The Effect of Health Insurance on Prescription Drug Use Among Low-Income Adults:Evidence from Recent Medicaid Expansions
Section snippets
INTRODUCTION
Prescription drugs represent one of the fastest-growing areas of healthcare spending (Martin et al., 2016) but because of the effective role medications play in treatment of a vast array of health conditions and because public programs are a large source of payment for medications, demand responsiveness to price changes is subject to intense study (Carrera et al., 2018) and to policy attention regulating price increases (The Brookings Institution, 2017; The Council of Economic Advisers, 2018).
BACKGROUND
Despite multiple studies demonstrating that declines in out-of-pocket (OOP) spending leads to higher use of pharmaceuticals among older Americans, the previous literature does not provide much direct evidence from which to draw inferences about the non-elderly adult population, particularly lower-income adults targeted for insurance expansion under the ACA. The ACA provides additional federal financing to states for extending Medicaid coverage to non-elderly adults earning less than 138 percent
CONCEPTUAL FRAMEWORK
The economic question we set out to answer is how does gaining insurance through Medicaid affect the use of prescription medications? Our identification strategy uses plausibly exogenous variation in likelihood of insurance coverage due to state Medicaid expansion decisions. Our conceptual model can be explained at the individual-level and follows the large literature in economics that studies the effect of insurance on healthcare use: we hypothesize that individuals whose out of pocket costs
DATA
The database we use in this study provides a combined view of U.S. pharmaceutical distribution sales for virtually all U.S. retail brick-and-mortar and mail-order pharmacy prescription activity, including large pharmacy chains, independent pharmacies and pharmacy benefit managers (PBMs). In our empirical specifications we use data that are aggregated to the level at which variation occurs (for example, state/year-quarter/therapeutic class/payer type). The underlying microdata contains
EMPIRICAL FRAMEWORK
Our main empirical strategy uses a state-level difference-in-difference model to compare pharmaceutical utilization in all expansion states to that in non-expansion states (first difference) before and after the expansions (second difference). In a specification check, we drop from our expansion group the five states (DC, DE, MA, NY and VT) that had large expansions of public coverage to nonelderly adults prior to 2014. Appendix Table A1 provides a detailed categorization of states into
Descriptive Trends
In Fig. 1, we plot unadjusted time trends for total prescriptions paid at the state level by Medicaid, Medicare, private insurance plans, or through cash and assistance programs available to the uninsured during the study period; we plot these trends separately for the expansion and non-expansion states. The blue and grey lines represent ACA Medicaid-expansion states and non-expansion states, respectively.6
Acknowledgements
We are grateful to seminar participants at the Fall 2015 and 2016 Association for Public Policy Analysis and Management (APPAM) conference, 2016 American Society of Health Economists conference, Indiana University and Vanderbilt University for helpful comments. Dr. Sommers’ work on this project was supported by grant number K02HS021291 from the Agency for Healthcare Research and Quality (AHRQ). The views presented here are those of the authors and do not represent AHRQ.
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