Year of Publication



Martin School of Public Policy and Administration

Date Available


Degree Name

Master of Public Administration

Committee Chair

Dr. Ronald Zimmer

Committee Member

Dr. Karen Blumenschein

Committee Member

Dr. Catherine Annis

Executive Summary

Compared to other states in the USA, Kentucky has one of the highest rates of diagnosed diabetes and one of the lowest levels of labor force participation. This project employs a multivariable linear regression model to estimate the correlation between county-level diabetes prevalence and county-level labor force participation rates in Kentucky counties from 2015-2019, using publicly available data from the Centers for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS) and the U.S. Census Bureau’s American Community Survey (ACS). Labor force participation rates serve as the dependent variable and diabetes prevalence rates serve as the primary independent variable, alongside nine other county-level variables meant to control for likely confounding factors: urban/rural categorization, mean age, sex ratio, proportion of the population aged 65 years or older, race, mean educational attainment, mean household income, poverty rate, and disability rate.

The correlation between diabetes prevalence and labor force participation rates in Kentucky counties was estimated at -0.425 (95% CI: -0.779, -0.0717). In other words, for every 1% increase in its diabetes prevalence, an average Kentucky county would expect to see its labor force participation rate decrease by 0.425%. Secondary analyses consistently found a negative relationship between diabetes prevalence and labor force participation rates, although some analyses returned diminished and non-significant results. Sensitivity analyses demonstrated that the relationship could lose statistical significance if the ACS labor force participation rate estimates used to build the model were significantly overestimated.



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