Using Prescription Drug Data for Timely Assessments of State Insurance Coverage Rates: A Validation Study
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2026
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Abstract
Abstract Introduction Timely assessments of insurance coverage are limited by lags and unpredictable data availability. We assessed the performance of real-time prescription data as a proxy for state-level coverage changes. Methods We analyzed correlations between quarterly state insurance coverage (Medicaid, private insurance, and uninsured) and counts of filled prescriptions per capita by payer (Medicaid, private insurance, and cash-pay/assistance programs) from 2013-2024. Regression models measured how predictive prescription counts were of within-state coverage changes. Results Medicaid prescriptions per capita were strongly correlated with Medicaid coverage (⍴=0.62). Cash-pay prescriptions per capita were moderately correlated with uninsured rates (⍴=0.49). Private coverage correlations were weak. Correlations were stronger for adults (⍴=0.74 for Medicaid, ⍴=0. 52 for uninsured) than children. Among adults, each 10% within-state increase in Medicaid prescriptions was associated with a 7.5% relative increase (95% CI, 5.7%-9.3%) in Medicaid coverage; each 10% increase in cash-pay prescriptions was associated with a 2.5% increase (95% CI, 1.0%-4.1%) in uninsured rates. Conclusions Real-time prescription drug data are moderately-to-highly correlated with state-level coverage changes in Medicaid and uninsured rates, particularly for adults. These data may be useful for timely assessments of upcoming policies affecting Medicaid coverage.
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| Authors | Benjamin N. Rome, Adrianna McIntyre, Jinwoo Kim, Jiali Han, Aaron S. Kesselheim, Benjamin D. Sommers |
| Journal | Health Affairs Scholar |
| Year | 2026 |
| DOI |
10.1093/haschl/qxag149
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| URL | |
| Keywords | Keywords not found |
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