Faculty Bibliography

Prior studies assessing the impact of calorie labels in fast-food settings have relied on comparisons across local and state jurisdictions with and without labeling mandates; several well-designed studies indicate a small reduction of calories purchased as a result of the labels. This study exploits a staggered roll-out of calorie labels in California to study the same issue using a novel comparison of in-store purchases with calorie information and drive-through purchases without calorie information at the same locations. With this design, consumers in both the treatment and comparison groups have been subject to the same social signals associated with the policy change and may have been exposed to calorie information during prior purchases, narrowing the intervention under study to the impact of posted menu labels at the point of purchase. Transactions (N = 201,418,976) at 424 unique restaurants at a single fast-food chain were included and a difference-in-differences design was used to examine changes one and two years after the implementation of labels at in-store counters compared to baseline. Using this comparison of consumer purchases within the same jurisdictions, we found no meaningful impact of posted calorie labels at the point of purchase, suggesting that such labels did not induce behavioral change. Additional methods to strengthen the impact of labeling policies are worthy of further study.

Multi-site and multi-organizational teams are increasingly common in epidemiologic research; however, there is a lack of standards or best practices for achieving success in collaborative research networks in epidemiology. We summarize our experiences and lessons learned from the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a collaborative agreement between the Centers for Disease Control and Prevention and research teams at Drexel University, New York University, Johns Hopkins University and Geisinger, and the University of Alabama at Birmingham. We present a roadmap for success in collaborative epidemiologic research, with recommendations focused on the following areas to maximize efficiency and success in collaborative research agreements: 1) operational and administrative considerations; 2) data access and sharing of sensitive data; 3) aligning network research aims; 4) harmonization of methods and measures; and 5) dissemination of findings. Future collaborations can be informed by our experiences and ultimately dedicate more resources to achieving scientific aims and efficiently disseminating scientific work products.

Exposure to unhealthy food and beverage marketing is a major contributor to excessive weight gain among young people and it may disproportionately affect Black and Latinx communities. Appropriate and comprehensive regulations on food and beverage companies are essential, particularly as companies expand their reach and leverage the latest technologies to create marketing experiences using immersive virtual reality (VR). Although immersive VR technology is in its infancy, the potential effects of immersive VR food and beverage marketing on consumption, coupled with the history of racially targeted marketing by food and beverage corporations toward Black and Latinx communities, and the heightened burden of diet-related illnesses in Black and Latinx communities underscore a critical need to investigate immersive VR marketing targeting young people of color. This viewpoint will provide a brief description of VR food and beverage marketing as the newest food and beverage marketing frontier, highlight key concerns and knowledge gaps, and underscore future directions in research.

PURPOSE/OBJECTIVE:We developed a comprehensive sleeve gastrectomy (SG) weight loss study cohort and biorepository to uncover mechanisms, biomarkers and predictive factors of weight loss, weight maintenance and amelioration of obesity-related comorbidities. For this purpose, we collected psychosocial, anthropometric, clinical data and a variety of samples pre-surgery, intraoperatively and 1.5, 3, 12 and 24 months post-surgery. For longer-term assessment, the collection of psychosocial and anthropometric data was extended to 10 years. Here, we present in-depth characterisation of the cohort and detailed overview of study procedures as a foundation for future analyses. PARTICIPANTS/METHODS:We consented 647 participants between June 2017 and March 2020 from two bariatric surgery clinics in New York City-one major urban hospital and one private hospital. Of 355 participants who provided baseline data, 300 underwent SG. Of these, 79% are females with an average age of 38 years, 68% are Hispanic, 20% are non-Hispanic Black and 11% are non-Hispanic White. FINDINGS TO DATE/RESULTS:We collected intraoperative adipose and stomach tissues from 282 patients and biosamples (blood, urine, saliva, stool) from 245 patients at 1.5 months, 238 at 3 month, 218 at 12 months and 180 at 24 months post-surgery. We are currently collecting anthropometric and psychosocial data annually until 10 years post-surgery. Data analysis is currently underway. FUTURE PLANS/UNASSIGNED:Our future research will explore the variability in weight loss outcomes observed in our cohort, particularly among Black and Hispanic patients in comparison to their White counterparts. We will identify social determinants of health, metabolic factors and other variables that may predict weight loss success, weight maintenance and remission of obesity-related comorbidities. Additionally, we plan to leverage our biorepository for collaborative research studies. We will complete long-term follow-up data by December 2031. We plan to apply for funding to expand biosample collection through year 10 to provide insights into the mechanisms of long-term weight maintenance.

OBJECTIVE:To perform a detailed characterisation of diabetes burden and pre-diabetes risk in a rural county with previously documented poor health outcomes in order to understand the local within-county distribution of diabetes in rural areas of America. DESIGN, SETTING, AND PARTICIPANTS/METHODS:In 2021, we prospectively mailed health surveys to all households in Sullivan County, a rural county with the second-worst health outcomes of all counties in New York State. Our survey included questions on demographics, medical history and the American Diabetes Association's Pre-diabetes Risk Test. PRIMARY OUTCOME AND METHODS/UNASSIGNED:Our primary outcome was an assessment of diabetes burden within this rural county. To help mitigate non-response bias in our survey, raking adjustments were performed across strata of age, sex, race/ethnicity and health insurance. We analysed diabetes prevalence by demographic characteristics and used geospatial analysis to assess for clustering of diagnosed diabetes cases. RESULTS:After applying raking procedures for the 4725 survey responses, our adjusted diagnosed diabetes prevalence for Sullivan County was 12.9% compared with the 2019 Behavioural Risk Factor Surveillance System (BRFSS) estimate of 8.6%. In this rural area, diagnosed diabetes prevalence was notably higher among non-Hispanic Black (21%) and Hispanic (15%) residents compared with non-Hispanic White (12%) residents. 53% of respondents without a known history of pre-diabetes or diabetes scored as high risk for pre-diabetes. Nearest neighbour analyses revealed that hotspots of diagnosed diabetes were primarily located in the more densely populated areas of this rural county. CONCLUSIONS:Our mailed health survey to all residents in Sullivan County demonstrated higher diabetes prevalence compared with modelled BRFSS estimates that were based on small telephone samples. Our results suggest the need for better diabetes surveillance in rural communities, which may benefit from interventions specifically tailored for improving glycaemic control among rural residents.

Widespread pain (WP) is associated with reduced function and disability. Importantly, three-fourths of the approximately 42% of U.S. adults with obesity have WP. Moreover, rates of adult obesity are higher, and WP outcomes are worse in racialized non-Hispanic Black and Hispanic/Latino/a/X groups, potentially exacerbating existing pain disparities. Bariatric surgery significantly reduces weight and improves pain. However, recurrent or unresolved pain after bariatric surgery can hinder weight loss or facilitate weight regain. The current study conducted a secondary analysis of a longitudinal study of predictors and mechanisms of weight loss after bariatric surgery to examine the point prevalence of WP and pain trajectories 24 months post surgery. Our secondary aim was to examine the association between weight loss and pain characteristics. Our exploratory aim was to longitudinally examine racial differences in pain trajectories after bariatric surgery. Our results showed that point prevalence decreased after bariatric surgery. Additionally, significant improvements in pain trajectories occurred within the first 3 months post surgery with a pattern of pain reemergence beginning at 12 months post surgery. Hispanic/Latino/a/X participants reported a higher number of painful anatomical sites before bariatric surgery, and the rate of change in this domain for this group was faster compared with the racialized non-Hispanic Black participants. These findings suggest that pain improvements are most evident during the early stages of surgical weight loss in racialized populations of adults with WP. Thus, clinicians should routinely monitor patients' weight changes after bariatric surgery as they are likely to correspond to changes in their pain experiences. PERSPECTIVE: This article presents the prevalence and pain trajectories of racialized adults with WP after surgical weight loss. Clinicians should evaluate changes in the magnitude and spatial distribution of pain after significant weight change in these populations so that pain interventions can be prescribed with greater precision.

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.