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The American Journal of Managed Care November 2008 - Special Issue
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How Similar Are States' Medicaid Preferred Drug Lists?
Jonathan D. Ketcham, PhD; and Jeffrey K. Ngai, MBA, MHSM
Medicare Part D Formulary Coverage Since Program Inception: Are Beneficiaries Choosing Wisely?
E. Anne Jackson, BS; and Kirsten J. Axelsen, MS
Impact of Therapeutic Switching in Long-Term Care
Bryan R. Cote, MA; and Elizabeth A. Petersen, MA
Medicare Part D's Effects on Elderly Patients' Drug Costs and Utilization
Jonathan D. Ketcham, PhD; and Kosali I. Simon, PhD
Kenneth E. Thorpe, PhD
Primary Care Physicians' Views of Medicare Part D
Andrew J. Epstein, PhD, MPP; Saif S. Rathore, MPH; G. Caleb Alexander, MD; and Jonathan D. Ketcham, PhD

How Similar Are States' Medicaid Preferred Drug Lists?

Jonathan D. Ketcham, PhD; and Jeffrey K. Ngai, MBA, MHSM
Comparison of the generosity and consistency of 10 states' Medicaid preferred drug lists for the top therapeutic classes revealed a large degree of inconsistency.
Table 4 shows the state-level coverage by therapeutic class overall and by patent status, as well as the level of coverage by the most-generous and least-generous states. The average difference between the most- and least-generous states in terms of percent covered for a therapeutic class was 54 percentage points. The greatest difference between states was for analgesics-narcotics (73 percentage points); New York covered only 13% and no on-patent drugs, whereas Virginia covered 87%, including 50% of on-patent and 92% of off-patent drugs. Analgesics-narcotics were followed in range in generosity by antipsychotics (63 percentage points), antiulcerants (60 percentage points), and antidepressants (56 percentage points); the range was smallest for antihyperlipidemics (34 percentage points). In 3 therapeutic classes, significantly fewer on-patent drugs were covered than off-patent drugs (P <.05). Every state except California does not cover any on-patent drugs for at least 1 therapeutic class, although which class varies by state. For example, only Florida does not cover any on-patent antidepressants, but Illinois, Florida, Michigan, and Virginia do not cover any on-patent inhaled steroids. In total, no on-patent products were covered in 13 of the 70 (18.6%) state-by-therapeutic-class pairs.

The results in Table 5 indicate that the greatest similarity in coverage was for anticonvulsants, which Table 4 also indicates was the most widely covered therapeutic class, with one third unanimously covered or not covered and no drugs with more than 3 states dissimilar from the others. This is because 4 states exempted anticonvulsants from their PDLs. The classes with the greatest amount of disagreement, based on the proportion of drugs with 4 to 5 states disagreeing, were antipsychotics (57.9%) and antidepressants (44.4%), whereas those with the least agreement (0% unanimous) were inhaled steroids and antiulcerants. The data (not shown) also indicate that the greatest amount of disagreement for on-patent drugs was for antidepressants (100% had 4-5 states dissimilar from the others), and the greatest amount of disagreement for off-patent drugs was for antipsychotics (80% had 4-5 dissimilar states).

States’ Medicaid PDLs differ substantially in their coverage of drugs in the highest-selling therapeutic categories. This variation across states could result from several sources, each with different implications. The variation might indicate that PDL coverage decisions do not reflect value-based purchasing despite recent efforts to facilitate it. This situation could result if a sufficient evidence base does not exist, or if state Medicaid agencies do not systematically incorporate existing evidence into coverage decisions. These shortcomings could be addressed through the development of more clinical evidence and more readily available evidence in an easily usable format through programs such as those currently conducted by the Foundation of Managed Care Pharmacy and the Drug Effectiveness Review Project.

The differences in PDLs could be consistent with value-based coverage if states differ with respect to the relative costs or benefits of specific drugs. Specific drugs’ relative benefits might vary, for example, because of differences in the characteristics of states’ Medicaid enrollees. Relative costs might vary because of supplemental discount programs, although the fact that some states receive systematically larger discounts cannot itself explain the observed inconsistencies in PDL coverage. Rather, if discount programs cause some states to receive larger discounts for one drug than others in the therapeutic class, while other states receive larger discounts for different drugs, these differences in relative prices could result in differences across states in the relative values of drugs in a class. That likely would result in less similarity for on-patent drugs than for off-patent drugs, the opposite of what we found. Nevertheless, additional research should consider whether states that achieve identical discounts through state purchasing pools such as the National Medicaid Pooling Initiative have more homogeneous PDLs as a result.17 PDL coverage decisions themselves can influence a state’s discount for a particular drug because they can be made before such discounts are negotiated in every state but Texas.18

We also found substantial differences across classes in consistency and generosity of PDL coverage. Because of concerns about quality and the clinical heterogeneity of patients’ responses to specific drugs, several but not all of the study states exempted anticonvulsants, antipsychotics, and antidepressants from PDL restrictions, resulting in their being the most widely covered classes. Despite 3 states agreeing to cover all antipsychotics and antidepressants, other states’ use of PDLs for these classes resulted in greater disagreement than that observed in the other therapeutic classes examined. States’ exemptions of these classes suggest that these drugs are not close substitutes and the differences in PDLs that we observed are, in fact, clinically important. Heterogeneity in clinical response to drugs across patients has been observed for other therapeutic classes studied here as well.19,20 Finally, we found that states somewhat frequently did not cover any on-patent drugs in a therapeutic class, limiting patients’ access to potentially beneficial medications. We analyzed only the therapeutic classes with the greatest sales, leaving open questions regarding coverage of other classes.

States might be similar or dissimilar along dimensions not considered here, including differences by brand, strength, and formulation of a given drug; the levels and tiers of copayments; the stringency of PA requirements and other supply-side controls such as stepwise (“fail-first”) requirements or monthly prescription count limits; and whether Medicaid managed care plans’ formularies also varied. Because we considered only coverage decisions, our findings likely under-represent the total degree of variation across states’ formularies. Research also should illuminate what drives this variation across states’ coverage decisions. Research focusing on the processes by which Medicaid P&T committees make their PDL coverage decisions will result in greater understanding of whether these differences result from differences in use of available evidence, the values of the committee (eg, the willingness to trade off access for cost reductions), drug discounts, the makeup of the P&T committee, or other differences in the political process. Finally, decision makers themselves could benefit from more evidence about the implications of specific formulary coverage decisions for costs and quality.

State pharmacy benefits managers should consider the extent to which the outcomes of their coverage decisions differ from those of their peers in other states. These managers have access to information not available to health services researchers such as ourselves to discern whether the inconsistencies reflect a lack of value-based coverage. For example, they can observe their states’ negotiated discounts. In addition, the state P&T committees include clinical experts to evaluate the quality effects of less generous and less consistent coverage, particularly whether consistency and generosity are more important for some therapeutic classes. States seeking to implement more value-based coverage decisions could benefit by learning from their counterparts in other states, as well as increasing their reliance on standardized information available from organizations such as the Drug Effectiveness Review Project.

The variation in prescription drug coverage that exists in private markets and in the managed competition approach of Medicare Part D benefits consumers by allowing them to choose a plan according to personal, heterogeneous values for specific drugs, although this comes at the cost of greater complexity for physicians.21 However, when patients cannot choose their plans, such as in Medicaid and under proposals for regional or national monopolies for Medicare Part D,22 variation in formulary design is not desirable if it reflects a lack of value-based coverage decisions. The variation across states’ Medicaid PDLs observed here raises concerns that such regional or national formularies in Part D would not reflect value to patients even on average, particularly given the higher stakes for various political interest groups in Medicare formulary design.

State Medicaid programs have implemented PDLs in an attempt to control the growth in prescription drug spending. We compared Medicaid PDLs for the 10 most populous states, examining their coverage of 110 drugs in 7 top-selling therapeutic classes. We found some similarity and many differences in the generosity and consistency of states’ PDLs. The large amount of variation indicates that states are not using a common set of clinical evidence to make consistent, value-based coverage decisions. More research is needed into Medicaid P&T committees and the processes by which they design PDLs. Those who make formulary coverage decisions would also benefit from additional evidence on the impact of these decisions on cost and quality outcomes.


Special thanks to Andrew J. Epstein, PhD, Michael F. Furukawa, PhD, Robert D. Harris, RPh, and Kosali I. Simon, PhD, for their insights on earlier versions of this manuscript, and to Rusty A. Jones, MBA, for access to Wolters Kluwer Health’s Pharmaceutical Audit Suite tool.

Author Affiliations: From the WP Carey School of Business, School of Health Management and Policy (JDK, JKN), Arizona State University, Tempe. Mr. Ngai is now with Pfizer, Inc.

Funding Source: None disclosed.

Author Disclosure: Dr Ketcham reports serving as a consultant for Pfizer, Inc. Mr Ngai reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JDK, JKN); acquisition of data (JDK, JKN); analysis and interpretation of data (JDK, JKN); drafting of the manuscript (JDK, JKN); critical revision of the manuscript for important intellectual content (JDK, JKN); and statistical analysis (JDK).

Address correspondence to: Jonathan D. Ketcham, PhD, WP Carey School of Business, School of Health Management and Policy, Arizona State University, 300 E Lemon St, Tempe, AZ 85287-4506. E-mail:

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