Faculty Bibliography

BACKGROUND:Limited research has addressed the obesity-COVID-19 severity association in paediatric patients. OBJECTIVE:To determine whether obesity is an independent risk factor for COVID-19 severity in paediatric patients and whether age modifies this association. METHODS:SARS-CoV-2-positive patients at NYU Langone Health from 1 March 2020 to 3 January 2021 aged 0-21 years with available anthropometric measurements: weight, length/height and/or body mass index (BMI). Modified log-Poisson models were utilized for the analysis. Main outcomes were 1) hospitalization and 2) critical illness (intensive care unit [ICU] admission). RESULTS:One hundred and fifteen of four hundred and ninety-four (23.3%) patients had obesity. Obesity was an independent risk factor for critical illness (adjusted risk ratio [ARR] 2.02, 95% CI 1.17 to 3.48). This association was modified by age, with obesity related to a greater risk for critical illness in adolescents (13-21 years) [ARR 3.09, 95% CI 1.48 to 6.47], but not in children (0-12 years). Obesity was not an independent risk factor for hospitalization for any age. CONCLUSION/CONCLUSIONS:Obesity was an independent risk factor for critical illness in paediatric patients, and this association was modified by age, with obesity related to a greater risk for critical illness in adolescents, but not in children. These findings are crucial for patient risk stratification and care.

The efficacy of COVID-19 mRNA vaccines is high, but breakthrough infections still occur. We compared the SARS-CoV-2 genomes of 76 breakthrough cases after full vaccination with BNT162b2 (Pfizer/BioNTech), mRNA-1273 (Moderna), or JNJ-78436735 (Janssen) to unvaccinated controls (February-April 2021) in metropolitan New York, including their phylogenetic relationship, distribution of variants, and full spike mutation profiles. The median age of patients in the study was 48 years; 7 required hospitalization and 1 died. Most breakthrough infections (57/76) occurred with B.1.1.7 (Alpha) or B.1.526 (Iota). Among the 7 hospitalized cases, 4 were infected with B.1.1.7, including 1 death. Both unmatched and matched statistical analyses considering age, sex, vaccine type, and study month as covariates supported the null hypothesis of equal variant distributions between vaccinated and unvaccinated in χ2 and McNemar tests (P > 0.1), highlighting a high vaccine efficacy against B.1.1.7 and B.1.526. There was no clear association among breakthroughs between type of vaccine received and variant. In the vaccinated group, spike mutations in the N-terminal domain and receptor-binding domain that have been associated with immune evasion were overrepresented. The evolving dynamic of SARS-CoV-2 variants requires broad genomic analyses of breakthrough infections to provide real-life information on immune escape mediated by circulating variants and their spike mutations.

PURPOSE/OBJECTIVE:To examine how the choice of neighborhood food environment definition impacts the association with diet. METHODS:) and relative measures (i.e., percentage of all food stores or restaurants); and various buffer distances, including administrative units (census tract) and empirically-derived buffers ("classic" network, "sausage" network) tailored to community type (higher-density urban, lower-density urban, suburban/small town, rural). Using generalized estimating equations, we estimated the association between each geospatial measure and DIS, controlling for individual- and neighborhood-level sociodemographics. RESULTS:The choice of buffer-based measure did not change the direction or magnitude of associations with DIS. Effect estimates derived from administrative units were smaller than those derived from tailored empirically-derived buffer measures. Substantively, a 10% increase in the percentage of fast food restaurants using a "classic" network buffer was associated with a 6.3 (SE=1.17) point higher DIS (p<0.001). The relationship between the percentage of supermarkets and DIS, however, was null. We observed high correlation coefficients between buffer-based density measures of supermarkets and fast food restaurants (r=0.73-0.83), which made it difficult to estimate independent associations by food outlet type. CONCLUSIONS:Researchers should tailor buffer-based measures to community type in future studies, and carefully consider the theoretical and statistical implications for choosing relative (vs. absolute) measures.

Restaurants are understudied yet increasingly important food environment institutions for tackling diet-related diseases. This scoping review analyzes research and gray literature (n = 171 records) to assess which healthy eating promotion strategies have been implemented in restaurants and the associated motivations, barriers, and outcomes, compared by restaurant type (corporate/chain vs. independently owned restaurants) and initiator (restaurant-initiated vs. investigator-initiated). We found that the most commonly reported strategy was the increase of generally healthy offerings and the promotion of such offerings. Changes in food availability were more common among corporate restaurants and initiated by restaurants, while environmental facilitators were more commonly initiated by investigators and associated with independently owned restaurants. Aside from those associated with revenue, motivations and barriers for healthy eating promoting strategies varied by restaurant type. While corporate restaurants were also motivated by public health criticism, independently owned restaurants were motivated by interests to improve community health. Revenue concerns were followed by food sourcing issues in corporate restaurants and lack of interest among independently owned restaurants. Among reporting sources, most outcomes were revenue positive. This study shows the need for practice-based evidence and accounting for restaurant business models to tailor interventions and policies for sustained positive changes in these establishments.

Importance/UNASSIGNED:Secondhand smoke (SHS) exposure is associated with many health conditions in children and adults. Millions of individuals in the US are currently exposed to SHS in their homes. Objective/UNASSIGNED:To investigate whether a federal ban on smoking in public housing settings was associated with a decrease in indoor SHS levels in New York City public housing developments 12 months after the policy's implementation. Design, Setting, and Participants/UNASSIGNED:This cohort study tracked indoor air quality longitudinally from April 2018 to September 2019 and used difference-in-differences analysis to examine SHS exposure before vs after implementation of the 2018 federal smoke-free housing (SFH) policy in 10 New York City Housing Authority (NYCHA) buildings vs 11 matched low-income buildings not subject to the SFH policy (ie, Section 8 buildings). Exposures/UNASSIGNED:Federal SFH policy implementation, beginning July 30, 2018. Main Outcomes and Measures/UNASSIGNED:Comparison of nicotine concentration levels from passive, bisulfate-coated filters before vs 12 months after implementation of the federal SFH policy. Secondary outcomes included changes in particulate matter less than 2.5 μm in diameter, measured with low-cost particle monitors, and counts of cigarette butts in common areas. Results/UNASSIGNED:Air quality was measured repeatedly in a total of 153 NYCHA and 110 Section 8 nonsmoking households as well as in 91 stairwells and hallways. Before the SFH policy implementation, air nicotine was detectable in 19 of 20 stairwells (95.0%) in NYCHA buildings and 15 of 19 stairwells (78.9%) in Section 8 buildings (P = .19) and in 17 of 19 hallways (89.5%) in NYCHA buildings and 14 of 23 hallways (60.9%) in Section 8 buildings (P = .004). Nicotine was detected less frequently inside nonsmoking apartments overall (26 of 263 [9.9%]) but more frequently in NYCHA apartments (20 of 153 [13.1%]) than in Section 8 apartments (6 of 110 [5.5%]) (P = .04). One year after policy implementation, there was no differential change over time in nicotine concentrations measured in stairwells (DID, 0.03 μg/m3; 95% CI, -0.99 to 1.06 μg/m3) or inside nonsmoking households (DID, -0.04 μg/m3; 95% CI, -0.24 to 0.15 μg/m3). Larger decreases in nicotine concentration were found in NYCHA hallways than in Section 8 hallways (DID, -0.43 μg/m3; 95% CI, -1.26 to 0.40 μg/m3). Conclusions and Relevance/UNASSIGNED:The findings suggest that there was no differential change in SHS in NYCHA buildings 12 months after SFH policy implementation. Additional support may be needed to ensure adherence to SFH policies.

BACKGROUND:Diabetes prevalence and incidence vary by neighborhood socioeconomic environment (NSEE) and geographic region in the United States. Identifying modifiable community factors driving type 2 diabetes disparities is essential to inform policy interventions that reduce the risk of type 2 diabetes. OBJECTIVE:This paper aims to describe the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a group funded by the Centers for Disease Control and Prevention to apply harmonized epidemiologic approaches across unique and geographically expansive data to identify community factors that contribute to type 2 diabetes risk. METHODS:The Diabetes LEAD Network is a collaboration of 3 study sites and a data coordinating center (Drexel University). The Geisinger and Johns Hopkins University study population includes 578,485 individuals receiving primary care at Geisinger, a health system serving a population representative of 37 counties in Pennsylvania. The New York University School of Medicine study population is a baseline cohort of 6,082,146 veterans who do not have diabetes and are receiving primary care through Veterans Affairs from every US county. The University of Alabama at Birmingham study population includes 11,199 participants who did not have diabetes at baseline from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a cohort study with oversampling of participants from the Stroke Belt region. RESULTS:The Network has established a shared set of aims: evaluate mediation of the association of the NSEE with type 2 diabetes onset, evaluate effect modification of the association of NSEE with type 2 diabetes onset, assess the differential item functioning of community measures by geographic region and community type, and evaluate the impact of the spatial scale used to measure community factors. The Network has developed standardized approaches for measurement. CONCLUSIONS:The Network will provide insight into the community factors driving geographical disparities in type 2 diabetes risk and disseminate findings to stakeholders, providing guidance on policies to ameliorate geographic disparities in type 2 diabetes in the United States. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/21377.

INTRODUCTION/BACKGROUND:Previous research on sugar-sweetened beverage trends has focused on self-reported consumption from surveys. Few studies used objective store sales or explored differences by area-level demographics and store type. METHODS:The average volume of beverages sold per store per 3-digit zoning improvement plan code from 2006 to 2015 was calculated using national Nielsen Retail Scanner point-of-sale data from 24,240 stores. A multilevel regression model analyzed annual trends, with random intercepts for state and separate models for beverage type (regular soda, no/low-calorie soda, other sugary drinks, 100% fruit juice, bottled water). Differences by store type (convenience, supermarkets, drug stores, mass merchandisers) and area-level demographics (categorized as tertiles) were examined. Data were analyzed in 2019. RESULTS:The model-based estimates indicated that sales of regular soda (-11.8%), no/low-calorie soda (-19.8%), and 100% fruit juice (-31.9%) decreased over time, whereas sales of bottled water (+34.4%) increased and sales of other sugary drinks remained stable (+2.4%). Decreases in sugar-sweetened beverage sales were largely concentrated in supermarkets and larger in areas with high income and education levels and a high percentage of black and Hispanic people. There were also relatively larger increases in bottled water sales in states located in the South and Midwest. CONCLUSIONS:The finding that sales of sugar-sweetened beverages decreased over time, whereas sales of bottled water increased is encouraging because sugar-sweetened beverage consumption is linked to obesity and other chronic conditions. This study provides a novel, rigorous assessment of U.S. beverage sales trends and differences by community and store characteristics.

We examine the causal link between proximity to fast food and the incidence of childhood obesity among low-income households in New York City. Using individual-level longitudinal data on students living in public housing linked to restaurant location data, we exploit the naturally occurring within-development variation in distance to fast food restaurants to estimate the impact of proximity on obesity. Since the assignment of households to specific buildings is based upon availability at the time of assignment to public housing, the distance between student residence and retail outlets-including fast food restaurants, wait-service restaurants, supermarkets, and corner stores-is plausibly random. Our credibly causal estimates suggest that childhood obesity increases with proximity to fast food, with larger effects for younger children who attend neighborhood schools.

Using objectively-measured height and weight data from academic years 2009-2013 (n = 1,114,010 student-year observations), we estimated the association between the food outlet in closest proximity to schools and the likelihood of obesity among New York City public high school students. Obesity risk was higher for students with a corner store as the nearest option to schools, regardless of whether other food outlet types were located within a quarter mile or a half mile of schools (i.e., benchmarks for zoning policies). Policymakers may want to consider introducing healthier food options near schools, in conjunction with programs to support changes within corner stores.

OBJECTIVE:s rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these two conditions relate, and which subgroups of children are more likely to develop diabetes with and without obesity. METHODS:To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from healthcare claims data, from 2009-2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots. RESULTS:We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents. CONCLUSIONS:Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric type 2 diabetes. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these two populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.