilience communities reach 50.5 percent despite lower income inequality (−0.68) and reduced Medicaid dependence (−0.62), which may suggest that geographic dispersion limits how socioeconomic stability translates into health benefits. The highest burdens concentrate in rural settings along three disadvantage pathways. Cluster 8 (155 ZIP codes) features aging demographics with elevated disability and Prevalence among the participants patients of 51.4 percent. Cluster 7 (127 ZIP codes) reflects educational constraints with reduced bachelor’s attainment and reaches 52.6 percent. Cluster 6 (42 Rural Underserved ZIP codes) shows compound disadvantages, including educational limitations (effect size 1.90), housing overcrowding (2.04), and substantially elevated uninsured rates (2.55), reaching 53.7 percent. The 11.1-percentage-point span across clusters underscores how distinct structural configurations are associated with markedly different burden trajectories across Wisconsin communities. 3.5 Resolution of Research Questions The evidence points to context-dependence rather than universal operation of social determinants. Food-access indicators showed selective associations concentrated in groups facing economic or mobility constraints: children in low-access areas (r = 0.39), low-income populations (r = 0.39), and households without a vehicle (r = 0.36). Racial patterns were asymmetric, with a positive association for the White low-access share (r = 0.38) and an inverse association for the Asian measure (r = −0.35). Taken together, these results suggest that distance-based metrics may miss how communities procure food. Educational attainment was the only measure that consistently aligned with lower Prevalence among the participants patients across all urbanization levels, although the magnitude ranged from modest to pronounced. Most other determinants varied by context, with direction and strength apparently shaped by density, infrastructure, and local resource environments. The UMAP analysis reinforced this view: communities grouped into eight archetypes defined by shared structural profiles, spanning an 11.1-percentage-point range in Prevalence among the participants patients from 42.6% to 53.7%. Affluent, well-educated communities aligned with lower values, whereas several rural clusters aligned with higher values through distinct pathways, including aging with elevated disability, limited higher-education attainment, and compound disadvantage. These patterns suggest that policies and interventions should be tailored to local structural conditions rather than applied uniformly. 4. Discussion 4.1 Summary of Principal Findings This analysis demonstrates that social determinants of health do not act through a single universal framework across communities. Educational attainment consistently shows a protective association in every urban classification level, while most other domains manifest only within specific settings. A narrow subset of demographic specific food access indicators aligns with the obesity burden, whereas many conventional subgroup metrics do not register a detectable association. Community clustering based on multivariate social determinant profiles further reveals distinct archetypes that correspond to differential prevalence among the participants patients’ level and change trajectories. These strands establish that context conditions the salience, direction, and practical leverage of structural factors. 4.2 Community Archetypes and Mechanistic Interpretation Clustering across 328 SDOH variables yielded eight community archetypes, with similarity driven more by shared determinant profiles than by geography (Figure 3). Educational attainment was the only determinant that behaved consistently, showing inverse associations with Prevalence among the participants patients in every urbanization level, with correlations in our data ranging from −0.16 in rural settings to −0.81 in urban settings (Figure 2). This pattern aligns with evidence that education tracks strongly with health and can function as a portable resource shaping access and decision-making across contexts[36], [37]. Concentrated attainment also coincided with the lowest burdens: in Cluster 2, degree attainment was nearly twice the state average (effect size 2.00), and Prevalence among the participants patients was 42.6 percent. This pairing may indicate that educational capital extends beyond individual knowledge, supporting steadier employment and, in turn, greater housing security and access to health-supporting resources. Advantages likely diffuse through institutions as well, as employers and long-term-care operators can reinforce them through workforce development and continuing education. In this view, education operates at multiple levels including individual, community, and organizational, which consists of the gradient observed in our data. Other determinants appeared contingent on place. Income inequality related to burden independent of average income, consistent with studies linking distributional features of income to community health [38], [39]. Signals around technology access were suggestive, as clusters with greater cellular data plan availability tended to show lower burden, and reviews indicate that digital health and telemedicine can improve cardiometabolic risk factors, including weight-related outcomes [40], [41]. Housing patterns were consistent with selection and exposure processes over time, in line with research on neighborhood deprivation trajectories and their association with health status [42]. Language diversity combined with high employment density did not uniformly correspond to lower burden; work on immigrant health notes that acculturative stress and related barriers can offset expected advantages, which may help explain these mixed patterns[43], [44]. Population density also appeared to cut both ways in our data, particularly where housing instability and segregation co-occurred with high density. These archetypes suggest that nominally similar indicators can operate differently across settings, which supports tailoring prevention strategies to local structural profiles rather than assuming uniform effects [45]. 4.3 Policy Innovation Opportunities Our findings indicate limits to uniform policy and support context-dependent strategies[46]. he classification guides tailored action: Urban Underserved areas, which showed the steepest increases in obesity, likely need intensive resource measures that expand insurance coverage, strengthen employment, and improve affordable food access. Urban Advantaged areas are better suited to prevention that sustains protective conditions, including provider availability, educational pathways, and wellness promotion. Rural and Rural Underserved cohorts warrant investment in telehealth, transportation support, and mobile services to overcome distance and capacity constraints, while Rural Advantaged communities should maintain educational and healthcare infrastructure and address localized inequities before they widen. In practice, the classification operates as a toolkit that translates descriptive patterns into policy levers aligned to structural realities. Technology access functions as a health determinant, so digital infrastructure should be treated as core health policy; broadband expansion and telemedicine capacity should be integrated into health promotion budgets and planning[47], [48]. Food-access patterns in our data concentrate in specific vulnerable subgroups, with strong correlations for children in low-access areas (r = 0.39, p < 0.001), low-income households (r = 0.39, p < 0.001), and families lacking vehicle access (r = 0.36, p < 0.01). From an operational perspective, priority should fall on child nutrition programs, targeted support for low-income households, and transportation-linked approaches such as mobile markets or delivery services. Broad geographic initiatives risk diluting resources; aligning interventions with these documented vulnerabilities offers a clearer path from statistical association to actionable policy. Housing investment patterns operating as health investment strategies indicate that housing policy frameworks inadequately capture health co-benefits of residential stability [49]. Housing assistance programs should incorporate health outcome metrics and neighborhood health infrastructure assessments into allocation criteria [50]. Language diversity patterns revealing complex acculturation-health trade-offs indicate that current immigrant health policies may inadvertently undermine traditional health protective factors [51]. Health policies should incorporate cultural adaptation support services and community-based interventions that strengthen traditional health support networks [52]. Population density constraints create health vulnerability despite urban infrastructure advantages reveal critical gaps in urban health policy frameworks [53]. Urban health policies must address segregation-specific mechanisms operating through social stress and discriminatory service provision [54]. Income inequality distribution effects suggest that community health policies should prioritize economic equality strategies over pure wealth accumulation approaches. Evidence on intergenerational transmission of disadvantages suggests that child-focused policies alone rarely interrupt the structural pathways through which disadvantage persists [55]. More promising approaches coordinate housing quality, healthcare access, and economic stability rather than siloed efforts in a single domain [56]. While broader reforms are pursued, organizations can act. Long-term care operators can strengthen staff competencies through targeted training and formal partnerships with local educational and health institutions [57]; employers and community partners can expand workplace education, digital wellness, nutrition access, and telehealth. Nursing-led telemedicine has produced measurable reductions in weight, body mass index, and waist circumference, indicating a feasible route to near-term gains within existing delivery systems [58]. These organizational steps complement longer-horizon policy change. 4.4 Study Limitations Interpretation is constrained by design and measurement features. As an ecological, cross-sectional study, the analysis cannot establish individual-level causality and remains vulnerable to ecological fallacy [59]. Spatial granularity is imperfect because ZIP codes may not align with lived community boundaries, and the cross-sectional frame limits inference to associations rather than temporal change. Measurement uncertainty persists standard food-access indicators may overlook culturally specific or informal provisioning networks, which could help explain subgroup asymmetries. The prevalence measure is derived from electronic health records among the participating patient population, so selection and coding heterogeneity are possible. Missing predictors were imputed with k-nearest neighbors within urbanization strata; performance depends on the missingness mechanism, and residual bias may remain. Dimensionality reduction and clustering are sensitive to parameterization and preprocessing, which can influence the specific archetypes recovered. These caveats suggest cautious interpretation of magnitudes, even as patterns appear context dependent. 4.5 Future Research Priority directions include longitudinal, individual-level studies within archetypes to test mechanisms; interaction models that formally quantify context modification; and multi-state validations to assess generalizability. Methodologically, sensitivity analyses of UMAP parameters and alternative clustering, plus tests of scale (tract, ZIP code, county), would clarify robustness. Policy research should evaluate cluster-tailored interventions against standard approaches and explore decision support that flags archetypes in near real time to guide targeted delivery. 5. Conclusion This study provides compelling empirical evidence challenging conventional assumptions about universal social determinant effects on health outcomes. Our comprehensive analysis reveals that while educational attainment operates as the sole truly universal protective factor across all community contexts, most social determinants demonstrate context-dependent effectiveness that varies dramatically by urbanization level. This fundamental distinction between universal and context-specific mechanisms necessitates a paradigm shift from one-size-fits-all interventions toward precision population health approaches that recognize community archetypes require tailored rather than standardized social determinant strategies. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or personal interest in the subject matter or materials discussed in this manuscript. References World Health Organization, “Obesity and Overweight.” Accessed: Aug. 19, 2025. [Online]. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8025002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":543875922,"identity":"390ba398-ec90-42ba-943e-f2c49b7afab1","order_by":0,"name":"Jake Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxgY2BoYHDAxyEC4bsVoSGBiMidcCVgXUkthAtBbm9mOJDxLbbNI33MhOYPhQdpgIh/WkHTZIbEvL3XAjdwPjjHPEaGlIb5NIbDucu+F27gZm3jZitPQ/b/8B1JJuANLylygtM9KOMQC1JIC1MBKn5VmyRMK5NMOZ999uONhzLp2wFsP+NMMPH8ps5PnOnN344EeZNRFaGpA4BwirBwJ5olSNglEwCkbByAYAHW9BKPXJMH8AAAAASUVORK5CYII=","orcid":"","institution":"University of Wisconsin Milwaukee","correspondingAuthor":true,"prefix":"","firstName":"Jake","middleName":"","lastName":"Luo","suffix":""},{"id":543875923,"identity":"5e7fc1bd-21be-45cd-aed9-eb0569792ec5","order_by":1,"name":"Zihao Yi","email":"","orcid":"","institution":"University of Wisconsin Milwaukee","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Yi","suffix":""},{"id":543875924,"identity":"d0c4a3db-a168-4c98-b42d-916f1f497d80","order_by":2,"name":"Jennifer T. Fink","email":"","orcid":"","institution":"University of Wisconsin–Milwaukee","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"T.","lastName":"Fink","suffix":""},{"id":543875925,"identity":"fda9b80c-fda6-4fe1-ac85-8a935bd7abe4","order_by":3,"name":"Robert Frediani","email":"","orcid":"","institution":"University of Wisconsin–Milwaukee","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Frediani","suffix":""},{"id":543875926,"identity":"b64d50da-7ba7-4881-98ce-051ad71746ec","order_by":4,"name":"Christopher Weber","email":"","orcid":"","institution":"Ascension Medical Group","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Weber","suffix":""},{"id":543875927,"identity":"f2c122cf-b152-4e27-9a36-6fc4b36557dc","order_by":5,"name":"Gabrielle Rude","email":"","orcid":"","institution":"Wisconsin Collaborative for Healthcare Quality","correspondingAuthor":false,"prefix":"","firstName":"Gabrielle","middleName":"","lastName":"Rude","suffix":""}],"badges":[],"createdAt":"2025-11-04 06:10:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8025002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8025002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101365585,"identity":"13a53882-408a-43e4-9eed-ac3763589c62","added_by":"auto","created_at":"2026-01-29 00:56:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1906722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of Prevalence among the participants patients and urbanization classification.\u003c/strong\u003eLeft panel: choropleth map of total Prevalence among the participants patients across Wisconsin ZIP codes (2023). Right panel: map of the urbanization classification used in stratified analyses. Data sources and classification procedures are described in Methods.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8025002/v1/b7881ffb8ad401c6d448de0d.png"},{"id":101751282,"identity":"55106c5b-78b4-4868-be0a-e99595f99778","added_by":"auto","created_at":"2026-02-03 10:18:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2621502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between social determinants of health and Prevalence among the participants patients across urbanization levels. \u003c/strong\u003eHeatmap showing correlation coefficients by determinant and urbanization stratum; color intensity reflects magnitude and sign. Asterisks indicate statistical significance at p \u0026lt; 0.05. SDOH denotes social determinants of health.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8025002/v1/d956a133cb0e10928749841f.png"},{"id":101365586,"identity":"49988ade-02f0-46d3-8808-efb2a786af57","added_by":"auto","created_at":"2026-01-29 00:56:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4355820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommunity structure from UMAP with burden overlay. \u003c/strong\u003eTwo-dimensional UMAP embedding of Wisconsin ZIP codes colored by urbanization classification; point size scales with Prevalence among the participants patients. Clusters were retained only when the cluster contained at least 20 ZIP codes; additional clustering details appear in Methods.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8025002/v1/8a441fee184a94b9681268e2.png"},{"id":101943094,"identity":"d831598b-507f-4bc7-aff2-a2f64d153d9c","added_by":"auto","created_at":"2026-02-05 09:40:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9398384,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8025002/v1/4729f089-9af7-4343-b276-73b19f7e1637.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Obesity, Urbanization, and Food Access in Wisconsin: The Intersection of Healthcare Provider Access and Population Health Outcomes","fulltext":[{"header":"1. Introduction","content":"\u003ch3\u003e1.1 Obesity as a Public Health Challenge\u003c/h3\u003e\n\u003cp\u003eObesity affects over 890 million adults globally, contributing to cardiovascular disease, type 2 diabetes, certain cancers, and premature mortality [1]. The United States faces particularly concerning rates, with obesity prevalence reaching 41.9% among adults and generating healthcare expenditures exceeding $173 billion annually [2]. Prevention approaches have traditionally emphasized individual behavioral modification, focusing on dietary choices and physical activity patterns, while often overlooking the structural environments that may constrain or enable healthy behaviors [3].This approach appears to have yielded limited population-level impact, suggesting that effective obesity prevention likely requires addressing social determinants of health (SDOH) and understanding how contextual factors shape their influence [4].\u003c/p\u003e\n\u003ch3\u003e1.2 Social Determinants and Food Access\u003c/h3\u003e\n\u003cp\u003eSocial determinants of health are the conditions in which people are born, grow, live, work, and age, including educational attainment, economic stability, housing quality, transportation access, and healthcare availability [5]. Research suggests these factors may influence obesity risk through multiple pathways [6]. Educational attainment appears to shape health literacy and employment opportunities, creating cascading effects on food purchasing power and healthcare access [7]. Economic stability may determine access to healthy foods and healthcare services while potentially influencing chronic stress exposure [8]. Housing quality seems to affect environmental exposures and proximity to health-promoting resources [9], while transportation systems govern accessibility to employment, healthcare, and food retail environments [10].\u003c/p\u003e\n\u003cp\u003eFood access social determinants of health factors include both physical accessibility to outlets providing affordable, nutritious foods and functional accessibility determined by transportation, economic resources, and cultural preferences [11]. Conventional measures focus primarily on distance to food retailers, yet emerging evidence suggests these indicators may obscure important variations among vulnerable populations [12]. Children, low-income households, households without vehicle access, and racial/ethnic minority groups often face distinct barriers inadequately captured by proximity measures [13]. These populations may rely on alternative procurement strategies, ethnic groceries, farmers markets, food assistance programs which conventional metrics fail to quantify [14].\u003c/p\u003e\n\u003ch3\u003e1.3 Urbanization and Context Effects\u003c/h3\u003e\n\u003cp\u003eUrbanization creates distinct structural environments that may fundamentally alter how social determinants influence health outcomes [15], [16]. In urban settings, higher population density, more varied transportation networks, and a broader mix of destinations are common; these features can support active travel and improve access to food retailers [17]. By contrast, many rural places have lower density and fewer transportation options, and social cohesion often appears stronger than in cities [18], [19]. However, urbanization effects are rarely uniform across communities. Urban areas include both advantaged neighborhoods and places of concentrated disadvantage, while rural areas range from economically stable communities to isolated locales with limited infrastructure [20], [21]. This heterogeneity suggests that identical SDOH factors may operate differently across contexts, yielding different health outcomes depending on surrounding structural conditions[22].\u003c/p\u003e\n\u003ch3\u003e1.4 Research Gaps\u003c/h3\u003e\n\u003cp\u003eDespite growing recognition of SDOH importance in obesity prevention, current research exhibits critical limitations that may constrain effective intervention development [23]. Many studies assess single determinants rather than their interactions or cumulative burden; as a result, compounding disadvantage and possible buffering effects may be missed [24]. Pooled national analyses and cumulative indices often assume uniform effects and can obscure place-specific variation [23], [25]. While studies demonstrate associations between selected social determinants and obesity outcomes [26], systematic tests that separate consistent effects from context-dependent patterns remain limited.\u003c/p\u003e\n\u003cp\u003eFood access measurement approaches rely heavily on aggregate distance-based indicators that may inadequately capture the complexity of food procurement behaviors across different demographic groups [27]. Current metrics often appear to miss population-specific barriers, alternative food sources, and cultural preferences that significantly influence actual food acquisition patterns [28]. Evidence suggests that food access effects may vary substantially across different population subgroups [29], yet few studies have systematically examined these variations. These evidence gaps collectively limit the development of targeted, context-appropriate obesity prevention strategies and highlight the need for comprehensive analytical approaches that can identify both universal and context-dependent determinant effects.\u003c/p\u003e\n\u003ch3\u003e1.5 Study Objectives\u003c/h3\u003e\n\u003cp\u003eThis study examines how social determinants relate to obesity in context, drawing on comprehensive SDOH data, stratified analyses by urbanization level, and clustering to summarize community profiles. Wisconsin is an appropriate setting given its pronounced urban\u0026ndash;rural variation, socioeconomic diversity, and surveillance infrastructure that supports population-level analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 1\u003c/strong\u003e: Do conventional food access measures demonstrate universal associations with obesity across all demographic groups, or do they exhibit selective effectiveness among specific vulnerable populations?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 2\u003c/strong\u003e: Which social determinants of health exhibit universal protective or harmful associate with obesity across all urbanization contexts, versus those whose associations with obesity outcomes are modified by community structural characteristics?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 3\u003c/strong\u003e: Do comprehensive social determinant profiles reveal distinct community archetypes with unique obesity patterns that transcend traditional administrative and urban-rural classification boundaries?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentral Hypothesis\u003c/strong\u003e: Social determinants operate through both universal and context-dependent mechanisms, with community structural characteristics modifying the strength and direction of SDOH-obesity associations, necessitating differentiated intervention approaches across community types.\u003c/p\u003e\n\u003cp\u003eBy addressing these questions systematically, our study may provide evidence-based guidance for developing context-appropriate obesity prevention strategies and establishing methodological frameworks for precision population health implementation.\u003c/p\u003e\n\u003ch3\u003e1.6 Expected Contributions\u003c/h3\u003e\n\u003cp\u003eOur aim is to move beyond \u0026ldquo;one-size-fits-all\u0026rdquo; interventions toward strategies calibrated to community structure. We classify social determinants as universal, context-dependent, or setting-specific to guide resource allocation: universal determinants may justify broad policies, whereas context-dependent determinants likely merit selective deployment where enabling conditions exist. The framework is portable and may be adapted to other regions and outcomes; population-level data and vulnerability indices can be used to target geographically localized groups, while integrating SDOH information into care workflows and EHRs can connect measurement to action and has been associated with improvements, even though community-level data integration remains uncommon.[30], [31]\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Context and Data Sources\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional ecological study of Wisconsin ZIP codes from 2019 to 2023. Analyses were stratified with a multi-level urbanization classification rather than a simple urban\u0026ndash;rural split, and the work was descriptive rather than causal or longitudinal. Prevalence among the participants patients was obtained from the Wisconsin Collaborative for Healthcare Quality at the ZIP level using EHR data. Food environment indicators came from the 2019 USDA Food Access Research Atlas, which reports demographic-specific access constraints, and additional social determinants were assembled from Agency for Healthcare Research and Quality resources. All sources provided de-identified aggregates.\u003c/p\u003e \u003cp\u003eAfter preprocessing, the analytic sample included 833 ZIP codes across 72 counties; exclusions were mainly special-purpose areas such as government facilities and airports. Urbanization classification was assigned before missingness screening to reduce potential selection bias. The primary outcome, prevalence among the participants patients, was defined as the share of patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;. Predictors covered established domains including education, transportation, housing, household composition, insurance, income and poverty, and demographic-specific food access. Variables were retained on native scales except where standardization was necessary for multivariate analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Processing and Quality Control\u003c/h2\u003e \u003cp\u003eCensus-tract food-access indicators were translated to ZIP codes using area-weighted correspondence, with population weighting when available. Social-determinant files were merged by standardized ZIP codes with checks for join integrity, and special-purpose or unstable ZIP codes (for example, PO boxes, universities) were excluded, consistent with prior ZIP-level population studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]Continuous variables were screened for outliers, and missing predictor values were imputed with k-nearest neighbors (k\u0026thinsp;=\u0026thinsp;5) within urbanization strata after standardization; outcomes were not imputed, in line with evidence that k-NN can accurately recover missing values in health datasets when appropriately tuned [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe summarized distributions and estimated bivariate associations within strata at α\u0026thinsp;=\u0026thinsp;0.05, classifying determinants as universal when direction and significance were consistent across strata and context dependent when confined to, or reversed in, particular strata. Community structure was characterized by UMAP applied to standardized features after collinearity reduction (|r| \u0026gt; 0.90); clusters were identified using density- and centroid-based algorithms with a minimum of 20 ZIP codes, and UMAP was selected for its ability to sharpen cluster separation while retaining broader gradients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Analyses were implemented in Python with standard scientific libraries, using scripted pipelines and fixed random seeds for reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study used exclusively public or aggregated datasets with no individually identifiable information. All analyses operated at ZIP code/county levels representing population aggregates. Under established guidelines for secondary analysis of de-identified community health indicators, institutional review board oversight was not required.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eObesity prevalence across 833 Wisconsin ZIP codes from 2019 through 2023 were analyzed in relation to the social and structural conditions that shape them. We begin with classification and demographic gradients in burden, then evaluate food access correlations, map community structure with a UMAP embedding, and conclude with a matrix that distinguishes universally protective factors from those whose influence depends on urbanization context.\u003c/p\u003e\n\u003ch2\u003e3.1 Demographic \u0026amp; Urban Level\u003c/h2\u003e\n\u003ch3\u003eUrban Classification Trends\u003c/h3\u003e\n\u003cp\u003ePrevalence among the participants patients increased across all cohorts from 2019 to 2023, with magnitudes ordered by the classification. Urban Underserved communities showed the largest rise (just over eight percentage points). Urban Advantaged communities increased by about three points, while Rural Underserved, Rural, and Rural Advantaged formed an intermediate band, each gaining roughly three to three and a half points. This gradient aligns with accumulated educational, economic, and service advantages, and the divergent growth rates, on top of baseline gaps, suggest widening separation in burden without targeted mitigation[35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Distribution of Wisconsin ZIP codes across urban/rural classification cohorts, showing obesity among the participants patients, recent trends, and defining characteristics.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevalence\u0026nbsp;distribution percentage\u0026nbsp;among the participants patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKey Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural Advantaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAreas with fewer health care providers and higher rates of poverty, uninsured status, and Medicaid enrollment. They also show lower educational attainment and poorer health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard rural areas with typical socioeconomic indicators and a moderate supply of health care providers. They experience moderate rates of poverty, uninsured status, Medicaid, educational attainment, and health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural Underserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAreas with fewer health care providers but lower rates of poverty, uninsured status, and Medicaid enrollment. They show moderate educational attainment and better health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Advantaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAreas with many health care providers and lower rates of poverty, uninsured status, and Medicaid enrollment. They have higher educational attainment and better health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard urban areas with typical socioeconomic indicators and fewer health care providers relative to advantaged urban areas. They show lower poverty, uninsured status, and Medicaid reliance along with moderate educational attainment and health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Underserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAreas with a moderate number of health care providers and higher poverty, unemployment, uninsured status, and Medicaid enrollment. They have lower educational attainment and poorer health status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnclassified ZIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZIP codes without a specific urban or rural classification.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eDemographic Burden\u003c/h3\u003e\n\u003cp\u003eTable 2 shows a stratified pattern shaped chiefly by race, age, and place. By race and ethnicity, Prevalence among the participants patients was highest among Black or African American patients (58.7%) and American Indian or Alaska Native patients (55.5%) and lowest among Asian or Pacific Islander patients (27.24%). Severe obesity showed a parallel contrast (18.65% vs 3.79%). Across age, prevalence rose from 34.76% in adults 18\u0026ndash;29 to a peak of 54.34% at 50\u0026ndash;59, then declined. Geography also differentiates risk. Urban Underserved and Rural Underserved both exceeded 54%, whereas Urban Advantaged communities were lower at 40.93%. Hispanic or Latino and White groups fell between these extremes, and sex differences were modest (48.61% in females vs 47.37% in males). Overall, the pattern suggests a multifactorial burden shaped by economic and insurance insecurity, constrained educational opportunity, mobility limits, and uneven access to health-supporting resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Demographic characteristics and obesity prevalence among the participants patients by race/ethnicity, age, gender, and urban/rural classification for Wisconsin ZIP codes in 2023.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClass 1 Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClass 2 Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClass 3 Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlack/African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e58.66%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.02%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.65%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsian/Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown/Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u0026nbsp;Classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural Underserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.44%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.49%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural Advantaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Underserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e55.17%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.22%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Advantaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown/Outside WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.35%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.34%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.89%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.02%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.10%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.61%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.38%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e49.80%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.74%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.47%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eSpatial Distribution\u003c/h3\u003e\n\u003cp\u003eMapped Prevalence among the participants patients clustered in diffuse Rural Underserved areas and in compact Urban Underserved pockets, while lower values concentrated in Urban Advantaged corridors. Rural and Rural Advantaged areas formed a transitional mosaic of intermediate values rather than a uniform band, which may suggest partial buffering where supportive conditions are mixed. Sharp boundaries between higher- and lower-burden zones highlight interface areas where improvements in access, transportation connectivity, or food retail placement could be most consequential. The correspondence between mapped clusters and cohort characteristics supports the interpretive utility of the classification schema.\u003c/p\u003e\n\u003ch2\u003e3.2 Food Access and Obesity Prevalence Correlations\u003c/h2\u003e\n\u003cp\u003eAt the ZIP-code level, conventional food-access indicators showed their largest positive correlations with Prevalence among the participants patients for children with low access (r = 0.3944, p \u0026lt; 0.001), low-income populations (r = 0.3861, p \u0026lt; 0.001), and households without a vehicle (r = 0.3644, p \u0026lt; 0.01). SNAP participation had a smaller positive association (r = 0.2864, p = 0.0147). Racial and ethnic patterns were asymmetric: the White low-access share correlated positively (r = 0.3793, p = 0.0010), whereas the Asian measure correlated inversely (r = \u0026minus;0.3454, p = 0.0030). Several other subgroup indicators, including the senior low-access measure, were not significant. The pattern suggests that measured access constraints align most closely with economic and mobility vulnerabilities and that current indicators may not fully capture culturally specific or alternative provisioning channels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Pearson correlations between low-access indicators and Prevalence among the participants patients at the ZIP-code level, sorted by absolute correlation. Statistically significant correlations are indicated by p-value thresholds.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood Access Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Low Food Access Population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Low-Income Population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHighly significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of SNAP Recipients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Low Food Access Households without Vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Children with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHighly significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Seniors with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Asian Population with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of White Population with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Native Hawaiian/Pacific Islander with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Black Population with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of American Indian/Alaska Native with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Hispanic Population with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Multiple Race Population with Low Food Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.3 Context-Dependent Associations Between Social Determinants of Health and Obesity Prevalence Across Urbanization Levels\u003c/h2\u003e\n\u003cp\u003eThe food access analysis reveals significant but limited associations, with effects concentrated among specific vulnerable groups rather than providing broad explanatory power. We therefore broadened the scope to the full set of SDOH and tested whether associations were universal or varied by urbanization level. A data-driven screen retained education indicators as the only consistently protective measures across strata, while for transportation and housing we selected variables whose significance or direction differed by setting to capture context-dependent. operation.\u003c/p\u003e\n\u003cp\u003eEducation emerges as the sole universal protective factor, with bachelor\u0026apos;s and graduate degree attainment showing consistent negative associations across all urbanization levels, though magnitude varies dramatically from modest rural effects (-0.16 to -0.28) to powerful urban associations reaching -0.81. In sharp contrast, three-quarters of other significant determinants operate only within specific contexts. Transportation illustrates this strikingly: walking and transit use protect against obesity exclusively in urban areas (correlations reaching -0.57), while commute duration matters only in rural settings where time constraints rather than activity opportunities drive obesity risk. Housing wealth indicators demonstrate protective associations only in urban environments where they proxy for neighborhood amenities yet show no rural significance. Most intriguingly, associate degrees prove harmful in urban environments (+0.82) yet protective in Rural Underserved Communities (-0.29), reflecting how credential value shifts with local educational saturation. The matrix reveals that effective interventions must account for how identical social determinant profiles produce markedly different health outcomes depending on the urbanization context.\u003c/p\u003e\n\u003ch2\u003e3.4 UMAP-Derived Community Structure and Outcomes\u003c/h2\u003e\n\u003cp\u003eWith 328 social determinant variables creating a high-dimensional space, we employ dimensional reduction to identify natural community groupings that synthesize context-dependent effects into coherent community archetypes, examining how social determinants combine real-world settings to create distinct obesity patterns.\u003c/p\u003e\n\u003cp\u003eCluster 2 includes 40 affluent, well-educated communities and shows the lowest Prevalence among the participants patients at 42.6 percent. Indicators of concentrated educational and economic capital are prominent: bachelor\u0026rsquo;s attainment is nearly twice the state average (effect size 2.00), per-capita income is elevated (2.23), mortgage costs are higher (1.99), and the share above 400 percent of the poverty threshold is larger (1.99). Together, these features are consistent with an integrated protective profile in which educational credentials, economic resources, and housing investments cluster.\u003c/p\u003e\n\u003cp\u003eAcross community types, burden rises in ordered fashion. Transitional zones in Clusters 1 and 5 (103 ZIP codes) show intermediate Prevalence among the participants patients of 45.3 and 47.8 percent, respectively, where protective factors appear present but less concentrated. Different structural configurations are associated with similar outcomes through distinct pathways. Cluster 4\u0026rsquo;s urban food-desert communities register 48.0 percent, combining density with access barriers and markedly reduced homeownership (effect size \u0026minus;2.21). Cluster 3\u0026rsquo;s rural resilience communities reach 50.5 percent despite lower income inequality (\u0026minus;0.68) and reduced Medicaid dependence (\u0026minus;0.62), which may suggest that geographic dispersion limits how socioeconomic stability translates into health benefits.\u003c/p\u003e\n\u003cp\u003eThe highest burdens concentrate in rural settings along three disadvantage pathways. Cluster 8 (155 ZIP codes) features aging demographics with elevated disability and Prevalence among the participants patients of 51.4 percent. Cluster 7 (127 ZIP codes) reflects educational constraints with reduced bachelor\u0026rsquo;s attainment and reaches 52.6 percent. Cluster 6 (42 Rural Underserved ZIP codes) shows compound disadvantages, including educational limitations (effect size 1.90), housing overcrowding (2.04), and substantially elevated uninsured rates (2.55), reaching 53.7 percent. The 11.1-percentage-point span across clusters underscores how distinct structural configurations are associated with markedly different burden trajectories across Wisconsin communities.\u003c/p\u003e\n\u003ch2\u003e3.5 Resolution of Research Questions\u003c/h2\u003e\n\u003cp\u003eThe evidence points to context-dependence rather than universal operation of social determinants. Food-access indicators showed selective associations concentrated in groups facing economic or mobility constraints: children in low-access areas (r = 0.39), low-income populations (r = 0.39), and households without a vehicle (r = 0.36). Racial patterns were asymmetric, with a positive association for the White low-access share (r = 0.38) and an inverse association for the Asian measure (r = \u0026minus;0.35). Taken together, these results suggest that distance-based metrics may miss how communities procure food.\u003c/p\u003e\n\u003cp\u003eEducational attainment was the only measure that consistently aligned with lower Prevalence among the participants patients across all urbanization levels, although the magnitude ranged from modest to pronounced. Most other determinants varied by context, with direction and strength apparently shaped by density, infrastructure, and local resource environments. The UMAP analysis reinforced this view: communities grouped into eight archetypes defined by shared structural profiles, spanning an 11.1-percentage-point range in Prevalence among the participants patients from 42.6% to 53.7%. Affluent, well-educated communities aligned with lower values, whereas several rural clusters aligned with higher values through distinct pathways, including aging with elevated disability, limited higher-education attainment, and compound disadvantage. These patterns suggest that policies and interventions should be tailored to local structural conditions rather than applied uniformly.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003ch3\u003e4.1 Summary of Principal Findings\u003c/h3\u003e\n\u003cp\u003eThis analysis demonstrates that social determinants of health do not act through a single universal framework across communities. Educational attainment consistently shows a protective association in every urban classification level, while most other domains manifest only within specific settings. A narrow subset of demographic specific food access indicators aligns with the obesity burden, whereas many conventional subgroup metrics do not register a detectable association. Community clustering based on multivariate social determinant profiles further reveals distinct archetypes that correspond to differential prevalence among the participants patients\u0026rsquo; level and change trajectories. These strands establish that context conditions the salience, direction, and practical leverage of structural factors.\u003c/p\u003e\n\u003ch3\u003e4.2 Community Archetypes and Mechanistic Interpretation\u003c/h3\u003e\n\u003cp\u003eClustering across 328 SDOH variables yielded eight community archetypes, with similarity driven more by shared determinant profiles than by geography (Figure 3). Educational attainment was the only determinant that behaved consistently, showing inverse associations with Prevalence among the participants patients in every urbanization level, with correlations in our data ranging from \u0026minus;0.16 in rural settings to \u0026minus;0.81 in urban settings (Figure 2). This pattern aligns with evidence that education tracks strongly with health and can function as a portable resource shaping access and decision-making across contexts[36], [37]. Concentrated attainment also coincided with the lowest burdens: in Cluster 2, degree attainment was nearly twice the state average (effect size 2.00), and Prevalence among the participants patients was 42.6 percent. This pairing may indicate that educational capital extends beyond individual knowledge, supporting steadier employment and, in turn, greater housing security and access to health-supporting resources. Advantages likely diffuse through institutions as well, as employers and long-term-care operators can reinforce them through workforce development and continuing education. In this view, education operates at multiple levels including individual, community, and organizational, which consists of the gradient observed in our data.\u003c/p\u003e\n\u003cp\u003eOther determinants appeared contingent on place. Income inequality related to burden independent of average income, consistent with studies linking distributional features of income to community health [38], [39]. Signals around technology access were suggestive, as clusters with greater cellular data plan availability tended to show lower burden, and reviews indicate that digital health and telemedicine can improve cardiometabolic risk factors, including weight-related outcomes [40], [41]. Housing patterns were consistent with selection and exposure processes over time, in line with research on neighborhood deprivation trajectories and their association with health status [42]. Language diversity combined with high employment density did not uniformly correspond to lower burden; work on immigrant health notes that acculturative stress and related barriers can offset expected advantages, which may help explain these mixed patterns[43], [44]. Population density also appeared to cut both ways in our data, particularly where housing instability and segregation co-occurred with high density. These archetypes suggest that nominally similar indicators can operate differently across settings, which supports tailoring prevention strategies to local structural profiles rather than assuming uniform effects [45].\u003c/p\u003e\n\u003ch3\u003e4.3 Policy Innovation Opportunities\u003c/h3\u003e\n\u003cp\u003eOur findings indicate limits to uniform policy and support context-dependent strategies[46]. he classification guides tailored action: Urban Underserved areas, which showed the steepest increases in obesity, likely need intensive resource measures that expand insurance coverage, strengthen employment, and improve affordable food access. Urban Advantaged areas are better suited to prevention that sustains protective conditions, including provider availability, educational pathways, and wellness promotion. Rural and Rural Underserved cohorts warrant investment in telehealth, transportation support, and mobile services to overcome distance and capacity constraints, while Rural Advantaged communities should maintain educational and healthcare infrastructure and address localized inequities before they widen. In practice, the classification operates as a toolkit that translates descriptive patterns into policy levers aligned to structural realities. Technology access functions as a health determinant, so digital infrastructure should be treated as core health policy; broadband expansion and telemedicine capacity should be integrated into health promotion budgets and planning[47], [48].\u003c/p\u003e\n\u003cp\u003eFood-access patterns in our data concentrate in specific vulnerable subgroups, with strong correlations for children in low-access areas (r = 0.39, p \u0026lt; 0.001), low-income households (r = 0.39, p \u0026lt; 0.001), and families lacking vehicle access (r = 0.36, p \u0026lt; 0.01). From an operational perspective, priority should fall on child nutrition programs, targeted support for low-income households, and transportation-linked approaches such as mobile markets or delivery services. Broad geographic initiatives risk diluting resources; aligning interventions with these documented vulnerabilities offers a clearer path from statistical association to actionable policy.\u003c/p\u003e\n\u003cp\u003eHousing investment patterns operating as health investment strategies indicate that housing policy frameworks inadequately capture health co-benefits of residential stability [49]. Housing assistance programs should incorporate health outcome metrics and neighborhood health infrastructure assessments into allocation criteria [50]. Language diversity patterns revealing complex acculturation-health trade-offs indicate that current immigrant health policies may inadvertently undermine traditional health protective factors [51]. Health policies should incorporate cultural adaptation support services and community-based interventions that strengthen traditional health support networks [52].\u003c/p\u003e\n\u003cp\u003ePopulation density constraints create health vulnerability despite urban infrastructure advantages reveal critical gaps in urban health policy frameworks [53]. Urban health policies must address segregation-specific mechanisms operating through social stress and discriminatory service provision [54]. Income inequality distribution effects suggest that community health policies should prioritize economic equality strategies over pure wealth accumulation approaches.\u003c/p\u003e\n\u003cp\u003eEvidence on intergenerational transmission of disadvantages suggests that child-focused policies alone rarely interrupt the structural pathways through which disadvantage persists [55]. More promising approaches coordinate housing quality, healthcare access, and economic stability rather than siloed efforts in a single domain [56]. While broader reforms are pursued, organizations can act. Long-term care operators can strengthen staff competencies through targeted training and formal partnerships with local educational and health institutions [57]; employers and community partners can expand workplace education, digital wellness, nutrition access, and telehealth. Nursing-led telemedicine has produced measurable reductions in weight, body mass index, and waist circumference, indicating a feasible route to near-term gains within existing delivery systems [58]. These organizational steps complement longer-horizon policy change.\u003c/p\u003e\n\u003ch3\u003e4.4 Study Limitations\u003c/h3\u003e\n\u003cp\u003eInterpretation is constrained by design and measurement features. As an ecological, cross-sectional study, the analysis cannot establish individual-level causality and remains vulnerable to ecological fallacy [59]. Spatial granularity is imperfect because ZIP codes may not align with lived community boundaries, and the cross-sectional frame limits inference to associations rather than temporal change. Measurement uncertainty persists standard food-access indicators may overlook culturally specific or informal provisioning networks, which could help explain subgroup asymmetries. The prevalence measure is derived from electronic health records among the participating patient population, so selection and coding heterogeneity are possible. Missing predictors were imputed with k-nearest neighbors within urbanization strata; performance depends on the missingness mechanism, and residual bias may remain. Dimensionality reduction and clustering are sensitive to parameterization and preprocessing, which can influence the specific archetypes recovered. These caveats suggest cautious interpretation of magnitudes, even as patterns appear context dependent.\u003c/p\u003e\n\u003ch3\u003e4.5 Future Research\u003c/h3\u003e\n\u003cp\u003ePriority directions include longitudinal, individual-level studies within archetypes to test mechanisms; interaction models that formally quantify context modification; and multi-state validations to assess generalizability. Methodologically, sensitivity analyses of UMAP parameters and alternative clustering, plus tests of scale (tract, ZIP code, county), would clarify robustness. Policy research should evaluate cluster-tailored interventions against standard approaches and explore decision support that flags archetypes in near real time to guide targeted delivery.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides compelling empirical evidence challenging conventional assumptions about universal social determinant effects on health outcomes. Our comprehensive analysis reveals that while educational attainment operates as the sole truly universal protective factor across all community contexts, most social determinants demonstrate context-dependent effectiveness that varies dramatically by urbanization level. This fundamental distinction between universal and context-specific mechanisms necessitates a paradigm shift from one-size-fits-all interventions toward precision population health approaches that recognize community archetypes require tailored rather than standardized social determinant strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or personal interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization, \u0026ldquo;Obesity and Overweight.\u0026rdquo; Accessed: Aug. 19, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=Worldwide%20adult%20obesity%20has%20more,16%25%20were%20living%20with%20obesity.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention, \u0026ldquo;Adult Obesity Facts,\u0026rdquo; Centers for Disease Control and Prevention. Accessed: Aug. 19, 2025. [Online]. Available: https://www.cdc.gov/obesity/adult-obesity-facts/index.html\u003c/li\u003e\n\u003cli\u003eS. 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Kakampakou \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects,\u0026rdquo; \u003cem\u003eBMC Med Res Methodol\u003c/em\u003e, vol. 25, no. 1, p. 79, Mar. 2025, doi: 10.1186/s12874-025-02504-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8025002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8025002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eSocial determinants of health (SDOH) are widely linked to obesity, but effects may depend on local structural conditions. We examined whether SDOH act uniformly or vary by urbanization and community profile in Wisconsin.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional ecological study of 833 ZIP codes (72 counties) from 2019\u0026ndash;2023. Obesity prevalence was derived from electronic health records. Predictors covered education, transportation, housing, household composition, insurance, income and poverty, and demographic specific food access. Analyses used a multi-level urbanization classification, bivariate associations to identify universal versus context dependent patterns, and UMAP with clustering to derive community archetypes.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eObesity prevalence increased in all cohorts. Urban Underserved communities rose by just over eight percentage points, Urban Advantaged by about three, and rural cohorts formed an intermediate band. Education was the only consistently protective factor across all strata, with stronger inverse associations in urban settings, whereas most other SDOH appeared context dependent. Food access associations are concentrated in specific subgroups, children in low access areas (r\u0026thinsp;=\u0026thinsp;0.39), low-income populations (r\u0026thinsp;=\u0026thinsp;0.39), and households without a vehicle (r\u0026thinsp;=\u0026thinsp;0.36), with asymmetric racial patterns. UMAP revealed eight community archetypes spanning 42.6% to 53.7% obesity prevalence, indicating that structural profiles aligned more closely with burden than geography alone.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eRelationships between SDOH and obesity were not uniform. Education behaved as a protective indicator, while most determinants varied by urbanization and community structure, supporting context matched strategies that prioritize high risk subgroups and tailor interventions to community archetypes.\u003c/p\u003e","manuscriptTitle":"Obesity, Urbanization, and Food Access in Wisconsin: The Intersection of Healthcare Provider Access and Population Health Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:55:55","doi":"10.21203/rs.3.rs-8025002/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fa224c3a-dde4-4eb7-b9a2-ff2440227a40","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57864798,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":57864799,"name":"Health sciences/Health care/Health policy"},{"id":57864800,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-29T00:55:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:55:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8025002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8025002","identity":"rs-8025002","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}