The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research
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The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research.
Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS).
Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes.
Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation).
KeywordsPolicy research Public health Fruits and vegetables Fast food Food environment Geographic information systems (GIS) mapping Neighborhoods
For reviewing early drafts of this manuscript: A. Hillier. and C. Branas, University of Pennsylvania; for statistical help, C. Schechter, Albert Einstein College of Medicine; for help with mapping: A. Wagner, University of Pennsylvania School of Design, J. Fletcher and V. Huang, Albert Einstein College of Medicine, and A. Maroko, Lehman College, City University of New York; for mentorship: J. A. Long, Veterans Affairs Center for Health Equity Research and Promotion (CHERP); for contributing to lead author’s training: the Pisacano Leadership Foundation and the Robert Wood Johnson Clinical Scholars Program; for providing access to the dataset: the Robert Wood Johnson Clinical Scholars Program at the University of Pennsylvania and the Public Health Management Corporation.
Statement of conflict of interests
The authors declare that they have no conflicts of interest.
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