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Amber Todd


Chronic illness is a significant contributor to disease burden in the US. Among these chronic conditions, diabetes is one of the most prevalent. Knowing the relationships between various factors and the prevalence of diabetes would be beneficial to creating targeted approaches to address this major public health problem. Therefore, the objective of this project was to elucidate the relationship between social determinants of health (unemployment, food environment index, and access to exercise opportunities) and access to care variables (ratio of population to primary care physicians and percent uninsured) with prevalence of diabetes by county in Ohio. This was done through Spearman correlation statistical analysis of data collected from the CDC County Health Rankings and Roadmaps Database. Results showed a weak negative correlation between diabetes prevalence and access to exercise, and a weak positive correlation between diabetes prevalence and percentage uninsured and PCP (primary care provider) ratio. Diabetes prevalence showed no correlation to food environment index. Results showed a moderate positive correlation between unemployment rates and diabetes prevalence. Additionally, a linear regression was performed to determine how well the selected social determinant of health and access to care variables accounted for the observed variance in diabetes between counties. The stepwise regression showed that the model was significant with percent unemployment having the greatest contribution to the observed variance in diabetes prevalence. Access to exercise opportunities was also found to have a significant contribution to the best model.

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