The data set initially received from D&B included 32,949 retail food businesses for New York City. After correction of geocoded addresses and removal of duplicate records, businesses likely to be defunct, and records likely to represent back offices and corporate offices, the data set included 29,976 businesses, of which 29,858 fell within the bounds of study subjects’ neighborhoods. displays descriptive statistics for the BMI-healthy, BMI-unhealthy, and BMI-intermediate categories as well as for specific food outlet types. Density of intermediate and unhealthy food outlets was much higher than density of healthy food outlets. Almost all study subjects lived within a half-mile of an unhealthy food outlet, with an average density of 31 such outlets per square kilometer. By contrast, only 82% lived within a half-mile of a healthy food outlet, with an average density of four outlets per square kilometer. Density measures for food outlet types were significantly correlated across neighborhoods, with correlation coefficients ranging from 0.38 (convenience stores and supermarkets) to 0.85 (non-fast-food restaurants and pizza restaurants).
Descriptive statistics for food outlet density (stores/km2).
maps the density of BMI-healthy food outlets, expressed in outlets per square kilometer, across the city. Outlet density was highest in high-walkability areas of the city, such as Manhattan, and lowest in low-walkability areas, such as Staten Island. Outlet density also varied by neighborhood income and race/ethnic composition, with higher densities in affluent and predominantly white neighborhoods in the southern half of Manhattan and lower densities in the poor and predominantly black or Latino neighborhoods in the northern half of Manhattan and in the South Bronx. To reduce the risk of confounding, the multivariate analyses controlled statistically for individual-level race/ethnicity and education and neighborhood-level poverty rate and race/ethnic composition, as well as indices of neighborhood walkability, including population density and land-use mix.
Figure 1 Density of BMI-healthy food outlets in New York City: Kernel Density Estimation (KDE) map illustrating the density of BMI-healthy food outlets. This KDE continuous surface was created with ArcGIS Spatial Analyst (ESRI, Redlands, CA), which uses a distance (more ...)
Multilevel analyses of the association between BMI and the food environment measures showed significant associations only with access to BMI-healthy food. We also assessed possible confounding effects of built environment variables. Population density, which has previously been inversely associated with BMI in analyses of the same data set, had an appreciable confounding effect, but further control for land-use mix, percent commercial area, and access to and neighborhood use of public transit did not alter the results. shows adjusted mean BMI for each quintile of the three food categories and the median density of food outlets for each category; displays the association between healthy food outlet density and BMI based on this analysis. The adjusted mean BMI in the fifth quintile of healthy food was 0.80 units [95% confidence interval (CI), 0.27–1.32, p < 0.01] lower than in the first quintile of healthy food. Population density and land-use mix remained significantly inversely associated with BMI after controlling for measures of the food environment. Increasing density of the BMI-unhealthy and BMI-intermediate food categories was not associated with BMI, and analyses of selected subcategories of BMI-unhealthy food (fast food, pizzerias, and convenience stores) found no significant associations.
Adjusted mean BMI by food density quintiles.
Figure 2 Adjusted mean BMI (± 95% CI) by BMI-healthy food density quintiles. Analysis is adjusted for the density of BMI-intermediate and BMI-unhealthy food outlets and for age, sex, race/ ethnicity, education, neighborhood sociodemographic characteristics, (more ...)
Because there was little difference in the adjusted mean BMI of individuals living in the first and second quintile of BMI-healthy food density, we collapsed these two categories into a single reference category to increase statistical power for analyses of the prevalence of overweight and obesity. The reference category had a median density of 0.76 healthy food outlets per square kilometer. shows the prevalence ratios for overweight and obesity by increasing density of healthy food outlets, increasing population density, and land-use mix. Controlling for population density and land-use mix, the prevalence of overweight and obesity were both lower among individuals with the highest density of healthy food outlets. Controlling for other features of the built environment did not alter the prevalence ratio for healthy food density.
Prevalence ratios (95% CIs) for overweight and obesity by increasing density of BMI-healthy food and indices of increasing neighborhood walkability.
Our previous work showed that increasing land-use mix and population density were inversely associated with BMI; this association remained after control for the density of BMI-healthy, BMI-unhealthy, and BMI-intermediate food outlets (Rundle et al. 2007
). The prevalence ratio for obesity comparing the fourth and first quartiles of land-use mix was 0.91 (95% CI, 0.86–0.97) and comparing the fourth and first quartiles of population density was 0.84 (95% CI, 0.73–0.96).