Racial and Ethnic Disparities in Adult Obesity Across Food and Physical Activity Environments in the United States: A Multilevel Analysis

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Swartz, Alexandra E. Berg, Stephen H. Linder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041950/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Adult obesity remains a critical public health issue in the United States, with marked disparities across racial and ethnic groups. Minority populations are often disproportionately exposed to unhealthy food and physical activity environments, yet little is known about how these exposures modify associations with obesity risk. Methods: We applied multilevel modeling to data from 3,194 U.S. counties across 50 states and the District of Columbia (2014–2024). County- and state-level measures of food and physical activity environments were examined in relation to adult obesity prevalence, with cross-level interactions tested between racial/ethnic population composition and key environmental variables (e.g., fast-food density, grocery access, supercenter prevalence, low-income low-access rates). Analyses were stratified by American Indian or Alaska Native (AIAN), Asian, Hispanic, Native Hawaiian or Other Pacific Islander (NHPI), Non-Hispanic Black (NHB), and Non-Hispanic White (NHW) populations. Results: Significant cross-level interactions were observed for counties with higher AIAN, NHB, and Hispanic populations. Fast-food restaurant density was more strongly associated with adult obesity in counties with larger AIAN and NHB populations. Convenience store counts were positively associated with obesity prevalence in counties with higher NHW populations. Conclusions: Associations between food environments and adult obesity vary by racial and ethnic composition. Geographic and demographic context should be incorporated into public health strategies in order to promote equity and lessen disparities in the prevention of obesity. adult obesity racial and ethnic disparities food environment physical activity environment multilevel analysis cross-level interactions united states health equity public health policy Figures Figure 1 1. Introduction Adult obesity is a critical public health problem in the United States. It is defined as a body mass index (BMI) of 30 kg/m² or higher. Obesity increases the risk of cardiovascular disease, stroke, type 2 diabetes, and several types of cancer [ 1 ]. The prevalence of adult obesity in the U.S. increased from 30.5% to 40.9% between 2017 and 2020, and the estimated yearly medical expenses associated with obesity are $ 173 billion [ 2 ]. Food and physical activity environments are critical levers for obesity prevention. Limited access to healthy food outlets, higher density of fast-food restaurants, and fewer recreational facilities have been consistently linked with higher obesity prevalence specifically among minorities [ 3 – 5 ]. Persistent health disparities are exacerbated by the disproportionate exposure of racial and ethnic minority communities to unhealthy environments [ 6 – 8 ]. Despite extensive research, limited evidence exists on whether food and physical activity environments differentially influence obesity risk depending on the racial and ethnic composition of communities. This study builds on earlier multilevel research and extends prior work by incorporating race-stratified models and cross-level interactions. By examining how these interactions shape obesity prevalence across diverse populations, we aim to inform more equitable and targeted public health interventions. According to data from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES), adult obesity rates are continuing to rise, surpassing 40% in many areas [ 3 ]. Significant racial and ethnic differences in the prevalence of obesity also exist, and it is crucial to fully understand these variations in order to tailor public health interventions to address them. Significant differences exist between racial and ethnic groups: according to NHANES data from 2017–2020, the prevalence of obesity was 16.1% among Asian adults, 49.9% among Non-Hispanic Black adults, 45.6% among Hispanic adults, and 41.4% among Non-Hispanic White adults [ 4 ]. These disparities are shaped in part by differences in food and physical activity environments. The USDA defines food environments as the physical, economic, and social factors that influence food access and choice [ 5 ]. Data sources such as the Food Environment Atlas and County Health Rankings provide measures of food outlet density, grocery store access, and related demographic and socioeconomic indicators [ 5 , 6 ]. While many studies have linked these environmental characteristics with obesity, little is known about whether the strength or direction of these associations varies across racial and ethnic subpopulations. Past research illustrates how risk for obesity is calibrated by structural aspects like food availability, economic environment, and neighborhoods [ 10 – 12 ]. Studies consistently report that Hispanic and Black groups are structurally disadvantaged in accessing healthy food, and residents of rural areas experience transportation and food availability limitations. Adequate access to parks, recreation centers, and green spaces are also associated with decreased obesity risk, particularly in urban and higher income settings [ 13 – 15 ].Most studies are regional and cross-sectional. Few assess how race interacts with environmental exposures, nationally. This study addresses this gap using a multilevel approach at a national level with a race-stratified analysis including cross- level interactions, and including confounders such as income, food insecurity, SNAP participation. We hypothesized that counties with higher proportions of Black and Hispanic populations would exhibit higher adult obesity prevalence compared with predominantly White counties. We further hypothesized that adverse food environment factors, characterised by limited grocery access and greater fast-food density, would be more strongly associated with adult obesity in counties with larger minority populations. Guided by the ecological model, this study focuses on community-level physical and food environment factors to advance understanding of structural contributors to obesity disparities. 2. Methods 2.1 Study design and sample This retrospective ecological study included all 3,194 counties from the 50 U.S. states and the District of Columbia for the period 2014–2024. The selected years represent the most recent and consistent data available on adult obesity and food and physical activity environments. The analysis applied a multilevel framework with counties nested within states to account for hierarchical data structure and regional policy differences. This approach enables estimation of both county- and state-level variance components and allows for testing of cross-level interactions between contextual factors and local environments. 2.2 Data sources Adult obesity prevalence was obtained from the County Health Rankings and Roadmaps dataset, while food and physical activity environment indicators were drawn from the U.S. Department of Agriculture (USDA) Food Environment Atlas. Variables were harmonized by aligning the closest available years across datasets to ensure temporal consistency. 2.3 Variables and measures The study examined socioeconomic, demographic, and food and physical activity environment variables at the county and state levels. The number of grocery stores, fast-food restaurants, supercenters, convenience stores, and SNAP-authorized establishments were among the food environment variables. Additional measures included food insecurity, household income, racial/ethnic composition, and access to recreational facilities. Table 1 summarizes all variables, their definitions, geographic level, source, and year of measurement. Table 1 Variable Summary: Geographic Level, Description, Data Source, and Year Variable Geographic Level Description Data Source Year Unit Soda, Chips, Pretzel Sales Tax State Additional sales tax on snack foods above standard food tax rates Bridging the Gap Program, MayaTech Corp. & Univ. of Illinois-Chicago 2014 Percent Adult Obesity Rate County Age-adjusted % of adults (20+) with BMI ≥ 30 BRFSS via County Health Rankings 2022 Percent SNAP Benefits State Federal/state-admin. program costs including education and training USDA ERS & Census Bureau 2024 Dollars % Households Low Access & Low Income County % of households without a vehicle & >1 mile from a large grocery store Supermarket directories, ACS, TDLinx 2015 Percent Poverty Rate County % of individuals living below federal poverty threshold USDA ERS, SAIPE (Census) 2015 Percent Grocery Stores County Number of grocery/supermarkets (excl. convenience & supercenters) U.S. Census Bureau CBP 2016 Count Farmers’ Markets County Markets with ≥ 2 vendors selling produce directly to consumers USDA Agricultural Marketing Service 2018 Count Farmers’ Markets (Fruit/Vegetable Sales) County Markets selling ≥ 51% fruits/vegetables USDA Agricultural Marketing Service 2018 Count Supercenters / Club Stores County Count of stores offering groceries & general goods U.S. Census Bureau CBP 2016 Count Convenience Stores County Stores selling limited range goods (e.g., snacks, soda) U.S. Census Bureau CBP 2016 Count SNAP-Authorized Stores County Avg. monthly number of stores accepting SNAP USDA FNS, SNAP Redemption Div. 2017 Count Recreation & Fitness Facilities County Fitness/recreation centers (e.g., gyms, sports facilities) U.S. Census Bureau CBP 2016 Count Demographics (Race/Ethnicity) County % population by race/ethnicity (White, Black, Hispanic, etc.) U.S. Census Bureau, 2010 Census 2010 Percent Median Household Income County Median income of households (age 15+) USDA ERS, Census Bureau 2015 Dollars Food Insecurity 1 County % of individuals without adequate food access Map the Meal Gap 2021 Percent Unemployment County % of labor force (16+) unemployed and seeking work Bureau of Labor Statistics 2022 Percent 1 Food insecurity is defined by the USDA and used by Feeding America to represent the estimated percentage of people in each area who lack consistent access to enough food for an active, healthy life. Estimates are modeled using indicators such as poverty, unemployment, disability, and median income (USDA, 2023). 2.4 Statistical analysis Multilevel linear regression models were estimated with counties nested within states. State-level predictors were grand-mean centered, and county-level predictors were group-mean centered to distinguish between- and within-state effects. To test whether associations between food environment characteristics and adult obesity varied by racial and ethnic composition, cross-level interaction terms were included between county-level racial/ethnic proportions and food environment indicators at both county and state levels. Models were stratified by racial/ethnic group - American Indian or Alaska Native (AIAN), Asian, Hispanic, Native Hawaiian or Other Pacific Islander (NHPI), Non-Hispanic Black (NHB), and Non-Hispanic White (NHW)—to identify group-specific associations. Analyses focused on the interaction of fast-food restaurant density, grocery store access, supercenter prevalence, and low-income low-access populations with racial/ethnic composition. All analyses were conducted in Stata version 19, and statistical significance was defined as p < 0.05. Detailed model results and interaction plots are provided in the Supplementary Information. 2.5 Ethical considerations This study used publicly available, de-identified data and was deemed exempt by the Institutional Review Board (IRB) of the University of Texas School of Public Health, Houston. 3. Results 3.1 Main effects The multilevel modeling results indicated that counties with a greater proportion of Hispanic (β = 0.121, p < 0.001), Non-Hispanic Black (β = 0.025, p = 0.02), and American Indian or Alaska Native (β = 0.185, p = 0.001) residents exhibited significantly higher levels of adult obesity. In contrast, counties with larger shares of Asian (β = − 0.158, p < 0.001) and Non-Hispanic White (β = − 0.020, p = 0.03) populations showed lower obesity prevalence. The percentages of Native Hawaiian or Other Pacific Islander populations did not show a statistically significant relationship with adult obesity prevalence (β = − 0.023, p = 0.69). Regarding socioeconomic indicators, median household income (β = − 0.0001, p < 0.001) was inversely associated with obesity prevalence, implying that higher-income counties tended to have lower obesity levels. Conversely, the poverty rate (β = 0.307, p < 0.001) displayed a positive association with adult obesity, indicating a greater obesity burden among economically disadvantaged areas. These findings establish the baseline relationships between food and social environment factors and adult obesity before accounting for potential effect modification by race and ethnicity. 3.2 Effect Modification by Race and Ethnicity To examine how racial and ethnic composition influences the association between food environments and adult obesity, cross-level interaction terms were included to assess effect modification . Significant moderating patterns were observed, indicating that the strength and direction of food-environment associations varied across racial and ethnic populations (Table 2 ). The multilevel results pointed to notable cross-level interactions, indicating that the strength and direction of food-environment effects on adult obesity were not uniform across racial and ethnic populations (Table 2 ). In counties where Hispanic populations comprised a larger share of residents, the relationship between fast-food outlet density and obesity prevalence was markedly stronger than in counties with fewer Hispanic inhabitants (see Table S3 in Supplementary Information). This pattern implies that frequent exposure to fast-food settings may intensify obesity risk within Hispanic communities. In counties containing greater proportions of Non-Hispanic Black populations, grocery-store availability showed a stronger inverse association with adult obesity, whereas the densities of convenience stores and supercenters exhibited more positive relationships with obesity compared with counties with smaller Black populations (see Table S5 in Supplementary Information). These results imply that increased availability of grocery stores provides a greater protective effect against obesity, whereas a higher presence of convenience stores or supercenters appears to elevate obesity risk within counties that have larger Non-Hispanic Black populations. In counties characterized by higher proportions of Native Hawaiian or Other Pacific Islander (NHPI) populations, a stronger positive link emerged between supercenter density and adult obesity—an effect that did not appear in counties with smaller NHPI populations (see Table S4 in Supplementary Information). Counties with larger Non-Hispanic White populations showed an inverse relationship between convenience store density and adult obesity, implying that the role of convenience stores may differ in these communities—potentially due to variations in product selection, food quality, or purchasing behaviors (see Table S6 in Supplementary Information). No statistically significant effects were identified for American Indian or Alaska Native(AIAN) or Asian populations (see Table S1 and S2 in Supplementary Information). These cross-level findings highlight how local food-environment influences operate within broader state contexts. By nesting counties within states, the multilevel model captures both local and contextual factors that shape obesity risk. This structure also allows for the detection of cross-level interactions , showing how state-level conditions—such as policy, economic, or cultural environments—can modify county-level relationships between food access and obesity. Table 2 Multilevel model estimates showing effect modification between racial/ethnic composition and food environment characteristics Racial/Ethnic Group Significant Main Effects Significant Interaction Effects AIAN AIAN population (+) None Asian Asian population (–) None Hispanic Hispanic population (+) Fast food density (+): Stronger effect in Hispanic-majority counties NHPI NHPI population (+) Supercenter density (+); % Households Low Access & Low Income (–): weaker effect NHB NHB population (+) Grocery access (–): More protective; Convenience store density (+); Supercenter density (–) NHW NHW population (–) Convenience store density (–): Lower obesity in NHW areas Note: “(+)” and “(–)” indicate the direction of association with adult obesity. Only statistically significant effects (typically p < 0.05) are reported. 3.3 Summary of effect modification. While not all cross-level interaction terms achieved statistical significance, distinct patterns were observed for certain racial and ethnic groups. Stronger associations between food environment variables and adult obesity prevalence were found in counties with larger proportions of Non-Hispanic Black, Native Hawaiian or Other Pacific Islander, Hispanic, and Non-Hispanic White residents, highlighting the need for racially and geographically tailored interventions(see Table S3 -S6 in Supplementary Information). Asian and American Indian or Alaska Native populations showed no significant interactions (see Table S1 amd S2 in Supplementary Information). This may be attributed to smaller sample sizes or unmeasured protective factors such as strong community cohesion or cultural dietary practices. These findings provide a nuanced understanding of how food environments and racial/ethnic composition interact to shape adult obesity outcomes, underscoring the importance of place-based, equity-oriented interventions. All detailed regression tables and interaction plots are provided in the Supplementary Information. 3.4 Interaction plots Figure 3 illustrates predicted adult obesity prevalence by fast-food restaurant density across counties with varying proportions of Hispanic residents. Counties with higher percentages of Hispanic residents (e.g., 40% and 50%) showed a steeper increase in predicted obesity prevalence as fast-food density increased. For brevity, only the most representative and statistically significant interaction plot is presented (e.g., for counties with higher proportions of Hispanic residents). Additional interaction plots for other racial and ethnic groups, along with state-level coefficient plots, are provided in the Supplementary Information. 4. Discussion Main Findings This study builds upon prior multilevel research by focusing on how food environments interact with racial and ethnic compositions to shape adult obesity rates across U.S. counties. In contrast to earlier models that examined structural predictors and demographic characteristics separately, this article centers on interaction effects to show how certain communities may be differentially affected by obesogenic environments. The finding that fast-food density has a stronger impact on obesity rates in Hispanic-majority counties underscores the need for policy interventions that are both place- and population-sensitive. These effects may reflect broader systemic inequities, such as targeted fast-food marketing or lack of zoning protections, and hence require structural solutions [ 13 – 15 ]. State-level coefficient plots further emphasize geographic variation in the effect of Hispanic population proportion on adult obesity prevalence, suggesting that regional policy contexts may either mitigate or magnify risk [ 10 ]. Comparison with Prior Studies Recent studies have also documented that racial and ethnic disparities in obesity are amplified by inequities in the food environment and neighborhood segregation. For instance, Bell et al. (2019) found that counties with greater racial inequalities and obesogenic environments experienced stronger associations between structural disadvantage and obesity [ 16 ]. Xu et al. (2021) and related spatial analyses reported that counties with higher fast-food density had higher obesity prevalence, particularly in areas with more Black and Hispanic residents [ 17 ]. More recently, a multi-county analysis showed that food desert exposure was positively associated with obesity and diabetes in the U.S. South, with magnified effects in minority-dense counties [ 18 ]. Another longitudinal study demonstrated that increases in fast-food outlets near residence were associated with increases in BMI over time, indicating environmental change can drive weight gain [ 19 ]. The findings also support the development of health policies and programs customized not only to community-level characteristics but also to the demographic groups who live there. Effective interventions such as healthy food retail incentives, culturally informed nutrition education, and improved access to recreational space should be designed with the community context in mind [ 15 , 19 ]. In order to better understand the lived experiences that underlie these statistical trends, future research should build on this work by utilizing time-series methodologies, integrating qualitative methods, and employing more granular local-level data (such as ZIP code or census tract). The study’s multilevel design adds value by recognizing that local food-environment effects are embedded within wider state and policy contexts, providing a clearer view of how structural factors interact across geographic scales to influence adult obesity. Overall, this research underscores the need for intersectional, equity-focused strategies to address adult obesity, particularly in communities facing the dual burden of structural disadvantage and limited access to healthy food. Limitations This study has several limitations. First, the ecological design, which relies on county- and state-level aggregate data, may mask within-county variability. Because the data are both ecological and cross-sectional, the findings cannot establish causality and should therefore be interpreted with caution. Second, although the County Health Rankings dataset provides model-based estimates of adult obesity, these estimates are derived from self-reported BRFSS data and hence may be subject to reporting bias like underreporting of weight or overreporting of height. Third, measures of food and physical activity environments were drawn from different years, introducing potential temporal misalignment. However, previous research indicates that most structural food environment indicators (e.g., grocery stores, supercenters) change slowly over time, whereas others—such as farmers’ markets—may fluctuate more quickly in response to policy or market dynamics [ 7 – 9 ]. Fourth, residual confounding remains a limitation. Though key confounders like food insecurity and SNAP benefits were included, factors like local zoning laws, healthcare access, and physical activity promotion were not available and hence this may bias results. Finally, raw counts were used over density measures due to consistency and comparability across geographies. While raw counts don’t capture outlet size or quality, they provide a standardized approach to analyzing environmental exposures nationwide. Future research Future studies should make use of data at the ZIP code or census tract level to capture finer spatial variation that county-level measures may overlook. Cross-level interactions with additional economic and environmental variables could further isolate race- and ethnicity-specific effects which were not done in this study. Cultural determinants of adult obesity were beyond the scope of this analysis; however, future research could incorporate variables such as acculturation, dietary customs, and food preparation behaviors to better account for cultural diversity when addressing obesity prevention [ 13 ]. Comparative or quasi-experimental designs, such as those involving soda taxes or SNAP expansions, can yield more convincing evidence of impact of policy changes over time. Furthermore, employing a mixed methods approach and integrating quantitative and qualitative research could yield more insightful information about perspectives and lived experiences in racially and economically diverse groups [ 15 ]. A multifaceted approach can help build more responsive, equitable, and effective public health interventions. 5. Conclusion This study adds new evidence to the literature on racial and ethnic disparities in obesity by showing how food environment factors interact with local demographic composition. Using a multilevel framework, we found that the relationship between environmental exposures—such as fast-food density, grocery access, and supercenter availability—and adult obesity differs across racial and ethnic groups. In particular, counties with larger Hispanic and Black populations depicted a stronger association with several food environment measures, suggesting a higher vulnerability shaped by both place and population dynamics. These findings emphasize the need to design obesity prevention strategies that are sensitive to both social and geographic context. Standard, one-size-fits-all approaches risk overlooking the ways structural and cultural factors intersect to influence health. Public health initiatives must therefore address not only the distribution of healthy food but also the demographic realities of the communities they are intended to serve. The results demonstrate that addressing obesity requires integrated approaches that consider data, context, and equity together. Further research is needed to understand how racialized environments contribute to disparities and to inform policy responses that meet the needs of varied communities. Declarations Ethics approval and consent to participate Not applicable. This study used publicly available, de-identified county- and state-level datasets and did not involve human subjects. Consent for publication Not applicable. No individual person’s data or identifiable images are included in this study. Availability of data and materials The datasets analyzed in this study are publicly available. Adult obesity data were obtained from County Health Rankings & Roadmaps (https://www.countyhealthrankings.org).Food environment indicators were obtained from the USDA Food Environment Atlas (https://www.ers.usda.gov/data-products/food-environment-atlas/). Additional datasets, including demographic and socioeconomic variables, were drawn from publicly available CDC and USDA sources as cited in the manuscript. Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this study. Authors’ contributions A.M.A. conceptualized the study, conducted the statistical analyses, and drafted the manuscript. S.H.L. contributed to study design and interpretation of results. M.D.S. provided critical revisions and input on the analytical approach. A.E.V.D.B. contributed to interpretation of findings and critical revision of the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. References Centers for Disease Control and Prevention. Adult Obesity Facts [Internet]. Atlanta (GA): CDC; 2024 [cited 2025 Sep 16]. Available from: https://www.cdc.gov/obesity/adult-obesity-facts/index.html Centers for Disease Control and Prevention. 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PLoS One. 2016;11(2):e0148394. doi:10.1371/journal.pone.0148394 Bukenya J. Determinants of food insecurity in Huntsville, Alabama, metropolitan area [Internet]. Huntsville (AL): Alabama A&M University; 2017 [cited 2025 Sep 16]. Available from: [insert URL or repository if available] Piontak JR, Schulman MD. Food insecurity in rural America. Contexts. 2014;13(3):75–7. doi:10.1177/1536504214545766 Polyzou EA, Polyzos SA. Outdoor environment and obesity: a review of current evidence. Metab Open. 2024;24:100331. doi:10.1016/j.metop.2024.100331 Li Y, Wang S, Cao G, Li D, Ng BP. Disentangling racial/ethnic and income disparities of food retail environments: impacts on adult obesity prevalence. Appl Geogr. 2021;137:102607. doi:10.1016/j.apgeog.2021.102607 Cereijo L, Gullón P, Del Cura I, Cebrecos A, Bilal U, Franco M. Exercise facilities and the prevalence of obesity and type 2 diabetes in the city of Madrid. Diabetologia. 2022;65(1):150–8. doi:10.1007/s00125-021-0558 Bell C. N., Thorpe R. J., & LaVeist T. A. (2019). Associations between Obesity, Obesogenic Environments, and Structural Racism in U.S. Counties. International Journal of Environmental Research and Public Health , 16(5), 861. https://www.mdpi.com/1660-4601/16/5/861 A Spatial Analysis of Obesity: Interaction of Urban Food Environments, Xu et al. (2021). PMC article. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280681/ Food deserts exposure, density of fast-food restaurants, and park access: county-level associations with obesity and diabetes in the U.S. PLOS ONE (2023). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301121 Effects of changes in residential fast-food outlet exposure on body mass index over time. PMC article. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10941418/ Additional Declarations No competing interests reported. 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09:58:04","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72916,"visible":true,"origin":"","legend":"","description":"","filename":"222aac2acde4450ba380b53a54fc850f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/c2d9e415b74931ff3406856f.xml"},{"id":96385095,"identity":"b6abd010-d23e-4752-84cb-2dc6d336f67d","added_by":"auto","created_at":"2025-11-20 13:02:26","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83001,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/32657d469ec305ee6df75f98.html"},{"id":96385089,"identity":"c56567f3-2826-4116-9f56-66e69d73164f","added_by":"auto","created_at":"2025-11-20 13:02:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. Predicted adult obesity prevalence by fast-food restaurant density across counties with varying proportions of Hispanic resident\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/9a2a2ecc98e31c00371e7262.jpeg"},{"id":105890876,"identity":"a319b61e-2cc0-4c79-89d0-df4333b06fc0","added_by":"auto","created_at":"2026-04-01 07:59:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1067254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/053e4fd3-7c1c-4093-a49a-cf417acd5f03.pdf"},{"id":96385090,"identity":"1c433ff9-2267-44fb-a0e6-977c5d814ea2","added_by":"auto","created_at":"2025-11-20 13:02:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":61246,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationFile1Abraham.docx","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/27230b62bda9bc10fc1ea943.docx"},{"id":96385103,"identity":"ec9677d2-af1a-4393-9a91-7b1c09524334","added_by":"auto","created_at":"2025-11-20 13:02:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15301328,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationFile2Abraham.docx","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/f02210338e4d3fd05dcdb444.docx"},{"id":96385099,"identity":"5d487ef7-ba08-456d-9ead-97e5e1cccba6","added_by":"auto","created_at":"2025-11-20 13:02:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":783061,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationFile3Abraham.docx","url":"https://assets-eu.researchsquare.com/files/rs-8041950/v1/1211f4effc2f39b26c11a3c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Racial and Ethnic Disparities in Adult Obesity Across Food and Physical Activity Environments in the United States: A Multilevel Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdult obesity is a critical public health problem in the United States. It is defined as a body mass index (BMI) of 30 kg/m\u0026sup2; or higher. Obesity increases the risk of cardiovascular disease, stroke, type 2 diabetes, and several types of cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of adult obesity in the U.S. increased from 30.5% to 40.9% between 2017 and 2020, and the estimated yearly medical expenses associated with obesity are \u003cspan\u003e$\u003c/span\u003e173\u0026nbsp;billion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFood and physical activity environments are critical levers for obesity prevention. Limited access to healthy food outlets, higher density of fast-food restaurants, and fewer recreational facilities have been consistently linked with higher obesity prevalence specifically among minorities [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Persistent health disparities are exacerbated by the disproportionate exposure of racial and ethnic minority communities to unhealthy environments [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite extensive research, limited evidence exists on whether food and physical activity environments differentially influence obesity risk depending on the racial and ethnic composition of communities. This study builds on earlier multilevel research and extends prior work by incorporating race-stratified models and cross-level interactions. By examining how these interactions shape obesity prevalence across diverse populations, we aim to inform more equitable and targeted public health interventions.\u003c/p\u003e\u003cp\u003eAccording to data from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES), adult obesity rates are continuing to rise, surpassing 40% in many areas [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Significant racial and ethnic differences in the prevalence of obesity also exist, and it is crucial to fully understand these variations in order to tailor public health interventions to address them.\u003c/p\u003e\u003cp\u003eSignificant differences exist between racial and ethnic groups: according to NHANES data from 2017\u0026ndash;2020, the prevalence of obesity was 16.1% among Asian adults, 49.9% among Non-Hispanic Black adults, 45.6% among Hispanic adults, and 41.4% among Non-Hispanic White adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese disparities are shaped in part by differences in food and physical activity environments. The USDA defines food environments as the physical, economic, and social factors that influence food access and choice [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Data sources such as the Food Environment Atlas and County Health Rankings provide measures of food outlet density, grocery store access, and related demographic and socioeconomic indicators [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While many studies have linked these environmental characteristics with obesity, little is known about whether the strength or direction of these associations varies across racial and ethnic subpopulations.\u003c/p\u003e\u003cp\u003ePast research illustrates how risk for obesity is calibrated by structural aspects like food availability, economic environment, and neighborhoods [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies consistently report that Hispanic and Black groups are structurally disadvantaged in accessing healthy food, and residents of rural areas experience transportation and food availability limitations. Adequate access to parks, recreation centers, and green spaces are also associated with decreased obesity risk, particularly in urban and higher income settings [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Most studies are regional and cross-sectional. Few assess how race interacts with environmental exposures, nationally. This study addresses this gap using a multilevel approach at a national level with a race-stratified analysis including cross- level interactions, and including confounders such as income, food insecurity, SNAP participation. We hypothesized that counties with higher proportions of Black and Hispanic populations would exhibit higher adult obesity prevalence compared with predominantly White counties. We further hypothesized that adverse food environment factors, characterised by limited grocery access and greater fast-food density, would be more strongly associated with adult obesity in counties with larger minority populations. Guided by the ecological model, this study focuses on community-level physical and food environment factors to advance understanding of structural contributors to obesity disparities.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study design and sample\u003c/h2\u003e\n \u003cp\u003eThis retrospective ecological study included all 3,194 counties from the 50 U.S. states and the District of Columbia for the period 2014\u0026ndash;2024. The selected years represent the most recent and consistent data available on adult obesity and food and physical activity environments.\u003c/p\u003e\n \u003cp\u003eThe analysis applied a multilevel framework with counties nested within states to account for hierarchical data structure and regional policy differences. This approach enables estimation of both county- and state-level variance components and allows for testing of cross-level interactions between contextual factors and local environments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n \u003cp\u003eAdult obesity prevalence was obtained from the County Health Rankings and Roadmaps dataset, while food and physical activity environment indicators were drawn from the U.S. Department of Agriculture (USDA) Food Environment Atlas. Variables were harmonized by aligning the closest available years across datasets to ensure temporal consistency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Variables and measures\u003c/h2\u003e\n \u003cp\u003eThe study examined socioeconomic, demographic, and food and physical activity environment variables at the county and state levels. The number of grocery stores, fast-food restaurants, supercenters, convenience stores, and SNAP-authorized establishments were among the food environment variables. Additional measures included food insecurity, household income, racial/ethnic composition, and access to recreational facilities. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes all variables, their definitions, geographic level, source, and year of measurement.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariable Summary: Geographic Level, Description, Data Source, and Year\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeographic Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoda, Chips, Pretzel Sales Tax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdditional sales tax on snack foods above standard food tax rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBridging the Gap Program, MayaTech Corp. \u0026amp; Univ. of Illinois-Chicago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdult Obesity Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge-adjusted % of adults (20+) with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRFSS via County Health Rankings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNAP Benefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFederal/state-admin. program costs including education and training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA ERS \u0026amp; Census Bureau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDollars\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Households Low Access \u0026amp; Low Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of households without a vehicle \u0026amp; \u0026gt;1 mile from a large grocery store\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupermarket directories, ACS, TDLinx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoverty Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of individuals living below federal poverty threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA ERS, SAIPE (Census)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrocery Stores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of grocery/supermarkets (excl. convenience \u0026amp; supercenters)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S. Census Bureau CBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFarmers\u0026rsquo; Markets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarkets with \u0026ge;\u0026thinsp;2 vendors selling produce directly to consumers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA Agricultural Marketing Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFarmers\u0026rsquo; Markets (Fruit/Vegetable Sales)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarkets selling\u0026thinsp;\u0026ge;\u0026thinsp;51% fruits/vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA Agricultural Marketing Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupercenters / Club Stores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount of stores offering groceries \u0026amp; general goods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S. Census Bureau CBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConvenience Stores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStores selling limited range goods (e.g., snacks, soda)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S. Census Bureau CBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNAP-Authorized Stores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvg. monthly number of stores accepting SNAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA FNS, SNAP Redemption Div.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecreation \u0026amp; Fitness Facilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitness/recreation centers (e.g., gyms, sports facilities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S. Census Bureau CBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics (Race/Ethnicity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% population by race/ethnicity (White, Black, Hispanic, etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S. Census Bureau, 2010 Census\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Household Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian income of households (age 15+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSDA ERS, Census Bureau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDollars\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood Insecurity\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of individuals without adequate food access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMap the Meal Gap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnemployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of labor force (16+) unemployed and seeking work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBureau of Labor Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eFood insecurity is defined by the USDA and used by Feeding America to represent the estimated percentage of people in each area who lack consistent access to enough food for an active, healthy life. Estimates are modeled using indicators such as poverty, unemployment, disability, and median income (USDA, 2023).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eMultilevel linear regression models were estimated with counties nested within states. State-level predictors were grand-mean centered, and county-level predictors were group-mean centered to distinguish between- and within-state effects.\u003c/p\u003e\n \u003cp\u003eTo test whether associations between food environment characteristics and adult obesity varied by racial and ethnic composition, cross-level interaction terms were included between county-level racial/ethnic proportions and food environment indicators at both county and state levels. Models were stratified by racial/ethnic group - American Indian or Alaska Native (AIAN), Asian, Hispanic, Native Hawaiian or Other Pacific Islander (NHPI), Non-Hispanic Black (NHB), and Non-Hispanic White (NHW)\u0026mdash;to identify group-specific associations.\u003c/p\u003e\n \u003cp\u003eAnalyses focused on the interaction of fast-food restaurant density, grocery store access, supercenter prevalence, and low-income low-access populations with racial/ethnic composition. All analyses were conducted in Stata version 19, and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Detailed model results and interaction plots are provided in the Supplementary Information.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Ethical considerations\u003c/h2\u003e\n \u003cp\u003eThis study used publicly available, de-identified data and was deemed exempt by the Institutional Review Board (IRB) of the University of Texas School of Public Health, Houston.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Main effects\u003c/h2\u003e\u003cp\u003eThe multilevel modeling results indicated that counties with a greater proportion of Hispanic (β\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Non-Hispanic Black (β\u0026thinsp;=\u0026thinsp;0.025, p\u0026thinsp;=\u0026thinsp;0.02), and American Indian or Alaska Native (β\u0026thinsp;=\u0026thinsp;0.185, p\u0026thinsp;=\u0026thinsp;0.001) residents exhibited significantly higher levels of adult obesity. In contrast, counties with larger shares of Asian (β = \u0026minus;\u0026thinsp;0.158, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Non-Hispanic White (β = \u0026minus;\u0026thinsp;0.020, p\u0026thinsp;=\u0026thinsp;0.03) populations showed lower obesity prevalence. The percentages of Native Hawaiian or Other Pacific Islander populations did not show a statistically significant relationship with adult obesity prevalence (β = \u0026minus;\u0026thinsp;0.023, p\u0026thinsp;=\u0026thinsp;0.69).\u003c/p\u003e\u003cp\u003eRegarding socioeconomic indicators, median household income (β = \u0026minus;\u0026thinsp;0.0001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was inversely associated with obesity prevalence, implying that higher-income counties tended to have lower obesity levels. Conversely, the poverty rate (β\u0026thinsp;=\u0026thinsp;0.307, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) displayed a positive association with adult obesity, indicating a greater obesity burden among economically disadvantaged areas.\u003c/p\u003e\u003cp\u003eThese findings establish the baseline relationships between food and social environment factors and adult obesity before accounting for potential \u003cb\u003eeffect modification\u003c/b\u003e by race and ethnicity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Effect Modification by Race and Ethnicity\u003c/h2\u003e\u003cp\u003eTo examine how racial and ethnic composition influences the association between food environments and adult obesity, cross-level interaction terms were included to assess \u003cb\u003eeffect modification\u003c/b\u003e. Significant moderating patterns were observed, indicating that the strength and direction of food-environment associations varied across racial and ethnic populations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The multilevel results pointed to notable cross-level interactions, indicating that the strength and direction of food-environment effects on adult obesity were not uniform across racial and ethnic populations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In counties where Hispanic populations comprised a larger share of residents, the relationship between fast-food outlet density and obesity prevalence was markedly stronger than in counties with fewer Hispanic inhabitants (see Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e in Supplementary Information). This pattern implies that frequent exposure to fast-food settings may intensify obesity risk within Hispanic communities.\u003c/p\u003e\u003cp\u003eIn counties containing greater proportions of Non-Hispanic Black populations, grocery-store availability showed a stronger inverse association with adult obesity, whereas the densities of convenience stores and supercenters exhibited more positive relationships with obesity compared with counties with smaller Black populations (see Table S5 in Supplementary Information). These results imply that increased availability of grocery stores provides a greater protective effect against obesity, whereas a higher presence of convenience stores or supercenters appears to elevate obesity risk within counties that have larger Non-Hispanic Black populations.\u003c/p\u003e\u003cp\u003eIn counties characterized by higher proportions of Native Hawaiian or Other Pacific Islander (NHPI) populations, a stronger positive link emerged between supercenter density and adult obesity\u0026mdash;an effect that did not appear in counties with smaller NHPI populations (see Table S4 in Supplementary Information).\u003c/p\u003e\u003cp\u003eCounties with larger Non-Hispanic White populations showed an inverse relationship between convenience store density and adult obesity, implying that the role of convenience stores may differ in these communities\u0026mdash;potentially due to variations in product selection, food quality, or purchasing behaviors (see Table S6 in Supplementary Information). No statistically significant effects were identified for American Indian or Alaska Native(AIAN) or Asian populations (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 in Supplementary Information).\u003c/p\u003e\u003cp\u003eThese cross-level findings highlight how local food-environment influences operate within broader state contexts. By nesting counties within states, the multilevel model captures both local and contextual factors that shape obesity risk. This structure also allows for the detection of \u003cb\u003ecross-level interactions\u003c/b\u003e, showing how state-level conditions\u0026mdash;such as policy, economic, or cultural environments\u0026mdash;can modify county-level relationships between food access and obesity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultilevel model estimates showing effect modification between racial/ethnic composition and food environment characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRacial/Ethnic Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificant Main Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant Interaction Effects\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIAN population (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian population (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic population (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFast food density (+): Stronger effect in Hispanic-majority counties\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNHPI population (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSupercenter density (+); % Households Low Access \u0026amp; Low Income (\u0026ndash;): weaker effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNHB population (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGrocery access (\u0026ndash;): More protective; Convenience store density (+); Supercenter density (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNHW population (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConvenience store density (\u0026ndash;): Lower obesity in NHW areas\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: \u0026ldquo;(+)\u0026rdquo; and \u0026ldquo;(\u0026ndash;)\u0026rdquo; indicate the direction of association with adult obesity. Only statistically significant effects (typically p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are reported.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Summary of effect modification.\u003c/h2\u003e\u003cp\u003eWhile not all cross-level interaction terms achieved statistical significance, distinct patterns were observed for certain racial and ethnic groups. Stronger associations between food environment variables and adult obesity prevalence were found in counties with larger proportions of Non-Hispanic Black, Native Hawaiian or Other Pacific Islander, Hispanic, and Non-Hispanic White residents, highlighting the need for racially and geographically tailored interventions(see Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e-S6 in Supplementary Information). Asian and American Indian or Alaska Native populations showed no significant interactions (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eamd S2 in Supplementary Information). This may be attributed to smaller sample sizes or unmeasured protective factors such as strong community cohesion or cultural dietary practices. These findings provide a nuanced understanding of how food environments and racial/ethnic composition interact to shape adult obesity outcomes, underscoring the importance of place-based, equity-oriented interventions. All detailed regression tables and interaction plots are provided in the Supplementary Information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Interaction plots\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates predicted adult obesity prevalence by fast-food restaurant density across counties with varying proportions of Hispanic residents. Counties with higher percentages of Hispanic residents (e.g., 40% and 50%) showed a steeper increase in predicted obesity prevalence as fast-food density increased. For brevity, only the most representative and statistically significant interaction plot is presented (e.g., for counties with higher proportions of Hispanic residents). Additional interaction plots for other racial and ethnic groups, along with state-level coefficient plots, are provided in the Supplementary Information.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cb\u003eMain Findings\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study builds upon prior multilevel research by focusing on how food environments interact with racial and ethnic compositions to shape adult obesity rates across U.S. counties. In contrast to earlier models that examined structural predictors and demographic characteristics separately, this article centers on interaction effects to show how certain communities may be differentially affected by obesogenic environments.\u003c/p\u003e\u003cp\u003eThe finding that fast-food density has a stronger impact on obesity rates in Hispanic-majority counties underscores the need for policy interventions that are both place- and population-sensitive. These effects may reflect broader systemic inequities, such as targeted fast-food marketing or lack of zoning protections, and hence require structural solutions [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. State-level coefficient plots further emphasize geographic variation in the effect of Hispanic population proportion on adult obesity prevalence, suggesting that regional policy contexts may either mitigate or magnify risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison with Prior Studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRecent studies have also documented that racial and ethnic disparities in obesity are amplified by inequities in the food environment and neighborhood segregation. For instance, Bell et al. (2019) found that counties with greater racial inequalities and obesogenic environments experienced stronger associations between structural disadvantage and obesity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Xu et al. (2021) and related spatial analyses reported that counties with higher fast-food density had higher obesity prevalence, particularly in areas with more Black and Hispanic residents [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. More recently, a multi-county analysis showed that food desert exposure was positively associated with obesity and diabetes in the U.S. South, with magnified effects in minority-dense counties [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another longitudinal study demonstrated that increases in fast-food outlets near residence were associated with increases in BMI over time, indicating environmental change can drive weight gain [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe findings also support the development of health policies and programs customized not only to community-level characteristics but also to the demographic groups who live there. Effective interventions such as healthy food retail incentives, culturally informed nutrition education, and improved access to recreational space should be designed with the community context in mind [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In order to better understand the lived experiences that underlie these statistical trends, future research should build on this work by utilizing time-series methodologies, integrating qualitative methods, and employing more granular local-level data (such as ZIP code or census tract).\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s multilevel design adds value by recognizing that local food-environment effects are embedded within wider state and policy contexts, providing a clearer view of how structural factors interact across geographic scales to influence adult obesity. Overall, this research underscores the need for intersectional, equity-focused strategies to address adult obesity, particularly in communities facing the dual burden of structural disadvantage and limited access to healthy food.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the ecological design, which relies on county- and state-level aggregate data, may mask within-county variability. Because the data are both ecological and cross-sectional, the findings cannot establish causality and should therefore be interpreted with caution.\u003c/p\u003e\u003cp\u003eSecond, although the County Health Rankings dataset provides model-based estimates of adult obesity, these estimates are derived from self-reported BRFSS data and hence may be subject to reporting bias like underreporting of weight or overreporting of height.\u003c/p\u003e\u003cp\u003eThird, measures of food and physical activity environments were drawn from different years, introducing potential temporal misalignment. However, previous research indicates that most structural food environment indicators (e.g., grocery stores, supercenters) change slowly over time, whereas others\u0026mdash;such as farmers\u0026rsquo; markets\u0026mdash;may fluctuate more quickly in response to policy or market dynamics [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFourth, residual confounding remains a limitation. Though key confounders like food insecurity and SNAP benefits were included, factors like local zoning laws, healthcare access, and physical activity promotion were not available and hence this may bias results.\u003c/p\u003e\u003cp\u003eFinally, raw counts were used over density measures due to consistency and comparability across geographies. While raw counts don\u0026rsquo;t capture outlet size or quality, they provide a standardized approach to analyzing environmental exposures nationwide.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture studies should make use of data at the ZIP code or census tract level to capture finer spatial variation that county-level measures may overlook. Cross-level interactions with additional economic and environmental variables could further isolate race- and ethnicity-specific effects which were not done in this study. Cultural determinants of adult obesity were beyond the scope of this analysis; however, future research could incorporate variables such as acculturation, dietary customs, and food preparation behaviors to better account for cultural diversity when addressing obesity prevention [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eComparative or quasi-experimental designs, such as those involving soda taxes or SNAP expansions, can yield more convincing evidence of impact of policy changes over time. Furthermore, employing a mixed methods approach and integrating quantitative and qualitative research could yield more insightful information about perspectives and lived experiences in racially and economically diverse groups [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A multifaceted approach can help build more responsive, equitable, and effective public health interventions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study adds new evidence to the literature on racial and ethnic disparities in obesity by showing how food environment factors interact with local demographic composition. Using a multilevel framework, we found that the relationship between environmental exposures\u0026mdash;such as fast-food density, grocery access, and supercenter availability\u0026mdash;and adult obesity differs across racial and ethnic groups. In particular, counties with larger Hispanic and Black populations depicted a stronger association with several food environment measures, suggesting a higher vulnerability shaped by both place and population dynamics. These findings emphasize the need to design obesity prevention strategies that are sensitive to both social and geographic context. Standard, one-size-fits-all approaches risk overlooking the ways structural and cultural factors intersect to influence health. Public health initiatives must therefore address not only the distribution of healthy food but also the demographic realities of the communities they are intended to serve.\u003c/p\u003e\u003cp\u003eThe results demonstrate that addressing obesity requires integrated approaches that consider data, context, and equity together. Further research is needed to understand how racialized environments contribute to disparities and to inform policy responses that meet the needs of varied communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study used publicly available, de-identified county- and state-level datasets and did not involve human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual person\u0026rsquo;s data or identifiable images are included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003eThe datasets analyzed in this study are publicly available. Adult obesity data were obtained from County Health Rankings \u0026amp; Roadmaps (https://www.countyhealthrankings.org).Food environment indicators were obtained from the USDA Food Environment Atlas (https://www.ers.usda.gov/data-products/food-environment-atlas/). Additional datasets, including demographic and socioeconomic variables, were drawn from publicly available CDC and USDA sources as cited in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.A. conceptualized the study, conducted the statistical analyses, and drafted the manuscript. S.H.L. contributed to study design and interpretation of results. M.D.S. provided critical revisions and input on the analytical approach. A.E.V.D.B. contributed to interpretation of findings and critical revision of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Adult Obesity Facts [Internet]. Atlanta (GA): CDC; 2024 [cited 2025 Sep 16]. Available from: https://www.cdc.gov/obesity/adult-obesity-facts/index.html\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Obesity Consequences [Internet]. Atlanta (GA): CDC; 2024 [cited 2025 Sep 16]. Available from: https://www.cdc.gov/obesity/data/adult.html\u003c/li\u003e\n\u003cli\u003eStierman B, Afful J, Carroll MD, Chen TC, Davy O, Fink S, et al. National Health and Nutrition Examination Survey 2017\u0026ndash;March 2020 Prepandemic Data Files\u0026mdash;Development of Files and Prevalence Estimates for Selected Health Outcomes. Natl Health Stat Report. 2021;(158):1\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. National Health and Nutrition Examination Survey [Internet]. Atlanta (GA): CDC; 2024 Jul 31 [cited 2025 Sep 16]. Available from: https://www.cdc.gov/nchs/nhanes/\u003c/li\u003e\n\u003cli\u003eEconomic Research Service, U.S. Department of Agriculture. Food Environment Atlas overview [Internet]. Washington (DC): USDA; 2024 [cited 2025 Sep 16]. Available from: https://www.ers.usda.gov/data-products/food-environment-atlas/go-to-the-atlas\u003c/li\u003e\n\u003cli\u003eUniversity of Wisconsin Population Health Institute. County Health Rankings \u0026amp; Roadmaps [Internet]. Madison (WI): University of Wisconsin; 2024 [cited 2025 Jul 31]. Available from: https://www.countyhealthrankings.org/health-data/how-to-use-your-county-health-snapshot\u003c/li\u003e\n\u003cli\u003ePowell LM, Slater S, Mirtcheva D, Bao Y, Chaloupka FJ. Food store availability and neighborhood characteristics in the United States. Prev Med. 2007;44(3):189\u0026ndash;95. doi:10.1016/j.ypmed.2006.08.008\u003c/li\u003e\n\u003cli\u003eLarson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the US. Am J Prev Med. 2009;36(1):74\u0026ndash;81. doi:10.1016/j.amepre.2008.09.025\u003c/li\u003e\n\u003cli\u003eChung C, Myers SL. Do the poor pay more for food? An analysis of grocery store availability and food price disparities. J Consum Aff. 1999;33(2):276\u0026ndash;96. doi:10.1111/j.1745-6606.1999.tb00071.x\u003c/li\u003e\n\u003cli\u003eMyers CA, Slack T, Martin CK, Broyles ST, Heymsfield SB. Change in obesity prevalence across the United States is influenced by recreational and healthcare contexts, food environments, and Hispanic populations. PLoS One. 2016;11(2):e0148394. doi:10.1371/journal.pone.0148394\u003c/li\u003e\n\u003cli\u003eBukenya J. Determinants of food insecurity in Huntsville, Alabama, metropolitan area [Internet]. Huntsville (AL): Alabama A\u0026amp;M University; 2017 [cited 2025 Sep 16]. Available from: [insert URL or repository if available]\u003c/li\u003e\n\u003cli\u003ePiontak JR, Schulman MD. Food insecurity in rural America. Contexts. 2014;13(3):75\u0026ndash;7. doi:10.1177/1536504214545766\u003c/li\u003e\n\u003cli\u003ePolyzou EA, Polyzos SA. Outdoor environment and obesity: a review of current evidence. Metab Open. 2024;24:100331. doi:10.1016/j.metop.2024.100331\u003c/li\u003e\n\u003cli\u003eLi Y, Wang S, Cao G, Li D, Ng BP. Disentangling racial/ethnic and income disparities of food retail environments: impacts on adult obesity prevalence. Appl Geogr. 2021;137:102607. doi:10.1016/j.apgeog.2021.102607\u003c/li\u003e\n\u003cli\u003eCereijo L, Gull\u0026oacute;n P, Del Cura I, Cebrecos A, Bilal U, Franco M. Exercise facilities and the prevalence of obesity and type 2 diabetes in the city of Madrid. Diabetologia. 2022;65(1):150\u0026ndash;8. doi:10.1007/s00125-021-0558\u003c/li\u003e\n\u003cli\u003eBell C. N., Thorpe R. J., \u0026amp; LaVeist T. A. (2019). Associations between Obesity, Obesogenic Environments, and Structural Racism in U.S. Counties. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, 16(5), 861. https://www.mdpi.com/1660-4601/16/5/861\u003c/li\u003e\n\u003cli\u003eA Spatial Analysis of Obesity: Interaction of Urban Food Environments, Xu et al. (2021). \u003cem\u003ePMC\u003c/em\u003e article. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280681/\u003c/li\u003e\n\u003cli\u003eFood deserts exposure, density of fast-food restaurants, and park access: county-level associations with obesity and diabetes in the U.S. \u003cem\u003ePLOS ONE\u003c/em\u003e (2023). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301121\u003c/li\u003e\n\u003cli\u003eEffects of changes in residential fast-food outlet exposure on body mass index over time. \u003cem\u003ePMC\u003c/em\u003e article. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10941418/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"adult obesity, racial and ethnic disparities, food environment, physical activity environment, multilevel analysis, cross-level interactions, united states, health equity, public health policy","lastPublishedDoi":"10.21203/rs.3.rs-8041950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8041950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdult obesity remains a critical public health issue in the United States, with marked disparities across racial and ethnic groups. Minority populations are often disproportionately exposed to unhealthy food and physical activity environments, yet little is known about how these exposures modify associations with obesity risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe applied multilevel modeling to data from 3,194 U.S. counties across 50 states and the District of Columbia (2014\u0026ndash;2024). County- and state-level measures of food and physical activity environments were examined in relation to adult obesity prevalence, with cross-level interactions tested between racial/ethnic population composition and key environmental variables (e.g., fast-food density, grocery access, supercenter prevalence, low-income low-access rates). Analyses were stratified by American Indian or Alaska Native (AIAN), Asian, Hispanic, Native Hawaiian or Other Pacific Islander (NHPI), Non-Hispanic Black (NHB), and Non-Hispanic White (NHW) populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSignificant cross-level interactions were observed for counties with higher AIAN, NHB, and Hispanic populations. Fast-food restaurant density was more strongly associated with adult obesity in counties with larger AIAN and NHB populations. Convenience store counts were positively associated with obesity prevalence in counties with higher NHW populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAssociations between food environments and adult obesity vary by racial and ethnic composition. Geographic and demographic context should be incorporated into public health strategies in order to promote equity and lessen disparities in the prevention of obesity.\u003c/p\u003e","manuscriptTitle":"Racial and Ethnic Disparities in Adult Obesity Across Food and Physical Activity Environments in the United States: A Multilevel Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 13:02:21","doi":"10.21203/rs.3.rs-8041950/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdec4bae-78a3-43f6-92b9-83f7f66fd49c","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T07:58:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 13:02:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8041950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8041950","identity":"rs-8041950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}