Faculty Bibliography

OBJECTIVE:The objective of this study was to evaluate potential sources of heterogeneity in the effect of calorie labeling on fast-food purchases among restaurants located in areas with different neighborhood characteristics. METHODS:In a quasi-experimental design, using transaction data from 2329 Taco Bell restaurants across the United States between 2008 and 2014, we estimated the relationships of census tract-level income, racial and ethnic composition, and urbanicity with the impacts of calorie labeling on calories purchased per transaction. RESULTS:Calorie labeling led to small, absolute reductions in calories purchased across all population subgroups, ranging between -9.3 calories (95% CI: -18.7 to 0.0) and -37.6 calories (95% CI: -41.6 to -33.7) 2 years after labeling implementation. We observed the largest difference in the effect of calorie labeling between restaurants located in rural compared with those located in high-density urban census tracts 2 years after implementation, with the effect of calorie labeling being three times larger in urban areas. CONCLUSIONS:Fast-food calorie labeling led to small reductions in calories purchased across all population subgroups except for rural census tracts, with some subgroups experiencing a greater benefit.

BACKGROUND:Online ordering in food pantries may support food security among adults with low socioeconomic status. PURPOSE/OBJECTIVE:Determine the impact of a transition from in-person ordering to online ordering on the food security status of food pantry clients. METHODS:For this quasi-experimental study, we recruited participants from Met Council's Kosher Food Network in New York City, including one pantry in Staten Island (intervention) and three pantries in the Bronx, Queens, and Brooklyn (comparison). The final sample included 114 and 90 adults in the intervention and comparison groups, respectively, at baseline (April-July 2023); and 77 and 58 adults in those groups during follow-up (October-December 2023). Using a six-item survey, we assessed food security status, where scores range from zero to six points and higher points indicate lower food security. Secondary outcomes included nutrition security status, fruit and vegetable intake, and pantry wait time. We used a difference-in-differences approach to assess differences in outcomes between conditions, including testing for differences by age (18-64 vs. ≥65 years). RESULTS:Food security scores decreased in the intervention and comparison groups over time, with no difference in the decrease between groups (P = .87). Yet, among younger adults in the intervention group, wait time decreased during follow-up, and increased in the comparison group (difference-in-differences = -12.1 minutes (95% CI: -21.9, -2.4); P = .02). We did not observe similar differences among older adults (P = .83), nor significant changes in other outcomes. CONCLUSIONS:The transition to online ordering did not influence food security status among food pantry clients but may help to save time, especially among younger adults. CLINICAL TRIALS REGISTRATION/BACKGROUND:NCT05752721.

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.

BACKGROUND:Nudges offer a promising tool to reduce sugary drink intake among children who are most at risk for diet-related disease. OBJECTIVE:To examine the impact of online store nudges on purchases of sugary drinks for children in lower-income households. METHODS:Caregivers with lower-income were recruited to an online shopping experiment and instructed to spend $10-$30 on three beverages for their child aged 1-5 years. Participants were randomized to navigate an online supermarket in its standard version (n = 1106) or a version with nudges (n = 1135), including a product placement nudge (i.e. placing healthy beverages in prominent positions) and a swap nudge (i.e. offering a swap of water, plain milk and/or 100% fruit juice upon selection of sugary drinks). RESULTS:On average, participants purchased 1887 (SD = 2113) and 620 (SD = 1528) calories from sugary drinks per basket in the control and experimental conditions, respectively. Model-based results indicate that those in the experimental condition purchased 1267 (95% CI: 1419, 1114) fewer calories from sugary drinks, and fewer grams of total sugar (β = -253.5 g (95% CI: -286.3, -220.6)) and added sugar (β = -287.8 g (95% CI: -323.1, -252.5)) purchased from sugary drinks. CONCLUSION/CONCLUSIONS:Nudges may be an effective, acceptable, scalable strategy for leading caregivers in lower-income households to purchase fewer sugary drinks for their children.

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.

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.

OBJECTIVE/UNASSIGNED:Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations. RESEARCH DESIGN AND METHODS/UNASSIGNED:We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE. RESULTS/UNASSIGNED:Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities. CONCLUSIONS/UNASSIGNED:The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.

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.

BACKGROUND:Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors. OBJECTIVE:This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US. METHODS:Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together. RESULTS:Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas. CONCLUSIONS:The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

OBJECTIVE:To assess the criterion validity of a dietary screener questionnaire adapted for Asian Americans (ADSQ) compared to Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA-24) food diary data amongst Chinese American Adults (CHAs). The ADSQ incorporated example ethnic foods from six Asian American groups. Lessons learned with respect to translating the ADSQ from English into Simplified Chinese were also documented. DESIGN/METHODS:Agreement between a two-day food diary (one weekend day and one weekday) and the ADSQ was assessed for vegetable, fruit, dairy, added sugar, fiber, calcium, and whole grain intake using paired t-tests to compare means and Spearman correlations to assess agreement between intake of food components. SETTING/METHODS:Data were collected online and via phone interviews. PARTICIPANTS/METHODS:Thirty-three CHAs aged 19-62 years (63.6% female). RESULTS:Mean differences were small for fruit, dairy, fiber, calcium, and whole grain intake, but were significantly different for vegetables and added sugar intake. Spearman correlations were < 0.5 and non-significant (p > 0.05) for all components. Both the ASA-24 and the ADSQ identified the same categories where CHAs intake is misaligned with dietary recommendations: whole grains, total fruit, and dairy. Difficulties were encountered in translating 13 out of 26 questions. CONCLUSIONS:The ADSQ may be a useful tool to identify intervention targets for improving dietary quality, but caution is warranted when interpreting vegetable and added sugar estimates. Differences in the English and Chinese languages underscore the need to take into account both literal translations and semantics in translating the ADSQ into other languages.