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A Mixed Methods Comparison of Urban and Rural Retail Corner Stores

1 Department of Nutrition, Gillings School of Global Public Health, UNC Center for Health Promotion and Disease Prevention, University of North Carolina-Chapel Hill, 2200 McGavran-Greenberg Hall, Chapel Hill NC ;
2 Department of Public Health, East Carolina University, Lakeside Annex 8, Room 126, Greenville NC;
3 UCLA Fielding School of Public Health, University of California, Los Angeles, CA;
4 Department of Kinesiology & Nutritional Science, California State University, Los Angeles, CA

Special Issues: Spatial Aspects of Health: Methods and Applications

Efforts to transform corner stores to better meet community dietary needs have mostly occurred in urban areas but are also needed in rural areas. Given important contextual differences between urban and rural areas, it is important to increase our understanding of the elements that might translate successfully to similar interventions involving stores in more rural areas. Thus, an in-depth examination and comparison of corner stores in each setting is needed. A mixed methods approach, including windshield tours, spatial visualization with analysis of frequency distribution, and spatial regression techniques were used to compare a rural North Carolina and large urban (Los Angeles) food environment. Important similarities and differences were seen between the two settings in regards to food environment context, spatial distribution of stores, food products available, and the factors predicting corner store density. Urban stores were more likely to have fresh fruits (Pearson chi2 = 27.0423; p < 0.001) and vegetables (Pearson chi2 = 27.0423; p < 0.001). In the urban setting, corner stores in high income areas were more likely to have fresh fruit (Pearson chi2 = 6.00; p = 0.014), while in the rural setting, there was no difference between high and low income area in terms of fresh fruit availability. For the urban area, total population, no vehicle and Hispanic population were significantly positively associated (p < 0.05), and median household income (p < 0.001) and Percent Minority (p < 0.05) were significantly negatively associated with corner store count. For the rural area, total population (p < 0.05) and supermarket count were positively associated (p < 0.001), and median household income negatively associated (P < 0.001), with corner store count. Translational efforts should be informed by these findings, which might influence the success of future interventions and policies in both rural and urban contexts.
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