Public directory data sources do not accurately characterize the food environment in two predominantly rural states.

Communities are being encouraged to develop locally based interventions to address environmental risk factors for obesity. Online public directories represent an affordable and easily accessible mechanism for mapping community food environments, but may have limited utility in rural areas. The primary aim of this study was to evaluate the efficacy of public directories vs rigorous onsite field verification to characterize the community food environment in 32 geographically dispersed towns from two rural states covering 1,237.6 square miles. Eight types of food outlets were assessed in 2007, including food markets and eating establishments, first using two publically available online directories followed by onsite field verification by trained coders. χ(2) and univariate binomial regression were used to determine whether the proportion of outlets accurately listed varied by food outlet type or town population. Among 1,340 identified outlets, only 36.9% were accurately listed through public directories; 29.6% were not listed but were located during field observation. Accuracy varied by outlet type, being most accurate for big box stores and least accurate for farm/produce stands. Overall, public directories accurately identified fewer than half of the food outlets. Accuracy was significantly lower for rural and small towns compared to mid-size and urban towns (P<0.001). In this geographic sample, public directories seriously misrepresented the actual distribution of food outlets, particularly for rural and small towns. To inform local obesity-prevention efforts, communities should strongly consider using field verification to characterize the food environment in low-population areas.

Smart density: A more accurate method of measuring rural residential density for health-related research.

BACKGROUND: Studies involving the built environment have typically relied on US Census data to measure residential density. However, census geographic units are often unsuited to health-related research, especially in rural areas where development is clustered and discontinuous. OBJECTIVE: We evaluated the accuracy of both standard census methods and alternative GIS-based methods to measure rural density. METHODS: We compared residential density (units/acre) in 335 Vermont school neighborhoods using conventional census geographic units (tract, block group and block) with two GIS buffer measures: a 1-kilometer (km) circle around the school and a 1-km circle intersected with a 100-meter (m) road-network buffer. The accuracy of each method was validated against the actual residential density for each neighborhood based on the Vermont e911 database, which provides an exact geo-location for all residential structures in the state. RESULTS: Standard census measures underestimate residential density in rural areas. In addition, the degree of error is inconsistent so even the relative rank of neighborhood densities varies across census measures. Census measures explain only 61% to 66% of the variation in actual residential density. In contrast, GIS buffer measures explain approximately 90% of the variation. Combining a 1-km circle with a road-network buffer provides the closest approximation of actual residential density. CONCLUSION: Residential density based on census units can mask clusters of development in rural areas and distort associations between residential density and health-related behaviors and outcomes. GIS-defined buffers, including a 1-km circle and a road-network buffer, can be used in conjunction with census data to obtain a more accurate measure of residential density.