As lockdown and school closure policies were implemented in response to the coronavirus, the federal government provided funding and relaxed its rules to support emergency food provision, but not guidance on best practices for effectiveness. Accordingly, cities developed a diverse patchwork of emergency feeding programs. This article uses qualitative data to provide insight into emergency food provision developed in five cities to serve children and families. Based on our qualitative analysis, we find that the effectiveness of local approaches appears to depend on: (i) cross‐sector collaboration, (ii) supply chains, and (iii) addressing gaps in service to increased risk populations.
Food insecurity and limited healthy food access are complex public health issues and warrant multi-level evaluations. The purpose of this paper was to present the overall study design and baseline results of the multi-pronged evaluation of a healthy food access (i.e., Fresh for Less (FFL)) initiative in Central Texas. The 2018–2021 FRESH-Austin study was a natural experiment that utilized a cluster random sampling strategy to recruit three groups of participants (total n = 400): (1) customers at FFL assets, (2) residents that lived within 1.5 miles of an FFL asset, and (3) residents from a comparison community. Evaluation measures included annual cohort surveys, accelerometers and GPS devices, store-level audits, and built environment assessments. Data are being used to inform and validate an agent-based model (ABM) to predict food shopping and consumption behaviors. Sociodemographic factors and food shopping and consumption behaviors were similar across the three groups; however, customers recruited at FFL assets were lower income and had a higher prevalence of food insecurity. The baseline findings demonstrate the need for multi-level food access interventions, such as FFL, in low-income communities. In the future, ABM can be used as a cost-effective way to determine potential impacts of future large-scale food environment programs and policies.
Food insecurity increased substantially in the USA during the early stages of the 2020 COVID-19 pandemic. The purpose of this study was to identify potential sociodemographic and food access-related factors that were associated with continuing or transitioning into food insecurity in a diverse population. An electronic survey was completed by 367 households living in low-income communities in Central Texas during June–July 2020. Multinomial logistic regression models were developed to examine the associations among food insecurity transitions during COVID-19 and various sociodemographic and food access-related factors, including race/ethnicity, children in the household, loss of employment/wages, language, and issues with food availability, accessibility, affordability, and stability during the pandemic. Sociodemographic and food access-related factors associated with staying or becoming newly food insecure were similar but not identical. Having children in the household, changes in employment/wages, changing shopping location due to food availability, accessibility and/or affordability issues, issues with food availability, and stability of food supply were associated with becoming newly food insecure and staying food insecure during the pandemic. Identifying as Latino and/or Black was associated with staying food insecure during COVID-19. These findings suggest that the COVID-19 pandemic did not create new food insecurity disparities. Rather, the pandemic exacerbated pre-existing disparities.
The purpose of this study was to explore the association between geographic food access and food insecurity and the potential role of race/ethnicity, income, and urbanicity among a low-income, diverse sample in Central Texas. Utilizing a cross-sectional study design, secondary data analysis of an existing cohort was used to examine the association between food insecurity; geographic food access; and sociodemographic factors of race/ethnicity, income, urbanicity, and additional covariates using binomial logistic regression models. The existing cohort was recruited from lower-income communities in Travis County, Texas. The sample (N = 393) was predominantly Hispanic, lived in urban areas, and nearly 40% were food insecure. Geographic food access was not found to be significantly associated with food insecurity. However, rural residents had greater odds of being food insecure than urban residents. Also, participants who earned USD 45,000–64,999 and over USD 65,000 had lower odds of being food insecure than participants who earned under USD 25,000. These findings add to the inconsistent literature about the association between geographic food access and food insecurity and contribute to urbanicity and income disparities in food-insecurity literature. Future work should consider urbanicity, income, and utilize community-specific data to gain greater understanding of the association between geographic food access and food insecurity.
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