ASU Food Policy and Environment Research Group

Beginning in March 2020, the COVID-19 pandemic triggered a sudden and severe economic downturn and led to disruptions in domestic and international food systems and supply chains. Over the first few months of the pandemic, in the United States, many stores had empty shelves, bars and restaurants closed, and children could no longer go to school. The unemployment rate increased from 3.5% in February 2020 to 14.8% in April 2020, leading to economic instability for many households. As a result, household food insecurity, defined as having limited or inconsistent access to nutritious and affordable food, increased rapidly.

During the first months of 2021, vaccinations began rolling out, more individuals returned to in-person work, children to schools, and restrictions were gradually phased out. Unemployment has decreased since the April 2020 peak to 5.4% in July 2021, but remains above pre-pandemic levels. This brief describes the prevalence of household food insecurity, job disruptions, and food-related behaviors as reported by a nationally representative sample of 1,643 U.S. adults, both in the year prior to the COVID-19 pandemic (March 2019 – March 2020) and during the first four months of 2021 (January – April 2021), a period representing approximately one year since the onset of the pandemic.

Background: Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists.

Methods: We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression.

Results: D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts.

Conclusion: Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status.

Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.

Setting: A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets.

Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.

Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status.

Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.