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Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States Census, and food establishment data

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dc.contributor.author Frankenfeld, Cara L.
dc.contributor.author Leslie, Timothy F.
dc.contributor.author Makara, Matthew A.
dc.date.accessioned 2015-09-22T15:27:38Z
dc.date.available 2015-09-22T15:27:38Z
dc.date.issued 2015-05-14
dc.identifier.citation Frankenfeld, Cara, Timothy Leslie, and Matthew Makara. “Diabetes, Obesity, and Recommended Fruit and Vegetable Consumption in Relation to Food Environment Sub-Types: A Cross-Sectional Analysis of Behavioral Risk Factor Surveillance System, United States Census, and Food Establishment Data.” BMC Public Health 15, no. 1 (2015): 491. en_US
dc.identifier.uri http://hdl.handle.net/1920/9896
dc.description.abstract Background Social and spatial factors are an important part of individual and community health. The objectives were to identify food establishment sub-types and evaluate prevalence of diabetes, obesity, and recommended fruit and vegetable consumption in relation to these sub-types in the Washington DC metropolitan area. Methods A cross-sectional study design was used. A measure of retail food environment was calculated as the ratio of number of sources of unhealthier food options (fast food, convenience stores, and pharmacies) to healthier food options (grocery stores and specialty food stores). Two categories were created: ≤1.0 (healthier options) and >1.0 (unhealthier options). k-means clustering was used to identify clusters based on proportions of grocery stores, restaurants, specialty food, fast food, convenience stores, and pharmacies. Prevalence data for county-level diabetes, obesity, and consumption of five or more fruits or vegetables per day (FV5) was obtained from the Behavioral Risk Factor Surveillance System. Multiple imputation was used to predict block-group level health outcomes with US Census demographic and economic variables as the inputs. Results The healthier options category clustered into three sub-types: 1) specialty food, 2) grocery stores, and 3) restaurants. The unhealthier options category clustered into two sub-types: 1) convenience stores, and 2) restaurants and fast food. Within the healthier options category, diabetes prevalence in the sub-types with high restaurants (5.9 %, p = 0.002) and high specialty food (6.1 %, p = 0.002) was lower than the grocery stores sub-type (7.1 %). The high restaurants sub-type compared to the high grocery stores sub-type had significantly lower obesity prevalence (28.6 % vs. 31.2 %, p <0.001) and higher FV5 prevalence (25.2 % vs. 23.1 %, p <0.001). Within the larger unhealthier options category, there were no significant differences in diabetes, obesity, or higher FV5 prevalence across the two sub-types. However, restaurants (including fast food) sub-type was significantly associated with lower diabetes and obesity, and higher FV prevalence compared to grocery store sub-type. Conclusions These results suggest that there are sub-types within larger categories of food environments that are differentially associated with adverse health outcomes. These observations support the specific food establishment composition of an area may be an important component of the food establishment-health relationship.
dc.description.sponsorship This study was partially funded by a George Mason University-Inova Health System Research Grant for inter-organizational research collaboration. Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject food environment en_US
dc.subject diabetes en_US
dc.subject obesity en_US
dc.subject diet en_US
dc.title Diabetes, obesity, and recommended fruit and vegetable consumption in relation to food environment sub-types: a cross-sectional analysis of Behavioral Risk Factor Surveillance System, United States Census, and food establishment data en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1186/s12889-015-1819-x


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