Outcomes of a Community-Wide Health Intervention in a Low-Income, Primarily Hispanic Community: The Go! Austin/Vamos! Austin (GAVA) Initiative

Abstract

Objectives To describe outcomes of a 4-year physical activity (PA) and nutrition intervention (2013–2017) in Dove Springs, a low-income urban community in Texas. Method Go! Austin/Vamos! Austin is a place-based intervention targeting the built and social environments of PA and nutrition. Baseline and follow-up measures related to PA and nutrition were obtained from 357 parent–child dyads (final n = 236) in the intervention community and a control community. A three-level dose of exposure measure was created to indicate the amount of exposure to intervention activities across the 4 years. Pre–post changes in key outcomes by level of exposure and contrasts across “high exposure” and “no exposure” categories were obtained using repeated-measures regression, adjusting for important confounders. Results “High exposure” adult participants showed consistently more favorable changes than “no exposure” participants across a variety of indicators, including positive perceptions and utilization of community PA resources, amount of moderate PA, utilization of retail outlets offering fresh produce, and measures of healthy eating. Few improvements were seen in child-level outcomes. Conclusions Community interventions can successfully improve health-promoting behaviors provided they ensure sufficient dose of exposure.

A Multi-Pronged Evaluation of a Healthy Food Access Initiative in Central Texas: Study Design, Methods, and Baseline Findings of the FRESH-Austin Evaluation Study

“…While there have been various interventions to improve economic and geographic food access among groups and communities' that experience the aforementioned disparities [25][26][27][28], the literature on comprehensive evaluations of these interventions or initiatives remains limited [25,26,[29][30][31][32]. Most interventions to date have relied on a single strategy, such as a healthy corner store model, and evaluations often only focus on more process data, such as the extent to which customers utilize the new assets, and do not include impact evaluation data on behavioral health outcomes.…”

“…Due to the fact of these inequities in food insecurity and healthy food access in Eastern Travis County, the City of Austin created the Fresh for Less (FFL) program [38]. Informed by formative qualitative work with key community stakeholders and successful strategies published in the literature [25,26,[29][30][31][32], FFL was designed to improve geographic and economic access to healthy foods in historically underserved areas. This was accomplished through a cross-sector partnership enabling the strategic placement of non-traditional food retail outlets (farm stands, mobile markets, and healthy corner stores) that offered subsidized healthy food products and accepted food assistance benefits in prioritized zip codes (Eastern Crescent of Travis County) that had a high prevalence of obesity, high prevalence of chronic disease, and historically limited geographic access to healthy food retail.…”

“…These factors included quality of food, variety, quality of store, cost, and cultural variety. These answer options were informed by our previous work with this community and the literature [32].…”

“…Furthermore, the baseline survey was administered after FFL had started implementation but was the first year of formal evaluation. We recognize that the lack of pre-implementation data is certainly a limitation, but we obtained data from other studies that drew community samples from the same areas of the city [32], and our longitudinal assessment will allow us to assess trends and understand if the placement and utilization of the new food access points are making a difference in food purchasing/intake patterns, and will inform the ABM in running several expansion simulation scenarios. Future work will explore outcome-related data as well as more specific associations with additional rigorous analyses as described in the overall analytic plan in Section 2.…”

Exploring the Impact of Policies to Improve Geographic and Economic Access to Vegetables among Low-Income, Predominantly Latino Urban Residents: An Agent-Based Model

“…Third, our model is time invariant, and as such, does not account for the time it may take to observe changes in agents’ behavior due to policy expansions, or if effects change over time. Fourth, the weights assigned in the function used to predict the probability of selecting a given store to purchase food were partially informed by real data from the FRESH-Austin Study, from past studies in the same setting and population [ 51 , 68 , 69 , 70 ], and by expert opinion by the author team. While our model calibration suggests that these weights adequately reflect the value community members assign to different factors when selecting a food store, it is possible that in other settings or low-income populations the weight allocation would vary.…”