The Impacts of Food Environment at Home and at the Neighborhood on Obesity

Obesity
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Institutional Affiliation

Obesity
The article “Food Environments and Obesity: Household Diet Expenditures Versus Food Deserts” by Edward Jaenicke, Danhong Chen, and Richard Volpe show the relationship between and other environmental aspects of where individuals reside, such as neighborhood and home. Researchers addressed various questions during this study as they tried to understand factors that contribute to overweight and obesity. For instance, they asked themselves how neighborhood, individual, and household factors influenced overweight and obesity status. The primary hypothesis of this research was that the food environments in the neighborhood and at home have a direct effect on overweight and obesity status (Chen, Jaenicke, & Volpe, 2016). As such, this study tries to prove the above hypothesis to understand how the three primary factors, namely household, individual, and neighborhood, affect people’s status of obesity and overweight.
Chen, Volpe, and Jaenicke came up with a set of questions so that they can prove their hypothesis. In particular, each question was related to one of the variables that were being tested, which include neighborhood, individual, and household. The following are the primary questions that researchers asked:
1. How do individual factors, namely gender, self-reported behaviors, and age, influence overweight, and obesity?
2. How do household factors, namely income, home food environment, ethnicity, education, and race, affect people’s overweight and obesity statuses?
3. How do neighborhood factors, namely metro-status, poverty rates, and county-level food store density, influence individuals’ overweight and obesity statuses? Notably, metro means an area that is divided into three sub-categories, while non-metro is a region that does not have these sub-categories. Additionally, food desert status refers to both low-access and low-income.
Chen, Jaenicke, and Volpe designed an observational study. Specifically, it entails a study where researchers cannot alter or control the variables directly. The study took place in the neighborhood and at home. Researchers observed the trend in overweight and obesity status based on the availability, location, and other aspects. Being an observational study, it is non-experimental. The data that researchers used came from pre-existing sources, where it was grouped to identify the patterns. This strategy was the best for an observational study since no experiment was conducted. The primary sources of the data were IRi MedProfiler, the Census Bureau of American Community Survey, IRi Consumer Panel, and the United States Census Bureau County Business (Chen, Jaenicke, & Volpe, 2016). The two primary categories of the data were overweight and obese. Based on these categories, numerous factors were examined in order to determine relationships. The study is considered categorical since the data was sorted in different categories. When performing a study, the use of pre-existing data reduces the energy and time. However, it can lead to potential weaknesses, particularly if the information collected is erroneous.
The study used a data analysis model known as a random-intercept logistic, which was done using version 13 of Stata (Chen, Jaenicke, & Volpe, 2016). By assuming that the slopes are fixed, researchers can identify intercepts for different groups using this model. In most cases, the random-intercept logistic is used to show the relationships between groups that are in a similar class. The above type of data analysis was deemed appropriate in the study since researchers wanted to know the correlation between obesity and other aspects of the food environment in the neighborhood and at home.
Participants of this study were approximately 38,650 in about 18,381 households who came from 2,104 counties in the United States of America (USA). They were divided into distinctive factor levels that were to be analyzed. These three subcategories include neighborhood, household, and individual. At the level of the individual, researchers used gender, self-reported responses, and age-related to diet. At the level of the household, researchers used marital status, income, food environment, household size, and ethnicity, and race. At the last level of the neighborhood, Chen and colleagues considered the status of a food desert, the density of food outlets, metro-status, and poverty rates (Chen, Jaenicke, & Volpe, 2016).
As researchers analyzed the data gathered throughout the study, they found out that “about 1/3 of the total sample was overweight and about 1/3 was obese” (Chen, Jaenicke,& Volpe, 2016). The results obtained were consistent with the national statistics. Additionally, adults had an average body mass index (BMI) of 28.5. “More than 85% of the households were non-Hispanic white, and more than half had a college-educated household head” (Chen, Jaenicke, & Volpe, 2016). Notably, the average income was more than $69,000, and the household size was two. A deeper analysis of the data collected indicated that “almost all the individual-level demographics and lifestyle choices were significantly associated with obesity or overweight status” (Chen, Jaenicke, & Volpe, 2016). Although the individual outcomes supported the primary hypothesis, the household-level results were adversely related to the obesity probability. Moreover, results identified “significant socioeconomic disparities in overweight or obesity status:”
“Compared with Whites, Non-Hispanic blacks were more likely to be obese or ove...