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GIS mapping of retail food access to assess risks of (chronic and acute) illness in populations of different socioeconomic status
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|Title: ||GIS mapping of retail food access to assess risks of (chronic and acute) illness in populations of different socioeconomic status|
|Authors: ||Darcey, Valerie L.|
|Keywords: ||Nutrition;Food consumption -- United States;Food consumption -- Health aspects|
|Issue Date: ||3-Jun-2010|
|Abstract: ||Characteristics of the built environment, including availability and type of retail food outlets, vary with area poverty. This affects consumption patterns of area residents and may, in turn, affect both local incidence of obesity and rates of food borne illness. This research utilizes a unique approach to analyze retail food access and food safety risk. Geographic information systems (GIS) were used to plot retail food listings, from two databases, and foodservice critical health code violations (CHV) over poverty in Philadelphia Co., Pennsylvania.
Retail listings were purchased from Dun and Bradstreet (D&B) and identified using inspection records from the Philadelphia Health Department (PDPH). Addresses were geocoded to census tracts (N=368). Tracts were classified into quintiles using Census Bureau poverty data. GIS overlay analysis was used to group locations within tracts.
To examine degree of retail food access produced by both data sources, Chi-square statistic was utilized to test interaction between poverty and store type. Using either database (D&B, N=4,263; PHD, N=5,847), a significant interaction was found between poverty and the distribution of food markets, indicating that rates of all grocery stores, including corner markets, were highest in high poverty areas. Further analysis revealed that high poverty areas contained both lower percentages of chain markets and supermarkets compared to low poverty areas. Though fast food was more prevalent in high poverty areas versus low, the interaction between poverty and the distribution of fast-food and full service restaurants was only significant using PDPH but not D&B. Significant differences in distances to convenience and grocery stores were similar between datasets. However, D&B failed to show significant differences in travel distance to supermarkets across poverty groups, while lowest poverty groups (highest income) weresignificantly different from other groups using PDPH. Significant differences in distance to fast food and full service restaurants between poverty groups were similar using both datasets. However, the relative literature-established direction of the relationship between poverty and proximity to fast food restaurants was conserved using PDPH but not D&B.
To examine distribution of CHV, PDPH inspection records from 2005 to 2008 for all public foodservice locations (N=10,859) were analyzed. Less than half (46.5%) of facilities had an average of zero CHV. The average rate of CHV for all foodservice facilities was 0.765 per inspection. Rates of CHV across poverty groups were significantly greater in the lowest poverty (highest income) group at 0.93 (0.04) compared to other groups. Average days between inspection was also significantly greater in the two lowest poverty (highest income) groups compared to higher poverty groups.
These results confirm an association of increased access to chain food markets for low poverty areas and increased access to corner markets/groceries for high poverty areas in Philadelphia. Furthermore, results suggest that data source can affect the assessment of food environments and subsequent interpretation of degree of impact on residents’ health. These results also indicate an association of higher rates of violations and longer periods between inspections with lowest poverty rates. This study demonstrates the use of GIS technology to assess food safety risks and the novel comparison of two data sources to assess community food access.|
|Appears in Collections:||Drexel Theses and Dissertations|
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