Document Type
Article
Publication Date
5-7-2024
Advisor
Jeannette Manger
Abstract
Background: Obesity has plagued the United States for decades, and while the implications of obesity have drastically changed over time (from a sign of wealth to a possible implication of mal/low nutrition), its impacts on the country’s population are abrasive and costly. Objective: The aim is to address and investigate how factors such as Median Household Income (MHI), Limited Access to Healthy Food and Limited Access to Exercise interplay with and correlate to obesity. Methods: Data acquired from the County Health Rankings was analyzed using several analysis strategies (paired and unpaired T-tests, and regression plots, etc.). Only Mississippi and Ohio adults were included in the data. Results: Mississippi had a statistically significant lower MHI than Ohio in both 2016 (p < .001) and 2022 (p < .001). Mississippi also had statistically significant higher rates of obesity than Ohio in both 2016 (p < .001) and 2022 (p < .001). A Spearman Correlation revealed that in Ohio, as the MHI increased, the percentage of obesity in adults decreased; this was a weak but significant correlation (r = -.430, p < .001). The same test revealed that in Mississippi, as the percentage of adults experiencing obesity decreased, MHI increased; this time there was a strong and significant correlation (r = -.753, p < .001). A Stepwise Linear Regression on Ohio’s data stated that the best fitting model was statistically significant (p < .001) and is responsible for 32.1% of the variance in the percent of obese adults; MHI was the most contributory to the results. Mississippi’s data resulted statistical significance (p < .001) and is responsible for 67.9% of the variance in the percent obesity, again, most correlated to MHI. An Enter Method Linear Regression for Mississippi suggested the best fitting model was statistically significant (p < .001) and is responsible for 59.4% of the variance in obesity. MHI is the largest and sole most important contributor to the model. Surprisingly, the percentage of people with Exercise Opportunities and with Limited Access to Healthy Foods did not have a statistically significant contribution to the model. Ohio yielded the same results (p < .001); however, Ohio’s data was responsible for 32.3% of the variance in obesity. Conclusion: Obesity prevalence within a state is most readily predicted by looking at the Median Household Income. Despite limited healthy food access and exercise opportunities being reasonable assumed correlates, they are not significant indicators of obesity. This paper later discusses future directions of research and discusses how other understudied factors could lead to more fruitful connections to obesity prevalence.
Repository Citation
Schlessel, B., & Williams, J. (2024). A Study on the 2016 and 2022 MS and OH Populations: How Obesity is Related to the Co-mingling of Median Household Income, Limited Access to Healthy Foods and Limited Access to Exercise Opportunities. Wright State University. Dayton, Ohio.
