Abstract
BackgroundBased on the need for better measurement of the retail food environment in rural settings and to examine how deprivation may be unique in rural settings, the aims of this study were: 1) to validate one commercially available data source with direct field observations of food retailers; and 2) to examine the association between modified neighborhood deprivation and the modified retail food environment score (mRFEI).MethodsSecondary data were obtained from a commercial database, InfoUSA in 2011, on all retail food outlets for each census tract. In 2011, direct observation identifying all listed food retailers was conducted in 14 counties in Kentucky. Sensitivity and positive predictive values (PPV) were compared. Neighborhood deprivation index was derived from American Community Survey data. Multinomial regression was used to examine associations between neighborhood deprivation and the mRFEI score (indicator of retailers selling healthy foods such as low-fat foods and fruits and vegetables relative to retailers selling more energy dense foods).ResultsThe sensitivity of the commercial database was high for traditional food retailers (grocery stores, supermarkets, convenience stores), with a range of 0.96-1.00, but lower for non-traditional food retailers; dollar stores (0.20) and Farmer’s Markets (0.50). For traditional food outlets, the PPV for smaller non-chain grocery stores was 38%, and large chain supermarkets was 87%. Compared to those with no stores in their neighborhoods, those with a supercenter [OR 0.50 (95% CI 0.27. 0.97)] or convenience store [OR 0.67 (95% CI 0.51, 0.89)] in their neighborhood have lower odds of living in a low deprivation neighborhood relative to a high deprivation neighborhood.ConclusionThe secondary commercial database used in this study was insufficient to characterize the rural retail food environment. Our findings suggest that neighborhoods with high neighborhood deprivation are associated with having certain store types that may promote less healthy food options.
Highlights
Based on the need for better measurement of the retail food environment in rural settings and to examine how deprivation may be unique in rural settings, the aims of this study were: 1) to validate one commercially available data source with direct field observations of food retailers; and 2) to examine the association between modified neighborhood deprivation and the modified retail food environment score
Results indicate that the sensitivity of the commercial database was very high for traditional food outlets, with a range of 0.96-1.00. These results indicate that InfoUSA commercial database is highly sensitive for traditional food retailers overall
Our findings provide further evidence to support conducting direct observation or ground-truthing in rural settings to verify the presence of food venues in the retail food environment [21,48] obtained from commercial data sources
Summary
Based on the need for better measurement of the retail food environment in rural settings and to examine how deprivation may be unique in rural settings, the aims of this study were: 1) to validate one commercially available data source with direct field observations of food retailers; and 2) to examine the association between modified neighborhood deprivation and the modified retail food environment score (mRFEI). Most recently in rural South Carolina, the positive predictive value (PPV) was 66% [21] between commercial data source and direct field observation The results from these two studies suggest that commercial data sources may perhaps have greater validity in urban settings relative to rural areas. One potential reason for the difference between rural and urban settings is that in urban settings the rate of store closings is lower than in rural areas, with 1 in 4 stores closing in 2007 in rural areas compared to 1 in 6 in urban settings Added to this issue is that a population of 3,252 is needed to support a grocery store in 2010, whereas in 2000 the population needed was only 2,843 [27]. In many of these small census tracts the population is not sufficient to support a store and there may be higher rate of store closings which are not captured with a commercial data source
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