Health Care Use for Pediatric Community Health Center Patients During Medicaid Coverage Unwinding

Abstract

Objective: In 2023, following 3 years of continuous Medicaid coverage, ‘unwinding’ led to disenrollments from Medicaid with some patients becoming uninsured. This study aimed to evaluate the impact of Medicaid unwinding on health care utilization, including preventive care, mental health care, and influenza vaccination, among pediatric patients at community-based health organizations. Methods: We used electronic health record data from a nationwide network of community-based health centers and identified pediatric patients who were Medicaid-insured during continuous coverage. The primary exposure was disenrollment from Medicaid to uninsured status during unwinding. We used generalized linear models with inverse probability of treatment weighting to estimate the association between disenrollment and health care utilization and influenza vaccination. Results: Among 448,123 Medicaid-insured pediatric patients, 8.4% were disenrolled to the uninsured. Disenrolled patients had 0.41 (95% confidence interval (CI): 0.40, 0.42) times the number of preventive visits (−1.25 [−1.43, −1.07] visits per year), 0.66 (95% CI: 0.65, 0.67) times the number of evaluation and management visits (−1.68 [−1.88, −1.48] visits per year), and fewer mental health visits with primary care providers and specialists. We also observed lower age-adjusted influenza vaccination rates among those patients who were uninsured (27.3% [95% CI: 26.7, 28.0]) than Medicaid-insured patients (39.3% [95% CI: 38.5, 40.0]), Conclusions: Disenrollment from Medicaid to the uninsured was associated with decreased preventive care, evaluation and management visits, mental health care, and influenza vaccination among pediatric patients. These findings highlight the potential health consequences of Medicaid unwinding and the importance of community-based health centers.

Publication
Academic Pediatrics
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Wyatt P. Bensken, PhD
Research Investigator & Adjunct Assistant Professor of Population and Quantitative Health Sciences

My expertise is in the use of complex health care data, paired with traditional statistical and novel machine learning approaches, to identify opportunities to improve health, health care, and health outcomes for all.