024 - Administrative Records Analysis and Data Visualization: Understanding Trends in Population Health Associated with MO Medicaid Expansion

Background: Since the Affordable Care Act (ACA) was signed into law in 2010, the number of Americans without health insurance has decreased to historically low levels. The number of adults between 18 and 64 who reported being uninsured for at least part of the previous 12 months dropped from 51.0 million in 2010 to 35.1 million in 2018, largely due to expansion in Medicaid eligibility. Evaluations of Medicaid Expansion have found mostly positive outcomes, including increased health care coverage, utilization of services, and improved quality of care. Early estimates indicated that 230,000 Missourians (MO), aged 19 to 64 and earning up to 138% of the federal poverty level, would gain health care insurance as MO Medicaid expansion took effect in 2021. The current study uses interactive dashboards to characterize MO's new Medicaid enrollees and associated health outcomes. This approach to the data provides a wide-ranging view of health indicators for traditionally underserved populations and identifies health disparities.
Methods: This study examines Missouri (MO) Medicaid administrative claims data to characterize MO's Medicaid expansion enrollees during three distinct time periods (CY2017-19, CY2020-6/30/2021, 7/1/21-23). A dashboard tracks the emergent enrollment population starting with a baseline population of enrollees for calendar years 2017-2019. This study characterized recipients into six populations (children, custodial parents, elderly, disabled, pregnant women, and women's health) based on their primary medical eligibility (ME) codes for understanding demographic health indicators (i.e., age groups, geographic location, sex, and race) and their similarities or differences to the new expansion population. We chose CMS core set measures that included populations or medical conditions of interest and expressible in terms of county- and state-level rates before and after expansion.
Results: The Demographic data dashboard provides profiles of the distribution of populations eligible for Medicaid by county and over time. Map hover and tooltip features offer relevant information, including the distribution of gender and race for that county. The Core Set Measures dashboard visualizes the distribution of rates for outcomes by county and across time. Selecting counties on the map will add that county's trend line to the trend line chart, allowing county-to-county and county-to-state comparisons over time for each measure.
Conclusion: Interactive dashboards are useful for characterizing and analyzing Medicaid population demographics, health status, and health care usage. The use of quality health indicators can increase understanding of health disparities and reveal areas for intervention. These population health visualizations inform health education, policies, and practice and contribute to an understanding of whether and how expansion improves Missourians' health.

Health Policy

Medicaid Expansion

Data Visualization

Health Equity