CHARACTERIZING THE U.S. MEDICAID POPULATION BY FEDERAL POVERTY LEVEL: DEMOGRAPHICS, REGIONAL PATTERNS, AND AFFORDABLE CARE ACT (ACA) EXPANSION STATUS TO ASSESS POLICY SHIFTS UNDER THE ONE BIG BEAUTIFUL BILL ACT (H.R.1)
Author(s)
Douglas L. Leslie, PhD1, Emily Dennis, MS1, Sameer Kotak, MBA, MS2;
1Penn State College of Medicine, Department of Public Health Sciences, Hershey, PA, USA, 2Yorker Health Corp., Glen Rock, NJ, USA
1Penn State College of Medicine, Department of Public Health Sciences, Hershey, PA, USA, 2Yorker Health Corp., Glen Rock, NJ, USA
OBJECTIVES: Federal Poverty Level (FPL) income is a key factor in determining Medicaid eligibility and coverage composition; understanding beneficiary distribution across FPL strata is essential for anticipating how large-scale proposals like H.R.1 influence Medicaid participation and equity. Accordingly, we characterized 2022 Medicaid beneficiaries by FPL and ACA expansion status to inform priorities for assessing potential H.R.1 impacts on coverage and access.
METHODS: A cohort of 59,089,575 enrollees was analyzed by expansion status and FPL (0-133% vs. ≥134%) using demographic and regional variables from administrative data across all 50 states and DC via the CMS Virtual Research Data Center at Penn State University. Descriptive statistics characterized proportional distributions and geographic clustering.
RESULTS: Nearly 87% (~51M) of beneficiaries had income ≤133% FPL; the majority were <40 years old, female, White, non-Hispanic, and in the South. The expansion group (38 states and DC; 36M enrollees, 62%) had a higher proportion of ≥40-year-olds (32% vs. 24%) and non-Hispanic White enrollees (43% vs. 32%), and a lower proportion of non-Hispanic Black enrollees (17% vs. 26%), compared with the non-expansion group (12 states; 38%). In expansion states, ~85% of enrollees were in the 0-133% FPL, compared with ~90% in the non-expansion states. Comparison of expansion groups stratified by FPL revealed larger demographic variations in the 0-133% FPL group than the ≥134% FPL group: expansion states had a lower proportion of <40-year-olds (66% vs. 75%) and a higher proportion of non-Hispanic Whites (43% vs. 31%). In the ≥134% FPL group, expansion states had a lower proportion of non-Hispanic Blacks (14%) than non-expansion states (24%).
CONCLUSIONS: Medicaid populations exhibit expansion-specific socioeconomic and geographic clustering. Claims-based analyses and impact modeling can help quantify potential H.R.1 impacts on eligibility, enrollment, and equity by drug or therapeutic area, providing a baseline for future evidence-based policy impact assessment.
METHODS: A cohort of 59,089,575 enrollees was analyzed by expansion status and FPL (0-133% vs. ≥134%) using demographic and regional variables from administrative data across all 50 states and DC via the CMS Virtual Research Data Center at Penn State University. Descriptive statistics characterized proportional distributions and geographic clustering.
RESULTS: Nearly 87% (~51M) of beneficiaries had income ≤133% FPL; the majority were <40 years old, female, White, non-Hispanic, and in the South. The expansion group (38 states and DC; 36M enrollees, 62%) had a higher proportion of ≥40-year-olds (32% vs. 24%) and non-Hispanic White enrollees (43% vs. 32%), and a lower proportion of non-Hispanic Black enrollees (17% vs. 26%), compared with the non-expansion group (12 states; 38%). In expansion states, ~85% of enrollees were in the 0-133% FPL, compared with ~90% in the non-expansion states. Comparison of expansion groups stratified by FPL revealed larger demographic variations in the 0-133% FPL group than the ≥134% FPL group: expansion states had a lower proportion of <40-year-olds (66% vs. 75%) and a higher proportion of non-Hispanic Whites (43% vs. 31%). In the ≥134% FPL group, expansion states had a lower proportion of non-Hispanic Blacks (14%) than non-expansion states (24%).
CONCLUSIONS: Medicaid populations exhibit expansion-specific socioeconomic and geographic clustering. Claims-based analyses and impact modeling can help quantify potential H.R.1 impacts on eligibility, enrollment, and equity by drug or therapeutic area, providing a baseline for future evidence-based policy impact assessment.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
PT20
Topic
Health Policy & Regulatory
Topic Subcategory
Reimbursement & Access Policy
Disease
No Additional Disease & Conditions/Specialized Treatment Areas