Non-communicable disease is currently the leading cause of morbidity and mortality all over the world. These diseases are responsible for 70% of global deaths, approximately 40 million people every year. Obesity, in particular, has been on the rise in the United States of America. There has been an increase in the number of Americans consuming poor-quality diets.
Due to the epidemic of obesity, millions of people all over the united states have a reduced quality of life and experience premature mortality (Cummins, 2017) . The health and economic effects of obesity have devastated the communities and families in the united states. It is therefore important to investigate the causes of this epidemic, particularly whether obesity is closely associated with the factors that influence that choice of food at home and within the individual’s neighboring community (Walker, 2020) . And why the factors such as desert status that influence food choice and availability are closely associated with obesity. The article “Food environment and Obesity: Household Diet Expenditure Versus Food Deserts.” is the one I chose as for my research question is because its objectives provide important answers to many of the questions I had.
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The study design was observational, 38,650 individuals drawn from 18, 381 households from 2104 counties in the united states, were enlisted. Their food habits were observed both within their families and in the surrounding communities to determine the relationship with no intervention from the members who conducted the research (Chen, 2016) . The tool used to measure the factors that influenced food choice at home was the USDAScore. The USDAScore was an appropriate method of data collection because it was reflected the dietary- quality indices that have previously been utilized by other researchers in the same field. Besides, the data from the scanners were more likely to reflect the true information about the food that is consumed in a particular family than traditional food frequency questionnaires (Chen, 2016) .
The researchers collected data categorically, the tool collected data that was used to investigate how effectively a household would stick to their expenditure on shares of 24 categories of food that were grouped compared to the values that the department of Agriculture and food plans have advised for families to use. Also, data was collected from individuals, their families and from the local community that surrounded a particular family from the census on business patterns in every county in the united states in 2008, while comprehensive details regarding the buying of food from the IRI Consumer Panel scanner data in the year 2008-2012) and the relevant data on MedProfiler for the year2012 in respective order. However, the method of data collection had some weaknesses, for instance, the data that was extracted from the scanners was generalized and accounted for food that was bought and with no specificity on the food that was consumed. Additionally, the data collected from the scanners took into consideration food that was bought for consumption at home and locked out the category of foods bought for consumption away from home (Chen, 2016) . This omission of the variables on the food that is consumed away from home may interfere with the results of the study.
During the research, confounders at all the levels of the study were taken into consideration especially in the analytic processes. The data obtained from all the participants of the study was used to calculate the mean and standard deviations for each variable by use of descriptive statistics. The method was appropriate for the answer to the research question on the factors affecting the choice of food and its relationship with obesity on the three levels of the research from personal to the family level and the community level. This is because the analysis was conducted based on multiple level models to keep the three levels of structure of the data into consideration. However, the method in which data analysis was conducted had some weaknesses, it was done using weights and heights that were reported by participants in the study. The self-reported height and weights are likely to be incorrect because people tend to increase their height and lower their weights while reporting and this may end in errors in measurement during the analytic processes. The BMIs might, therefore, be underestimated in the analysis and there may be a higher number of overweight and obese individuals than suggested by the results. Accordingly, the overweight and obesity prevalence might be higher than the analysis suggests.
The key demographics of the participants' data included age, gender, race/ethnicity, household size, income, education, and marital status. The researchers also took into consideration the exercise habits of an individual and their fast-food culture. The participants specified the number of times that they ate fast foods and how frequently they participated in exercise activity in a week (Chen, 2016) . T he inclusion criteria for participation in the study was the presence of one's data in the data sets that were used in data collection while the exclusion criteria from the study were when an individual declined participation in the study.
In conclusion, the factors that influence food availability, choice, and consumption, and in particular, the desert status of food is shown to have an impact on the likelihood of the development of obesity in a community (Welker, 2016) . Besides, the environment surrounding food at a personal level, and the family level have a higher impact than the food environment in a particular community.
References
Cummins, S. C. (2017). Enduring challenges in estimating the effect of the food environment on obesity.
Walker, B. B. (2020). The local food environment and obesity: evidence from three cities. Obesity, 28(1), , 40-45.
Welker, E. L. (2016). The school food environment and obesity prevention: progress over the last decade. . Current obesity reports, 5(2), , 145-155.
Chen, D.J.(2016). Food environments and obesity: household diet expenditure versus food deserts. American journal of public health , 106 (5), 881-888.
