Faculty Bibliography

Naturalistic online grocery stores could provide a novel setting for evaluating nutrition interventions. In 2021-2022, we recruited US adults (n 144, 59% low-income) to complete two weekly study visits: one in a naturalistic ('mock') online grocery store developed for research and one in a real online grocery store. Participants selected groceries and responded to survey questions. Analyses examined survey responses and expenditures on fifteen food categories (e.g., bread, sugar-sweetened beverages). Nearly all enrolled participants completed both visits (98% retention). Moreover, nearly participants all reported that their selections in the naturalistic store were similar to their usual purchases (95%) and that the naturalistic store felt like a real store (92%). Participants' spending on food categories in the naturalistic store were moderately-to-strongly correlated with their spending in the real store (range of correlation coefficients: 0⋅36-0⋅67, all P-values < 0⋅001). Naturalistic online grocery stores may offer a promising platform for conducting nutrition research.

How online grocery shopping behaviors differ among Asian American (AA) ethnic subgroups and acculturation level is unknown. From June 9-15, 2020, we administered an online survey to a nationally-derived nonprobability sample of 2,895 AA adults, including 1,737 East, 570 South, and 587 Southeast Asian adults, assessing online grocery shopping (yes/no, frequency, reasons). We used logistic regression to compare responses by subgroup and acculturation score, controlling for sociodemographics. Thirty-percent of participants reported shopping online for groceries in a typical month, with a higher percentage among South (45%) versus East Asian adults (23%). Participants with low (vs. high) acculturation scores were more likely to report a lack of special foods (OR = 0.7; 95% CI: 0.5-0.98) and poor food quality (OR = 0.6; 95% CI: 0.4-0.7) as preventing them from shopping online. Online grocery shopping has the capacity to address inequities in health, potentially via culturally-tailored programs designed for less-acculturated AA adults.

BACKGROUND:Online grocery shopping has surged in popularity, but we know little about online grocery shopping behaviors and attitudes of adults with low income, including differences by age. METHODS:= 3526). Participants completed an online survey designed to assess diet and online food shopping behaviors. Using logistic regression, we examined the relationship between participant characteristics, including age, and the likelihood of online grocery shopping, and separately examined variation in the reasons for online grocery shopping by age. RESULTS:&lt; 0.001)). CONCLUSION/CONCLUSIONS:Strategies for making online grocery shopping more affordable for adults with lower income may be promising, especially online produce. For older adults, additional support may be needed to make online shopping a suitable replacement for in-store shopping, such as education on technology and combining it with opportunities for social support.

PURPOSE/OBJECTIVE:The Supplemental Nutrition Assistance Program (SNAP) Online Purchasing Pilot (OPP) allows for the online purchase of groceries using SNAP benefits. First implemented in California in late April 2020, little is known about program usage. This study assessed initial implementation of SNAP Online in California using SNAP OPP transaction data from April - October 2020. Insights can identify usage differences by demographics, store availability, and rurality to help inform future pilot programs and nutrition initiatives. DESIGN/METHODS:Using generalized estimating equations, we modeled county-level associations between transactions and county-level demographics, rurality, and retailer availability. SETTING/METHODS:Transaction data from California's Department of Social Services (CDSS) was linked with publicly-available, county-level demographics. SUBJECTS/METHODS:Anonymized county-level data on SNAP Online transactions and CalFresh households. MEASURES/METHODS:The primary outcome was successful SNAP Online food transactions per county. ANALYSIS/METHODS:Generalized estimating equation models with clustering by county was used. RESULTS:During the first 7 months, median SNAP Online transactions per county per month was 665; 2.7% of total SNAP redemptions were from SNAP Online. Counties with more female-led, disabled, Latino, or Asian CalFresh households had fewer Amazon transactions. Each additional Walmart per county corresponded to 260.7 more Walmart transactions (P < .001). Each percent increase in county zip codes covered by Amazon Fresh corresponded to 45.4 fewer Walmart transactions (P < .05) and 37.3 more Amazon transactions (P < .001). CONCLUSION/CONCLUSIONS:Number of stores per county was associated with greater online grocery transactions, whereas rurality was not. County-level SNAP demographics correlated with transactions at particular retailers.

Importance/UNASSIGNED:The establishment and renovation of supermarkets may promote healthy diet practices among youth by increasing retail infrastructure for fresh foods. Objective/UNASSIGNED:To estimate the association between the Food Retail Expansion to Support Health (FRESH) program and the weight status of children and adolescents. Design, Setting, and Participants/UNASSIGNED:Using a difference-in-differences (DiD) design and including 12 months before and after a FRESH supermarket opened, data were analyzed for residentially stable public school students in kindergarten through 12th grade with objectively measured height and weight data from the academic years 2009 through 2016. Of the 8 FRESH-subsidized supermarkets in residential neighborhoods in New York City, New York, 5 were new and 3 were renovation projects between December 2011 and June 2014. Data were analyzed from June 2021 to January 2022. Interventions/UNASSIGNED:The treatment group included students who resided within 0.50 miles of a FRESH-subsidized supermarket and had at least 1 body mass index (BMI) measurement within 12 months before and 3 to 12 months after the month a FRESH supermarket opened (n = 22 712 student-year observations). A 2-stage matching-weighting approach was used to construct a control group of students who resided more than 0.50 miles from a FRESH supermarket in a FRESH-eligible area (n = 86 744 student-year observations). Main Outcomes and Measures/UNASSIGNED:BMI z score was calculated using objectively measured height and weight data from FITNESSGRAM, an annual, school-based, standardized fitness assessment of every New York City public school student. Obesity was defined as 95th percentile or greater of the BMI z score using Centers for Disease Control and Prevention growth charts. Results/UNASSIGNED:The treatment group in the analytic sample had 11 356 students (22 712 student-year observations), and the control group had 43 372 students (86 744 student-year observations). The students were predominately Black (18.8%) and Hispanic and Latino (68.5%) and eligible for free or reduced-priced lunch (84.6%). There was a significant decrease in BMI z score among students who resided within 0.50 miles of a FRESH supermarket (vs control group students) in the 3- to 12-month follow-up period (DiD, -0.04; 95% CI, -0.06 to -0.02). This was true for those exposed to supermarkets that were either new (DiD, -0.07; 95% CI, -0.11 to -0.03) or renovated (DiD, -0.03; 95% CI, -0.06 to -0.01). A statistically significant decrease was also observed in the likelihood of obesity (DiD, -0.01; 95% CI, -0.02 to -0.002). Conclusions and Relevance/UNASSIGNED:Government-subsidized supermarkets may contribute to a small decrease in obesity risk among children residing near those supermarkets, if part of a comprehensive policy approach.

During the COVID-19 pandemic in 2020, the Supplemental Nutrition Assistance Program (SNAP) Online Purchasing Pilot (OPP) was rapidly expanded across the US. This program, enabling direct-to-home grocery delivery, could be a transformative step towards improving fresh-food access. However, lack of information on which areas are serviced by SNAP OPP hinders the identification of potential demographic and regional disparities in access. Lessons from the initial implementation period are critical for understanding continuing inequities and informing the implementation of future programs. In California, SNAP OPP expanded food access for 85.9% of the state's SNAP households in 2020-21. Coverage was significantly greater in urban areas, covering 87.2% of CalFresh households in urban limited food access areas as compared with 29.9% of CalFresh households in rural limited food access areas. County-level COVID-19 rates did not have a meaningful association with SNAP OPP coverage.

BACKGROUND:There are persistent disparities in weight- and diet-related diseases by sexual orientation. Lesbian and bisexual females have a higher risk of obesity and cardiovascular disease compared to heterosexual females. Gay and bisexual males have a higher risk of diabetes and cardiovascular disease compared to heterosexual males. However, it remains unknown how sexual orientation groups differ in their dietary quality. OBJECTIVE:This study aimed to determine whether dietary quality differs by sexual orientation and sex among US adults. DESIGN/METHODS:This was a cross-sectional study of 24-h dietary recall data from a nationally representative sample of adults aged 20-65 participating in the 2011-2016 National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS/SETTING/METHODS:Study participants were adults (n=8,851) with complete information on dietary intake, sexual orientation, and sex. MAIN OUTCOME MEASURES/METHODS:The main outcome measures were daily energy intake from 20 specific food and beverage groups and Healthy Eating Index-2015 (HEI-2015) scores for sexual orientation groups (heterosexual vs. gay/lesbian/bisexual (GLB)). STATISTICAL ANALYSES PERFORMED/METHODS:Ordinary least squares regressions were used to calculate adjusted means for each food and beverage group and HEI-2015, stratified by sex and controlling for covariates (e.g., age, race/ethnicity) and survey cycles (2011-2012; 2013-2014; 2015-2016). RESULTS:Among males, red and processed meat/poultry/seafood (p=0.01) and sandwiches (p=0.02) were smaller contributors to energy intake for gay/bisexual males compared to heterosexual males. Among females, cereals (p=0.04) and mixed dishes (p=0.02) were smaller contributors to energy intake for lesbian/bisexual females compared to heterosexual females. Gay/bisexual males had significantly higher total HEI-2015 scores than heterosexual males (53.40 ± 1.36 vs. 49.29 ± 0.32, difference=4.14, p=0.004). Lesbian/bisexual females did not differ in total or component HEI-2015 scores from heterosexual females. CONCLUSIONS:While GLB groups were similar for a variety of dietary outcomes compared to heterosexual groups, gay and bisexual males displayed healthier dietary quality for processed meat (by consuming smaller amounts) and overall dietary quality (according to HEI-2015) compared to heterosexual males.

OBJECTIVE:We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS/METHODS:As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: (1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); (2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and (3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of (1) fast-food restaurants and (2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural. RESULTS:Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways. CONCLUSIONS:Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

BACKGROUND:Large-scale longitudinal studies evaluating influences of the built environment on risk for type 2 diabetes (T2D) are scarce, and findings have been inconsistent. OBJECTIVE:To evaluate whether land use environment (LUE), a proxy of neighborhood walkability, is associated with T2D risk across different US community types, and to assess whether the association is modified by food environment. METHODS:The Veteran's Administration Diabetes Risk (VADR) study is a retrospective cohort of diabetes-free US veteran patients enrolled in VA primary care facilities nationwide from January 1, 2008, to December 31, 2016, and followed longitudinally through December 31, 2018. A total of 4,096,629 patients had baseline addresses available in electronic health records that were geocoded and assigned a census tract-level LUE score. LUE scores were divided into quartiles, where a higher score indicated higher neighborhood walkability levels. New diagnoses for T2D were identified using a published computable phenotype. Adjusted time-to-event analyses using piecewise exponential models were fit within four strata of community types (higher-density urban, lower-density urban, suburban/small town, and rural). We also evaluated effect modification by tract-level food environment measures within each stratum. RESULTS:In adjusted analyses, higher LUE had a protective effect on T2D risk in rural and suburban/small town communities (linear quartile trend test p-value <0.001). However, in lower density urban communities, higher LUE increased T2D risk (linear quartile trend test p-value <0.001) and no association was found in higher density urban communities (linear quartile trend test p-value = 0.317). Particularly strong protective effects were observed for veterans living in suburban/small towns with more supermarkets and more walkable spaces (p-interaction = 0.001). CONCLUSION/CONCLUSIONS:Among veterans, LUE may influence T2D risk, particularly in rural and suburban communities. Food environment may modify the association between LUE and T2D.