The study examined the relationship between food swamps and self-reported food shopping frequency and perceived food access, while considering transportation mode and travel time. This Community-Based Participatory Research study surveyed residents from six neighborhoods in Hartford. Individual-level food swamp exposure (the ratio of unhealthy to healthy food stores within a 0.5-mile radius of participants’ homes) was measured both objectively (using GIS-based methods) and subjectively (through self-reporting). Poisson regression models assessed the associations between food swamps and outcomes (shopping frequency by store types and perceived access to food), with transportation mode and travel time as moderators. Of 304 participants, 51% lived in subjective (n = 153) and 71% in objective (n = 198) food swamps. Food swamp exposure was associated with greater shopping frequency at unhealthy outlets (β = 0.12, p < 0.001), less access to healthier food (β = −0.13, p < 0.001), and increased access to unhealthy food (β = 0.08, p < 0.001). Transportation significantly moderates these relationships; bus riders reported the highest rates of unhealthy food purchasing (β = 0.17, p < 0.001). Longer travel times increased both healthy and unhealthy food access (β = 0.01, p < 0.001 for each). Food swamps interact with public transportation to contribute to food shopping and access, underscoring the need for integrated food and transportation policies to address structural barriers and promote health equity in underserved urban communities.

Background: Food insecurity is defined as having limited access to food, while nutrition insecurity is characterized as a lack of consistent access to affordable and acceptable foods that support health, manage or prevent disease, and meet daily nutritional needs. College students face increased risks of food and nutrition insecurity, yet the issue is understudied. This study examined the patterns of food and nutrition insecurity among students at a public university across main and regional campuses. Methods: We conducted a cross-sectional survey using Qualtrics for participant recruitment in November 2023. The USDA’s 10-item toolFood insecurity was measured using the USDA’s 10-item tooland housing security was measured using the U.S. Census Bureau’s National Survey of Income and Program Participation 6-item tool. We used ANOVAs and logistic regression to examine differences across demographics. Data analysis was done using SPSS version 29. Results: There were 6538 student responses. Of these, 36% of students were food insecure, while 20% were nutritionally insecure. Comparatively, food and nutrition insecurity were significantly higher among students with low-income (p < 0.001), housing instability (p < 0.001), a higher number of dependents, and those indicating that they were single/unmarried (p = 0.005), first-generation (p < 0.001), and Pell grant eligible (p < 0.001). Annual income and housing security emerged as significant predictors: lower income was approximately twice as likely to be associated with food insecurity, while those reporting housing insecurity were six times more likely to experience food insecurity. Conclusions: The study findings reveal disparities in food and nutrition insecurity among a diverse student population at a public university. Addressing the issue among them is crucial and requires a multifaceted, inclusive approach. Emergency financial assistance and structural interventions that promote housing security are warranted.

This study examined the convergent and predictive validity of objective and subjective food swamp measures within a Community-Based Participatory Research project conducted in six neighborhoods. Using resident survey data and geocoded audit data for 20 food store types, food swamps were measured using three indices: Food Swamp Index (FSI), modified Retail Food Environment Index (mRFEI), and Physical Food Environment Index (PFEI), calculated within a ½ mile buffer of respondents’ homes. Convergent validity was assessed using Spearman correlations. At the individual level, no significant correlations were found between objective and subjective measures (all rho < 0.06, p > .05). At the neighborhood level, results varied by measurement approach: as continuous variables, only mRFEI showed a significant but divergent correlation (rho = -0.81, p < .001); when dichotomized, mRFEI showed perfect convergent validity (rho = 1.00, p < .001), PFEI showed modest convergent validity (rho = 0.12, p = .039), while FSI showed divergent validity (rho = -0.28, p < .001). Subjective measures consistently predicted shopping frequency at unhealthy food stores (all p < .004), while objective measures showed null associations. These findings suggest subjective measures better capture the behaviorally relevant food environment and may be preferable for research examining dietary behaviors.

Background: Food swamps, which have a high density of outlets selling unhealthy food relative to healthier options, are a major driver of diet-related disparities in urban communities. However, no validated food swamp measurement tools exist at the neighborhood level. Methods: We developed the Food Swamp Environment Audit Tool (FS-EAT) using community-based participatory research. FS-EAT includes 19 items as-sessing street-level food outlet types, accessibility features, food marketing, pricing, social features, and store-level information and food availability. Reliability and validity testing were performed using stakeholder surveys and feedback from the community advisory board (CAB). We compared secondary National Establishment Time Series (NETS) 2020 with FS-EAT food store data using positive predictive values and sensitivity scores. We computed Spearman's rank correlation coefficients to measure the alignment between block group-level food swamp and non-food swamp classifications, comparing FS-EAT data with NETS 2020 data. Results: Interrater reliability was strong (κ = 0.82), and 80% of stakeholders rated FS-EAT items as relevant/extremely relevant. CAB members con-firmed that FS-EAT maps aligned better with lived experiences than the secondary NETS data. GIS analyses showed that FS-EAT maps captured more accurate and timely food swamp exposure compared to NETS. When validated against the NETS 2020 business list, the FS-EAT demonstrated an overall sensitivity of 45.3% and a PPV of 68.2%. We con-firmed fewer than half of NETS-listed outlets through FS-EAT ground observation, yet more than two-thirds of audit-identified stores were also present in the NETS database. Sensitivity was highest for convenience stores (74.4%) and lowest for limited-service restaurants (29.2%). Resident perceptions were moderately correlated with FS-EAT scores (r = 0.5). While both sources agreed on the food swamp classification in 18 block groups, 15 block groups (38.5%) were classified as food swamps by the FS-EAT audit data but as non-food swamps by the NETS data. Discussion: The FS-EAT tool captures nuanced features needed to assess the level of neighborhood food swamp exposure and contributes a systematic, cost-effective way to identify neighborhood-level food swamps.