Faculty Bibliography

OBJECTIVE:Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias. METHODS:Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states. RESULTS:(95% CI: -0.72 to -0.31) decrease in BMI over time. CONCLUSIONS:These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

OBJECTIVE:This study aimed to examine the relationship between proximity to healthy and unhealthy food outlets around children's homes and their weight outcomes. METHODS:A total of 3,507,542 student-year observations of height and weight data from the 2009-2013 annual FitnessGram assessment of New York City public school students were used. BMI z scores were calculated, student obesity or obesity/overweight was determined using Centers for Disease Control and Prevention growth charts, and these data were combined with the locations of four food outlet types (fast-food restaurants, wait-service restaurants, corner stores, and supermarkets) to calculate distance to the nearest outlet. Associations between weight status outcomes and distance to these food outlet types were examined using neighborhood (census tract) fixed effects. RESULTS:Living farther than 0.025 mile (about half of a city block) from the nearest fast-food restaurant was associated with lower obesity and obesity/overweight risk and lower BMI z scores. Results ranged from 2.5% to 4.4% decreased obesity. Beyond this distance, there were generally no impacts of the food environment and little to no impact of other food outlet types. CONCLUSIONS:Proximity to fast-food restaurants was inversely related to childhood obesity, but no relationships beyond that were seen. These findings can help better inform policies focused on food access, which could, in turn, reduce childhood obesity.

BACKGROUND:Exposure to food advertisements is associated with poor diet among youth, and food and beverage companies are increasingly advertising on social media sites that are popular among youth. OBJECTIVE:To identify the prevalence of social media advertising among fast food, beverage, and snack companies and examine advertising techniques they use on Instagram, Facebook, Twitter, Tumblr, and Vine. METHODS:We quantified the increase in the creation of social media accounts from 2007 to 2016 among 200 fast food, beverage, and snack brands from the United States. We conducted content analyses to examine the marketing themes and healthfulness of products featured in 2000 posts from a subset of 20 brands and used multilevel regression to assess associations between marketing themes (eg, adolescents socializing) and interactive tools (eg, hashtags). RESULTS:Two hundred brands collectively managed 568 accounts in 2016. Content analyses revealed that unique social media features (eg, geo-tags) appeared in 74.5% (n = 1489) of posts, and 31.5% (n = 630) were interactive. Posts featuring adolescents were more likely to be interactive than posts featuring adults (P < 0.001). Two-thirds (67.9%; n = 362) of foods shown were unhealthy, and 61.2% (n = 435) of beverages were sugar sweetened. CONCLUSIONS:Social media food advertising is pervasive and uses interactive tools to engage with users.

BACKGROUND:Tobacco remains the leading cause of preventable death in the United States, with 41,000 deaths attributable to secondhand smoke (SHS) exposure. On July 30, 2018, the U.S. Department of Housing and Urban Development passed a rule requiring public housing authorities to implement smoke-free housing (SFH) policies. OBJECTIVES/OBJECTIVE:Prior to SFH policy implementation, we measured self-reported and objective SHS incursions in a purposeful sample of 21 high-rise buildings (>15 floors) in New York City (NYC): 10 public housing and 11 private sector buildings where most residents receive federal housing subsidies (herein 'Section 8' buildings). METHODS:) from low-cost particle monitors. SHS was measured for 7-days in non-smoking households (NYCHA n = 157, Section 8 n = 118 households) and in building common areas (n = 91 hallways and stairwells). RESULTS:was observed between and within buildings; on average nicotine concentrations were higher in NYCHA apartments and hallways than in Section 8 buildings (p < 0.05), and NYCHA residents reported seeing smokers in common areas more frequently. CONCLUSIONS:SFH policies may help in successfully reducing SHS exposure in public housing, but widespread pre-policy incursions suggest achieving SFH will be challenging.

OBJECTIVE/UNASSIGNED:To assess the accuracy of government inspection records, relative to ground observation, for identifying businesses offering foods/drinks. DESIGN/UNASSIGNED:Agreement between city and state inspection records v. ground observations at two levels: businesses and street segments. Agreement could be 'strict' (by business name, e.g. 'Rizzo's') or 'lenient' (by business type, e.g. 'pizzeria'); using sensitivity and positive predictive value (PPV) for businesses and using sensitivity, PPV, specificity and negative predictive value (NPV) for street segments. SETTING/UNASSIGNED:The Bronx and the Upper East Side (UES), New York City, USA. PARTICIPANTS/UNASSIGNED:All food/drink-offering businesses on sampled street segments (n 154 in the Bronx, n 51 in the UES). RESULTS/UNASSIGNED:By 'strict' criteria, sensitivity and PPV of government records for food/drink-offering businesses were 0·37 and 0·57 in the Bronx; 0·58 and 0·60 in the UES. 'Lenient' values were 0·40 and 0·62 in the Bronx; 0·60 and 0·62 in the UES. Sensitivity, PPV, specificity and NPV of government records for street segments having food/drink-offering businesses were 0·66, 0·73, 0·84 and 0·79 in the Bronx; 0·79, 0·92, 0·67, and 0·40 in the UES. In both areas, agreement varied by business category: restaurants; 'food stores'; and government-recognized other storefront businesses ('gov. OSB', i.e. dollar stores, gas stations, pharmacies). Additional business categories - 'other OSB' (barbers, laundromats, newsstands, etc.) and street vendors - were absent from government records; together, they represented 28·4 % of all food/drink-offering businesses in the Bronx, 22·2 % in the UES ('other OSB' and street vendors were sources of both healthful and less-healthful foods/drinks in both areas). CONCLUSIONS/UNASSIGNED:Government records frequently miss or misrepresent businesses offering foods/drinks, suggesting caveats for food-environment assessments using such records.

Background/UNASSIGNED:Tobacco spending may exacerbate financial hardship in low-income populations by using funds that could go toward essentials. This study examined post-quit spending plans among low-income smokers and whether financial hardship was positively associated with motivation to quit in the sample. Methods/UNASSIGNED:= 410). Linear regression was used to examine the relationship between financial distress, food insecurity, smoking-induced deprivation (SID) and motivation to quit (measured on a 0-10 scale). We performed summative content analyses of open-ended survey questions to identify the most common plans among participants with and without SID for how to use their tobacco money after quitting. Results/UNASSIGNED:The top three spending plans among participants with and without SID were travel, clothing and savings. There were three needs-based spending plans unique to a small number of participants with SID: housing, health care and education. Conclusions/UNASSIGNED:Financial distress and food insecurity did not enhance overall motivation to quit, while smokers with SID were less motivated to quit. Most low-income smokers, including those with SID, did not plan to use their tobacco money on household essentials after quitting.

[This corrects the article DOI: 10.1371/journal.pone.0215571.].

BACKGROUND:Tobacco remains a leading cause of preventable death in the U.S., responsible for more than 440,000 deaths each year. Approximately 10% of these deaths are attributable to exposure of non-smokers to secondhand smoke (SHS). Residents living in public multi-unit housing (MUH) are at excess risk for SHS exposure compared to the general population. On November 30, 2016, the U.S. Department of Housing and Urban Development (HUD) passed a rule requiring all public housing agencies to implement smoke-free housing (SFH) policies in their housing developments by July 30, 2018. METHODS:As part of a larger natural experiment study, we designed a protocol to evaluate indoor SHS levels before and after policy implementation through collection of repeat indoor air samples in non-smoking apartments and common areas of select high-rise NYCHA buildings subject to the HUD SFH rule, and also from socio-demographically matched private-sector high-rise control buildings not subject to the rule. A baseline telephone survey was conducted in all selected buildings to facilitate rapid recruitment into the longitudinal study and assess smoking prevalence, behaviors, and attitudes regarding the SFH policy prior to implementation. Data collection began in early 2018 and will continue through 2021. DISCUSSION/CONCLUSIONS:The baseline survey was completed by 559 NYCHA residents and 471 comparison building residents (response rates, 35, and 32%, respectively). Smoking prevalence was comparable between study arms (15.7% among NYCHA residents and 15.2% among comparison residents). The majority of residents reported supporting a building-wide smoke-free policy (63.0 and 59.9%, respectively). We enrolled 157 NYCHA and 118 comparison non-smoking households into the longitudinal air monitoring study and performed air monitoring in common areas. Follow up surveys and air monitoring in participant households occur every 6 months for 2.5 years. Capitalizing on the opportunity of this federal policy rollout, the large and diverse public housing population in NYC, and robust municipal data sources, this study offers a unique opportunity to evaluate the policy's direct impacts on SHS exposure. Methods in this protocol can inform similar SFH policy evaluations elsewhere.

Demographic and income disparities may impact food accessibility. Research has not yet well documented the precise location of healthy and unhealthy food resources around children's homes and schools. The objective of this study was to examine the food environment around homes and schools for all public school children, stratified by race/ethnicity and poverty status. This cross-sectional study linked data on the exact home and school addresses of a population-based sample of public school children in New York City from 2013 to all corner stores, supermarkets, fast-food restaurants, and wait-service restaurants. Two measures were created around these addresses for all children: 1) distance to the nearest outlet, and 2) count of outlets within 0.25 miles. The total analytic sample included 789,520 K-12 graders. The average age was 11.78 years (SD ± 4.0 years). Black, Hispanic, and Asian students live and attend schools closer to nearly all food outlet types than White students, regardless of poverty status. Among not low-income students, Black, Hispanic, and Asian students were closer from home and school to corner stores and supermarkets, and had more supermarkets around school than White students. The context in which children live matters, and more nuanced data is important for development of appropriate solutions for childhood obesity. Future research should examine disparities in the food environment in other geographies and by other demographic characteristics, and then link these differences to health outcomes like body mass index. These findings can be used to better understand disparities in food access and to help design policies intended to promote healthy eating among children.