Dylan Barker

Background/Purpose: Diabetes prevalence in the Deep South remains among the highest in the United States. Yet few studies have examined the role of community level determinants, such as poverty, food insecurity, healthcare shortages, and unsafe environments in the geographic clustering of high diabetes burden in counties with higher proportions of racial/ethnic minoritized populations. Methods: We conducted an ecological, county-level study of 418 Deep South counties using publicly available data. The outcome was percentage of adults diagnosed with diabetes (2022). Exposures included food insecurity, access to exercise opportunities and parks, firearm mortality, healthcare workforce and insurance indicators, education, broadband access, and racial/ethnic composition (percent non-Hispanic Black, non-Hispanic White, Hispanic). We used descriptive statistics, Pearson correlations, and LISA mapping to identify hotspots and cold spots. Results: Average county level diabetes burden was 15.6%. Strong positive correlations were found with physical inactivity ( r = 0.82), obesity ( r = 0.78), poverty ( r = 0.74), firearm fatalities ( r = 0.67), and food insecurity ( r = 0.59). Inverse correlations were observed for broadband access ( r = -0.69), education ( r = -0.59), access to exercise opportunities ( r = -0.42), and primary care physician availability ( r = -0.28). LISA maps showed hotspots concentrated in the Mississippi Delta and parts of Alabama and Louisiana, co-occurring with higher poverty, higher firearm mortality, and higher proportions of non-Hispanic Black residents, while cold spots aligned with counties with stronger infrastructure and healthcare resources. Implications for Public Health: Diabetes in the Deep South reflects structural and environmental conditions beyond individual behaviors. Policies that expand health-promoting infrastructure, strengthen healthcare access, reduce food insecurity, and improve community safety are warranted, particularly in hotspot counties with higher proportions of racial/ethnic minoritized populations.

Background: Cardiovascular disease remains a leading cause of preventable mortality in the United States, with rural counties in the Deep South experiencing disproportionately high burdens. Grounded in the Andersen healthcare utilization model, this study examined how enabling resources, predisposing characteristics, and access-related barriers relate to coronary heart disease (CHD) prevalence and mortality. Methods: This ecological analysis included 418 counties across Alabama, Georgia, Louisiana, Mississippi, and South Carolina. Using Local Indicators of Spatial Association (LISA) and multivariable linear regression, we tested three theory-based hypotheses and assessed the spatial clustering of CHD outcomes, while identifying key structural and sociodemographic predictors. Results: Counties with greater rurality and fewer healthcare providers exhibited significantly higher rates of CHD prevalence and mortality. Primary care provider availability and higher household income were protective factors. Digital exclusion, measured by lack of access to computers or mobile devices, was significantly associated with higher CHD prevalence and mortality. Spatial analysis identified the counties with better-than-expected cardiovascular outcomes despite structural disadvantages, suggesting the potential role of localized resilience factors and unmeasured community-level interventions. Conclusions: The findings affirm the relevance of the Andersen model for understanding rural health disparities and highlight the importance of investing in both digital infrastructure and healthcare capacity. Expanding telehealth without addressing provider shortages and social determinants may be insufficient. Local policy innovations and community resilience mechanisms may offer scalable models for improving cardiovascular health in disadvantaged areas.

Background: Mississippi has one of the highest rates of food insecurity in the United States, with many counties experiencing significant disparities in access to healthy food. Therefore, understanding the spatial distribution of these disparities is essential for developing targeted interventions. Purpose: This study aims to investigate the spatial distribution of food insecurity and its associated social and health factors across Mississippi counties to identify areas in need of intervention. Methods: Cross sectional study was conducted using publicly available datasets to examine food insecurity and its associated factors at the county level. Pearson correlations and bivariate Moran’s I were used to explore relationships between food insecurity, food banks, health outcomes, and social determinants of health. Results: Food insecurity was highest in counties bordering Louisiana, with significant correlations between food insecurity and poverty, poor health outcomes, and limited broadband access. Conversely, food banks were concentrated in Northeast Mississippi, where food insecurity rates were lower. Conclusions: The findings highlight the need for more equitable distribution of resources across Mississippi counties, particularly in areas with higher food insecurity rates. This study provides valuable insights for public health officials to address food insecurity through targeted, region-specific interventions.

Academic adaptation is crucial for the success and well-being of international students, especially in rural areas where cultural and social support resources may be limited. This study aimed to identify predictors of academic adaptation among international students in the rural southern United States, focusing on social support, cultural integration, alcohol use, and acculturative stress. A cross-sectional study was conducted with 141 international students from two universities: the University of Mississippi/Oxford campus and the University of Alabama. Data were collected through a web-based survey using validated scales to measure cultural integration, acculturative stress, social support, alcohol use, and academic adaptation. Pearson correlation and multiple linear regression analyses were performed to identify significant predictors of academic adaptation. The sample consisted of 54.6% male and 45.4% female students, with a mean age of 29.4 years. The majority were graduate students (89.4%), with the majority being funded by graduate teaching or research assistantships (78.7%). Correlation analyses showed that social support was positively correlated with academic adaptation (r = 0.62, p < 0.01). Also, cultural integration was positively corelated with academic adaptation (r = 0.33, p < 0.001). However, acculturative stress was negatively correlated with academic adaptation (r = −0.20, p < 0.05). The fully adjusted regression analyses identified social support as a positive predictor of academic adaptation among international students in rural U.S. universities. The model demonstrated that each unit increase in the social support score was associated with a 0.61 increase in the academic adaptation score (95% CI [0.44, 0.79], p < 0.001). These findings highlight the need for tailored support programs addressing the unique challenges faced by this population to enhance their academic success and overall well-being.