Differences in the cost of antidepressants across state medicaid programs

Christina M.L. Kelton, Robert P. Rebelein, Pamela C. Heaton, Yann Ferrand, Jeff J. Guo

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Background: Depression is the most prevalent major mental health disorder, affecting between eight and ten percent of the population in the United States. The U.S. Medicaid programs spent in total over $2.3 billion on antidepressant drugs in 2003, across three categories of antidepressants including selective serotonin reuptake inhibitors, tricyclic antidepressants, and others. Each state has its own set of cost-containment policies with respect to antidepressants, as well as other drugs, including preferred drug lists, prior authorization policies, copay systems, drug utilization reviews, and physician and patient education. Aims: Our objectives for this study are to describe in detail state Medicaid spending on antidepressants and to determine the magnitude and significance of the effects of Medicaid drug policies on reimbursement expense. Methods: Data from the Centers for Medicare & Medicaid Services are used to calculate state expenditures on antidepressants and number of prescriptions for antidepressants. Policy variables are taken from a 2003 Kaiser Commission report. Additional data on state population, employment, and weather are found in Census 2000 and other government sources. Descriptive summary tables are used to explain reimbursement per capita (die per capita "burden" of depression) and reimbursement per prescription. Results: Per-capita reimbursement ranges from less than $5 in Nevada and Wisconsin to over $20 in Tennessee and Maine. We find that the burden of depression is heaviest in states in which the amount of annual sunshine is low; the percent of people living in rural areas is high; and the employment rate is low. Those states in which the depression burden is heaviest are those states in which cost-containment policies are pursued most vigorously. The state of Michigan has the lowest per-prescription reimbursement ($50), followed closely by Wisconsin. Meanwhile, California, Texas, and Oklahoma have the highest reimbursement per prescription (over $75 in each of these states). Reimbursement per antidepressant prescription is highest in states in which the population is high; the percentage of generic prescriptions is low; and there does not exist a tiered-copay system. Discussion: Of all the Medicaid policies considered, the tieredcopay system is the only policy with a statistically significant negative correlation with per-prescription cost. Since an important limitation of the study is only a single year of observation, we cannot establish the direction of causation between policy and drug cost. Another limitation of the study is that actual acquisition costs are lower than reimbursements due to manufacturer rebates. For other cost-containment programs besides cost sharing, it is the quality of the program, not its existence per se, that seems to matter. Moreover, states that have high percentages of generic drugs, regardless of policy, enjoy significantly lower costs per prescription. The results of the study also lend support to the importance of sunlight and urbanization in reducing the depression burden. Implications for Policy and Research: Policy makers in state Medicaid programs can learn from experiences in other states. Additional research is required to ensure that the results hold up across different years and for other therapeutic classes of drugs.

Original languageEnglish (US)
Pages (from-to)33-47
Number of pages15
JournalJournal of Mental Health Policy and Economics
Volume11
Issue number1
Publication statusPublished - Mar 1 2008
Externally publishedYes

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ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Psychiatry and Mental health

Cite this

Kelton, C. M. L., Rebelein, R. P., Heaton, P. C., Ferrand, Y., & Guo, J. J. (2008). Differences in the cost of antidepressants across state medicaid programs. Journal of Mental Health Policy and Economics, 11(1), 33-47.