Background A number of studies have measured college student food insecurity prevalence higher than the national average; however, no multicampus regional study among students at 4-y institutions has been undertaken in the Appalachian and Southeast regions of the United States. Objectives The aims of this study were to determine the prevalence of food insecurity among college students in the Appalachian and Southeastern regions of the United States, and to determine the association between food-insecurity status and money expenditures, coping strategies, and academic performance among a regional sample of college students. Methods This regional, cross-sectional, online survey study included 13,642 college students at 10 public universities. Food-insecurity status was measured through the use of the USDA Adult Food Security Survey. The outcomes were associations between food insecurity and behaviors determined with the use of the money expenditure scale (MES), the coping strategy scale (CSS), and the academic progress scale (APS). A forward-selection logistic regression model was used with all variables significant from individual Pearson chi-square and Wilcoxon analyses. The significance criterion α for all tests was 0.05. Results The prevalence of food insecurity at the universities ranged from 22.4% to 51.8% with an average prevalence of 30.5% for the full sample. From the forward-selection logistic regression model, MES (OR: 1.47; 95% CI: 1.40, 1.55), CSS (OR: 1.19; 95% CI: 1.18, 1.21), and APS (OR: 0.95; 95% CI: 0.91, 0.99) scores remained significant predictors of food insecurity. Grade point average, academic year, health, race/ethnicity, financial aid, cooking frequency, and health insurance also remained significant predictors of food security status. Conclusions Food insecurity prevalence was higher than the national average. Food-insecure college students were more likely to display high money expenditures and exhibit coping behaviors, and to have poor academic performance.

Objective: To assess the relationship between food insecurity, sleep quality, and days with mental and physical health issues among college students. Design: An online survey was administered. Food insecurity was assessed using the 10-item Adult Food Security Survey Module. Sleep was measured using the 19-item Pittsburgh Sleep Quality Index (PSQI). Mental health and physical health were measured using three items from the Healthy Days Core Module. Multivariate logistic regression was conducted to assess the relationship between food insecurity, sleep quality, and days with poor mental and physical health. Setting: Twenty-two higher education institutions. Participants: College students (n=17,686) enrolled at one of 22 participating universities. Results: Compared to food secure students, those classified as food insecure (43.4%) had higher PSQI scores indicating poorer sleep quality (p<.0001) and reported more days with poor mental (p<.0001) and physical (p<.0001) health as well as days when mental and physical health prevented them from completing daily activities (p<.0001). Food insecure students had higher adjusted odds of having poor sleep quality (adjusted odds ratio [AOR]:1.13, 95% confidence interval [CI]: 1.12-1.14), days with poor physical health (AOR: 1.01, 95% CI: 1.01-1.02), days with poor mental health (AOR: 1.03, 95% CI: 1.02-1.03), and days when poor mental or physical health prevented them from completing daily activities (AOR: 1.03, 95% CI: 1.02-1.04). Conclusions: College students report high food insecurity which is associated with poor mental and physical health and sleep quality. Multi-level policy changes and campus wellness programs are needed to prevent food insecurity and improve student health-related outcomes.

This project is an analysis of the spatial inequality that exists between rural and urban areas in access to food assistance agencies. I gathered the population of all food pantries and soup kitchens in 24 sample counties in Indiana and mapped the location of these agencies using geographic information system analysis. Using the population center of the census block group, I measured the distance from the population center to the nearest food assistance agency. If the closest agency was more than a mile away, the census block group was considered a food assistance desert, a concept I created that draws on the food desert measurement. I found that rural high‐poverty counties in my sample are the most likely to contain census block groups that are food assistance deserts, and urban high‐poverty counties are the least likely to contain food assistance deserts. From these findings, I determine that access to assistance agencies needs to be increased in rural areas, especially rural areas with high‐poverty rates.

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