Eating behaviors and the perceived nutrition environment among college students.

Objectives: To assess eating behaviors and identify whether there is an association between eating behaviors and the perceived nutrition environment among college students. Participants: College students (n = 180) actively living on campus during the 2019-2020 academic school year. Methods: Cross- sectional study utilizing the validated NEMS-P survey tool to collect all data. A multivariate logistics regression was used to assess the association between eating behaviors and the perceived nutrition environment. Results: Statistically significant association noted between perceived higher cost of healthy eating and decreased consumption of fruit (p = .027), availability of nutrition information (healthy eating signs) and increased vegetable and fruit consumption (p = .018, p = 0.010) and increased ease of purchasing fruits and vegetables and increased consumption (p = 0.037). Conclusion: The campus nutrition environment can provide students the opportunity to learn about and practice healthy eating through available foods and beverages, nutrition education and signs that encourage healthy eating throughout the campus.

Food Security, Access, and Challenges in New Mexico during COVID-19.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic disrupted food systems and economies across the United States. Public health measures, including stay-at-home orders, led to employment disruptions and food system shocks that increased barriers to food access. OBJECTIVES: We aimed to examine food insecurity and food access challenges in New Mexico (NM) during the COVID-19 pandemic. METHODS: A cross-sectional study using a validated survey was conducted in NM in May and June 2020. Adults 18 y and older were recruited through convenience sampling via email, websites, and targeted social media ads from major universities, nongovernmental organizations, state agencies, and media outlets. Survey questions assessed food insecurity and food-related challenges and worry. Bivariate and multivariate logistic regression examined relations between food insecurity and demographic characteristics. z Tests were used to compare the proportions of individuals who responded affirmatively to food challenge and worry questions between food-secure and food-insecure respondents. RESULTS: A total of 1487 residents participated in the study. Thirty percent of respondents reported experiencing food insecurity and 16% experienced very low food security since the pandemic started. Food insecurity was associated with each of 7 characteristics examined in bivariate logistic regression analyses. Multivariate logistic regression results showed that Hispanic (adjusted OR: 1.70; 95% CI: 1.18, 2.44) and female (adjusted OR: 1.78; 95% CI: 1.09, 2.90) respondents were more likely to experience food insecurity than non-Hispanic white and male respondents. Larger household sizes were associated with higher odds of food insecurity except for those in the lowest and highest income categories. z Tests showed that a higher proportion of food-insecure respondents experienced food-related challenges and worry than food-secure respondents. CONCLUSIONS: Disparities in food insecurity persisted during the COVID-19 pandemic and food-insecure individuals were more likely to report experiencing food-related challenges and worry. Researchers and policy makers in NM may consider continuing efforts to mitigate food access issues as the pandemic continues.

A Multi-Site Analysis of the Prevalence of Food Insecurity in the United States, before and during the COVID-19 Pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic profoundly affected food systems including food security. Understanding how the COVID-19 pandemic impacted food security is important to provide support and identify long-term impacts and needs. OBJECTIVE: The National Food Access and COVID research Team (NFACT) was formed to assess food security over different US study sites throughout the pandemic, using common instruments and measurements. This study presents results from 18 study sites across 15 states and nationally over the first year of the COVID-19 pandemic. METHODS: A validated survey instrument was developed and implemented in whole or part through an online survey of adults across the sites throughout the first year of the pandemic, representing 22 separate surveys. Sampling methods for each study site were convenience, representative, or high-risk targeted. Food security was measured using the USDA 6-item module. Food security prevalence was analyzed using ANOVA by sampling method to assess statistically significant differences. RESULTS: Respondents (n = 27,168) indicate higher prevalence of food insecurity (low or very low food security) since the COVID-19 pandemic, compared with before the pandemic. In nearly all study sites, there is a higher prevalence of food insecurity among Black, Indigenous, and People of Color (BIPOC), households with children, and those with job disruptions. The findings demonstrate lingering food insecurity, with high prevalence over time in sites with repeat cross-sectional surveys. There are no statistically significant differences between convenience and representative surveys, but a statistically higher prevalence of food insecurity among high-risk compared with convenience surveys. CONCLUSIONS: This comprehensive study demonstrates a higher prevalence of food insecurity in the first year of the COVID-19 pandemic. These impacts were prevalent for certain demographic groups, and most pronounced for surveys targeting high-risk populations. Results especially document the continued high levels of food insecurity, as well as the variability in estimates due to the survey implementation method.

Differences in Overweight/Obesity Among Youth in a Midwest State by Rural-Urban Continuum Codes.

BACKGROUND: A disparity in overweight/obesity prevalence exists between rural and urban youth; however, definitions of 'rural' vary widely and the degree to which rurality impacts overweight/obesity prevalence is unclear. Therefore, the purpose of this study was to examine the school height and weight data in a rural Midwest state to determine differences in overweight and obesity prevalence among youth by using Rural-Urban Continuum (RUC) codes to define county-level degree of urbanization. METHODS: De-identified statewide data were obtained in electronic format from the state Department of Health. Height, weight, sex and age were used to calculate body mass index (BMI) z-scores, which were used to determine BMI percentile and categories. The county variable was used to assign a RUC code to each individual. Logistic regression was used to examine binary weight classifications by rural status while controlling for age, sex and race/ethnicity. RESULTS: Odds of obesity and of overweight/obesity were higher among rural youth compared to non-rural. Odds of overweight/obesity increased with increasing rurality. Compared to youth who lived in counties with a RUC code of 3, youth who lived in counties with RUC codes of 5, 7, 8 and 9 had greater odds of overweight/obesity. The number of youth classified as 'rural' ranged from 11-48 percent, depending on how 'rural' was defined. Likewise, overweight/obesity prevalence differed by 4.6 percent depending on how 'rural' was defined. CONCLUSIONS: Consistently defining 'rural' and determining degree of rurality is important in understanding how geographic location plays a role in overweight/obesity among youth. Future research should work to assess the physical and social environments of these different types of rural areas to better understand the role that rurality plays in contributing to overweight/obesity among youth. Assessing social determinants of health and its impact on health in rural youth is essential for designing effective public health interventions that can be implemented to address the issue.