Faculty Bibliography

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: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.

INTRODUCTION/BACKGROUND:The present study measures how racially-targeted food and beverage ads affect adolescents' attitudes toward ads and brands, purchase intentions for advertised products, and willingness to engage with brands on social media. METHODS:Black and White adolescents were recruited through Survey Sampling International in 2016. Participants completed an online survey in which they were randomized to view either four food and beverage ads (e.g., soda, candy commercials) featuring Black actors or four food and beverage ads featuring White actors. RESULTS:For the two components of the attitudinal outcome, Black participants were more likely to report a positive affective response toward racially-similar ads compared to Whites. However, White participants were more likely to like ads that were racially-dissimilar compared to Black participants. Data were analyzed in 2016-2017, and we used an alpha level of 0.05 to denote statistical significance. CONCLUSIONS:Both Black and White adolescents reported more positive affective responses to ads that featured Blacks compared to ads that featured Whites. Because there were no differences on two outcomes, future research should examine the influence of racially-targeted marketing in real-world contexts (e.g., social media) and longitudinal exposure to targeted advertising on dietary behavior.

BACKGROUND:The decisions that individuals make about the food and beverage products they purchase and consume directly influence their energy intake and dietary quality and may lead to excess weight gain and obesity. However, gathering and interpreting data on food and beverage purchase patterns can be difficult. Leveraging novel sources of data on food and beverage purchase behavior can provide us with a more objective understanding of food consumption behaviors. OBJECTIVE:Food and beverage purchase receipts often include time-stamped location information, which, when associated with product purchase details, can provide a useful behavioral measurement tool. The purpose of this study was to assess the feasibility, reliability, and validity of processing data from fast-food restaurant receipts using crowdsourcing via Amazon Mechanical Turk (MTurk). METHODS:Between 2013 and 2014, receipts (N=12,165) from consumer purchases were collected at 60 different locations of five fast-food restaurant chains in New Jersey and New York City, USA (ie, Burger King, KFC, McDonald's, Subway, and Wendy's). Data containing the restaurant name, location, receipt ID, food items purchased, price, and other information were manually entered into an MS Access database and checked for accuracy by a second reviewer; this was considered the gold standard. To assess the feasibility of coding receipt data via MTurk, a prototype set of receipts (N=196) was selected. For each receipt, 5 turkers were asked to (1) identify the receipt identifier and the name of the restaurant and (2) indicate whether a beverage was listed in the receipt; if yes, they were to categorize the beverage as cold (eg, soda or energy drink) or hot (eg, coffee or tea). Interturker agreement for specific questions (eg, restaurant name and beverage inclusion) and agreement between turker consensus responses and the gold standard values in the manually entered dataset were calculated. RESULTS:Among the 196 receipts completed by turkers, the interturker agreement was 100% (196/196) for restaurant names (eg, Burger King, McDonald's, and Subway), 98.5% (193/196) for beverage inclusion (ie, hot, cold, or none), 92.3% (181/196) for types of hot beverage (eg, hot coffee or hot tea), and 87.2% (171/196) for types of cold beverage (eg, Coke or bottled water). When compared with the gold standard data, the agreement level was 100% (196/196) for restaurant name, 99.5% (195/196) for beverage inclusion, and 99.5% (195/196) for beverage types. CONCLUSIONS:Our findings indicated high interrater agreement for questions across difficulty levels (eg, single- vs binary- vs multiple-choice items). Compared with traditional methods for coding receipt data, MTurk can produce excellent-quality data in a lower-cost, more time-efficient manner.

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

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

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.