Faculty Bibliography

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

The objective of this study was to measure change in obesity prevalence among New York City (NYC) adults from 2004 to 2013-2014 and assess variation across sociodemographic subgroups. We used objectively measured height and weight data from the NYC Health and Nutrition Examination Survey to calculate relative percent change in obesity (≥ 30 kg/m²) between 2004 (n = 1987) and 2013-2014 (n = 1489) among all NYC adults and sociodemographic subgroups. We also examined changes in self-reported proxies for energy imbalance. Estimates were age-standardized and statistical significance was evaluated using two-tailed T tests and multivariable regression (p < 0.05). Between 2004 and 2013-2014, obesity increased from 27.5 to 32.4% (p = 0.01). Prevalence remained stable and high among women (31.2 to 32.8%, p = 0.53), but increased among men (23.4 to 32.0%, p = 0.002), especially among non-Latino White men and men age ≥ 65 years. Black adults had the highest prevalence in 2013-2014 (37.1%) and Asian adults experienced the largest increase (20.1 to 29.2%, p = 0.06), especially Asian women. Foreign-born participants and participants lacking health insurance also had large increases in obesity. We observed increases in eating out and screen time over time and no improvements in physical activity. Our findings show increases in obesity in NYC in the past decade, with important sociodemographic differences.

Readers should note the following two typographical errors in this article.

Research has examined how the food environment affects the risk of cardiovascular disease (CVD). Many studies have focused on residential neighbourhoods, neglecting the activity spaces of individuals. The objective of this study was to investigate whether food environments in both residential and global positioning system (GPS)-defined activity space buffers are associated with body mass index (BMI) and blood pressure (BP) among low-income adults. Data came from the New York City Low Income Housing, Neighborhoods and Health Study, including BMI and BP data (n=102, age=39.3±14.1 years), and one week of GPS data. Five food environment variables around residential and GPS buffers included: fast-food restaurants, wait-service restaurants, corner stores, grocery stores, and supermarkets. We examined associations between food environments and BMI, systolic and diastolic BP, controlling for individual- and neighbourhood-level sociodemographics and population density. Within residential buffers, a higher grocery store density was associated with lower BMI (β=- 0.20 kg/m2, P<0.05), and systolic and diastolic BP (β =-1.16 mm Hg; and β=-1.02 mm Hg, P<0.01, respectively). In contrast, a higher supermarket density was associated with higher systolic and diastolic BP (β=1.74 mm Hg, P<0.05; and β=1.68, P<0.01, respectively) within residential buffers. In GPS neighbourhoods, no associations were documented. Examining how food environments are associated with CVD risk and how differences in relationships vary by buffer types have the potential to shed light on determinants of CVD risk. Further research is needed to investigate these relationships, including refined measures of spatial accessibility/exposure, considering individual's mobility.

BACKGROUND:Local food environments include food stores (eg, supermarkets, grocery stores, bakeries) and restaurants. However, the extent to which other storefront businesses offer food/drink is not well described, nor is the extent to which food/drink availability through a full range of storefront businesses might change over time. OBJECTIVES/OBJECTIVE:This study aimed to assess food/drink availability from a full range of storefront businesses and the change over time and to consider implications for food-environment research. DESIGN/METHODS:Investigators compared direct observations from 2010 and 2015. PARTICIPANTS/SETTING/METHODS:Included were all storefront businesses offering foods/drinks on 153 street segments in the Bronx, NY. MAIN OUTCOME MEASURES/METHODS:The main outcome was change between 2010 and 2015 as determined by matches between businesses. Matches could be strict (businesses with the same name on the same street segment in both years) or lenient (similar businesses on the same street segment in both years). Investigators categorized businesses as general grocers, specialty food stores, restaurants, or other storefront businesses (eg, barber shops/beauty salons, clothing outlets, hardware stores, laundromats, and newsstands). STATISTICAL ANALYSES PERFORMED/METHODS:Investigators quantified change, specifically calculating how often businesses in 2015 were present in 2010 and vice versa. RESULTS:Strict matches for businesses in 2015 present in 2010 ranged from 29% to 52%, depending on business category; lenient matches ranged from 43% to 72%. Strict matches for businesses in 2010 present in 2015 ranged from 34% to 63%; lenient matches ranged from 72% to 83%. In 2015 compared with 2010, on 22% more of the sampled street segments, 30% more businesses were offering food/drink: 66 vs 46 general grocers, 22 vs 19 specialty food stores, 99 vs 99 restaurants, 98 vs 56 other storefront businesses. CONCLUSIONS:Over 5 years, an urban food environment changed substantially, even by lenient standards, particularly among "other storefront businesses" and in the direction of markedly greater food availability (more businesses offering food on more streets). Failure to consider a full range of food/drink sources and change in food/drink sources could result in erroneous food-environment conclusions.

BACKGROUND:Excess calorie consumption and poor diet are major contributors to the obesity epidemic. Food retailers, in particular at supermarkets, are key shapers of the food environment which influences consumers' diets. This study seeks to understand the decision-making processes of supermarket retailers-including motivators for and barriers to promoting more healthy products-and to catalogue elements of the complex relationships between customers, suppliers, and, supermarket retailers. METHODS:We recruited 20 supermarket retailers from a convenience sample of full service supermarkets and national supermarket chain headquarters serving low- and high-income consumers in urban and non-urban areas of New York. Individuals responsible for making in-store decisions about retail practices engaged in online surveys and semi-structured interviews. We employed thematic analysis to analyze the transcripts. RESULTS:Supermarket retailers, mostly representing independent stores, perceived customer demand and suppliers' product availability and deals as key factors influencing their in-store practices around product selection, placement, pricing, and promotion. Unexpectedly, retailers expressed a high level of autonomy when making decisions about food retail strategies. Overall, retailers described a willingness to engage in healthy food retail and a desire for greater support from healthy food retail initiatives. CONCLUSIONS:Understanding retailers' in-store decision making will allow development of targeted healthy food retail policy approaches and interventions, and provide important insights into how to improve the food environment.

Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: 1) TV watching (>2 hours/day), 2) video games (>2 hours/day), 3) total screen time (>2 hours/day); and 4) 20 minutes/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report > 2 hours/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policymakers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.

OBJECTIVE:Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control. RESEARCH DESIGN AND METHODS/METHODS:Census tracts in New York City were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the A1C Registry data. RESULTS:Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9, 89.6, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which was 86.2%. CONCLUSIONS:Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C Registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.

BACKGROUND:Local and national policies to encourage supermarket opening or expansion are popular strategies for improving access to healthy food for residents in neighborhoods lacking these types of stores, yet few evaluations of such initiatives exist. OBJECTIVE:Our aim was to test whether a newly opened supermarket in the Bronx, NY, changed household availability of healthy and unhealthy food items and reported daily consumption of these items among respondents residing in close proximity (≤0.5 mile) to the new supermarket. DESIGN/METHODS:This quasi-experimental study evaluated changes in purchasing and consumption habits of residents within 0.5 mile of the new supermarket as compared to residents living more than 0.5 mile from the supermarket. Data were collected through street intercept surveys at three different times: once before the store opened (March to August 2011) and in two follow-up periods (1 to 5 months and 13 to 17 months after the store opened). This study analyzed a subset of successfully geocoded resident intersections from the larger study. PARTICIPANTS/SETTING/METHODS:We surveyed 3,998 residents older than the age of 18 years in two Bronx neighborhoods about their food-purchasing behaviors before the store opened and in two follow-up periods. Responses from residents whose intersections were successfully geocoded (N=3,378) were analyzed to examine the consumption and purchasing behaviors of those in close proximity to the new store. INTERVENTION/METHODS:A new supermarket opened in a low-access neighborhood in the Bronx with the help of financial incentives through New York City's Food Retail Expansion to Support Health (FRESH) program. MAIN OUTCOME MEASURES/METHODS:The primary outcome evaluated was the change in percent of respondents reporting that the following food items were "always available" in the home: milk, fruit juice, soda, pastries, packaged snacks, fruits, and vegetables. As a secondary outcome, we explored changes in self-reported daily servings of these items. STATISTICAL ANALYSIS PERFORMED/METHODS:A difference-in-difference analysis was performed, controlling for age, education, marital status, income, sex, race, and ethnicity. RESULTS:Residents within 0.5 mile of the store had increased household availability of both healthy and unhealthy foods. After the introduction of the supermarket, the percent of residents in close proximity to the store who reported always having produce available in the home increased by 8.8% compared to those living >0.5 mile from the store in the first post-period and by 10.6% compared to those living >0.5 mile from the store in the second post-period. A similar positive increase in household availability of salty snacks and pastries was observed. Residents living in close proximity also reported greater consumption of healthy foods like produce and water, and lower intake of soft drinks and pastries. CONCLUSIONS:Given the financial support at the national and local levels to encourage supermarket development and expansion in high-need communities, it is imperative to evaluate the impact of such initiatives. Although the findings have so far been equivocal, our findings give weight to the argument that, at a micro-level, the siting of a new supermarket can indeed impact local purchasing and consumption behavior. Although purchasing for both healthy and unhealthy food items increased, reported consumption showed an increase in servings of healthy items (water, vegetables, and fruit) and a decrease in servings of unhealthy foods (soft drinks, salty snacks, and pastries).

BACKGROUND:Little is known about the role of financial hardship as it relates to drug use, especially among men who have sex with men (MSM). As such, this study aimed to investigate potential associations between financial hardship status and drug use among MSM. METHODS:We conducted a cross-sectional survey of 580 MSM in Paris recruited using a popular geosocial-networking smartphone application (GSN apps). Descriptive analyses and multivariate analyses were performed. A modified Poisson model was used to assess associations between financial hardship status and use of drugs (any drugs, tobacco, alcohol, marijuana, inhalant nitrites, and club drugs). RESULTS:In our sample, 45.5% reported that it was somewhat, very, or extremely difficult to meet monthly payments of bills (high financial hardship). In multivariate analyses, a high level of financial hardship was significantly associated with an increased likelihood of reporting use of any substance use (adjusted risk ratio [aRR] = 1.15; 95% CI = 1.05-1.27), as well as use of tobacco (aRR = 1.45; 95% CI = 1.19-1.78), marijuana (aRR = 1.48; 95% CI =1.03-2.13), and inhalant nitrites (aRR = 1.24; 95% CI = 1.03-1.50). CONCLUSIONS:Financial hardship was associated with drug use among MSM, suggesting the need for interventions to reduce the burden of financial hardship in this population.