Faculty Bibliography

OBJECTIVES/OBJECTIVE:To support efforts to improve urban population health, we created a City Health Dashboard with area-specific data on health status, determinants of health, and equity at city and subcity (census tract) levels. METHODS:We developed a Web-based resource that includes 37 metrics across 5 domains: social and economic factors, physical environment, health behaviors, health outcomes, and clinical care. For the largest 500 US cities, the Dashboard presents metrics calculated to the city level and, where possible, subcity level from multiple data sources, including national health surveys, vital statistics, federal administrative data, and state education data sets. RESULTS:Iterative input from city partners shaped Dashboard development, ensuring that measures can be compared across user-selected cities and linked to evidence-based policies to spur action. Reports from early deployment indicate that the Dashboard fills an important need for city- and subcity-level data, fostering more granular understanding of health and its drivers and supporting associated priority-setting. CONCLUSIONS:By providing accessible city-level data on health and its determinants, the City Health Dashboard complements local surveillance efforts and supports urban population health improvement on a national scale. (Am J Public Health. Published online ahead of print February 21, 2019: e1-e8. doi:10.2105/AJPH.2018.304903).

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.

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.

Traditional methods of health surveillance often under-represent racial and ethnic minorities. Our objective was to use geospatial analysis and emergency claims data to estimate local chronic disease prevalence separately for specific racial and ethnic groups. We also performed a regression analysis to identify associations between median household income and local disease prevalence among Black, Hispanic, Asian, and White adults in New York City. The study population included individuals who visited an emergency department at least once from 2009 to 2013. Our main outcomes were geospatial estimates of diabetes, hypertension, and asthma prevalence by Census tract as stratified by race and ethnicity. Using emergency claims data, we identified 4.9 million unique New York City adults with 28.5% of identifying as Black, 25.2% Hispanic, and 6.1% Asian. Age-adjusted disease prevalence was highest among Black and Hispanic adults for diabetes (13.4 and 13.1%), hypertension (28.7 and 24.1%), and asthma (9.9 and 10.1%). Correlation between disease prevalence maps demonstrated moderate overlap between Black and Hispanic adults for diabetes (0.49), hypertension (0.57), and asthma (0.58). In our regression analysis, we found that the association between low income and high disease prevalence was strongest for Hispanic adults, whereas increases in income had more modest reductions in disease prevalence for Black adults, especially for diabetes. Our geographically detailed maps of disease prevalence generate actionable evidence that can help direct health interventions to those communities with the highest health disparities. Using these novel geographic approaches, we reveal the underlying epidemiology of chronic disease for a racially and culturally diverse population.

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.

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).

OBJECTIVE: To examine Supplemental Nutrition Assistance Program (SNAP) recipients' use of the first online supermarket accepting Electronic Benefit Transfer (EBT) payment. METHODS: In this mixed-methods study, the authors collected EBT purchase data from an online grocer and attempted a randomized controlled trial in the South Bronx, New York City, followed by focus groups with SNAP beneficiaries aged >/=18 years. Participants were randomized to shop at their usual grocery store or an online supermarket for 3 months. Focus groups explored barriers and motivators to online EBT redemption. RESULTS: Few participants made online purchases, even when incentivized in the randomized controlled trial. Qualitative findings highlighted a lack of perceived control over the online food selection process as a key barrier to purchasing food online. Motivators included fast, free shipping and discounts. CONCLUSIONS AND IMPLICATIONS: Electronic Benefit Transfer for online grocery purchases has the potential to increase food access among SNAP beneficiaries, but challenges exist to this new food buying option. Understanding online food shopping barriers and motivators is critical to the success of policies targeting the online expansion of SNAP benefits.

PURPOSE/OBJECTIVE:To develop a composite Cancer Burden Index and produce 95% confidence intervals (CIs) as measures of uncertainties for the index. METHODS:The Kentucky Cancer Registry has developed a cancer burden Rank Sum Index (RSI) to guide statewide comprehensive cancer control activities. However, lack of interval estimates for RSI limits its applications. RSI also weights individual measures with little inherent variability equally as ones with large variability. To address these issues, a Modified Sum Index (MSI) was developed to take into account of magnitudes of observed values. A simulation approach was used to generate individual and simultaneous 95% CIs for the rank MSI. An uncertainty measure was also calculated. RESULTS:At the Area Development Districts (ADDs) level, the ranks of the RSI and the MSI were almost identical, while larger variation was found at the county level. The widths of the CIs at the ADD level were considerably shorter than those at the county level. CONCLUSION/CONCLUSIONS:The measures developed for estimating composite cancer burden indices and the simulated CIs provide valuable information to guide cancer prevention and control effort. Caution should be taken when interpreting ranks from small population geographic units where the CIs for the ranks overlap considerably.

BACKGROUND:Walkable neighborhoods are purported to impact a range of cardiometabolic outcomes through increased walking, but there is limited research that examines multiple cardiometabolic outcomes. Additionally, few Walk Score (a novel measure of neighborhood walkability) studies have been conducted in a European context. We evaluated associations between neighborhood Walk Score and selected cardiometabolic outcomes, including obesity, hypertension and heart rate, among adults in the Paris metropolitan area. METHODS AND RESULTS/RESULTS:We used data from the second wave of the RECORD Study on 5993 participants recruited in 2011-2014, aged 34-84 years, and residing in Paris (France). To this existing dataset, we added Walk Score values for participants' residential address. We used multilevel linear models for the continuous outcomes and modified Poisson models were used for our categorical outcomes to estimate associations between the neighborhood Walk Score (both as a continuous and categorical variable) (0-100 score) and body mass index (BMI) (weight/height2 in kg/m2), obesity (kg/m2), waist circumference (cm), systolic blood pressure (SBP) (mmHg), diastolic blood pressure (DBP) (mmHg), hypertension (mmHg), resting heart rate (RHR) (beats per minute), and neighborhood recreational walking (minutes per week). Most participants lived in Walker's Paradise (48.3%). In multivariate models (adjusted for individual variables, neighborhood variables, and risk factors for cardiometabolic outcomes), we found that neighborhood Walk Score was associated with decreased BMI (β: -0.010, 95% CI: -0.019 to -0.002 per unit increase), decreased waist circumference (β: -0.031, 95% CI: -0.054 to -0.008), increased neighborhood recreational walking (β: +0.73, 95% CI: +0.37 to +1.10), decreased SBP (β: -0.030, 95% CI: -0.063 to -0.0004), decreased DBP (β: -0.028, 95% CI: -0.047 to -0.008), and decreased resting heart rate (β: -0.026 95% CI: -0.046 to -0.005). CONCLUSIONS:In this large population-based study, we found that, even in a European context, living in a highly walkable neighborhood was associated with improved cardiometabolic health. Designing walkable neighborhoods may be a viable strategy in reducing cardiovascular disease prevalence at the population level.