Jessica K. Athens

Proximity to Fast-Food Outlets and Supermarkets as Predictors of Fast-Food Dining Frequency

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 respondents 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.

Using Empirical Bayes Methods to Rank Counties on Population Health Measures

The 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.

The local geographic distribution of diabetic complications in New York City: Associated population characteristics and differences by type of complication

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.

Geospatial clustering in sugar-sweetened beverage consumption among Boston youth

Abstract 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.

Using Geospatial Analysis and Emergency Claims Data to Improve Minority Health Surveillance

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.