Purpose:                              The aim of this study was to critically evaluate dietary quality and adequacy of low-income South Africans using the NOVA food processing classification system and World Health Organization (WHO) dietary guidelines.

Methods:                            2546 participants (18-50 years) from three low-income neighborhoods in two provinces in South Africa (SA) (Langa and Khayalitsha in the Western Cape, and Mount Frere in the Eastern Cape) were included. Fieldworkers trained in standardized techniques collected household data, anthropometry measurements and a 1-day 24-hour recall, which was analyzed according to the SA Medical Research Council food composition manual. Foods reported consumed were classified according to the NOVA food processing classification system. Compliance with WHO dietary guidelines and trends in ultra-processed food (UPF) consumption was evaluated.

Results/findings:              Participants had a mean energy intake of 7734kJ/day. Females were more likely to be living with obesity (43.9%) than males (7.9%). Most (79.81%) participants were within the acceptable WHO guideline range for saturated fat (mean intake of 11.6g/day), total fat (67.8%, mean: 39.4g/day), sodium (72.5%, mean: 1845mg/day) and free sugar (60.3%, mean: 12.8g/day). Only 7.2% of participants met the WHO guideline for fruit and vegetables (>=400g/per day), and 18.1% met the guideline for fiber (>=25g/day). UPF consumption accounted for 13.0% of the total energy consumption for the lowest quartile of UPF consumers, and 63.6% of the total energy for the highest quartile, who were also the highest energy consumers overall (mean energy intake of 11042kJ/day). Those within the highest quartile remained within acceptable WHO consumption range for sugar and saturated fat (although mean saturated fat intake of 26.7g/day was 17.7g higher that the lowest quartile). The highest quartile consumed excessive total fat and sodium, and inadequate fiber, fruits and vegetables.

Conclusions:                       UPF consumption is prevalent amongst low-income consumers in SA. Highest UPF consumers have higher overall energy consumption, higher sodium and higher fat intake. Most low-income South Africans assessed in this study had inadequate intake of fiber, fruits and vegetables. Policy measures are urgently needed in SA to protect against the proliferation of harmful UPF, and to promote and enable the consumption of whole and less processed foods.

Purpose: The purpose of the cross-sectional analysis was to examine whether residents of a diverse, low-income urban community shop at their nearest supermarket, and if not, how far they travel for groceries. Differences in overall distance travelled for groceries, and excess distance beyond nearest store, by race/ethnicity and food insecurity status were explored.

Methods: This study employed a cross-sectional study design, analyzing 2018-2019 baseline data from the FRESH-Austin cohort (n=393), all supermarkets/grocery stores in Travis County and participant home addresses were geocoded. Network distances to the nearest store from each participant's home, and to the stores where they shop for groceries, were calculated using GIS. We categorized participants as using a) their nearest supermarket, b) a supermarket somewhat further than their nearest store (1.1-5 times the distance), or c) a supermarket very far from their nearest store (>5 times the distance). Race/ethnicity was self-reported and food insecurity status was measured using a validated screener. Linear regression and chi-squared tests were used to determine differences in distance-based outcomes by race/ethnicity and food insecurity status.

Results: Under 19% of the sample reported shopping at the supermarket closest to their home (mean distance closest supermarket=1.66 miles, mean distance to self-reported supermarkets=5.26 miles). There were no significant differences in the unadjusted linear regression models assessing the distance to the closest supermarket by race/ethnicity or food insecurity status, or distance to self-reported location by food insecurity status. However, individuals who identified as Hispanic (Beta=1.26, SE=0.51, p<0.05) and Black (Beta=2.71, SE=0.84, p<0.01) traveled further to their self-reported than non-Hispanic White cohort members. Similarly, the chi-squared tests found there were not significant differences in the ratio categorical variable by food insecurity status, but there were significant differences by race/ethnicity (p<0.05).
Conclusion: Our study presents more nuanced evidence of racial/ethnic disparities in food access. Although some researchers and policymakers focus on strategies to improve geographic food access, other domains of access (affordability, quality, cultural adequacy) need greater consideration.

Purpose: In response to the COVID-19 pandemic, government agencies and community-based organizations pursued multiple strategies to provide food to in-need households, with a major component being increased support and expansion of the emergency food system. Our aim was to examine the extent to which food insecure, low-income households received emergency food from a church, food pantry, or food bank during 2020 and whether the reach of this part of the food safety net was equitable.

Methods: We used data from the 2020 Current Population Survey Food Security Supplement. We produced population estimates for the receipt of emergency food in the previous year by food insecure households earning below 185% of the federal poverty level (n=2,097). We then modeled emergency food receipt with demographic (primary respondents’ age, sex, race, Hispanic ethnicity, education, marital status, and employment and disability status) and household (low vs. very low food security, SNAP participation, perceived monetary amount required to meet food needs, number of children, income, and metropolitan status) characteristics using logistic regression and calculated predicted probabilities of receiving emergency food.

Results: The overall proportion of low-income, food insecure households receiving emergency food was 40.3%. The predicted probability of receiving emergency food was 46.5% for Hispanic versus 38.2% for non-Hispanic (p=0.01); 36.5% for employed versus 42.2% for unemployed (p=0.20) or 50.7% for not working, living with a disability (p<0.001); 35.1% for low versus 48.8% for very low food security (p<0.001); and 48.8% for SNAP participants versus 32.2% for non-participants (p<0.001). There were no significant differences in the predicted probability of receiving emergency food based on the other variables in the model.

Conclusions: Even with a crisis providing increased media attention to emergency food resources and governmental and community-based organizations working to increase availability, a substantial proportion of low-income, food insecure households did not use these resources during 2020. Despite low overall use of these services, there did not appear to be disparities in receipt of emergency food with regard to historically marginalized groups. These results suggest that barriers to accessing emergency food must be identified and addressed to increase the proportion of households accessing these resources.

Purpose: To understand facilitators, barriers, and awareness of farmers market (FM) usage among Oklahoma Supplemental Nutrition Assistance Program (SNAP) participants, and determine how FMs are associated with fruit and vegetable (FV) intake and food literacy.

Methods: This cross-sectional study included email and in-person recruitment of SNAP participants living within 10 miles of 5 SNAP-accepting FM representing different Oklahoma counties (n=20,798). In-person, convenience recruitment at each FM was used to include actual FM shoppers due to low SNAP participant FM shopping in the literature. Independent sample t-tests were used to compare food literacy scores, and FM awareness scores, and a Fischer’s exact test to compare demographic characteristics between shoppers and non-shoppers. Frequencies were calculated for facilitators and barriers. Independent sample t-test was used to determine differences in FV intake.

Results/findings: Response rate for online surveys was 0.97% (n=127) and 69.6% for in-person (n=32). The median participant (n = 159) was 40 years and 82.1% of participants were female. The majority of participants identified as non-Hispanic/Latino (85.7%) and white (68.0%). Shoppers had higher rates of education, employment, and marriage compared to non-shoppers (p < 0.0001). Shoppers reported a median distance of 6 miles (IQR of 3.0, 12.0) and 10 minutes (IQR of 5.0, 20.0) travel. Shoppers were not significantly more aware of FM location than non-shoppers in any county (p = 0.2128, 0.4646, 0.9090, 0.7490). The top facilitator in FM shopping was “fresher produce available at FMs” (43.3%). The top barrier was “I don’t know where any markets are” (31.6%). FM shoppers had significantly higher FV intake (16.3 ± 3.6, p = 0.0006) and food literacy (27.7 ± 4.9, p = 0.0025) compared to non-shoppers (14.2 ± 3.7 and 25.0 ± 5.5 respectively).

Conclusions: Results can be used to improve SNAP-Ed interventions and FM initiatives that address the diet quality of low-income groups by incorporating food literacy and encouraging the use of FM produce. The difference in food literacy scores suggest that higher food literacy may positively influence FM shopping among SNAP participants. Future research should investigate differences in barriers and facilitators to FM shopping in rural versus urban settings.

Purpose: Food insecurity (FI) is a persistent public health concern that is particularly problematic during the behavior- and health-formative period of young adulthood. Dietary patterns and specific nutrients have been shown to alter the gut microbiome (GM) and its function with important implications on overall health. Due to the paucity of research related to FI, the purpose of this study was to assess the relationship between FI and the GM. We hypothesized that FI college students would have microbial and metabolomic features distinct from food secure (FS) college students.

Methods: Based on food security status within the last 30 days, 60 participants from a cohort of diverse college students were classified as FS (n=38) or FI (n=22). As a cross-sectional study design, the fecal microbiome and metabolome were analyzed using 16S amplicon sequencing, targeted liquid chromatography-tandem mass spectrometry (MS/MS), and targeted detection of short-chain fatty acids using gas chromatography-MS. The functional profile of the GM was predicted via the PICRUSt 2 pipeline and mapped to the KEGG database.

Results: The overall community of the GM between FI and FS students showed significant separation as informed by beta-diversity analysis (Jaccard metric, p=0.05). Moreover, FI students had significantly greater alpha diversity values compared to those who were FS (Pielou’s evenness, p=0.05; Shannon index, p=0.04). FS students had a greater abundance of the taxa Clostridia, Megasphaera, and Holdemanella (log-FC > 2.0; p’s < 0.05), whereas FI students had a greater abundance Enterobacteriaceae and Eisenbergiella (log-FC > 2.0; p’s < 0.05). Output from PICRUSt suggested that FI students had greater metabolic pathway activity for hydrolysis reactions, energy substrate biosynthesis, and macronutrient metabolism (e.g., proteins) (p’s < 0.05). Metabolomic data revealed three significant between-group metabolites: Picolinic acid, phosphocreatine, and 2-pyrrolidinone (p’s < 0.05).

Conclusions: The GM of FI college students showed distinct differences in overall community structure, key microbes, predicted metabolic pathways, and objective metabolites, when compared to FS counterparts. This study provides one of the first omic-informed works in elucidating the critical influence of FI on the GM, highlighting the significance of this behavioral mechanism for future research.

Purpose: 

     Food insecurity (FI), inconsistent access to adequate, healthy food, is associated with poorer cognitive function.  College students are known to report the highest rates of food insecurity in any population.  Despite this, no studies have ever investigated possible neural mechanisms which may account for cognitive differences associated with FI and which may impact FI students’ nutrition behaviors.  This cross-sectional study examined executive function (EF) and resting-state brain connectivity associations with FI in college students.

Methods:

     FI was assessed via the USDA Adult Food Security Survey Module, and forty students experiencing either very low food security (FI; n = 20) or high food security (FS; n=20) were recruited and matched by sex and age.  Participants completed the Behavior Rating Inventory of Executive Function (BRIEF) and Adverse Childhood Experience (ACEs) Questionnaire.  Resting-state functional magnetic resonance imaging (rs-fMRI) scans were taken.  Images were preprocessed using Statistical Parametric Mapping, and group differences in functional connectivity were analyzed using Independent Component Analysis in CONN Toolbox. All models were adjusted for ACEs.

Results: 

     FI was associated with poorer Global BRIEF scores (adjusted b=8.36; 95% CI: 2.32, 14.40) and five BRIEF subscales: Inhibit, Initiate, Working Memory, Plan, and Organize (p’s<0.05).  For rs-fMRIs, FI students showed greater connectivity between the frontoparietal network (FPN) and left middle temporal gyrus (L-MTG; Cluster size p-FWE = 0.029), the salience network (SN) and precuneus (Cluster size p-FWE < 0.001), and the SN and right middle frontal gyrus (R-MFG; Cluster size p-FWE = 0.016) as compared to FS students.  Greater connectivity between the FPN and L-MTG was associated with poorer EF on the Organize subscale (p=0.024), and greater connectivity between the SN and R-MFG was associated with poorer EF on the Inhibit subscale (p=0.038) for FI students. 

Conclusion:

     Research has previously linked anti-connectivity between networks at rest (i.e. greater network segregation) to healthy cognitive functioning.  This rs-fMRI study of college students with FI reveals greater between network connectivity, which might be a mechanism contributing to EF impairment and poorer behavioral choices.