Date of Completion

2025

Document Type

Honors College Thesis

Department

College of Nursing and Health Sciences

Thesis Type

Honors College

First Advisor

Bikki Tran Smith

Second Advisor

Jeremy Sibold

Third Advisor

Thomas Griffin

Keywords

Medicaid unwinding, Partisanship, Medicaid disenrollment

Abstract

Background

Annual Medicaid renewal requirements were temporarily paused during the COVID-19 pandemic, and the ending of the public health emergency brought on the process of unwinding in which millions of Americans lost their health insurance coverage. As the Medicaid program is deeply intertwined with politics, partisanship has been shown to correlate with states' Medicaid decisions and their respective outcomes.

Methods

This study was a cross-sectional secondary data analysis that used publicly available descriptive data on unwinding rates by state in September 2024 and respective state partisanship of the legislature, governor, and overall state. Covariates included age demographics on Medicaid, metrics used in the unwinding process, and the uninsured population which were gathered from the U.S. Census data and Kaiser Family Foundation (KFF). Analyses conducted included descriptive statistics and linear regressions to observe the political factors which influenced differing disenrollment rates across states in the unwinding process.

Results

States with a Republican governor, Republican control of the legislature and, overall Republican control were found to disenroll beneficiaries at higher rates when compared to states with Democratic control. In addition, increases in the elderly population on Medicaid were associated with decreased disenrollment rates.

Conclusion

These findings indicate that partisanship has a significant impact on Medicaid unwinding, as was indicated in existing research on Medicaid expansion decisions amongst states. Further research is necessary for more accurate depictions of partisan patterns in unwinding, and identification of specific policies that encourage these results will help to inform health insurance policies that mitigate these effects.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Available for download on Wednesday, April 22, 2026

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