This scoping review summarized findings and key measures from U.S.-based studies that 1) examined associations between geographic indicators of structural racism (e.g., redlining, racial segregation) and access to food retailers (e.g., supermarkets, convenience stores) or 2) documented disparities in access by neighborhood racial/ethnic composition. In 2022, relevant scientific literature was reviewed using Covidence software. Independent reviewers examined 13,069 citations; 163 citations advanced to the full-text review stage and 70 were selected for inclusion. Twenty-one studies (30%) linked one or more indicator of structural racism to food retailer access while 49 (70%) solely examined differences in access by neighborhood racial/ethnic composition. All studies featuring indicators of structural racism reported significant findings; however, indicators varied across studies making it difficult to make direct comparisons. Key indicators of structural racism in the food access literature included redlining (n = 3), gentrification (n = 3), and racial segregation (n = 4). Many U.S.-based studies have evaluated food retailer access by neighborhood racial/ethnic composition. Moving forward, studies should model indicators of structural racism and determine their influence on geographic access to large and small food retailers.

Disparities in diet quality persist in the U.S. Examining consumer food purchasing can provide unique insight into the nutritional inequities documented by race/ethnicity, socioeconomic status (SES), and geographic location (i.e., urban vs. rural). There remains limited understanding of how these three factors intersect to influence consumer food purchasing. This study aimed to summarize peer-reviewed scientific studies that provided an intersectional perspective on U.S. consumer food purchasing. Thirty-four studies were examined that presented objectively measured data on purchasing outcomes of interest (e.g., fruits, vegetables, salty snacks, sugar-sweetened beverages, Healthy Eating Index, etc.). All studies were of acceptable or high quality. Only six studies (17.6%) assessed consumer food purchases at the intersection of race/ethnicity, SES, or geographic location. Other studies evaluated racial/ethnic or SES differences in food purchasing or described the food and/or beverage purchases of a targeted population (example: low-income non-Hispanic Black households). No study assessed geographic differences in food or beverage purchases or examined purchases at the intersection of all three factors. Overall, this scoping review highlights the scarcity of literature on the role of intersectionality in consumer food and beverage purchasing and provides recommendations for future studies to grow this important area of research.

Farmers markets (FM) have been proposed by researchers, policymakers, and health agencies as a potential community-level strategy to prevent obesity and reduce disparities in healthy food access in the U.S. Information about disparities in farmers market availability is scarce. This research aimed to examine county-level associations between farmers market (FM) availability and demographic, socioeconomic, health, and environmental measures in an effort to determine if disparities in availability exist in the U.S. An ecological study of 3,135 U.S. counties was conducted in 2013 using 2009–2010 data extracted from the United States Department of Agriculture Food Environment Atlas. Logistic regression and Poisson regression models were used to determine associations between county-level FM availability and measures such as percentage of non-Hispanic black residents, median household income, and number of grocery stores per 100,000 residents (i.e., per capita). Regression models were stratified by metro county status and all analyses were adjusted for state-level clustering. There were 1,774 (56.6%) counties with at least one FM available. Median household income was associated with increased odds of having at least one FM available among non-metro counties, but not metro counties. Percentage of non-Hispanic black residents and residents living in poverty were negatively associated with per capita FMs among metro and non-metro counties. Per capita fast food restaurants was negatively associated with per capita FMs among metro counties. Disparities in FM availability exist in the U.S. More research on the behavioral and health implications of farmers market availability should be conducted.

Recent studies have reported that SNAP participants have poorer diet quality than non-participants. This study aimed to examine how differences in socio-demographic, household, and health-related measures explain disparities in diet quality between SNAP participants and non-participants using Oaxaca-Blinder decomposition analysis. We analyzed cross-sectional data on 14,331 adult respondents of the National Health and Nutrition Examination Survey (NHANES) 2009 - 2014. To measure diet quality, we applied the Healthy Eating Index (HEI)-2015 to respondents' 24-hour dietary recall data (scale: 0-100 points). We used Oaxaca-Blinder decomposition analysis to determine how much of the disparity in HEI-2015 total score between SNAP participants and non-participants was explained by socio-demographic (e.g., age, race/ethnicity, educational), household (e.g., household size, food security status), and health-related measures (e.g., BMI, smoking status). Analyses performed revealed significant differences in HEI-2015 total score by SNAP participation status (p < 0.001). We found that the total gap in HEI-2015 total score between SNAP participants and income-ineligible non-participants was 6.30 points. Socio-demographic measures alone explained 72.40% of the disparity. All measures together explained 86.31% of the disparity. The total gap between SNAP participants and income-eligible non-participants was 3.24 points. Socio-demographic measures alone explained 35.51% of this disparity while all measures together explained 56.86%. We observed disparities in diet quality between SNAP participants and non-participants. Socio-demographic, household, and health-related measures explained a significant amount of the disparity that existed between SNAP participants and income-ineligible non-participants; they explained less of the disparity between SNAP participants and income-eligible non-participants.