Faculty Bibliography

BACKGROUND:Small food retailers often stock energy-dense convenience foods and are ubiquitous in low-income urban settings. With the rise in e-commerce, little is known about the acceptability of online grocery shopping from small food retailers. OBJECTIVE:To explore perceptions of the role of small food retailers (bodegas) in food access and the acceptability of online grocery shopping from bodegas among customers and owners in a diverse NYC urban neighborhood with low-income. DESIGN/METHODS:In-depth interviews were conducted with bodega owners and adult customers between May-July 2022. PARTICIPANTS/SETTING/METHODS:Bodega owners who had either implemented a locally designed online grocery system (n=4) or not (n=2). Customers (n=25) were recruited through purposive sampling and eligible if they purchased at bodegas ( > once/month), had low-income (household income ≤ 130% of the federal poverty level or Supplemental Nutrition Assistance Program (SNAP) participants), and owned smartphones. ANALYSES PERFORMED/UNASSIGNED:All interviews were transcribed and analyzed in MAXQDA using grounded theory. RESULTS:To owners and customers, bodegas were seen as good neighbors providing culturally appropriate foods and an informal financial safety net. Their perceptions concerning food cost and availability of healthy foods in bodegas diverged. Although most perceived online grocery from bodegas as a positive community resource, they also believed it was not suited to their own community due to the bodega's proximity to customers' homes and the low digital literacy of some community members. Customers reported social norms of pride in not using online grocery shopping. Owners and customers believed the service would more likely be used if government benefits like SNAP allowed payment for online orders. Both suggested improved outreach to increase program awareness and uptake. CONCLUSIONS:Online grocery shopping from small food retailers may be acceptable in urban communities with low-income and was perceived as a community resource. However, important barriers need to be addressed such as social norms related to pride in not using online grocery services, digital literacy, program awareness, and allowing SNAP payment for online orders from bodegas.

OBJECTIVE/UNASSIGNED:To perform a geospatial analysis of food insecurity in a rural county known to have poor health outcomes and assess the effect of the COVID-19 pandemic. METHODS/UNASSIGNED:In 2020, we mailed a comprehensive cross-sectional survey to all households in Sullivan County, a rural county with the second-worst health outcomes among all counties in New York State. Surveys of households included validated food insecurity screening questions. Questions were asked in reference to 2019, prior to the pandemic, and for 2020, in the first year of the pandemic. Respondents also responded to demographic questions. Raking adjustments were performed using age, sex, race/ethnicity, and health insurance strata to mitigate non-response bias. To identify significant hotspots of food insecurity within the county, we also performed geospatial analysis. FINDINGS/UNASSIGNED:From the 28,284 households surveyed, 20% of households responded. Of 4725 survey respondents, 26% of households reported experiencing food insecurity in 2019, and in 2020, this proportion increased to 35%. In 2020, 58% of Black and Hispanic households reported experiencing food insecurity. Food insecurity in 2020 was also present in 58% of unmarried households with children and in 64% of households insured by Medicaid. The geospatial analyses revealed that hotspots of food insecurity were primarily located in or near more urban areas of the rural county. CONCLUSIONS/UNASSIGNED:Our countywide health survey in a high-risk rural county identified significant increases of food insecurity in the first year of the COVID-19 pandemic, despite national statistics reporting a stable rate. Responses to future crises should include targeted interventions to bolster food security among vulnerable rural populations.

BACKGROUND:There is interest in reshaping the Supplemental Nutrition Assistance Program (SNAP) to better support family nutrition. OBJECTIVE:The Grocery Assistance Program Study (GAPS) for Families evaluated the effects of prohibiting the purchase of certain sugary foods using program funds on the nutritional quality of foods purchased and consumed by program participants. DESIGN/METHODS:A randomized experimental trial was carried out with participants randomized to one of three food benefit conditions. Baseline and follow-up measures collected included interviewer administered 24-hour dietary recalls, food purchase receipts, food security, height, and weight. PARTICIPANT/SETTING/UNASSIGNED:Adult-child dyads in households eligible for SNAP but currently not enrolled were recruited from the Minneapolis/St Paul MN metropolitan area from May of 2018 through May of 2019. A total of 293 adult-child dyads received the intervention as allocated. Of these dyads, 233 adults completed follow-up measures and met criteria for inclusion in the analytic sample, resulting in an attrition rate of 20.5%. For children, a total of 224 completed follow-up measures and met criteria for inclusion in the analytic sample, resulting in an attrition rate of 23.5%. INTERVENTION/METHODS:Participants were randomized to one of three conditions: restriction (not allowed to buy sugar sweetened beverages [SSB], sweet baked goods or candy with program funds); restriction paired with incentive (30% incentive for fruits and vegetables [FV] purchased with funds); and control (funds provided with no restrictions or incentives). Funds were provided on a four-week cycle for 20 weeks via a study provided debit card. MAIN OUTCOME MEASURES/METHODS:The primary outcome was the Healthy Eating Index (HEI)-2015 total score. Additional outcomes included selected HEI-2015 component scores; energy intake; food security; body weight; and purchasing of SSB, sweet baked goods, candies, fruits, and vegetables. STATISTICAL ANALYSIS/METHODS:Linear regression analyses were conducted with change in the outcome regressed on treatment condition for the primary outcome analyses. RESULTS:There were no differences observed between conditions in change in the nutrition and food security measures examined. Purchases of SSB and sweet baked goods and candies significantly differed by experimental condition. Purchase of restricted foods was lower at follow-up in the restriction and restriction paired with incentive conditions compared to the control condition. For example, spending on SSB at follow-up was significantly lower in the restriction ($2.66/week) and restriction paired with incentive ($2.06/week) conditions in comparison to control condition ($4.44/week) (p<.0003 and p<.0001 respectively). CONCLUSIONS:This study failed to find evidence in support of prohibiting the purchase of sugary foods with food program funds as a strategy to improve program participant nutrition, even when paired with a FV incentive. Research carried out in the context of the SNAP program is needed for a more robust evidence base.

OBJECTIVE:The aim of this study was to examine relationships between the food environment and obesity by community type. METHODS:Using electronic health record data from the US Veterans Administration Diabetes Risk (VADR) cohort, we examined associations between the percentage of supermarkets and fast-food restaurants with obesity prevalence from 2008 to 2018. We constructed multivariable logistic regression models with random effects and interaction terms for year and food environment variables. We stratified models by community type. RESULTS:Mean age at baseline was 59.8 (SD = 16.1) years; 93.3% identified as men; and 2,102,542 (41.8%) were classified as having obesity. The association between the percentage of fast-food restaurants and obesity was positive in high-density urban areas (odds ratio [OR] = 1.033; 95% CI: 1.028-1.037), with no interaction by time (p = 0.83). The interaction with year was significant in other community types (p < 0.001), with increasing odds of obesity in each follow-up year. The associations between the percentage of supermarkets and obesity were null in high-density and low-density urban areas and positive in suburban (OR = 1.033; 95% CI: 1.027-1.039) and rural (OR = 1.007; 95% CI: 1.002-1.012) areas, with no interactions by time. CONCLUSIONS:Many healthy eating policies have been passed in urban areas; our results suggest such policies might also mitigate obesity risk in nonurban areas.

OBJECTIVE:The study objective was to determine the effect of gentrification on the weight outcomes of New York City public school students living in public housing. METHODS:In a prospective cohort of 19,022 New York City public school students in public housing followed during 2009-2017, weight outcomes of students living in public housing buildings in gentrified neighborhoods were compared to those living in consistently low-socioeconomic-status neighborhoods; assignment was quasi-random in each borough. RESULTS:Among the 42,182 student-year observations, gentrification did not increase weight outcomes significantly, for BMI z scores (0.037; 95% CI: -0.012 to 0.086), obesity (0.6 percentage points [pp]; 95% CI: -0.9 to 2.1), or overweight (1.3 pp; 95% CI: -0.7 to 3.2). However, heterogeneous effects by borough were found, where the gentrification in Manhattan increased students' BMI z scores by 0.19 (95% CI: 0.09-0.29), obesity by 3.4 pp (95% CI: 0.03-6.5), and overweight by 9.2 pp (95% CI: 6.3-12.1). No heterogeneity by race and ethnicity, gender, or age was found. CONCLUSIONS:With strong internal validity, this study shows that neighborhood gentrification differentially influenced children's health through obesity, based on borough of residence. Such findings could inform policies or interventions focused on subpopulations and geographies.

BACKGROUND:In the United States, there has been a concerning rise in the prevalence of opioid use disorders (OUD) among transition-age (TA) adults, 18 to 25-years old, with a disproportionate impact on individuals and families covered by Medicaid. Of equal concern, the treatment system continues to underperform for many young people, emphasizing the need to address the treatment challenges faced by this vulnerable population at a pivotal juncture in their life course. Pharmacotherapy is the most effective treatment for OUD, yet notably, observational studies reveal gaps in the receipt of and retention in medications for opioid use disorder (MOUD), resulting in poor outcomes for many TA adults in treatment. Few current studies on OUD treatment quality explicitly consider the influence of individual, organizational, and contextual factors, especially for young people whose social roles and institutional ties remain in flux. METHODS:We introduce a retrospective, longitudinal cohort design to study treatment quality practices and outcomes among approximately 65,000 TA adults entering treatment for OUD between 2012 and 2025 in New York. We propose to combine data from multiple sources, including Medicaid claims and encounter data and a state registry of substance use disorder (SUD) treatment episodes, to examine three aspects of OUD treatment quality: 1) MOUD use, including MOUD option (e.g., buprenorphine, methadone, or extended-release [XR] naltrexone); 2) adherence to pharmacotherapy and retention in treatment; and 3) adverse events (e.g., overdoses). Using rigorous analytical methods, we will provide insights into how variation in treatment practices and outcomes are structured more broadly by multilevel processes related to communities, treatment programs, and characteristics of the patient, as well as their complex interplay. DISCUSSION/CONCLUSIONS:Our findings will inform clinical decision making by patients and providers as well as public health responses to the rising number of young adults seeking treatment for OUD amidst the opioid and polysubstance overdose crisis in the U.S.

IMPORTANCE/UNASSIGNED:Menu labeling has been implemented in restaurants in some US jurisdictions as early as 2008, but the extent to which menu labeling is associated with calories purchased is unclear. OBJECTIVE/UNASSIGNED:To estimate the association of menu labeling with calories and nutrients purchased and assess geographic variation in results. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:A cohort study was conducted with a quasi-experimental design using actual transaction data from Taco Bell restaurants from calendar years 2007 to 2014 US restaurants with menu labeling matched to comparison restaurants using synthetic control methods. Data were analyzed from May to October 2023. EXPOSURE/UNASSIGNED:Menu labeling policies in 6 US jurisdictions. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was calories per transaction. Secondary outcomes included total and saturated fat, carbohydrates, protein, sugar, fiber, and sodium. RESULTS/UNASSIGNED:The final sample included 2329 restaurants, with menu labeling in 474 (31 468 restaurant-month observations). Most restaurants (94.3%) were located in California. Difference-in-differences model results indicated that customers purchased 24.7 (95% CI, 23.6-25.7) fewer calories per transaction from restaurants in the menu labeling group in the 3- to 24-month follow-up period vs the comparison group, including 21.9 (95% CI, 20.9-22.9) fewer calories in the 3- to 12-month follow-up period and 25.0 (95% CI, 24.0-26.1) fewer calories in the 13- to 24-month follow-up period. Changes in the nutrient content of transactions were consistent with calorie estimates. Findings in California were similar to overall estimates in magnitude and direction; yet, among restaurants outside of California, no association was observed in the 3- to 24-month period. The outcome of menu labeling also differed by item category and time of day, with a larger decrease in the number of tacos vs other items purchased and a larger decrease in calories purchased during breakfast vs other times of the day in the 3- to 24-month period. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this quasi-experimental cohort study, fewer calories were purchased in restaurants with calorie labels compared with those with no labels, suggesting that consumers are sensitive to calorie information on menu boards, although associations differed by location.

Despite higher chronic disease prevalence, minoritized populations live in highly walkable neighborhoods in US cities more frequently than non-minoritized populations. We investigated whether city-level racial residential segregation (RRS) was associated with city-level walkability, stratified by population density, possibly explaining this counterintuitive association. RRS for Black-White and Latino-White segregation in large US cities was calculated using the Index of Dissimilarity (ID), and walkability was measured using WalkScore. Median walkability increased across increasing quartiles of population density, as expected. Higher ID was associated with higher walkability; associations varied in strength across strata of population density. RRS undergirds the observed association between walkability and minoritized populations, especially in higher population density cities.

OBJECTIVES:This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS:We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS:We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS:We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

Vaccination is an indispensable tool to reduce negative outcomes due to COVID-19. Although COVID-19 disproportionately affected lower income and Black and Hispanic communities, these groups have had lower population-level uptake of vaccines. Using detailed cross-sectional data, we examined racial and ethnic group differences in New York City schoolchildren becoming fully vaccinated (two doses) within 6 months of vaccine eligibility. We matched school enrollment data to vaccination data in the Citywide Immunization Registry, a census of all vaccinations delivered in New York City. We used ordinary least squares regression models to predict fully vaccinated status, with key predictors of race and ethnicity using a variety of different control variables, including residential neighborhood or school fixed effects. We also stratified by borough and by age. The sample included all New York City public school students enrolled during the 2021-2022 school year. Asian students were most likely to be vaccinated and Black and White students least likely. Controlling for student characteristics, particularly residential neighborhood or school attended, diminished some of the race and ethnicity differences. Key differences were also present by borough, both overall and by racial and ethnic groups. In sum, racial and ethnic disparities in children's COVID-19 vaccination were present. Vaccination rates varied by the geographic unit of borough; controlling for neighborhood characteristics diminished some disparities by race and ethnicity. Neighborhood demographics and resources, and the attributes, culture and preferences of those who live there may affect vaccination decisions and could be targets of future efforts to increase vaccination rates.