Childhood obesity rates in the U.S. have reached epidemic proportions, and an urgent need remains to identify evidence-based strategies for prevention and treatment. Multi-level, multi-component interventions are needed due to the multi-factorial nature of obesity, and its proven links to both the social and built environment. However, there are huge gaps in the literature related to doing these kinds of interventions among low-income, urban, minority groups. The B’More Healthy Communities for Kids (BHCK) intervention is a multi-level, multi-component intervention, targeting low-income African American youth ages 10–14 and their families in Baltimore, Maryland. This intervention prevents childhood obesity by working at multiple levels of the food and social environments to increase access to, demand for, and consumption of healthier foods. BHCK works to create systems-level change by partnering with city policy-makers, multiple levels of the food environment (wholesalers, corner stores, carryout restaurants), and the social environment (peers and families). In addition, extensive evaluation will be conducted at each level of the intervention to assess intervention effectiveness via both process and impact measures. This project is novel in multiple ways, including: the inclusion of stakeholders at multiple levels (policy, institutional, and at multiple levels of the food system); that it uses novel computational modeling methodologies to engage policy makers and guide informed decisions of intervention effectiveness; it emphasizes both the built environment (intervening with food sources) and the social environment (intervening with families and peers). The design of the intervention and the evaluation plan of the BHCK project are documented here. NCT02181010 (July 2, 2014).

Little is known about the mechanisms through which neighborhood-level factors (e.g., social support, economic opportunity) relate to suboptimal availability of healthy foods in low-income urban communities. We engaged a diverse group of chain and local food outlet owners, residents, neighborhood organizations, and city agencies based in Baltimore, MD. Eighteen participants completed a series of exercises based on a set of pre-defined scripts through an interactive, iterative group model building process over a two-day community-based workshop. This process culminated in the development of causal loop diagrams, based on participants’ perspectives, illustrating the dynamic factors in an urban neighborhood food system. Synthesis of diagrams yielded 21 factors and their embedded feedback loops. Crime played a prominent role in several feedback loops within the neighborhood food system: contributing to healthy food being “risky food,” supporting unhealthy food stores, and severing social ties important for learning about healthy food. Findings shed light on a new framework for thinking about barriers related to healthy food access and pointed to potential new avenues for intervention, such as reducing neighborhood crime.

Scholars in planning and public health have increasingly called attention to inequities in the food system. To evaluate and address these challenges, local and regional governments have developed a...

Objective: To investigate acquisition and mobility experiences of food-insecure individuals across urbanicity levels (i.e., urban, suburban, rural) in the early months of the COVID-19 pandemic.Design: Cross-sectional study using a nationally representative online panel to measure where food-insecure individuals acquired food, food acquisition barriers and mobility to food sources, which were evaluated across urbanicity levels using chi-squared tests and 95 % CI.Setting: USA.Participants: 2011 adults (18 years or older).Results: Food insecurity impacted 62·3 % of adults in urban areas, 40·5 % in rural areas and 36·7 % in suburban areas (P < 0·001). Food acquisition barriers that were significantly more prevalent among food-insecure adults in urban areas were a change in employment status (34·2 %; 95 % CI 27·2 %, 41·1 %; P < 0·0001) and limited availability of food in retailers (38·8 %; 95 % CI 31·7 %, 45·9 %; P < 0·001). In rural areas, food-insecure adults primarily acquired food for the household from supercentres (61·5 %; 95 % CI 50·4 %, 72·5 %; P < 0·05), while locally sourced foods were less common among food-insecure adults in rural areas (6·9 %; 95 % CI 0·01 %, 13·0 %) compared to urban areas (19·8 %; 95 % CI 14·3 %, 25·4 %; P < 0·01). Transportation as a barrier did not vary significantly by urbanicity, but food-insecure adults across urbanicity levels reported utilising a range of transportation modes to acquire food.Conclusions: A planning approach that links urban and rural areas could address food insecurity by enhancing the integration of food production, transportation and food distribution, building towards a more resilient and equitable food system for all Americans.

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.