Innovative Data Linkage for Enhancing Identification and Understanding of Social Determinants of Health Among the Medicaid Population

Vollständiger Abstract

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Background: The COVID-19 pandemic led to economic and policy changes that increased Medicaid enrollment and reduced disenrollment. Tracking the impact of these changes on the composition of enrollees is crucial for resource allocation and program evaluation. However, Medicaid data currently lack the necessary demographic, social, and economic information about enrollees. Objective: Enhance Medicaid enrollment data by linking it with nationally representative survey data to compare the composition of enrollees across various social determinants of health characteristics before and after the COVID-19 pandemic. Research Design: We utilize individual-level Medicaid enrollment records (TAF, 2018–2021) before and during the pandemic, linked to restricted American Community Survey (ACS, 2021) microdata. Results: Almost 95% of enrollees in our analytic sample received an anonymous identifier (Protected Identification Key), and nearly 1% were found in ACS data. By comparing Medicaid enrollees in different enrollment cohorts, we find that the pandemic caused significant compositional changes, particularly among the newly enrolled. Our findings indicate that those experiencing a major health or economic shock, either directly or through a family member, relied on Medicaid, likely as a temporary source of health insurance during the pandemic. Conclusions: Linking individual-level records between Medicaid and ACS data effectively addresses a crucial gap in current data capacity. The integrated data can be utilized, repurposed, and expanded by incorporating additional survey and administrative records to enhance the utility of Medicaid data for future research.