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.

Purpose: Little is known about the dietary behaviors of African-American breast cancer survivors (AABCS). We sought to describe dietary intake and quality in AABCS and examine associations with demographic, social, lifestyle, and body composition factors to potentially inform the development of effective dietary interventions. Methods: Baseline data from a prospective weight loss trial of 210 AABCS were assessed. A food frequency questionnaire was used to evaluate dietary intake and diet quality via the Healthy Eating Index 2010 (HEI-2010) and Alternative Healthy Eating Index 2010 (AHEI-2010). Linear regression analysis was conducted to determine the most influential variables on diet quality. Results: Mean HEI- and AHEI-2010 total scores were 65.11 and 56.83 indicating that diet quality needs improvement. Women were the least adherent to recommendations for intake of whole grains, dairy, sodium, empty calories, sugary beverages, red/processed meats, and trans-fat. Increased self-efficacy for healthy eating behaviors, more years of education (AHEI only), negative smoking status, smaller waist circumference, and increased physical activity (HEI only) were significantly associated with higher diet quality scores. Conclusion: Our findings suggest the diet quality of AABCS needs improvement. Intervention programs may achieve higher diet quality in AABCS by focusing on increasing self-efficacy for healthy eating behaviors.