GEOSPATIAL FOOD ENVIRONMENT EXPOSURE, OBESITY, AND FOOD SECURITY AMONG LOW INCOME BALTIMORE CITY CHILDREN AND CAREGIVERS

Background. In recent years, the volume geospatial food environment research has increased sharply. However, the extent to which methodological decisions influence associations between food environment exposure, BMI, and food security, has been understudied. Research Aims. I sought to characterize the geospatial exposure to food retailers for a sample of households in low-income areas of Baltimore City, Maryland. Then, to examine the cross-sectional associations between those exposures and: adults’ BMI, children’s BMI z-scores, and household food security. And lastly, I compared the findings across several data sources, data extraction, and data processing methods. Methods. Participants were children ages 10 to 14 (n = 335) and their caregivers (n = 324). Food retailer listings were obtained from: (1) ReferenceUSA, (2) the Supplemental Nutritional Assistance Program, and (3) the Johns Hopkins Center for a Livable Future (CLF). Then, a Combined Database, that contained all available data, was created. Exposure was assessed as counts of food retailers within a 0.25, 0.5, and one-mile Euclidian distances of participants’ homes, and as distances to the closest retailers. Two data processing approaches were compared. One was time intensive and resulted in extensive revisions to the original data. The other was less time intensive and resulted in minimal revisions. Results. With few exceptions, the choice of data sources and data processing methods produced large differences in estimates of participants’ exposures, especially to food retailers such as supermarkets. However, for caregivers, no statistically significant associations were detected between most geospatial food environment exposure measures and BMI. Surprisingly, greater counts of supermarkets and fruit and vegetable specialty stores near participants’ homes, were associated with lower food security. For children, greater number of corner stores, convenience stores, gas stations, carryouts, and restaurants near homes, was statistically significantly associated with slightly higher BMI z-scores. Living further from those stores was associated with lower BMI z-scores. Overall, findings were consistent across data sources and data processing methods. Conclusions. The choice of data sources and data processing methods produced large differences in estimates of participants’ exposures to certain store types, but had less impact on the associations with the study outcomes

and Challenges

# The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Systems modeling represents an innovative ap-proach for addressing the obesity epidemic at the community level. We developed an agent-based model of the Baltimore City food environment that permits us to assess the relative impact of different programs and policies, alone and in combination, and potential unexpected consequences. Based on this experience, and a review of literature, we have identified a set of principles, potential benefits, and challenges. Some of the key principles include the impor-tance of early and multilevel engagement with the com-munity prior to initiating model development and contin-ued engagement and testing with community stakeholders. Important benefits include improving community stake-holder understanding of the system, testing of interven-tions before implementation, and identification of unex-pected consequences. Challenges in these models include deciding on the most important, yet parsimonious factors to consider, how to model food source and food selection behavior in a realistic yet transferable manner, and identi-fying the appropriate outcomes and limitations of the model

Healthy versus Unhealthy Suppliers in Food Desert Neighborhoods: A Network Analysis of Corner Stores’ Food Supplier Networks

Background: Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. Methods: Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. Results: Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p < 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82; p < 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average −1.53 (p < 0.001) fewer connections to suppliers in the HSN (p < 0.001). Conclusions: Our findings indicate clear differences between corner stores’ HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider

Relation between the Supplemental Nutritional Assistance Program cycle and dietary quality in low-income African Americans in Baltimore, Maryland

Background: There has been limited research regarding the Supplemental Nutritional Assistance Program (SNAP) and recipients' dietary quality during the days and weeks after benefit disbursement.Objective: We examined the relation between participants' stages in the SNAP cycle and their macronutrient consumption, Healthy Eating Index (HE!) scores, and fruit and vegetable intake.Design: in this cross-sectional study, we analyzed single 24-h dietary recalls collected from 244 African American SNAP participants recruited near 24 corner stores in Baltimore City. A multiple linear regression analysis and bootstrapping were used.Results: Among participants who received a SNAP benefit 15 d before being surveyed, energy intake (1.35%; 95% CI: 0.01%, 2.73%), energy intake adjusted for minimum energy requirements (3.86%; 95% CI: 0.06%. 7.96%), total fat intake (1.96%; 95% CI: 0.29%, 3.8%), saturated fat intake (2.02%; 95% Cl: 0.23%, 4.01%), and protein intake (2.09%; 95% Cl: 0.70%, 3.62%) were higher per each 1-d increase in the TSSD.Conclusions: These findings suggest that the relation between the TSSD and macronutrient intake might be U-shaped, with higher intake of calories, fat, and protein in individuals in the very early and late stages of their SNAP cycles. Foods high in these nutrients might be cheaper, more accessible, and have a longer shelf-life than healthier options, such as fruit, vegetables, and whole grains, for SNAP participants when their benefits run out. Additional efforts are needed to investigate the effect of the TSSD on dietary intake by using a longitudinal design and to improve the quality of dietary intake in African American SNAP participants.National Heart, Lung, and Blood InstituteKruse Family Publications AwardJohns Hopkins Sch Publ Health, Dept Int Hlth, Baltimore, MD USAJohns Hopkins Sch Publ Health, Dept Hlth Behav & Soc, Baltimore, MD USAUniversidade Federal de São Paulo, Dept Hlth Sci, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Hlth Sci, São Paulo, BrazilNational Heart, Lung, and Blood Institute: 1R21HL102812-01A1Web of Scienc