Geospatial Analysis of County-level Social, Behavioral, and Demographic Factors Associated with Youth Obesity in Tennessee, United States, 2024

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Mooney, Qian Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9240369/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Youth obesity remains a major public health concern in the United States, with high burdens in the Southeastern region. Tennessee consistently reports elevated youth obesity rates, yet the geographic distribution of youth obesity and its relationship with county-level factors are not well understood. This study examined spatial patterns of youth obesity across Tennessee counties and associations with social, behavioral, and demographic determinants. Methods: An ecological analysis was conducted using county-level data from Tennessee’s 95 counties. Youth obesity prevalence served as the outcome variable. Spatial patterns were evaluated using Global Moran’s I, Anselin Local Moran’s I, and Getis-Ord Gi* hotspot analysis. Ordinary least squares (OLS) regression was used as the primary multivariable model, with diagnostic tests assessing multicollinearity, heteroskedasticity, residual normality, and spatial dependence. All analyses were conducted using ArcGIS Pro and Python. Results: Youth obesity prevalence ranged from 23.9% to 55.5% across counties, revealing substantial geographic variation. Global Moran’s I indicated no significant statewide spatial autocorrelation; however, local analyses identified distinct clusters and hotspots in parts of West and East Tennessee. Bivariate correlations revealed strong interrelationships among socioeconomic indicators. In the multivariable OLS model, physical inactivity emerged as the only significant predictor of youth obesity (β = 1.02, p < 0.0004). Diagnostic tests showed no evidence of heteroskedasticity or spatial non-stationarity. Conclusion: Youth obesity in Tennessee displays localized geographic clustering and is strongly associated with county-level physical inactivity. These findings highlight the importance of spatially informed, place-based interventions that prioritize increasing physical activity, particularly in high-burden counties. youth obesity GIS spatial epidemiology physical inactivity social determinants of health Tennessee Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Childhood and Adolescent obesity remains a persistent and escalating public health concern in the United States (U.S.), with substantial implications for long-term physical, mental, and social well-being. 1 – 3 Early-life obesity trajectories often persist into adulthood, increasing the risk of chronic diseases such as type 2 diabetes, hypertension, and cardiovascular disease. 3 Beyond medical morbidity, youth obesity is associated with diminished academic performance, social stigma, and reduced quality of life. 4 In the U.S., nearly one in five children and adolescents aged 2–19 years meet criteria for obesity, with even higher rates reported in many Southern states, including Tennessee. 2 , 5 In 2023, about 20% of adolescents aged 10 to 17 are affected by obesity and based on the most recent "State of the Child" report, the state has the fourth highest rate of childhood obesity in the U.S. 6–9 However, there is significant variation across counties in Tennessee, for example, the rate was as low as 23.9% in Moore County and as high as 55.5% in Pickett County. 7 – 9 Despite decades of public health attention, marked geographic disparities continue to characterize childhood and adolescent obesity in the U.S. as well as Tennessee, suggesting that upstream social, behavioral, and environmental determinants play a defining role. Youth obesity is strongly shaped by the interplay of individual behaviors and broader structural conditions. 10 – 12 Physical inactivity, limited access to healthy foods, low socioeconomic status, and restricted transportation options are well-established determinants that vary considerably across communities. 10 , 13 Area-level characteristics, such as poverty concentration, housing burden, limited recreational infrastructure, neighborhood safety, and reduced walkability, can exacerbate obesity risk by constraining opportunities for healthy behaviors. 10 These determinants often cluster spatially, producing regional risk patterns that may not be visible through traditional non-spatial analyses. 14 Understanding the geographic distribution of youth obesity and how it aligns with county-level social and behavioral conditions is therefore essential for informing local interventions and designing tailored public health strategies. Tennessee presents a compelling context in which to examine these relationships. The state has consistently ranked among those with the highest levels of childhood and adolescent obesity nationwide. 5 , 7 – 9 Its 95 counties are geographically and socioeconomically diverse, encompassing metropolitan hubs like Nashville and Memphis, mid-sized urban centers, Appalachian counties in East Tennessee, and widespread rural regions characterized by economic distress, limited health care access, and infrastructural barriers. 15 , 16 Rurality is also associated with higher obesity prevalence due to reduced access to recreational facilities, lower food environment quality, and increased transportation dependence. 17 Tennessee’s demographic heterogeneity further reinforces the need for granular analysis. More recently, attention has also turned to broader community-level determinants such as social cohesion, linguistic isolation, and housing cost burden, each of which shapes families’ capacity to engage in health-promoting behaviors. 18 – 21 Yet, few studies have explicitly applied spatial methods to youth obesity in Tennessee, leaving a critical gap in understanding how local conditions shape county-level disparities. Understanding whether certain counties experience disproportionately high youth obesity due to concentrated social disadvantages or behavioral risk factors remains critical for targeted prevention. This study addresses that gap by examining the county-level geographic distribution of youth obesity in Tennessee and assessing how it aligns with key social, behavioral, and environmental determinants. We mapped the geospatial distribution of youth obesity in Tennessee and examined its bivariate associations with physical inactivity and rurality. We used the Getis-Ord Gi* statistics to identify statistically significant hotspot and coldspot regions. Using Global and Local Moran’s I, we evaluate whether youth obesity exhibits significant spatial clustering across the state. We then explore how county-level predictors relate to youth obesity using ordinary least squares (OLS) regression. Findings from this work aim to deepen understanding of the geographic patterning of youth obesity in Tennessee and inform the development of place-based interventions and policies tailored to local needs. Methods Data Sources Tennessee operates a decentralized public health system in which county health departments provide services and coordinate local programming. 22 Because counties function as key administrative and decision-making units for public health planning and resource allocation, county-level data offer a policy-relevant scale for examining geographic differences. 22 This study, therefore, utilized county-level indicators from multiple state and federal sources to characterize youth obesity patterns across Tennessee’s 95 counties. 16 , 23 The primary outcome youth obesity prevalence (%) was obtained from the Tennessee Department of Education’s Coordinated School Health surveillance system for the 2021-2022. 8 Social, economic, behavioral, and environmental predictors were drawn from domains highlighted in the County Data Package, the Tennessee Office of Vital Statistics, HUD's Comprehensive Housing Affordability Strategy (CHAS) Data, Bureau of Labor Statistics (Local Area Unemployment Statistics), and the U.S. Census Bureau’s American Community Survey (ACS). 16 , 24 – 26 Behavioral risk indicators, including physical inactivity and adult smoking, were obtained from County Health Rankings (2023). 16 , 27 Rural-urban classification was assigned using the 2023 USDA Rural-Urban Continuum Codes, dichotomized into metropolitan (codes 1–3) and non-metropolitan (codes 4–9) counties. 28 ( Table 1 ) Measures Youth obesity was defined as the percentage of obese (BMI ≥ 95th percentile) based on county-level assessments reported through Tennessee’s Coordinated School Health surveillance system. 8 , 9 Social and behavioral determinants were measured at the county level, including physical inactivity (%), uninsured rate (%), unemployment rate (%), and severe housing cost burden (%), all of which represent established indicators of health-related social and economic conditions. Environmental and infrastructure-related measures included the percentage of households without car access (%), and non-English speaking households (%), each of which may affect opportunities for physical activity, access to services, and overall health behaviors. Demographic characteristics such as median age, White population, and population size were included to capture differences in population structure that may influence county-level health patterns. Rural–urban status was defined using the 2023 USDA Rural-Urban Continuum Codes (RUCC). 28 Statistical Analysis and Models Choropleth maps were generated to visualize the geographic distribution of youth obesity prevalence, and bivariate maps were developed to examine the spatial relationship between youth obesity and physical inactivity as well as rurality. The Getis-Ord Gi* statistic was further employed to identify statistically significant hotspots and coldspots of youth obesity across the state. In addition, the percentage of youth obesity was analyzed using Global Moran’s I and Optimized Local Moran’s I to assess spatial autocorrelation across Tennessee counties. Global Moran’s I quantified the overall spatial pattern of youth obesity statewide, while Local Moran’s I identified statistically significant county-level clusters and spatial outliers. An inverse-distance spatial weights structure with a Euclidean distance metric was applied to capture proximity-based relationships among counties. To evaluate the association between youth obesity and county-level social and behavioral characteristics, youth obesity (%) served as the dependent variable. Explanatory variables included physical inactivity, uninsured rate, households without car access, severe housing cost burden, rurality/urbanicity, non-English speaking households, median age, unemployment rate, White population, and population size. All predictors were expressed as percentages (or continuous values, where applicable) and retained in their original scales to preserve interpretability in terms of percentage-point changes. Z-score standardization was not applied for this reason. Bivariate relationships between youth obesity and each predictor were assessed using Spearman’s rank correlation coefficients (two-tailed, α = 0.05). An ordinary least squares (OLS) model was estimated as the primary global regression model. Diagnostic tests included assessment of multicollinearity via variance inflation factors, evaluation of heteroskedasticity using the Koenker-Bassett test, and examination of residual normality using the Jarque-Bera statistic. To determine whether spatial regression models were warranted, Global Moran’s I was applied to OLS residuals. Because residuals exhibited no significant spatial autocorrelation, neither spatial lag nor spatial error models were required. Due to the absence of spatial dependence in OLS residuals, geographically weighted regression (GWR) was not conducted to explore spatial non-stationarity in predictor effects. All spatial analyses were performed using ArcGIS Pro 3.4.0 (Esri). Correlation analyses were completed in Python 3.12.12 on the Google Colab platform. This study used exclusively publicly available, aggregated county-level data, and therefore did not involve human subjects or identifiable information. Accordingly, the project was exempt from Institutional Review Board oversight. Table 1 Description, sources, and rationale for variables used in the analysis of youth obesity across Tennessee counties, 2024. Variable Timeframe Variable Description Source Rationale Youth obesity (%) 2024 Percent of school-aged children classified as overweight or obese based on BMI percentiles Tennessee Department of Education, Coordinated School Health; Tennessee Department of Health County Data Package 16 Childhood obesity is a key population health indicator linked to long-term chronic disease risk. 2 Physical inactivity (%) 2023 Percent of adults reporting no leisure-time physical activity County Health Rankings 27 Physical inactivity is consistently associated with elevated obesity risk and obesogenic environments. 29 – 31 Uninsured rate (%) 2022 Percent of population without health insurance coverage U.S. Census Bureau, ACS 5-Year Estimates 24 Insurance status is associated with obesity by influencing access to preventive care, physical activity programs, and essential health services. 32 Households without car access (%) 2022 Percent of occupied households with no vehicle available U.S. Census Bureau, ACS 5-Year Estimates 24 Lack of transportation limits access to healthy foods, recreation, and health-promoting resources. 17 , 33 Severe housing burden (%) 2022 Percent of households spending ≥ 50% of income on housing costs HUD's Comprehensive Housing Affordability Strategy (CHAS) Data 25 Housing burden reflects economic strain linked to reduced capacity for healthy behaviors leading to obesity. 21 Rurality/urbanicity 2023 Classification using USDA Rural–Urban Continuum Codes (RUCC): 1–3 urban; 4–9 rural USDA Economic Research Service 28 Rural areas face higher obesity risk due to limited infrastructure, food deserts, and fewer activity resources. 17 , 31 , 34 Median age (years) 2022 Median age of county population U.S. Census Bureau, ACS 5-Year Estimates 24 Demographic age structure reflects population distribution and may influence community health behaviors, leading to obesogenic risk. 35 Non-English speaking households (%) 2022 Percent of households in which English is not the primary language U.S. Census Bureau, ACS 5-Year Estimates 24 Linguistic isolation can impede access to health programs and services. 20 White population (%) 2022 Percent of county population identifying as White, non-Hispanic U.S. Census Bureau, ACS 5-Year Estimates 24 Demographic composition is linked to cultural, economic, and structural conditions affecting obesity. 20 Population size 2022 Total county population U.S. Census Bureau, ACS 5-Year Estimates 24 As the global population size continue to increase, so also the obesity rate. 36 Unemployment rate (%) 2022 Percent of the civilian labor force unemployed Bureau of Labor Statistics, (Local Area Unemployment Statistics) 26 Unemployment is associated with economic hardship and increased behavioral risk factors which can exacerbate weight gain. 37 Results Spatial Distribution of Youth Obesity Youth obesity varied considerably across Tennessee’s 95 counties, revealing distinct geographic patterns across the state (Fig. 1 ). County-level rates ranged from 23.9% to 55.5%, with the lowest levels concentrated primarily in portions of Middle Tennessee, including Williamson, Wilson, Sumner, and Macon counties. In contrast, the highest youth obesity levels were observed in parts of West Tennessee (e.g., Haywood, Henderson, and Lake) and East Tennessee (e.g., Hancock, Hamblen, Grainger, Cocke, and Carter). A gradient pattern was evident, with many counties in central Tennessee clustering in the moderate (42.1–45.4%) range, while peripheral regions (both in the west and east) displayed more extreme values. Bivariate Distribution Counties in West Tennessee, including Lake, Obion, Haywood, Lauderdale, Crockett, Hardeman, and McNairy, demonstrated consistently high youth obesity accompanied by high physical inactivity, forming a broad belt of elevated behavioral and health risk. Similar high-high combinations were observed in portions of East Tennessee, particularly Cocke, Hancock, Campbell, and Morgan counties. In contrast, several counties in Middle Tennessee, such as White, Rutherford, Putnam, Grundy, Cannon, and Lincoln, exhibited low youth obesity and low physical inactivity. (Fig. 2 (a) ) High levels of youth obesity co-occurred with rural classification in several regions, particularly throughout West Tennessee (Lake, Obion, Lauderdale, Haywood, Hardeman, Weakley, and Carroll) and parts of Upper East Tennessee, including Cocke, Hancock, Clairborne, and Greene counties. In contrast, many of the urban and suburban counties in Middle Tennessee, such as Williamson, Davidson, Rutherford, Sumner, and Wilson, displayed low youth obesity. (Fig. 2 (b) ) Spatial Autocorrelation: Global Moran’s I and Local Moran’s I (LISA) The Global Moran’s I statistic was 0.12, with a z-score of 1.45 and a p-value of 0.15, indicating that youth obesity did not exhibit statistically significant global spatial autocorrelation across Tennessee counties. This suggests that, at the statewide level, the spatial distribution of youth obesity does not deviate significantly from a random pattern. Despite the absence of significant global clustering, Anselin’s Local Moran’s I (LISA) analysis identified several localized clusters and spatial outliers (Fig. 3 ), highlighting meaningful subregional patterns. High-High clusters, representing counties with high youth obesity surrounded by similarly high-rate neighbors, were observed in Western and Eastern Tennessee, including Carroll, Obion, Jefferson, and Hamblen counties. In contrast, Low-Low clusters, indicating counties with low youth obesity surrounded by low-rate neighbors, were detected in Montgomery, Davidson, Bradley, Lincoln, and Franklin counties. Several spatial outliers were also identified. High-Low outliers, where counties exhibited high youth obesity despite neighboring counties having lower rates, included Robertson, Dickson, Bedford, and Coffee, suggesting localized risk factors not shared by adjacent counties. Low-High outliers, where counties had relatively low youth obesity but were surrounded by higher-rate neighbors, were observed in Dyer and Overton counties. Hotspot Analysis (Getis-Ord Gi*) Counties identified as hotspots of youth obesity were observed primarily in West Tennessee and parts of East Tennessee. A 95% confidence hotspot was detected in Hamblen County, while additional hotspots at the 90% confidence level were identified in Obion and Carroll counties. These counties represent areas where elevated youth obesity is not only high in magnitude but also spatially reinforced by neighboring counties. In contrast, coldspots, representing clusters of significantly lower youth obesity rates, were identified in Middle Tennessee. Counties such as Franklin and Lincoln emerged as coldspots at a 99% confidence level. While Montgomery, Robertson, Davidson, Coffee, Moore, and Bradley were at a 95% confidence level. Overall, the Getis–Ord Gi* results complement the Local Moran’s I findings by highlighting areas of intensified concentration of high and low youth obesity. ( Fig. 4 ) Bivariate Spearman Correlations Several indicators of socioeconomic disadvantage, poverty rate, educational attainment, food insecurity, and adult smoking were found to be highly correlated with one another (ρ ≥ 0.70) and were therefore excluded from multivariable analyses. The final set of variables retained for further modeling included youth obesity, uninsured rate, physical inactivity, households without car access, severe housing cost burden, rurality/urbanicity, median age, non-English speaking households, White population, population size, and unemployment rate. (Table 2 ) Among the retained variables, physical inactivity demonstrated strong and consistent associations with youth obesity (ρ = 0.51, p < 0.01) and multiple structural factors, including unemployment rate (ρ = 0.65, p < 0.01), rurality (ρ = 0.53, p < 0.01), uninsured rate (ρ = 0.46, p < 0.01), and households without car access (ρ = 0.36, p < 0.01), and was inversely correlated with population size (ρ = -0.61, p < 0.01). Similar patterns were observed for the unemployment rate, which was positively associated with uninsured status, physical inactivity, and rurality, and negatively associated with population size. Median age was positively correlated with White population (ρ = 0.63, p < 0.01) and negatively correlated with severe housing cost burden, non-English speaking households, and population size (all p < 0.01). Overall, the correlation structure highlights the clustering of behavioral risk and socioeconomic disadvantage within less populous, more rural counties, informing subsequent multivariable and spatial analyses. Table 2 Bivariate Spearman Correlations Among County-Level Social, Behavioral, and Demographic Variables in Tennessee Counties, 2024 Youth Obesity Youth Obesity Uninsured Rate Physical Inactivity Households Without Car Access Severe Housing Cost Burden Rurality/ Urbanicity Median Age Non-English Speaking Households White Population Population Unemployment Rate - 0.25* 0.51** 0.38** -0.03 0.25* 0.21* -0.19 0.08 -0.28** 0.43** Uninsured Rate 0.25* - 0.46** 0.26** -0.03 0.3** 0.03 -0.05 0.07 -0.27** 0.41** Physical Inactivity 0.51** 0.46** - 0.36** -0.11 0.53** 0.25* -0.33** 0.27** -0.61** 0.65** Households Without Car Access 0.38** 0.26** 0.36** - 0.12 0.27** 0.17 -0.02 -0.04 -0.18 0.49** Severe Housing Cost Burden -0.03 -0.03 -0.11 0.12 - -0.06 -0.43** 0.3** -0.39** 0.31** -0.18 Rurality/Urbanicity 0.25* 0.3** 0.53** 0.27** -0.06 - 0.23* -0.26** 0.16 -0.4** 0.43** Median Age 0.21* 0.03 0.25* 0.17 -0.43** 0.23* - -0.43** 0.63** -0.44** 0.43** Non-English Speaking Households -0.19 -0.05 -0.33** -0.02 0.3** -0.26** -0.43** - -0.51** 0.51** -0.34** White Population 0.08 0.07 0.27** -0.04 -0.39** 0.16 0.63** -0.51** - -0.44** 0.29** Population -0.28** -0.27** -0.61** -0.18 0.31** -0.4** -0.44** 0.51** -0.44** - -0.44** Unemployment Rate -0.43** 0.41** 0.65** 0.49** -0.18 0.43** 0.43** -0.34** 0.29** -0.44** - Statistical test: Spearman’s rho (2-tailed): * p ≤ 0.05; ** p ≤ 0.01 (2-tailed). Ordinary Least Squares Regression The overall model was statistically significant (Joint F-statistic = 3.74, p < 0.0004), explaining approximately 31% of the variance in youth obesity prevalence (R² = 0.31; adjusted R² = 0.23). Variance inflation factors for all predictors were below commonly accepted thresholds (VIF < 3.5), indicating no evidence of problematic multicollinearity. Among all predictors, physical inactivity emerged as the only variable associated with youth obesity. Higher levels of physical inactivity were associated with significantly higher youth obesity prevalence (β = 1.02, robust p < 0.0003), indicating that a one–percentage point increase in physical inactivity corresponded to approximately one-percentage point increase in youth obesity, holding other factors constant. Median age showed a positive association with youth obesity that approached statistical significance (β = 0.29, p = 0.09), while other variables were not significantly associated with youth obesity in the fully adjusted model. ( Table 3 ) Model diagnostics indicated no evidence of heteroskedasticity (Koenker-Bassett statistic p = 0.34), supporting the stability of coefficient estimates. However, the Jarque-Bera test was statistically significant ( p < 0.0001), suggesting departures from residual normality. Subsequent analyses, therefore, relied on robust standard errors for inference. Taken together, the OLS results highlight physical inactivity as the dominant county-level determinant of youth obesity in Tennessee; however, diagnostic tests showed no evidence of spatial non-stationarity, indicating that the global OLS model adequately captured the observed relationships without the need for geographically weighted regression. ( Table 4 ) Table 3 Ordinary Least Squares (OLS) Regression of County-Level Youth Obesity in Tennessee (N = 95) Variable Coefficient (β) Robust SE Robust t Robust p-value VIF Intercept 2.81 10.26 0.27 0.785 - Rurality/Urbanicity -0.85 1.12 -0.77 0.446 1.48 Uninsured rate (%) -0.05 0.26 -0.21 0.837 1.74 Physical inactivity (%) 1.02 0.27 3.81 0.0003** 3.34 Unemployment rate (%) 0.62 1.53 0.41 0.686 2.69 Non-English speaking households (%) 0.22 0.21 1.05 0.298 2.05 Severe housing cost burden (%) 0.06 0.25 0.25 0.805 1.74 Households without car access (%) 0.007 0.05 0.13 0.894 1.12 Population 0.000003 0.000004 0.70 0.486 2.90 Median age (years) 0.29 0.17 1.71 0.091 1.93 White population (%) -0.03 0.04 -0.75 0.456 2.16 Note: * p ≤ 0.05; ** p ≤ 0.01 (2-tailed). Table 4 Table of Model Diagnostics Ordinary Least Squares (OLS) Regression Statistic Value p-value Dependent variable Youth obesity (%) - Number of counties 95 - R² 0.308 - Adjusted R² 0.226 - Akaike Information Criterion (AICc) 565.42 - Joint F-statistic 3.74 < 0.0004 Joint Wald Statistic 63.04 < 0.0001 Koenker–Bassett test 11.21 0.34 Jarque–Bera test 24.07 < 0.0001 Discussion This study examined the geographic distribution of youth obesity across Tennessee counties and assessed its association with county-level social, behavioral, and demographic factors using spatial and regression-based methods. Youth obesity varied markedly across the state, with pronounced regional differences and localized concentrations of elevated burden. Although global spatial autocorrelation was not statistically significant, both Local Moran’s I and Getis-Ord Gi* analyses revealed meaningful subregional clustering, indicating that youth obesity in Tennessee is shaped by localized contextual influences rather than a uniform statewide spatial process. Among the social and behavioral factors examined, physical inactivity emerged as the dominant county-level determinant of youth obesity, while diagnostic tests indicated that a global OLS model adequately captured the observed relationships. The spatial analyses provide important insight into how youth obesity is distributed across Tennessee. High-prevalence counties were concentrated primarily in West Tennessee and parts of East Tennessee, while lower prevalence was observed across much of Middle Tennessee. The presence of High-High clusters and statistically significant hotspots in specific counties underscores the importance of examining local patterns, even in the absence of significant global spatial autocorrelation. 38 , 39 This finding is consistent with prior spatial epidemiologic research demonstrating that health outcomes driven by social and behavioral factors often exhibit localized clustering that may be obscured at broader geographic scales. 38 – 42 These localized clusters likely reflect region-specific combinations of socioeconomic conditions, built environment characteristics, and behavioral norms that vary across Tennessee’s diverse geographic landscape. 39 , 43 Regression analyses further clarified the drivers underlying these spatial patterns. Physical inactivity was the only predictor independently associated with youth obesity in the multivariable OLS model, with a one-percentage point increase in inactivity corresponding to an approximate one-point increase in youth obesity. This finding aligns with existing literature identifying physical inactivity as a central contributor to childhood and adolescent obesity through reduced energy expenditure, limited recreational opportunities, and constrained opportunities for active transport. 29 – 31 , 34 , 44 Other indicators, such as uninsured rate, unemployment rate, housing cost burden, transportation access, rurality, and demographic composition, were not statistically significant after adjustment. This may be due to their high intercorrelation in bivariate analyses, suggesting that their influence on youth obesity may be indirect or mediated through behavioral pathways, particularly physical inactivity. 45 Importantly, diagnostic tests provided no evidence of heteroskedasticity or spatial non-stationarity, and residuals exhibited no spatial autocorrelation (Koenker-Bassett test, p-value = 0.34). 46 These findings indicate that the relationship between physical inactivity and youth obesity is relatively stable across Tennessee counties, supporting the use of a global OLS model rather than spatial lag, spatial error, or geographically weighted regression. Although exploratory geographically weighted regression analyses were considered, diagnostic tests provided no evidence of spatial non-stationarity, indicating that the global OLS model adequately captured the observed relationships. This study advances understanding of youth obesity in Tennessee by demonstrating that local geographic context matters, even when global spatial dependence is absent. The findings emphasize physical inactivity as a central driver of county-level youth obesity and underscore the need for targeted, context-sensitive public health strategies. Future research incorporating longitudinal data and multilevel designs could further elucidate how individual behaviors interact with local environments to shape youth obesity risk across Tennessee and similar settings in the southeastern United States. Strengths and Limitations This study has several strengths, including the use of statewide county-level data, integration of multiple spatial analytic techniques, and a diagnostics-driven modeling approach. However, limitations should be noted. Ecological design precludes inference at the individual level, and associations observed at the county level may not reflect individual risk relationships. The analysis was cross-sectional, limiting causal interpretation, and some potentially relevant determinants, such as food environment quality, school nutrition policies, and neighborhood safety, were not available at the county level. Additionally, counties vary in geographic size and internal heterogeneity, which may mask within-county disparities. Implications for Policy and Practice The identification of localized clusters and hotspots of youth obesity highlights the need for targeted, place-based public health strategies rather than uniform statewide approaches. Counties in West and East Tennessee with persistently elevated youth obesity may benefit from focused investments in physical activity-promoting infrastructure, including parks, recreational facilities, safe walking and biking routes, and school- and community-based physical activity programs. Because physical inactivity emerged as the dominant determinant of youth obesity in this study, policies that reduce structural barriers to physical activity, such as transportation limitations, limited access to safe recreational spaces, and rural infrastructure gaps, are likely to yield the greatest impact. 29 , 47 Local health departments can use these findings to prioritize high-burden counties for resource allocation, program expansion, and cross-sector collaboration. At the state level, these results support integrating geographic data into obesity prevention planning and evaluation. Incorporating spatial analyses into routine surveillance can help identify emerging hotspots, monitor progress, and tailor interventions to local contexts. Policies that incentivize active school environments, expand access to community recreation in rural areas, and support land-use planning that encourages physical activity may be particularly effective. Importantly, the stability of the inactivity-obesity relationship across counties suggests that statewide policies addressing physical inactivity can be broadly applied, while allowing flexibility for local adaptation based on community needs. Together, these approaches can enhance the effectiveness of obesity prevention efforts and contribute to reducing persistent geographic disparities in youth health across Tennessee. Conclusion This study demonstrates that youth obesity in Tennessee exhibits substantial geographic variation, characterized by localized clusters and hotspots rather than uniform statewide patterns. Physical inactivity emerged as the primary county-level determinant of youth obesity, while other social and demographic factors appeared to operate indirectly through behavioral pathways. Spatial analyses revealed meaningful local concentrations of elevated and reduced youth obesity burden. Together, these findings underscore the importance of integrating spatial perspectives into obesity surveillance and highlight the need for targeted, place-based interventions that prioritize increasing physical activity, particularly in high-burden counties. Declarations Ethics approval and accordance Not applicable. This study utilized publicly available, aggregated data and did not involve human participants or identifiable information. Consent to participate Not applicable. Consent to publish Not applicable. Clinical trial number Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution MAM & BA conceptualized the study and developed the methodology. MAM performed the data curation, software implementation, and formal analysis. QH assisted with code validation and supported model evaluation. All authors contributed to the manuscript writing, reviewed the final draft, and approved the submitted version. Data Availability All data used in this study are publicly available, through the following sources: Tennessee Department of Health County Data Packages ( [https://www.tn.gov/health/health-program-areas/county-health-councils/cha-chip-resources/county-profiles.html](https:/www.tn.gov/health/health-program-areas/county-health-councils/cha-chip-resources/county-profiles.html) ), Tennessee Department of Education and Tennessee Department of Health Coordinated School Health BMI Report (2021–2022) ( [https://www.tn.gov/content/dam/tn/education/csh/CSH\_BMI\_Report\_2021-22.pdf](https:/www.tn.gov/content/dam/tn/education/csh/CSH_BMI_Report_2021-22.pdf) ), the U.S. Census Bureau American Community Survey ( [https://data.census.gov/](https:/data.census.gov) ), the U.S. Department of Housing and Urban Development CHAS dataset ( [https://www.huduser.gov/portal/datasets/cp.html](https:/www.huduser.gov/portal/datasets/cp.html) ), the U.S. Bureau of Labor Statistics Local Area Unemployment Statistics ( [https://www.bls.gov/lau/data.htm](https:/www.bls.gov/lau/data.htm) ), the County Health Rankings & Roadmaps ( [https://www.countyhealthrankings.org/](https:/www.countyhealthrankings.org) ), and the 2023 USDA Rural–Urban Continuum Codes ( [https://www.ers.usda.gov/data-products/rural-urban-continuum-codes](https:/www.ers.usda.gov/data-products/rural-urban-continuum-codes) ). All datasets were accessed between November and December 2025. References Daniels SR, Jacobson MS, McCrindle BW, Eckel RH, Sanner BM. American Heart Association Childhood Obesity Research Summit. Circulation. 2009;119(15):2114–23. 10.1161/CIRCULATIONAHA.109.192215 . Centers for Disease Control and Prevention, CDC. Childhood Obesity Facts. Obesity. December 20. 2024. Accessed November 26, 2025. https://www.cdc.gov/obesity/childhood-obesity-facts/childhood-obesity-facts.html The American Academy of Pediatrics. Addressing Childhood Obesity is Complex and Requires a Holistic Approach. Accessed November 26. 2025. https://www.aap.org/en/news-room/fact-checked/fact-checked-addressing-childhood-obesity-is-complex-and-requires-a-holistic-approach/ Rankin J, Matthews L, Cobley S, et al. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Ther. 2016;7:125–46. 10.2147/AHMT.S101631 . Tennessee Department of Human Services. Tennessee Tipping the Scales Against Childhood Obesity. Accessed November 26. 2025. https://www.tn.gov/humanservices/news/2019/10/4/tennessee-tipping-the-scales-against-childhood-obesity.html Tennessee State Government, Tennessee Commission on Children & Youth. Kids Count State of the Child 2022. 2022. https://www.tn.gov/content/dam/tn/tccy/documents/kids-count/tccy-kcsoc/State_of_the_Child_2022.pdf Kidcentral Tennessee Home. Battling Obesity in Tennessee Children. Accessed November 26. 2025. https://www.kidcentraltn.com/health/nutrition/battling-obesity-in-tennessee-children-.html Tennessee Department of Health (TDH), Tennessee Department of Education. Tennessee Public Schools: A Summary of Student Body Mass Index Data 2021-22. 2023. https://www.tn.gov/content/dam/tn/education/csh/CSH_BMI_Report_2021-22.pdf Tennessee Department of Health (TDH), Tennessee Department of Education. Tennessee Public Schools: A Summary of Student Body Mass Index Data 2023-24. 2025. https://www.tn.gov/content/dam/tn/education/csh/CSH_BMI_Report_2021-22.pdf Oudat Q, Messiah SE, Ghoneum AD. A Multi-Level Approach to Childhood Obesity Prevention and Management: Lessons from Japan and the United States. Nutrients. 2025;17(5):838. 10.3390/nu17050838 . De Lorenzo A, Romano L, Di Renzo L, Di Lorenzo N, Cenname G, Gualtieri P. Obesity: A preventable, treatable, but relapsing disease. Nutrition. 2020;71:110615. 10.1016/j.nut.2019.110615 . Lee A, Cardel M, Donahoo WT et al. Social and Environmental Factors Influencing Obesity. In: Feingold KR, Ahmed SF, Anawalt B, eds. Endotext . MDText.com, Inc.; 2000. Accessed November 26, 2025. http://www.ncbi.nlm.nih.gov/books/NBK278977/ Moschonis G, Trakman GL. Overweight and Obesity: The Interplay of Eating Habits and Physical Activity. Nutrients. 2023;15(13):2896. 10.3390/nu15132896 . Dahu BM, Khan S, Toubal IE, et al. Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study. JMIR AI. 2024;3:e64362. 10.2196/64362 . The Tennessee Comptroller of the Treasury. Tennessee County Profiles. July 2024. Accessed November 26. 2025. https://comptroller.tn.gov/maps/tennessee-county-profiles.html Tennessee Department of Health (TDH). County Data Packages. 2024. Accessed November 26, 2025. https://www.tn.gov/health/health-program-areas/county-health-councils/cha-chip-resources/county-profiles.html Lenardson JD, Hansen AY, Hartley D. Rural and Remote Food Environments and Obesity. Curr Obes Rep. 2015;4(1):46–53. 10.1007/s13679-014-0136-5 . Suglia SF, Shelton RC, Hsiao A, Wang YC, Rundle A, Link BG. Why the Neighborhood Social Environment Is Critical in Obesity Prevention. J Urban Health. 2016;93(1):206–12. 10.1007/s11524-015-0017-6 . Chan JA, Koster A, Lakerveld J, Schram MT, van Greevenbroek M, Bosma H. Associations of neighborhood social cohesion and changes in BMI—The Maastricht Study. Eur J Public Health. 2024;34(5):949–54. 10.1093/eurpub/ckae109 . Taverno SE, Rollins BY, Francis LA, Generation, Language BM. Index, and Activity Patterns in Hispanic Children. Am J Prev Med. 2010;38(2):145–53. 10.1016/j.amepre.2009.09.041 . Nobari TZ, Whaley SE, Blumenberg E, Prelip ML, Wang MC. Severe housing-cost burden and obesity among preschool-aged low-income children in Los Angeles County. Prev Med Rep. 2018;13:139–45. 10.1016/j.pmedr.2018.12.003 . Tennessee Department of Health (TDH). Local and Regional Health Departments. Accessed November 26. 2025. https://www.tn.gov/health/health-program-areas/localdepartments.html Tennessee County Health Profiles. The Sycamore Institute. Accessed November 26. 2025. https://sycamoretn.org/health/county-profiles/ United States Census Bureau. Census Bureau Data. Accessed November 26. 2025. https://data.census.gov/ Office of Policy Development and Research (PD & R). Consolidated Planning/CHAS Data | HUD USER. Accessed December 16. 2025. https://www.huduser.gov/portal/datasets/cp.html U.S. Bureau of Labor Statistics. Featured LAU Searchable Databases. Bureau of Labor Statistics. Accessed December 16. 2025. https://www.bls.gov/lau/data.htm County Health Rankings & Roadmaps. Accessed November 26. 2025. https://www.countyhealthrankings.org/ Economic Research Service U.S. Department of Agriculture. Rural-Urban Continuum Codes | Economic Research Service. July 2025. Accessed November 26. 2025. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes Rosenkranz RR, Ridley K, Guagliano JM, Rosenkranz SK. Physical activity capability, opportunity, motivation and behavior in youth settings: theoretical framework to guide physical activity leader interventions. Int Rev Sport Exerc Psychol. 2023;16(1):529–53. 10.1080/1750984X.2021.1904434 . Gierbolini-Rivera RD, de Paula da Silva AA, Ramírez-Marrero FA. Opportunities for physical activity research in Puerto Rico: a review of the literature. Discov Public Health. 2025;22(1):639. 10.1186/s12982-025-01041-3 . Forseth B, Carlson JA, Ortega A, et al. O the places rural children will go… to get physical activity: a cross sectional analysis. BMC Public Health . 2025;25:1188. doi:10.1186/s12889-025-22442-8. Mylona EK, Benitez G, Shehadeh F, et al. The association of obesity with health insurance coverage and demographic characteristics: a statewide cross-sectional study. Med (Baltim). 2020;99(27):e21016. 10.1097/MD.0000000000021016 . Patterson R, Webb E, Hone T, Millett C, Laverty AA. Associations of Public Transportation Use With Cardiometabolic Health: A Systematic Review and Meta-Analysis. Am J Epidemiol. 2019;188(4):785–95. 10.1093/aje/kwz012 . Crouch E. Rural–Urban Differences in Overweight and Obesity, Physical Activity, and Food Security Among Children and Adolescents. Prev Chronic Dis. 2023;20. 10.5888/pcd20.230136 . Jura M, Kozak LP. Obesity and related consequences to ageing. Age (Dordr). 2016;38(1):23. 10.1007/s11357-016-9884-3 . Ahmed SK, Mohammed RA, Obesity. Prevalence, causes, consequences, management, preventive strategies and future research directions. Metabolism Open. 2025;27:100375. 10.1016/j.metop.2025.100375 . Dietrich H, Hebebrand J, Reissner V. The bidirectional relationship of obesity and labor market status - Findings from a German prospective panel study. Int J Obes (Lond). 2022;46(7):1295–303. 10.1038/s41366-022-01105-3 . Wong DWS. Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods. Int J Environ Res Public Health. 2021;18(18):9848. 10.3390/ijerph18189848 . Brakefield WS, Olusanya OA, Shaban-Nejad A. Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach. JMIR Public Health Surveill. 2022;8(8):e37039. 10.2196/37039 . Elliott P, Wartenberg D. Spatial Epidemiology: Current Approaches and Future Challenges. Environ Health Perspect. 2004;112(9):998–1006. 10.1289/ehp.6735 . Mazza O, Gluck C, Haim A, Bornstein RJ. Spatial patterns of childhood obesity clusters linked to socioeconomic inequalities. Front Public Health. 2025;13. 10.3389/fpubh.2025.1497090 . Ng M, Dai X, Cogen RM, et al. National-level and state-level prevalence of overweight and obesity among children, adolescents, and adults in the USA, 1990–2021, and forecasts up to 2050. Lancet. 2024;404(10469):2278–98. 10.1016/S0140-6736(24)01548-4 . Tennessee Department of Health (TDH). Built Environment and Health. Accessed December 15. 2025. https://www.tn.gov/health/health-program-areas/office-of-primary-prevention/redirect-opp/built-environment-and-health.html Verde L, Barrea L, Bowman-Busato J, Yumuk VD, Colao A, Muscogiuri G. Obesogenic environments as major determinants of a disease: It is time to re-shape our cities. Diab/Metab Res Rev. 2024;40(1):e3748. 10.1002/dmrr.3748 . Kim Y, Liao Y, Colabianchi N. Examining the Long-term Association Between Neighborhood Socioeconomic Status and Obesity and Obesity-related Unhealthy Behaviors Among Children: Results From the Fragile Families and Child Wellbeing Study. Ann Behav Med. 2023;57(8):640–8. 10.1093/abm/kaad001 . Koenker R, Bassett G. Robust Tests for Heteroscedasticity Based on Regression Quantiles. Econometrica. 1982;50(1):43–61. 10.2307/1912528 . Wei J, Wu Y, Zheng J, Nie P, Jia P, Wang Y. Neighborhood sidewalk access and childhood obesity. Obes Rev. 2021;22(Suppl 1):e13057. 10.1111/obr.13057 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialYouthObesityTennesseeGIS.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9240369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623904988,"identity":"c9c7d56f-68dd-41a2-88d1-c04bd5e254e9","order_by":0,"name":"Mustapha Aliyu Muhammad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACHjDJ2MDAfAAqkkC0FrYEhgMkauExIE6L7ozcg59u5tjJrm0/8/Hxhz+HGfjZcwzwajG7kZcsnbst2XjbmdzNBgfbDjNI9rwhpCXHAKiFOXHbgdxtEgcbDjMY3CBoS47x79xt9Ynbzr95/uMA0GH2RGgxA9pyOHHbjRw2hgNsQFskCGk588bMOnfbceNtN54ZS5xtS+eROPOsAL+W4znGt3O3VctuO5/88EPFH2s5/vbkDXi1YAAe0pSPglEwCkbBKMAKAEvXUbDSQzzqAAAAAElFTkSuQmCC","orcid":"","institution":"East Tennessee State University","correspondingAuthor":true,"prefix":"","firstName":"Mustapha","middleName":"Aliyu","lastName":"Muhammad","suffix":""},{"id":623904990,"identity":"63537a2d-eef8-44fd-98bb-90171a9908e2","order_by":1,"name":"Bless-me Ajani","email":"","orcid":"","institution":"East Tennessee State University","correspondingAuthor":false,"prefix":"","firstName":"Bless-me","middleName":"","lastName":"Ajani","suffix":""},{"id":623904992,"identity":"b054e5f6-c612-40c9-9944-7a885b055994","order_by":2,"name":"Hopelyn A. Mooney","email":"","orcid":"","institution":"East Tennessee State University","correspondingAuthor":false,"prefix":"","firstName":"Hopelyn","middleName":"A.","lastName":"Mooney","suffix":""},{"id":623904995,"identity":"fd1416ae-54f6-4998-999f-982fa2c2d5de","order_by":3,"name":"Qian Huang","email":"","orcid":"","institution":"East Tennessee State University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-27 05:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9240369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9240369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107486400,"identity":"e9951e11-61ba-4bbe-afe5-0851c4a46b2a","added_by":"auto","created_at":"2026-04-22 02:38:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197109,"visible":true,"origin":"","legend":"\u003cp\u003eCounty-Level Youth Obesity Prevalence in Tennessee, 2024\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/97c8d2438c65c41155dd5477.png"},{"id":107487883,"identity":"e52e6eab-b322-4933-bf23-91ac7715e2ea","added_by":"auto","created_at":"2026-04-22 02:43:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2709055,"visible":true,"origin":"","legend":"\u003cp\u003eBivariate Choropleth Maps of Youth Obesity with Physical Inactivity and Rurality in Tennessee, 2024\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/784b6a780d9bf64251255e02.png"},{"id":107486604,"identity":"104c2214-f29f-4fb6-a62e-f4d84320947a","added_by":"auto","created_at":"2026-04-22 02:38:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165964,"visible":true,"origin":"","legend":"\u003cp\u003eLocal Moran’s I Cluster and Outlier Analysis of Youth Obesity in Tennessee Counties, 2024\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/3873d1e93c5a0d37569862fb.png"},{"id":107488592,"identity":"d5caad4b-c513-497e-a4ad-7971e9613057","added_by":"auto","created_at":"2026-04-22 02:45:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181788,"visible":true,"origin":"","legend":"\u003cp\u003eHot Spot Analysis (Getis–Ord Gi*) of Youth Obesity in Tennessee Counties, 2024\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/f9f642f895cf6a86563bf836.png"},{"id":107705466,"identity":"f7c92ba4-5fab-46da-814f-739b0a3cd774","added_by":"auto","created_at":"2026-04-24 09:12:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3604934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/50e42acd-0d4d-4798-958c-15b1a46232cc.pdf"},{"id":107359268,"identity":"1e83f82b-6caf-4201-985e-64597e060d46","added_by":"auto","created_at":"2026-04-20 17:48:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":203488,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialYouthObesityTennesseeGIS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9240369/v1/252f7a20de78c4e04fd6351b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Analysis of County-level Social, Behavioral, and Demographic Factors Associated with Youth Obesity in Tennessee, United States, 2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildhood and Adolescent obesity remains a persistent and escalating public health concern in the United States (U.S.), with substantial implications for long-term physical, mental, and social well-being.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Early-life obesity trajectories often persist into adulthood, increasing the risk of chronic diseases such as type 2 diabetes, hypertension, and cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Beyond medical morbidity, youth obesity is associated with diminished academic performance, social stigma, and reduced quality of life.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In the U.S., nearly one in five children and adolescents aged 2\u0026ndash;19 years meet criteria for obesity, with even higher rates reported in many Southern states, including Tennessee.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In 2023, about 20% of adolescents aged 10 to 17 are affected by obesity and based on the most recent \"State of the Child\" report, the state has the fourth highest rate of childhood obesity in the U.S.\u003csup\u003e6\u0026ndash;9\u003c/sup\u003e However, there is significant variation across counties in Tennessee, for example, the rate was as low as 23.9% in Moore County and as high as 55.5% in Pickett County.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Despite decades of public health attention, marked geographic disparities continue to characterize childhood and adolescent obesity in the U.S. as well as Tennessee, suggesting that upstream social, behavioral, and environmental determinants play a defining role.\u003c/p\u003e \u003cp\u003eYouth obesity is strongly shaped by the interplay of individual behaviors and broader structural conditions.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Physical inactivity, limited access to healthy foods, low socioeconomic status, and restricted transportation options are well-established determinants that vary considerably across communities.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Area-level characteristics, such as poverty concentration, housing burden, limited recreational infrastructure, neighborhood safety, and reduced walkability, can exacerbate obesity risk by constraining opportunities for healthy behaviors.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e These determinants often cluster spatially, producing regional risk patterns that may not be visible through traditional non-spatial analyses.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Understanding the geographic distribution of youth obesity and how it aligns with county-level social and behavioral conditions is therefore essential for informing local interventions and designing tailored public health strategies.\u003c/p\u003e \u003cp\u003eTennessee presents a compelling context in which to examine these relationships. The state has consistently ranked among those with the highest levels of childhood and adolescent obesity nationwide.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Its 95 counties are geographically and socioeconomically diverse, encompassing metropolitan hubs like Nashville and Memphis, mid-sized urban centers, Appalachian counties in East Tennessee, and widespread rural regions characterized by economic distress, limited health care access, and infrastructural barriers.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Rurality is also associated with higher obesity prevalence due to reduced access to recreational facilities, lower food environment quality, and increased transportation dependence.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Tennessee\u0026rsquo;s demographic heterogeneity further reinforces the need for granular analysis.\u003c/p\u003e \u003cp\u003eMore recently, attention has also turned to broader community-level determinants such as social cohesion, linguistic isolation, and housing cost burden, each of which shapes families\u0026rsquo; capacity to engage in health-promoting behaviors.\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Yet, few studies have explicitly applied spatial methods to youth obesity in Tennessee, leaving a critical gap in understanding how local conditions shape county-level disparities. Understanding whether certain counties experience disproportionately high youth obesity due to concentrated social disadvantages or behavioral risk factors remains critical for targeted prevention.\u003c/p\u003e \u003cp\u003eThis study addresses that gap by examining the county-level geographic distribution of youth obesity in Tennessee and assessing how it aligns with key social, behavioral, and environmental determinants. We mapped the geospatial distribution of youth obesity in Tennessee and examined its bivariate associations with physical inactivity and rurality. We used the Getis-Ord Gi* statistics to identify statistically significant hotspot and coldspot regions. Using Global and Local Moran\u0026rsquo;s I, we evaluate whether youth obesity exhibits significant spatial clustering across the state. We then explore how county-level predictors relate to youth obesity using ordinary least squares (OLS) regression. Findings from this work aim to deepen understanding of the geographic patterning of youth obesity in Tennessee and inform the development of place-based interventions and policies tailored to local needs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eTennessee operates a decentralized public health system in which county health departments provide services and coordinate local programming.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Because counties function as key administrative and decision-making units for public health planning and resource allocation, county-level data offer a policy-relevant scale for examining geographic differences.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e This study, therefore, utilized county-level indicators from multiple state and federal sources to characterize youth obesity patterns across Tennessee\u0026rsquo;s 95 counties.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The primary outcome youth obesity prevalence (%) was obtained from the Tennessee Department of Education\u0026rsquo;s Coordinated School Health surveillance system for the 2021-2022.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Social, economic, behavioral, and environmental predictors were drawn from domains highlighted in the County Data Package, the Tennessee Office of Vital Statistics, HUD's Comprehensive Housing Affordability Strategy (CHAS) Data, Bureau of Labor Statistics (Local Area Unemployment\u003c/p\u003e \u003cp\u003eStatistics), and the U.S. Census Bureau\u0026rsquo;s American Community Survey (ACS).\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Behavioral risk indicators, including physical inactivity and adult smoking, were obtained from County Health Rankings (2023).\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Rural-urban classification was assigned using the 2023 USDA Rural-Urban Continuum Codes, dichotomized into metropolitan (codes 1\u0026ndash;3) and non-metropolitan (codes 4\u0026ndash;9) counties.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eYouth obesity was defined as the percentage of obese (BMI \u0026ge;\u0026thinsp;95th percentile) based on county-level assessments reported through Tennessee\u0026rsquo;s Coordinated School Health surveillance system.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Social and behavioral determinants were measured at the county level, including physical inactivity (%), uninsured rate (%), unemployment rate (%), and severe housing cost burden (%), all of which represent established indicators of health-related social and economic conditions. Environmental and infrastructure-related measures included the percentage of households without car access (%), and non-English speaking households (%), each of which may affect opportunities for physical activity, access to services, and overall health behaviors. Demographic characteristics such as median age, White population, and population size were included to capture differences in population structure that may influence county-level health patterns. Rural\u0026ndash;urban status was defined using the 2023 USDA Rural-Urban Continuum Codes (RUCC).\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis and Models\u003c/h3\u003e\n\u003cp\u003eChoropleth maps were generated to visualize the geographic distribution of youth obesity prevalence, and bivariate maps were developed to examine the spatial relationship between youth obesity and physical inactivity as well as rurality. The Getis-Ord Gi* statistic was further employed to identify statistically significant hotspots and coldspots of youth obesity across the state. In addition, the percentage of youth obesity was analyzed using Global Moran\u0026rsquo;s I and Optimized Local Moran\u0026rsquo;s I to assess spatial autocorrelation across Tennessee counties. Global Moran\u0026rsquo;s I quantified the overall spatial pattern of youth obesity statewide, while Local Moran\u0026rsquo;s I identified statistically significant county-level clusters and spatial outliers. An inverse-distance spatial weights structure with a Euclidean distance metric was applied to capture proximity-based relationships among counties.\u003c/p\u003e \u003cp\u003eTo evaluate the association between youth obesity and county-level social and behavioral characteristics, youth obesity (%) served as the dependent variable. Explanatory variables included physical inactivity, uninsured rate, households without car access, severe housing cost burden, rurality/urbanicity, non-English speaking households, median age, unemployment rate, White population, and population size. All predictors were expressed as percentages (or continuous values, where applicable) and retained in their original scales to preserve interpretability in terms of percentage-point changes. Z-score standardization was not applied for this reason. Bivariate relationships between youth obesity and each predictor were assessed using Spearman\u0026rsquo;s rank correlation coefficients (two-tailed, α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAn ordinary least squares (OLS) model was estimated as the primary global regression model. Diagnostic tests included assessment of multicollinearity via variance inflation factors, evaluation of heteroskedasticity using the Koenker-Bassett test, and examination of residual normality using the Jarque-Bera statistic. To determine whether spatial regression models were warranted, Global Moran\u0026rsquo;s I was applied to OLS residuals. Because residuals exhibited no significant spatial autocorrelation, neither spatial lag nor spatial error models were required. Due to the absence of spatial dependence in OLS residuals, geographically weighted regression (GWR) was not conducted to explore spatial non-stationarity in predictor effects.\u003c/p\u003e \u003cp\u003eAll spatial analyses were performed using ArcGIS Pro 3.4.0 (Esri). Correlation analyses were completed in Python 3.12.12 on the Google Colab platform. This study used exclusively publicly available, aggregated county-level data, and therefore did not involve human subjects or identifiable information. Accordingly, the project was exempt from Institutional Review Board oversight.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription, sources, and rationale for variables used in the analysis of youth obesity across Tennessee counties, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimeframe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYouth obesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of school-aged children classified as overweight or obese based on BMI percentiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTennessee Department of Education, Coordinated School Health; Tennessee Department of Health County Data Package\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChildhood obesity is a key population health indicator linked to long-term chronic disease risk.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical inactivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of adults reporting no leisure-time physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounty Health Rankings\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhysical inactivity is consistently associated with elevated obesity risk and obesogenic environments.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of population without health insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsurance status is associated with obesity by influencing access to preventive care, physical activity programs, and essential health services.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds without car access (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of occupied households with no vehicle available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLack of transportation limits access to healthy foods, recreation, and health-promoting resources.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere housing burden (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of households spending\u0026thinsp;\u0026ge;\u0026thinsp;50% of income on housing costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHUD's Comprehensive Housing Affordability Strategy (CHAS)\u003c/p\u003e \u003cp\u003eData\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHousing burden reflects economic strain linked to reduced capacity for healthy behaviors leading to obesity.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality/urbanicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification using USDA Rural\u0026ndash;Urban Continuum Codes (RUCC): 1\u0026ndash;3 urban; 4\u0026ndash;9 rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSDA Economic Research Service\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural areas face higher obesity risk due to limited infrastructure, food deserts, and fewer activity resources.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian age of county population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDemographic age structure reflects population distribution and may influence community health behaviors, leading to obesogenic risk.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-English speaking households (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of households in which English is not the primary language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistic isolation can impede access to health programs and services.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite population (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of county population identifying as White, non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDemographic composition is linked to cultural, economic, and structural conditions affecting obesity.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal county population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU.S. Census Bureau, ACS 5-Year Estimates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAs the global population size continue to increase, so also the obesity rate.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of the civilian labor force unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBureau of Labor Statistics, (Local Area Unemployment\u003c/p\u003e \u003cp\u003eStatistics)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnemployment is associated with economic hardship and increased behavioral risk factors which can exacerbate weight gain.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Distribution of Youth Obesity\u003c/h2\u003e \u003cp\u003eYouth obesity varied considerably across Tennessee\u0026rsquo;s 95 counties, revealing distinct geographic patterns across the state (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). County-level rates ranged from 23.9% to 55.5%, with the lowest levels concentrated primarily in portions of Middle Tennessee, including Williamson, Wilson, Sumner, and Macon counties. In contrast, the highest youth obesity levels were observed in parts of West Tennessee (e.g., Haywood, Henderson, and Lake) and East Tennessee (e.g., Hancock, Hamblen, Grainger, Cocke, and Carter). A gradient pattern was evident, with many counties in central Tennessee clustering in the moderate (42.1\u0026ndash;45.4%) range, while peripheral regions (both in the west and east) displayed more extreme values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Distribution\u003c/h2\u003e \u003cp\u003eCounties in West Tennessee, including Lake, Obion, Haywood, Lauderdale, Crockett, Hardeman, and McNairy, demonstrated consistently high youth obesity accompanied by high physical inactivity, forming a broad belt of elevated behavioral and health risk. Similar high-high combinations were observed in portions of East Tennessee, particularly Cocke, Hancock, Campbell, and Morgan counties. In contrast, several counties in Middle Tennessee, such as White, Rutherford, Putnam, Grundy, Cannon, and Lincoln, exhibited low youth obesity and low physical inactivity. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(a)\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eHigh levels of youth obesity co-occurred with rural classification in several regions, particularly throughout West Tennessee (Lake, Obion, Lauderdale, Haywood, Hardeman, Weakley, and Carroll) and parts of Upper East Tennessee, including Cocke, Hancock, Clairborne, and Greene counties. In contrast, many of the urban and suburban counties in Middle Tennessee, such as Williamson, Davidson, Rutherford, Sumner, and Wilson, displayed low youth obesity. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial Autocorrelation: Global Moran’s I and Local Moran’s I (LISA)\u003c/h3\u003e\n\u003cp\u003eThe Global Moran\u0026rsquo;s I statistic was 0.12, with a z-score of 1.45 and a p-value of 0.15, indicating that youth obesity did not exhibit statistically significant global spatial autocorrelation across Tennessee counties. This suggests that, at the statewide level, the spatial distribution of youth obesity does not deviate significantly from a random pattern. Despite the absence of significant global clustering, Anselin\u0026rsquo;s Local Moran\u0026rsquo;s I (LISA) analysis identified several localized clusters and spatial outliers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), highlighting meaningful subregional patterns. High-High clusters, representing counties with high youth obesity surrounded by similarly high-rate neighbors, were observed in Western and Eastern Tennessee, including Carroll, Obion, Jefferson, and Hamblen counties. In contrast, Low-Low clusters, indicating counties with low youth obesity surrounded by low-rate neighbors, were detected in Montgomery, Davidson, Bradley, Lincoln, and Franklin counties. Several spatial outliers were also identified. High-Low outliers, where counties exhibited high youth obesity despite neighboring counties having lower rates, included Robertson, Dickson, Bedford, and Coffee, suggesting localized risk factors not shared by adjacent counties. Low-High outliers, where counties had relatively low youth obesity but were surrounded by higher-rate neighbors, were observed in Dyer and Overton counties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eHotspot Analysis (Getis-Ord Gi*)\u003c/h3\u003e\n\u003cp\u003eCounties identified as hotspots of youth obesity were observed primarily in West Tennessee and parts of East Tennessee. A 95% confidence hotspot was detected in Hamblen County, while additional hotspots at the 90% confidence level were identified in Obion and Carroll counties. These counties represent areas where elevated youth obesity is not only high in magnitude but also spatially reinforced by neighboring counties. In contrast, coldspots, representing clusters of significantly lower youth obesity rates, were identified in Middle Tennessee. Counties such as Franklin and Lincoln emerged as coldspots at a 99% confidence level. While Montgomery, Robertson, Davidson, Coffee, Moore, and Bradley were at a 95% confidence level. Overall, the Getis\u0026ndash;Ord Gi* results complement the Local Moran\u0026rsquo;s I findings by highlighting areas of intensified concentration of high and low youth obesity. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Spearman Correlations\u003c/h2\u003e \u003cp\u003eSeveral indicators of socioeconomic disadvantage, poverty rate, educational attainment, food insecurity, and adult smoking were found to be highly correlated with one another (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.70) and were therefore excluded from multivariable analyses. The final set of variables retained for further modeling included youth obesity, uninsured rate, physical inactivity, households without car access, severe housing cost burden, rurality/urbanicity, median age, non-English speaking households, White population, population size, and unemployment rate. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAmong the retained variables, physical inactivity demonstrated strong and consistent associations with youth obesity (ρ\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and multiple structural factors, including unemployment rate (ρ\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), rurality (ρ\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), uninsured rate (ρ\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and households without car access (ρ\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and was inversely correlated with population size (ρ = -0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similar patterns were observed for the unemployment rate, which was positively associated with uninsured status, physical inactivity, and rurality, and negatively associated with population size. Median age was positively correlated with White population (ρ\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and negatively correlated with severe housing cost burden, non-English speaking households, and population size (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Overall, the correlation structure highlights the clustering of behavioral risk and socioeconomic disadvantage within less populous, more rural counties, informing subsequent multivariable and spatial analyses.\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\u003eBivariate Spearman Correlations Among County-Level Social, Behavioral, and Demographic Variables in Tennessee Counties, 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYouth Obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYouth Obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUninsured Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical Inactivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHouseholds Without Car Access\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSevere Housing Cost Burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRurality/\u003c/p\u003e \u003cp\u003eUrbanicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedian Age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon-English Speaking Households\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWhite Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUnemployment Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Inactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds Without Car Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere Housing Cost Burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality/Urbanicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-English Speaking Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.34**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.34**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistical test: Spearman\u0026rsquo;s rho (2-tailed): * p\u0026thinsp;\u0026le;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026le;\u0026thinsp;0.01 (2-tailed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOrdinary Least Squares Regression\u003c/h2\u003e \u003cp\u003eThe overall model was statistically significant (Joint F-statistic\u0026thinsp;=\u0026thinsp;3.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0004), explaining approximately 31% of the variance in youth obesity prevalence (R\u0026sup2; = 0.31; adjusted R\u0026sup2; = 0.23). Variance inflation factors for all predictors were below commonly accepted thresholds (VIF\u0026thinsp;\u0026lt;\u0026thinsp;3.5), indicating no evidence of problematic multicollinearity. Among all predictors, physical inactivity emerged as the only variable associated with youth obesity. Higher levels of physical inactivity were associated with significantly higher youth obesity prevalence (β\u0026thinsp;=\u0026thinsp;1.02, robust \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0003), indicating that a one\u0026ndash;percentage point increase in physical inactivity corresponded to approximately one-percentage point increase in youth obesity, holding other factors constant. Median age showed a positive association with youth obesity that approached statistical significance (β\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09), while other variables were not significantly associated with youth obesity in the fully adjusted model. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eModel diagnostics indicated no evidence of heteroskedasticity (Koenker-Bassett statistic \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34), supporting the stability of coefficient estimates. However, the Jarque-Bera test was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting departures from residual normality. Subsequent analyses, therefore, relied on robust standard errors for inference. Taken together, the OLS results highlight physical inactivity as the dominant county-level determinant of youth obesity in Tennessee; however, diagnostic tests showed no evidence of spatial non-stationarity, indicating that the global OLS model adequately captured the observed relationships without the need for geographically weighted regression. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrdinary Least Squares (OLS) Regression of County-Level Youth Obesity in Tennessee (N\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRobust t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality/Urbanicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical inactivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-English speaking households (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere housing cost burden (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds without car access (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite population (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: * p\u0026thinsp;\u0026le;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026le;\u0026thinsp;0.01 (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of Model Diagnostics Ordinary Least Squares (OLS) Regression\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\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYouth obesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of counties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkaike Information Criterion (AICc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint Wald Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKoenker\u0026ndash;Bassett test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque\u0026ndash;Bera test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the geographic distribution of youth obesity across Tennessee counties and assessed its association with county-level social, behavioral, and demographic factors using spatial and regression-based methods. Youth obesity varied markedly across the state, with pronounced regional differences and localized concentrations of elevated burden. Although global spatial autocorrelation was not statistically significant, both Local Moran\u0026rsquo;s I and Getis-Ord Gi* analyses revealed meaningful subregional clustering, indicating that youth obesity in Tennessee is shaped by localized contextual influences rather than a uniform statewide spatial process. Among the social and behavioral factors examined, physical inactivity emerged as the dominant county-level determinant of youth obesity, while diagnostic tests indicated that a global OLS model adequately captured the observed relationships.\u003c/p\u003e \u003cp\u003eThe spatial analyses provide important insight into how youth obesity is distributed across Tennessee. High-prevalence counties were concentrated primarily in West Tennessee and parts of East Tennessee, while lower prevalence was observed across much of Middle Tennessee. The presence of High-High clusters and statistically significant hotspots in specific counties underscores the importance of examining local patterns, even in the absence of significant global spatial autocorrelation.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e This finding is consistent with prior spatial epidemiologic research demonstrating that health outcomes driven by social and behavioral factors often exhibit localized clustering that may be obscured at broader geographic scales.\u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e These localized clusters likely reflect region-specific combinations of socioeconomic conditions, built environment characteristics, and behavioral norms that vary across Tennessee\u0026rsquo;s diverse geographic landscape.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRegression analyses further clarified the drivers underlying these spatial patterns. Physical inactivity was the only predictor independently associated with youth obesity in the multivariable OLS model, with a one-percentage point increase in inactivity corresponding to an approximate one-point increase in youth obesity. This finding aligns with existing literature identifying physical inactivity as a central contributor to childhood and adolescent obesity through reduced energy expenditure, limited recreational opportunities, and constrained opportunities for active transport.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Other indicators, such as uninsured rate, unemployment rate, housing cost burden, transportation access, rurality, and demographic composition, were not statistically significant after adjustment. This may be due to their high intercorrelation in bivariate analyses, suggesting that their influence on youth obesity may be indirect or mediated through behavioral pathways, particularly physical inactivity.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eImportantly, diagnostic tests provided no evidence of heteroskedasticity or spatial non-stationarity, and residuals exhibited no spatial autocorrelation (Koenker-Bassett test, p-value\u0026thinsp;=\u0026thinsp;0.34).\u003csup\u003e46\u003c/sup\u003e These findings indicate that the relationship between physical inactivity and youth obesity is relatively stable across Tennessee counties, supporting the use of a global OLS model rather than spatial lag, spatial error, or geographically weighted regression. Although exploratory geographically weighted regression analyses were considered, diagnostic tests provided no evidence of spatial non-stationarity, indicating that the global OLS model adequately captured the observed relationships.\u003c/p\u003e \u003cp\u003eThis study advances understanding of youth obesity in Tennessee by demonstrating that local geographic context matters, even when global spatial dependence is absent. The findings emphasize physical inactivity as a central driver of county-level youth obesity and underscore the need for targeted, context-sensitive public health strategies. Future research incorporating longitudinal data and multilevel designs could further elucidate how individual behaviors interact with local environments to shape youth obesity risk across Tennessee and similar settings in the southeastern United States.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths, including the use of statewide county-level data, integration of multiple spatial analytic techniques, and a diagnostics-driven modeling approach. However, limitations should be noted. Ecological design precludes inference at the individual level, and associations observed at the county level may not reflect individual risk relationships. The analysis was cross-sectional, limiting causal interpretation, and some potentially relevant determinants, such as food environment quality, school nutrition policies, and neighborhood safety, were not available at the county level. Additionally, counties vary in geographic size and internal heterogeneity, which may mask within-county disparities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Policy and Practice\u003c/h2\u003e \u003cp\u003eThe identification of localized clusters and hotspots of youth obesity highlights the need for targeted, place-based public health strategies rather than uniform statewide approaches. Counties in West and East Tennessee with persistently elevated youth obesity may benefit from focused investments in physical activity-promoting infrastructure, including parks, recreational facilities, safe walking and biking routes, and school- and community-based physical activity programs. Because physical inactivity emerged as the dominant determinant of youth obesity in this study, policies that reduce structural barriers to physical activity, such as transportation limitations, limited access to safe recreational spaces, and rural infrastructure gaps, are likely to yield the greatest impact.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Local health departments can use these findings to prioritize high-burden counties for resource allocation, program expansion, and cross-sector collaboration.\u003c/p\u003e \u003cp\u003eAt the state level, these results support integrating geographic data into obesity prevention planning and evaluation. Incorporating spatial analyses into routine surveillance can help identify emerging hotspots, monitor progress, and tailor interventions to local contexts. Policies that incentivize active school environments, expand access to community recreation in rural areas, and support land-use planning that encourages physical activity may be particularly effective. Importantly, the stability of the inactivity-obesity relationship across counties suggests that statewide policies addressing physical inactivity can be broadly applied, while allowing flexibility for local adaptation based on community needs. Together, these approaches can enhance the effectiveness of obesity prevention efforts and contribute to reducing persistent geographic disparities in youth health across Tennessee.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that youth obesity in Tennessee exhibits substantial geographic variation, characterized by localized clusters and hotspots rather than uniform statewide patterns. Physical inactivity emerged as the primary county-level determinant of youth obesity, while other social and demographic factors appeared to operate indirectly through behavioral pathways. Spatial analyses revealed meaningful local concentrations of elevated and reduced youth obesity burden. Together, these findings underscore the importance of integrating spatial perspectives into obesity surveillance and highlight the need for targeted, place-based interventions that prioritize increasing physical activity, particularly in high-burden counties.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study utilized publicly available, aggregated data and did not involve human participants or identifiable information.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMAM \u0026amp;amp; BA conceptualized the study and developed the methodology. MAM performed the data curation, software implementation, and formal analysis. QH assisted with code validation and supported model evaluation. All authors contributed to the manuscript writing, reviewed the final draft, and approved the submitted version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used in this study are publicly available, through the following sources: Tennessee Department of Health County Data Packages ( [https://www.tn.gov/health/health-program-areas/county-health-councils/cha-chip-resources/county-profiles.html](https:/www.tn.gov/health/health-program-areas/county-health-councils/cha-chip-resources/county-profiles.html) ), Tennessee Department of Education and Tennessee Department of Health Coordinated School Health BMI Report (2021\u0026ndash;2022) ( [https://www.tn.gov/content/dam/tn/education/csh/CSH\\_BMI\\_Report\\_2021-22.pdf](https:/www.tn.gov/content/dam/tn/education/csh/CSH_BMI_Report_2021-22.pdf) ), the U.S. Census Bureau American Community Survey ( [https://data.census.gov/](https:/data.census.gov) ), the U.S. Department of Housing and Urban Development CHAS dataset ( [https://www.huduser.gov/portal/datasets/cp.html](https:/www.huduser.gov/portal/datasets/cp.html) ), the U.S. Bureau of Labor Statistics Local Area Unemployment Statistics ( [https://www.bls.gov/lau/data.htm](https:/www.bls.gov/lau/data.htm) ), the County Health Rankings \u0026amp;amp; Roadmaps ( [https://www.countyhealthrankings.org/](https:/www.countyhealthrankings.org) ), and the 2023 USDA Rural\u0026ndash;Urban Continuum Codes ( [https://www.ers.usda.gov/data-products/rural-urban-continuum-codes](https:/www.ers.usda.gov/data-products/rural-urban-continuum-codes) ). All datasets were accessed between November and December 2025.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDaniels SR, Jacobson MS, McCrindle BW, Eckel RH, Sanner BM. American Heart Association Childhood Obesity Research Summit. Circulation. 2009;119(15):2114\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.109.192215\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.109.192215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention, CDC. Childhood Obesity Facts. Obesity. December 20. 2024. 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Obes Rev. 2021;22(Suppl 1):e13057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/obr.13057\u003c/span\u003e\u003cspan address=\"10.1111/obr.13057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"youth obesity, GIS, spatial epidemiology, physical inactivity, social determinants of health, Tennessee","lastPublishedDoi":"10.21203/rs.3.rs-9240369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9240369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eYouth obesity remains a major public health concern in the United States, with high burdens in the Southeastern region. Tennessee consistently reports elevated youth obesity rates, yet the geographic distribution of youth obesity and its relationship with county-level factors are not well understood. This study examined spatial patterns of youth obesity across Tennessee counties and associations with social, behavioral, and demographic determinants.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eAn ecological analysis was conducted using county-level data from Tennessee\u0026rsquo;s 95 counties. Youth obesity prevalence served as the outcome variable. Spatial patterns were evaluated using Global Moran\u0026rsquo;s I, Anselin Local Moran\u0026rsquo;s I, and Getis-Ord Gi* hotspot analysis. Ordinary least squares (OLS) regression was used as the primary multivariable model, with diagnostic tests assessing multicollinearity, heteroskedasticity, residual normality, and spatial dependence. All analyses were conducted using ArcGIS Pro and Python.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eYouth obesity prevalence ranged from 23.9% to 55.5% across counties, revealing substantial geographic variation. Global Moran\u0026rsquo;s I indicated no significant statewide spatial autocorrelation; however, local analyses identified distinct clusters and hotspots in parts of West and East Tennessee. Bivariate correlations revealed strong interrelationships among socioeconomic indicators. In the multivariable OLS model, physical inactivity emerged as the only significant predictor of youth obesity (β\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0004). Diagnostic tests showed no evidence of heteroskedasticity or spatial non-stationarity.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eYouth obesity in Tennessee displays localized geographic clustering and is strongly associated with county-level physical inactivity. These findings highlight the importance of spatially informed, place-based interventions that prioritize increasing physical activity, particularly in high-burden counties.\u003c/p\u003e","manuscriptTitle":"Geospatial Analysis of County-level Social, Behavioral, and Demographic Factors Associated with Youth Obesity in Tennessee, United States, 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 17:48:21","doi":"10.21203/rs.3.rs-9240369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-15T22:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35022600730307068406390341285567182684","date":"2026-04-15T12:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202340240415106060462042971691211417213","date":"2026-04-14T22:42:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T10:39:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T16:12:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T10:41:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T09:40:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-02T09:08:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f277eb96-e604-45f5-8a9e-59a5034fd8eb","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T17:48:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 17:48:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9240369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9240369","identity":"rs-9240369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}