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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., Competing Interests: The authors have declared that no competing interests exist.

Objectives: 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.

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.

Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.

Little is known about how neighborhood noise influences cardiovascular disease (CVD) risk among low-income populations. The aim of this study was to investigate associations between neighborhood noise complaints and body mass index (BMI) and blood pressure (BP) among low-income housing residents in New York City (NYC), including the use of global positioning system (GPS) data. Data came from the NYC Low-Income Housing, Neighborhoods and Health Study in 2014, including objectively measured BMI and BP data (N = 102, Black = 69%), and 1 week of GPS data. Noise reports from "NYC 311" were used to create a noise complaints density (unit: 1000 reports/km 2 ) around participants' home and GPS-defined activity space neighborhoods. In fully-adjusted models, we examined associations of noise complaints density with BMI (kg/m 2 ), and systolic and diastolic BP (mmHg), controlling for individual- and neighborhood-level socio-demographics. We found inverse relationships between home noise density and BMI (B = -2.7 [kg/m 2 ], p = 0.009), and systolic BP (B = -5.3 mmHg, p = 0.008) in the fully-adjusted models, and diastolic BP (B = -3.9 mmHg, p = 0.013) in age-adjusted models. Using GPS-defined activity space neighborhoods, we observed inverse associations between noise density and systolic BP (B = -10.3 mmHg, p = 0.019) in fully-adjusted models and diastolic BP (B = -7.5 mmHg, p = 0.016) in age-adjusted model, but not with BMI. The inverse associations between neighborhood noise and CVD risk factors were unexpected. Further investigation is needed to determine if these results are affected by unobserved confounding (e.g., variations in walkability). Examining how noise could be related to CVD risk could inform effective neighborhood intervention programs for CVD risk reduction.

The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13-19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.

Objective: To use novel geographic methods and large-scale claims data to identify the local distribution of pediatric chronic diseases in New York City., Methods: Using a 2009 all-payer emergency claims database, we identified the proportion of unique children aged 0 to 17 with diagnosis codes for specific medical and psychiatric conditions. As a proof of concept, we compared these prevalence estimates to traditional health surveys and registry data using the most geographically granular data available. In addition, we used home addresses to map local variation in pediatric disease burden., Results: We identified 549,547 New York City children who visited an emergency department at least once in 2009. Though our sample included more publicly insured and uninsured children, we found moderate to strong correlations of prevalence estimates when compared to health surveys and registry data at prespecified geographic levels. Strongest correlations were found for asthma and mental health conditions by county among younger children (0.88, P = .05 and 0.99, P < .01, respectively). Moderate correlations by neighborhood were identified for obesity and cancer (0.53 and 0.54, P < .01). Among adolescents, correlations by health districts were strong for obesity (0.95, P = .05), and depression estimates had a nonsignificant, but strong negative correlation with suicide attempts (-0.88, P = .12). Using SaTScan, we also identified local hot spots of pediatric chronic disease., Conclusions: For conditions easily identified in claims data, emergency department surveillance may help estimate pediatric chronic disease prevalence with higher geographic resolution. More studies are needed to investigate limitations of these methods and assess reliability of local disease estimates., (Copyright © 2016 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)

Aims: To identify population characteristics associated with local variation in the prevalence of diabetic complications and compare the geographic distribution of different types of complications in New York City., Methods: Using an all-payer database of emergency visits, we identified the proportion of unique adults with diabetes who also had cardiac, neurologic, renal and lower extremity complications. We performed multivariable regression to identify associations of demographic and socioeconomic factors, and diabetes-specific emergency department use with the prevalence of diabetic complications by Census tract. We also used geospatial analysis to compare local hotspots of diabetic complications., Results: We identified 4.6million unique New York City adults, of which 10.5% had diabetes. Adjusting for demographic and socioeconomic factors, diabetes-specific emergency department use was associated with severe microvascular renal and lower extremity complications (p-values<0.001), but not with severe macrovascular cardiac or neurologic complications (p-values of 0.39 and 0.29). Our hotspot analysis demonstrated significant geographic heterogeneity in the prevalence of diabetic complications depending on the type of complication. Notably, the geographic distribution of hotspots of myocardial infarction were inversely correlated with hotspots of end-stage renal disease and lower extremity amputations (coefficients: -0.28 and -0.28)., Conclusions: We found differences in the local geographic distribution of diabetic complications, which highlight the contrasting risk factors for developing macrovascular versus microvascular diabetic complications. Based on our analysis, we also found that high diabetes-specific emergency department use was correlated with poor diabetic outcomes. Emergency department utilization data can help identify the location of specific populations with poor glycemic control., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: This study used cross-sectional data to test the independent relationship of proximity to chain fast-food outlets and proximity to full-service supermarkets on the frequency of mealtime dining at fast-food outlets in two major urban areas, using three approaches to define access. Interactions between presence of a supermarket and presence of fast-food outlets as predictors of fast-food dining were also tested., Methods: Residential intersections for respondents in point-of-purchase and random-digit-dial telephone surveys of adults in Philadelphia, PA, and Baltimore, MD, were geocoded. The count of fast-food outlets and supermarkets within quarter-mile, half-mile, and 1-mile street network buffers around each respondent's intersection was calculated, as well as distance to the nearest fast-food outlet and supermarket. These variables were regressed on weekly fast-food dining frequency to determine whether proximity to fast food and supermarkets had independent and joint effects on fast-food dining., Results: The effect of access to supermarkets and chain fast-food outlets varied by study population. Among telephone survey respondents, supermarket access was the only significant predictor of fast-food dining frequency. Point-of-purchase respondents were generally unaffected by proximity to either supermarkets or fast-food outlets. However, ≥1 fast-food outlet within a 1-mile buffer was an independent predictor of consuming more fast-food meals among point-of-purchase respondents. At the quarter-mile distance, ≥1 supermarket was predictive of fewer fast-food meals., Conclusions: Supermarket access was associated with less fast-food dining among telephone respondents, whereas access to fast-food outlets were associated with more fast-food visits among survey respondents identified at point-of-purchase. This study adds to the existing literature on geographic determinants of fast-food dining behavior among urban adults in the general population and those who regularly consume fast food., (Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.)

Objectives: The University of Wisconsin Population Health Institute has published the County Health Rankings since 2010. These rankings use population-based data to highlight health outcomes and the multiple determinants of these outcomes and to encourage in-depth health assessment for all United States counties. A significant methodological limitation, however, is the uncertainty of rank estimates, particularly for small counties. To address this challenge, we explore the use of longitudinal and pooled outcome data in hierarchical Bayesian models to generate county ranks with greater precision., Methods: In our models we used pooled outcome data for three measure groups: (1) Poor physical and poor mental health days; (2) percent of births with low birth weight and fair or poor health prevalence; and (3) age-specific mortality rates for nine age groups. We used the fixed and random effects components of these models to generate posterior samples of rates for each measure. We also used time-series data in longitudinal random effects models for age-specific mortality. Based on the posterior samples from these models, we estimate ranks and rank quartiles for each measure, as well as the probability of a county ranking in its assigned quartile. Rank quartile probabilities for univariate, joint outcome, and/or longitudinal models were compared to assess improvements in rank precision., Results: The joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates. Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent., Conclusion: Incorporating longitudinal or pooled outcome data may improve rank certainty, depending on characteristics of the measures selected. For measures with different determinants, joint modeling neither improved nor degraded rank precision. This approach suggests a simple way to use existing information to improve the precision of small-area measures of population health.

University of Wisconsin Population Health Institute has published County Health Rankings (The Rankings) since 2010. These rankings use population-based data to highlight variation in health and encourage health assessment for all US counties. However, the uncertainty of estimates remains a limitation. We sought to quantify the precision of The Rankings for selected measures. We developed hierarchical models for 5 health outcome measures and applied empirical Bayes methods to obtain county rank estimates for a composite health outcome measure. We compared results using models with and without demographic fixed effects to determine whether covariates improved rank precision. Counties whose rank had wide confidence intervals had smaller populations or ranked in the middle of all counties for health outcomes. Incorporating covariates in the models produced narrower intervals, but rank estimates remained imprecise for many counties. Local health officials, especially in smaller population and mid-performing communities, should consider these limitations when interpreting the results of The Rankings.

Background: The prevalence of morbid obesity is increasing throughout Wisconsin and the United States. In 2004, we published a study, "Trends in Bariatric Surgery for Morbid Obesity in Wisconsin." We determined that surgery rates were increasing but felt the demand exceeded the capacity of the surgeons. This is a 6-year follow-up., Methods: Data was gathered from 3 sources: the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System, the Wisconsin Hospital Association, and a survey administered to Wisconsin bariatric surgeons., Results: From 2003-2008, an average of 2.8% of Wisconsin adults were morbidly obese. Although the number of bariatric surgeries performed in Wisconsin remained steady (1311 surgeries in 2003 and 1343 in 2008), the types of procedures shifted from open gastric bypass (73% in 2003) to laparoscopic gastric bypass (80% in 2008). The rate of surgery was 1 for every 100 morbidly obese adults. The majority of surgeons surveyed (70%) report that a lack of insurance benefits is the biggest barrier to performing bariatric surgery., Conclusion: The prevalence of morbid obesity continues to increase in Wisconsin compared to our previously published data. Bariatric surgery volumes have remained stable but the type of procedure has changed. Approximately 1% of bariatric surgery candidates have surgery each year.