 To compare the generosity and consistency of 10 statesâ€™ Medicaid preferred drug lists (PDLs) in high-volume therapeutic classes.

 Descriptive comparisons of 7 of the top 10 therapeutic classes by Medicaid sales and of the top 10 most populous states with Medicaid PDLs.

 A PDL specifies which drugs are available to patients without receiving prior approval from the state. State PDLs were collected in January 2008 to determine the status (covered or not covered) of 110 different drugs in each state. The US Food and Drug Administration Orange Book provided patent status for each drug. States were compared for generosity and similarity of coverage overall, by patent status, and by therapeutic class.

 For 42 (38%) of the drugs, there was wide consistency in PDL design, with at least 9 states classifying the drug with the same PDL status. For the other 62% of drugs, there was greater variation, with 2 or more states classifying the drugs differently than the others. Generosity and consistency also varied by therapeutic class and patent status. 

 For most drugs, Medicaid PDLs are not implemented consistently across states, suggesting that states do not rely on common clinical evidence to make value-based coverage decisions. Greater involvement by the federal government in designing or regulating monopolistic Medicare Part D PDLs may result in similar inconsistencies.

(Am J Manag Care. 2008;14(11 Spec No.):SP46-SP52)

Medicaid programs use preferred drug lists (PDLs) to control prescription drug spending. Comparison of the generosity and consistency of PDL coverage in the 10 most populous states, with PDLs for 110 active ingredients in 7 of the top therapeutic classes, showed that: 

For 62% of ingredients, at least 2 states differed from the others in their coverage decisions. 

States did not consistently apply clinical evidence to make value-based coverage decisions.

State and federal governments have changed the prescription drug benefits available to Medicaid and Medicare enrollees throughout the past decade. In response to high growth rates in prescription drug spending, state Medicaid programs have implemented numerous policies to control costs. One common strategy is to use preferred drug lists (PDLs). Preferred drug lists establish which drugs are available to patients immediately and which require patients&#8217; physicians or pharmacists to request prior authorization (PA) from the government. Prior authorization often is used in conjunction with &#8220;stepwise&#8221; or &#8220;fail-first&#8221; regulations that require a patient to unsuccessfully try a drug covered by the PDL before PA is granted.

Preferred drug lists fall in the middle of the range of formulary design, with open formularies at one end and closed formularies at the other. Open formularies provide insurance coverage for any drug, whereas closed formularies provide coverage only for drugs on the formularies. The credible threat to exclude certain drugs from coverage is shared by both closed formularies and PDLs, and enhances payers&#8217; ability to bargain for lower prices. In addition to lowering negotiated prices, closed formularies and PDLs can reduce spending by shifting prescribing decisions toward lower-cost alternatives.

Previous research indicates that PDLs have lowered states&#8217; growth in prescription drug spending.

 Other work using microdata found that PDLs have altered prescribing decisions,

 and in some contexts this appears to have negative implications for quality.

 PDLs also appear to restrict patients&#8217; access to medications, particularly for newer drugs that have been linked to higher quality,

 and to reduce patient adherence to continuing medication therapy.

 These effects of PDLs occur despite their coupling with PAs, likely because the unreimbursed administrative burden of requesting PA promotes greater prescribing of drugs covered by the PDL.

Despite these concerns with Medicaid PDLs, state PDL design might reflect value-based purchasing principles if the cost savings exceed the costs associated with the negative effects of PDLs. If states&#8217; pharmacy and therapeutics (P&T) committees rely on scientific evidence to weigh these tradeoffs, they can create PDLs that promote value-based prescribing, for example, by covering generic products but excluding much costlier on-patent therapeutic substitutes with potentially higher marginal effectiveness. If states&#8217; P&T committees rely on a consistent base of national or international clinical evidence, then the resulting PDLs themselves should be largely consistent across states. Recent efforts have focused on promoting Medicaid programs&#8217; use of clinical evidence; for example, the Foundation of Managed Care Pharmacy established a standardized format for drug manufacturers to report data regarding drugs&#8217; value for use by P&T committees.

 Additionally, a number of state Medicaid agencies have joined the Drug Effectiveness Review Project, which combines existing research about the comparative value and outcomes of drugs in a given therapeutic class into a single source available to both member and nonmember states.

We examined the level of generosity and extent of agreement among states&#8217; prescription drug coverage decisions, as well as how coverage and consistency varied with patent status and therapeutic class. The purpose was to gain insight into whether states&#8217; formulary designs rely on a consistent evidence base, or whether states differ in their criteria for determining coverage. Existing research indicates that states had diverse coverage prior to the recent growth in adoption of PDLs and the growth in evidence-based medicine and economic evaluations of drugs.

 Research on private insurers likewise indicates substantial variation in formulary design because of various organizational characteristics.

 Other researchers found that neither Florida&#8217;s Medicaid formulary nor a private formulary had evidence-based or value-based designs.

Because state Medicaid PDLs are not available in an electronic, easily comparable format, we limited the scope of the analysis to the largest states and therapeutic classes. Specifically, we gathered state Medicaid PDLs in place as of January 2008 to examine 1100 formulary design decisions (110 drugs in 10 states). We determined which states had PDLs from a survey conducted by the Kaiser Family Foundation15 and, for nonrespondents, by examining each state&#8217;s Medicaid Web site. Of the 37 states that have implemented PDLs, we included in this study the 10 most populated: Illinois, Florida, Texas, New York, California, Ohio, Michigan, Georgia, Virginia, and Massachusetts. 

To choose which therapeutic classes to study, we ranked them using the Universal System of Classification (USC) based on their Medicaid sales in the 12 months before March 2008 as reported by the Wolters Kluwer Health&#8217;s Pharmaceutical Audit Suite tool.

 We included 7 of the top 10 3-digit USCs: analgesics-narcotics (USC 022), anticonvulsants (USC 202), antidepressants (USC 643), antihyperlipidemics (USC 321), antipsychotics (USC 641), antiulcerants (USC 234), and inhaled steroids (USC 284), and excluded antibiotics (USC 151), HIV antivirals (USC 821), and analeptics (USC 645). Because of legal requirements preventing states from limiting access to critical care, states have exempted certain therapeutic classes from their PDLs, essentially leaving the formularies open for those classes. Thus, we excluded HIV antivirals, which were exempted from all of the state PDLs in this study. We excluded antibiotics because states often covered some of a drug&#8217;s strengths and formulations but not others. We excluded the tenth highest-selling class, analeptics, because as central nervous system stimulants they were subject to additional restrictions beyond the PDL in Michigan and New York, and PDL coverage of a given drug varied by patient age in Georgia and California. We examined PDL status at the active-ingredient level, which we refer to as a &#8220;drug.&#8221; Because we considered only the drug level, we excluded differences across states by formulation, strength, brand name, or other specific aspects of a product. Together, the 7 therapeutic categories included 110 different drugs that accounted for 39% of Medicaid sales (by dollars) nationwide for the 12 months before March 2008.

 Each of these therapeutic classes had been reviewed previously by the Drug Effectiveness Review Project. Patent status for each drug was obtained from the US Food and Drug Administration (FDA) Orange Book. Drugs were characterized as off patent when the FDA Orange Book listed therapeutic-equivalent drugs that were sold by drug manufacturers other than the proprietary manufacturer, or when any form or strength of the drug became off patent. 

 lists the therapeutic classes chosen and the number of drugs, the proportion of drugs on patent, and which states exempted the category from their PDLs. 

We examined each state&#8217;s PDL to determine whether each drug was covered. Drugs were deemed covered if they were listed with preferred status or did not require PA, or if the class was exempted from the PDL altogether. (The Massachusetts PDL occasionally designates for some specific drugs that PA is required if more than 30 units are required per month. In these cases, if PA was required only for supplies for more than 1 month, the drug was still counted as being on the PDL.) 

Because states sometimes distinguish between a drug&#8217;s brand and generic versions or between different formulations (eg, extended release vs short acting) in their coverage decisions, we considered a drug to be covered if any version was available without PA. Combination products of 2 or more drugs were viewed as distinct drugs, and they were considered on patent if any of the drugs were on patent. 

Using these data, we generated descriptive results and where appropriate performed Fisher&#8217;s exact tests or t tests to indicate the generosity of coverage and the degree of consistency in coverage across states. Specifically, generosity was measured by the proportion of drugs that were covered by each state. Consistency was measured by the number of states that had the same coverage for each drug. For example, states were considered unanimous on a given drug&#8217;s status either if none included it on the PDL or if all 10 did, but they had the greatest disagreement if 5 states included the drug and the other 5 did not.

States&#8217; 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&#8217; relative benefits might vary, for example, because of differences in the characteristics of states&#8217; 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.

 PDL coverage decisions themselves can influence a state&#8217;s discount for a particular drug because they can be made before such discounts are negotiated in every state but Texas.

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&#8217; 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&#8217; use of PDLs for these classes resulted in greater disagreement than that observed in the other therapeutic classes examined. States&#8217; 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.

 Finally, we found that states somewhat frequently did not cover any on-patent drugs in a therapeutic class, limiting patients&#8217; 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 (&#8220;fail-first&#8221;) requirements or monthly prescription count limits; and whether Medicaid managed care plans&#8217; formularies also varied. Because we considered only coverage decisions, our findings likely under-represent the total degree of variation across states&#8217; formularies. Research also should illuminate what drives this variation across states&#8217; 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&#8217; 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.

 However, when patients cannot choose their plans, such as in Medicaid and under proposals for regional or national monopolies for Medicare Part D,

 variation in formulary design is not desirable if it reflects a lack of value-based coverage decisions. The variation across states&#8217; 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.

 Physiciansâ€™ prescribing responses to a restricted formulary: the impact of Medicaid preferred drug lists in Illinois and Louisiana. 

 The effect of access restriction on the vintage of drugs used by Medicaid enrollees. 

 Medicaid prescription drug access restrictions: exploring the effect on patient persistence with hypertension medications.

 Medicaid preferred drug listsâ€™ costs to physicians. 

Insights into Formulary Decision Processes: Findings from a National Survey of Health Plans

. Silver Spring, MD: Social & Scientific Systems, Inc; March 2008. 

http://www.fmcpnet.org/cfr/waSys/f.cfc?method=getListFile&id=3EE4422A

 Drug formulary restrictions as a costcontainment policy in Medicaid programs. 

14. Neumann PJ, Lin PJ, Greenberg D, et al.

 Do drug formulary policies reflect evidence of value? 

 SourceÂ® Pharmaceutical Audit Suite Tool. 

http://www.wkhealth.com/pt/re/ps/source.htm

Analysis of Multi-state Medicaid Drug Purchasing Pool

http://www.hhsc.state.tx.us/reports/Multi-State_MedicaidDrugPurchasing.pdf

. Clinical implications of pharmacogenomics of statin treatment. 

22. Huskamp HA, Rosenthal MB, Frank RG, Newhouse JP

. The Medicare prescription drug benefit: how will the game be played? 

Articles

SUPPLEMENT CONTENT

Comparison of the generosity and consistency of 10 states' Medicaid preferred drug lists for the top therapeutic classes revealed a large degree of inconsistency.

How Similar Are States' Medicaid Preferred Drug Lists?

 To evaluate prior analyses that quantify how drug management programs impact health plans and patients with respect to those analysesâ€™ effectiveness in measuring program quality. 

 We examined 77 US studies from 1996 to 2007 that evaluated the respective drug management programs of managed care, indemnity insurance, Veterans Health Administration, and Medicaid health plans. Our review included those studies that assessed the ways in which specific drug management tools and the drug management program overall impacted economic, clinical, and/or humanistic outcomes. 

 The 77 studies included in our review used 11 types of outcome end points. A total of 52 studies (68%) incorporated an economic end point; of these, 35 (68%) reported economic data only and did not address clinical or humanistic outcomes. Overall, 33 (43%) evaluated clinical or humanistic end points; of these, 17 (52%) also reported on an economic end point. 

 Although the number of studies evaluating drug management programs has trended upward, only a handful have integrated economic, clinical, and/or humanistic outcomes when assessing the effects these programs have on health plans and patients. The efforts of these researchers to assess the overall quality of drug management programs have fallen short. To ensure that drug management tools have a desired effect on outcomes and medical costs, measures used to evaluate drug management programs must be improved.

(Am J Manag Care. 2008;14(11 Spec No.):SP36-SP45)

Few studies designed to evaluate the impact of drug management programs have adequately assessed their quality, and more comprehensive assessments are needed to ensure the following:

Drug management program tools improve or remain neutral to patient outcomes. 

Health plans use drug management tools that deliver the most value (best health outcomes) per dollar spent.

Health plans generally introduce drug management programs and tools to manage and control how members use prescription drugs. These programs influence the way patients are treated medically and must be viewed as healthcare interventions; they also have health outcome and cost implications. Just as with other healthcare interventions, it is essential to ensure that drug management programs do not lead to negative outcomes for patients that may subsequently increase overall medical costs. To this end, it is important to accurately assess the quality of existing programs. 

Before quality can be measured, however, it must be defined. Genichi Taguchi was one of the earliest promoters for improving quality in manufacturing. He defined poor quality as &#8220;the loss a product imposes on society after it is shipped.&#8221;

 If we apply Taguchi&#8217;s definition to healthcare, poor quality might be described as the loss an intervention imposes on society after it is implemented. Although the manufacturing industry now has procedures for quality assessment firmly in place, measuring the quality of healthcare services&#8212;including prescription drug programs&#8212;and pinpointing a precise definition of &#8220;quality&#8221; appear much more elusive. The Institute of Medicine has defined quality in healthcare as &#8220;the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.&#8221;

The US healthcare industry has made progress in developing measures to evaluate medical care delivery and patient outcomes for individuals with private and public health insurance. National Committee for Quality Assurance accreditation and quality reporting (based on the Healthcare Effectiveness Data and Information Set) and the Joint Commission on Accreditation of Healthcare Organizations performance measurement initiatives are quality measurement programs

 that specify disease treatment and management measures, and incorporate appropriate indices for prescribing medication. Neither program outlines specific criteria for assessing drug management programs. The following organizations have made independent efforts to develop and implement quality measures for pharmaceutical care, broadly and in the payer/managed care setting: 

&#8226; The Academy of Managed Care Pharmacy Formulary Submission Format designed an evidence-based, effectiveness-driven approach to formulary management.

&#8226; The National Quality Forum is developing voluntary standards for reporting the quality of therapeutic drug management.

&#8226; The URAC Pharmacy Benefit Management Accreditation Format has established a benchmark for the quality of care in pharmacy benefit management.

Although these programs make a good beginning, none is comprehensive and each restricts itself to a particular stakeholder perspective. There is no consensus among the programs as to what should be measured, how it should be measured, and what outcomes should constitute thresholds for different quality measures. This lack of consensus has led to significant variations in data and results. Without standardized quality measures and public reports on drug management programs&#8217; performance, it is difficult for stakeholders to assess or compare quality claims presented in the literature or by health plans.

In addition to analyzing the methodologies used in the literature to quantify the impact of drug management programs, we wanted to offer insight as to whether these initiatives help ensure high quality overall (eg, the best possible outcomes for health plans and patients). This may help managed care organizations and other stakeholders in the United States improve the processes for measuring quality, better determine the effectiveness of drug management programs in the United States, and provide a framework for improving quality. 

Applying the search terms listed in the eAppendix Table, we identified 1099 potentially relevant articles. After careful screening to exclude studies that did not meet inclusion criteria, we identified 77 articles suitable for this review. The 

 details each of the 77 articles included in our analysis.

 Research on the effects of drug management programs evolved over the study period. Only 5 (6.5%) of the 77 studies we reviewed that evaluated drug management programs were published before 2001; after 2001, the number of these types of studies increased dramatically, as did the breadth of end points considered. Although only 1 study published in 1996-1999 met our inclusion criteria, there were 4 qualifying studies published in 2000-2001, 15 in 2002-2003, 31 in 2004-2005, and 26 in 2006-2007. 

The studies we examined measured the effects of 1 or more of 10 drug management tool types on various end points. Cumulatively, the studies encompassed the following 10 drug management tool types: formulary restrictions, preferred drug list, use of copayments or cost-sharing measures, prior authorization procedures, quantity level limits, step-therapy edits, therapeutic substitutions, generic drug programs, pharmacy cognitive services, and over-the-counter drug coverage (

). A majority of the studies (77%) evaluated only 1 drug management tool type, with cost sharing being the most common (n = 39; 50.6%), followed by formulary restrictions (n = 27; 35.1%). 

W. Edwards Deming, one of the founders of quality theories, surmised that &#8220;Better quality at lower price has a chance to capture a market. Cutting costs without improvement of quality is futile.&#8221;85 Operating on this tenet, researchers must strive to prove that healthcare interventions positively impact the quality of healthcare in addition to measuring how they affect cost and consumption. Otherwise, improved health outcomes might come at unacceptable costs or, conversely, economic savings might be achieved at the expense of a patient&#8217;s health.

One of the main purposes of our analysis was to identify the extent to which evaluations of drug management programs measure quality, or applying the Institute of Medicine definition, identify the degree to which health services for individuals and populations increase the likelihood of desired health-related outcomes and are consistent with current professional knowledge.

 Although the studies we reviewed quantified the impact of drug management programs, they usually included a narrow scope of outcomes and were not specifically designed to measure quality.

Although plans and researchers are demonstrating growing interest in the effects of managed care drug management initiatives on clinical outcomes, even the newer studies fall short of providing a comprehensive quality assessment as a result of their failure to consider both economic and patient -centered end points. Despite the increasing number of studies to incorporate a diverse panel of outcomes, none appeared to have a predefined purpose of quality measurement by which to proceed with their assessments. 

. To help ensure that quality is appropriately assessed, several aspects need to be considered when designing evaluations for drug management programs. The study&#8217;s objective should include a patient outcomes measure. Ideally, outcomes measures should be end-point measures, such as physiologic response to an intervention, hospitalizations because of treatment failures or complications, or patient-reported outcomes. In cases where patient follow-up time makes some of these measures unfeasible, intermediate outcomes (eg, medication compliance, short-term resource use) may be the most viable options until long-term data are available to evaluate the program&#8217;s impact. Any analysis of patient outcomes should be accompanied by a comprehensive economic analysis that goes beyond the program&#8217;s impact on the pharmacy budget and encompasses its effect on the total costs to plans and patients.

. When planning how to execute a study on drug management programs, researchers should carefully consider data collection methods. They must be prepared to use appropriate data from multiple sources, with the goal of considering a combination of economic, clinical, and/or humanistic outcomes. For example, to evaluate economic and physiologic outcomes, researchers could augment claims data with EMR; to validate patient-reported outcomes and support an economic assessment, patient survey data could be combined with claims data. 

. A comprehensive quality analysis of a drug management program considers the program from a long-term perspective. This may compete with a health plan&#8217;s need to deliver short-term benefits. Even if the average retention for health plan members is only 3 years, however, the drug management program&#8217;s long-term impact is important for several reasons. It is conceivable&#8212;if not likely&#8212;that patients subject to drug management program interventions who leave the plan will later return. In these cases, the plan would benefit from implementing a drug management program that encourages positive outcomes in the long term; conversely, the plan would suffer from earlier short-sighted decisions. Additionally, innovative employers or payers will recognize benefits from interventions that deliver long-term cost savings and improve patient outcomes because these results align with their goal of having a healthy and productive workforce.

Historically, studies that have evaluated the effects of drug management programs have neglected to assess quality as a measurement of health outcome per cost. We believe our analysis is the first to consider the methodologic approaches used in assessing how drug management programs or tools impact outcomes in the United States and the degree to which previous studies have evaluated program quality. Other reviews have synthesized the findings of drug management program research, such as the assessment of previous economic findings by Hadley,

 or reviewed the findings of studies that evaluated prior authorization, like the one by MacKinnon and Kumar.

 Goldman et al recently reviewed literature that assessed the association between cost sharing, medication utilization, and outcomes.

 None of these reviews considered the methodologic approaches used in other studies to evaluate the impact of drug management programs and whether they were comprehensive enough to draw valid conclusions about overall program quality.

Deming said that &#8220;Cutting costs without improvement of quality is futile.&#8221;

 Healthcare resources are limited; therefore, assessments of drug management programs need to evolve beyond an analysis of short-term benefits. They need to ensure that drug management tools live up to their intent, which is to control medication use and lower overall medical costs without hindering patient outcomes. Although some studies we examined did look beyond a single outcome perspective, only a small number considered both economic and healthrelated outcomes. This limits the usefulness of current literature as a tool to guide plan designers working to enhance the effectiveness of drug management programs. 

The continued publication of drug management program assessments is highly encouraged. Such evaluations serve to disseminate the best practices and facilitate consensus on quality assessment approaches for drug management program tools.

The authors would like to thank Diana I. Brixner, PhD, RPh, for her leadership; and Brian T. Agnew, MB A, Marie Howson, RN , MB A, and Barbara L. Roper, BA, for their support of this project. 

 From the Department of Pharmacotherapy (CM-M, A-PH, GMO), University of Utah, Salt Lake City; Millcreek Outcomes Group (CM-M, A-PH, GMO), Salt Lake City; and Pfizer, Inc (DTS, BE), New York.

 Ms McAdam-Marx, Dr Holtorf, and Mr Oderda are consultants with Millcreek Outcomes Group, which received payment from Pfizer, Inc for the development and preparation of this manuscript. Drs Schaaf and Eng are employees of Pfizer, Inc and report owning stock in that company. 

 Concept and design (CM-M, DTS, A-PH, BE, GMO); acquisition of data (CM-M, A-PH, BE); analysis and interpretation of data (CM-M, DTS, A-PH, BE, GMO); drafting of the manuscript (CM-M, DTS, A-PH); critical revision of the manuscript for important intellectual content (CM-M, DTS, A-PH, GMO); statistical analysis (BE); obtaining funding (DTS, BE, GMO); administrative, technical, or logistic support (A-PH); and supervision (GMO).

 Carrie McAdam-Marx, RPh, MS, Department of Pharmacotherapy, University of Utah, 421 Wakara Wy, Rm 208, Salt Lake City, UT 84108. E-mail: 

3. Agency for Health Care Research and Quality.

 National voluntary consensus standards for the reporting of therapeutic drug management quality. 

http://www.fmcpnet.org/cfr/waSys/%20f.cfc?method=getListFile&amp;id=A2D0F502

7. Pharmacy Benefit Management Accreditation.

http://www.pbm.urac.org/files/URAC-PBM-Brochure.pdf

8. Abourjaily P, Gouveia WA, Selker HP, Zucker DR.

 Evaluating the nondrug costs of formulary coverage restrictions. 

10. Adams AS, Soumerai SB, Ross-Degnan D.

 Use of antihypertensive drugs by Medicare enrollees: does type of drug coverage matter? 

12. Artz MB, Hadsall RS, Schondelmeyer SW.

 Impact of generosity level of outpatient prescription drug coverage on prescription drug events and expenditure among older persons. 

14. Briesacher B, Kamal-Bahl S, Hochberg M, Orwig D, Kahler KH

. Three-tiered-copayment drug coverage and use of nonsteroidal anti-inflammatory drugs. 

16. Bukstein DA, Cherayil GA, Gepner AD, Luskin AT, Kooistra JB, Olson RM

. The economic burden associated with prior authorizations in an allergist office. 

18. Christian-Herman J, Emons M, George D

. Effects of generic-only drug coverage in a Medicare HMO. 

. 2004;(suppl web exclusives):w4-w455-w468.

. Health plan member experience with point-of-service prescription step therapy. 

. Effects of a polypharmacy edit and reduced quantity limits on the utilization of triptans and overall costs in an integrated health system. 

24. Fairman KA, Motheral BR, Henderson RR

. Retrospective, long-term follow-up study of the effect of a three-tier prescription drug copayment system on pharmaceutical and other medical utilization and costs. 

. Changing prescribing patterns and increasing prescription expenditures in Medicaid. 

. Economic consequences of underuse of generic drugs: evidence from Medicaid and implications for prescription drug benefit plans. 

30. Gershovich OE, Billups SJ, Delate T, Hoffman CK, Carroll N. 

Assessment of clinical, service, and cost outcomes of a conversion program of sumatriptan to rizatriptan ODT in primary care patients with migraine headaches. 

32. Gibson TB, Mark TL, McGuigan KA, Axelsen K, Wang S

. The effects of prescription drug copayments on statin adherence. 

. Consumer response to dual incentives under multi-tiered prescription drug formularies. 

36. Harris BN, West DS, Johnson JT, Hong SH, Stowe CD

. Effects on the cost and utilization of proton pump inhibitors from adding over-the-counter omeprazole to drug benefit coverage in a state employee health plan. 

. An evaluation of Oregonâ€™s evidence-based Practitioner-Managed Prescription Drug Plan. 

Unintended consequences of caps on Medicare drug benefits. 

42. Huskamp HA, Deverka PA, Epstein AM, et al

. Impact of 3-tier formularies on drug treatment of attention-deficit/hyperactivity disorder in children. 

. Comparison of mailorder with community pharmacy in plan sponsor cost and member cost in two large pharmacy benefit plans. 

. How do incentive-based formularies influence drug selection and spending for hypertension? 

48. Landon BE, Rosenthal MB, Normand SL, et al

. Incentive formularies and changes in prescription drug spending. 

50. Lurk JT, DeJong DJ, Woods TM, Knell ME, Carroll CA

. Effects of changes in patient cost sharing and drug sample policies on prescription drug costs and utilization in a safety-net-provider setting. 

. Lowering patient drug acquisition costs to improve health outcomes and costs. 

54. Meissner BL, Moore WM, Shinogle JA, Reeder CE, Little JM

. Effects of an increase in prescription copayment on utilization of low-sedating antihistamines and nasal steroids. 

. Medication costs, adherence, and health outcomes among Medicare beneficiaries. 

. Effect of a three-tier prescription copay on pharmaceutical and other medical utilization. 

. Predictors of satisfaction of health plan members with prescription drug benefits. 

62. Nair KV, Ganther JM, Valuck RJ, McCollum MM, Lewis SJ

. Impact of multi-tiered pharmacy benefits on attitudes of plan members with chronic disease states. 

. Managed care organizationâ€™s experience with a therapeutic conversion program in hypertensive patients. 

66. Panzer PE, Regan TS, Chiao E, Sarnes MW

. Implications of an SSRI generic step therapy pharmacy benefit design: an economic model in anxiety disorders. 

 A review of prior authorization programs: antihemophilic, antineoplastic, and antiretroviral agents. The Center for Pharmacoeconomic Studies. 

http://www.utexas.edu/pharmacy/research/institutes/pharmacoeconomics/pareport.pdf

 Consumer knowledge and perceptions of formularies. 

 Impact of a formulary switch from amlodipine to felodipine at a VA medical center. 

. Pharmacy costs associated with nonformulary drug requests. 

76. Thiebaud P, Patel BV, Nichol MB, Berenbeim DM

. The effect of switching on compliance and persistence: the case of statin treatment. 

. Evaluation of product switching after a state Medicaid program began covering loratadine OTC 1 year after market availability.

80. Tseng CW, Brook RH, Keeler E, Steers WN, Mangione CM

. Cost-lowering strategies used by Medicare beneficiaries who exceed drug benefit caps and have a gap in drug coverage. 

. Medicaid prescription drug access restrictions: exploring the effect on patient persistence with hypertension medications. 

84. Zeber JE, Grazier KL, Valenstein M, Blow FC, Lantz PM

. Effect of a medication copayment increase in veterans with schizophrenia. 

. Cost Containment Strategies for Prescription Drugs: Assessing the Evidence in the Literature. March 2005. Kaiser Family Foundation report 7295. 

http://www.kff.org/rxdrugs/7295.cfm.Accessed

. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. 

Adopting quality measurement standards for drug management programs might increase the effectiveness and long-term sustainability of positive health outcomes for patients.

Systematic Analysis of Outcomes Evaluations Applied to Drug Management Programs

 To evaluate how Medicare Part D formulary composition has changed since program inception, including comparison of plans eligible for full premium subsidy (ie, benchmark plans) with their counterparts. 

 The study used publicly available data released by the Centers for Medicare & Medicaid Services to generate snapshots of formulary coverage and enrollment levels in each plan year. The analysis included all Part D plans and tracked formulary coverage of 152 of the most common brand name and generic drugs prescribed to seniors. 

 Since 2006, the number of products available without restriction has increased and the number of drugs not on formulary has decreased. However, it appears that beneficiaries (subsidized beneficiaries in particular) may not be using their open-enrollment periods to reevaluate the available plan offerings.

 Beneficiaries need to reevaluate the Part D options available on an annual basis to maintain enrollment with the most appropriate plan available. Although all plans meet the proscribed formulary requirements, some plans offer richer drug coverage with more drugs available on an unrestricted basis. Benchmark plan status allows Part D plans to maintain or gain significant Medicare enrollment from year to year. Careful oversight should be provided to ensure that the level of formulary coverage offered at benchmark and other plans remains consistent.

(Am J Manag Care. 2008;14(11 Spec No.):SP29-SP35)

A review of coverage for 152 commonly prescribed drugs suggests beneficiaries may not be using their open-enrollment periods to reevaluate the available plan offerings.

Other beneficiaries may be renewing their original plan without considering alternatives.

Effective January 2006, Medicare Part D expanded the Medicare program to include coverage for prescription drugs. Prior to 2006, low-income Medicare  beneficiaries who qualified for their state Medicaid program received drug coverage through Medicaid, while Medicare remained the primary payer for most other medical services. Individuals who are eligible for both Medicare and Medicaid are called dual-eligible beneficiaries. Under the Medicare drug benefit, dual-eligible beneficiaries qualify for reduced cost-sharing requirements and are exempt from premium payments if they are enrolled in basic plans with a premium below the minimum premium benchmark, designated as benchmark plans. Other Medicare beneficiaries with low income and minimal assets also may qualify for reduced cost-sharing requirements and are referred to as the other low-income subsidy (LIS) population in this report. If they do not choose a Medicare Part D plan, full-benefit LIS and dual-eligible beneficiaries are automatically enrolled in a benchmark plan based on a random assignment. 

A May 2007 report by the Government Accountability Office acknowledged the impact beneficiary-specific assignments had in a pilot program launched in Maine, but concluded that the existing random assignment process should be maintained.

 The report found that the dual-eligible beneficiaries&#8217; right to change plans at any time and the minimum proscribed formulary requirements provide beneficiaries with the flexibility to meet their coverage needs.

This study was conducted to assess (1) whether there are any differences in the formulary composition of the benchmark plans versus other plans and (2) whether it appears that dual-eligible and other LIS beneficiaries are actively making enrollment decisions based on formulary composition.

The analysis included stand-alone prescription drug plans (PDPs) and Medicare Advantage plans that provide a Part D benefit (MAPDs). The analysis was limited to non&#8211;employer-sponsored plans to be consistent with the formulary information available in the PUF. 

For the purposes of counting plans, we defined a plan as the unique combination of contract ID and plan ID. The contract ID and plan ID are referenced in plan documents published by the Part D plan, and they are used in the PUF files and enrollment reports to track carrier or plan information. The contract ID corresponds to the carrier providing the coverage (eg, Humana Insurance Company of New York). The plan ID corresponds to the benefit offered (eg, Silver, Gold, or Bronze). PDP carriers may offer no more than 3 plans in any region (with some exceptions due to mergers or acquisitions). As such, a PDP offered in all regions would have no more than 114 unique combinations of contract ID and plan ID filed with CMS (ie, maximum of 3 plans filed in 38 regions). There may be many more combinations of contract ID and plan ID associated with the Part D component of MA-PD plans, however. Most MA-PD plans are filed at the county level (rather than the multistate region level, like PDPs). Approximately 1% of Part D enrollment is in regional MA-PD plans that are filed for a multistate area. 

We reviewed the top 168 drugs dispensed to persons age >65 years as of October 2007, as identified by Verispan based on script volume.6 The drugs were stratified into the following categories:

&#8226; Drugs in excluded therapeutic classes were not included in the study. Eight of the most common 168 drugs were in this category: alprazolam, lorazepam, folic acid, clonazepam, temazepam, diazepam, Viagra, and enteric-coated aspirin.

&#8226; Four of the most common drugs were the generic form of a brand drug also listed among the most common drugs. These products had recent patent expirations. Coverage for the drugs in each year was included at the best coverage available for either the brand or generic form. The 4 pairs of drugs were Norvasc/amlodipine besylate, Toprol XL/metoprolol succinate, Ambien/zolpidem tartrate, and Lotrel/amlodipine besylate and benazepril HCl.

 summarizes the total enrollment and count of plans included in the analysis. The enrollment associated with benchmark plans includes all enrollees, not limited to those who qualify for the subsidy. As seen in Table 1, the enrollment in the benchmark plans decreased significantly between 2007 and 2008. The number of individuals eligible for the subsidy did not decline over this time. There were 9.2 million Part D enrollees eligible for the subsidy in 2007 and 9.4 million Part D enrollees eligible for the subsidy in 2008.

The decline in enrollment in the benchmark plans shown in Table 1 is the result of increased disruption in the plans with benchmark status in 2008. Between plan years 2006 and 2007, few plans lost benchmark status (notifications of premium increase were mailed to 0.6 million beneficiaries

). A greater number of plans lost benchmark status between plan years 2007 and 2008 (notifications of premium increase were mailed to 2.6 million beneficiaries

). From year to year, the number of beneficiaries who change plans has remained consistent. The CMS indicated that 10% of Part D enrollees changed plans between 2006 and 20079 and 12% of Part D enrollees changed plans between 2007 and 2008.10 Between 2007 and 2008, it is likely that many of the nonsubsidized enrollees renewed with their plans, even when the plan lost benchmark status. As a result, it appears the nonbenchmark plans gained enrollment, although most of the movement in enrollment would have been between plans with benchmark status.

, drugs were categorized into 3 levels of coverage: (1) not listed, (2) restricted, and (3) unrestricted. Drugs that are not listed in the PUF for a plan are generally not covered, although beneficiaries may appeal to the plan for a coverage exception. A drug was classified as restricted if a prior authorization or step therapy requirement was reported in the PUF for a plan. A drug was classified as unrestricted regardless of its tier status if there were no reported prior authorization or step therapy requirements. There may be quantity limits associated with these drugs. As shown in Figure 1, across all plans, the number of study drugs available without restriction peaked in 2007. The peak in unrestricted study drugs in 2007 appears to be related to expanded formulary coverage between 2006 and 2007. Plans may have been conservative when they filed their 2006 benefits. On average, the plans that renewed between 2006 and 2007 increased the number of drugs available on an unrestricted basis by 4.5 (MA-PDs) and 5.5 (PDPs). From 2006 to 2008, the number of study drugs available on an unrestricted basis increased by 2 or more and the number of drugs not listed decreased by more than 2 drugs. The number of drugs not listed on the formulary decreased most significantly at MA-PD plans (from 12 to 6) and nonbenchmark PDPs (from 10 to 6).

The values in Figure 1 were calculated on a straight-average basis. That is, all plans were equally weighted. This calculation is analogous to a beneficiary reviewing the PDP offerings during the open-enrollment period. An enrollment-weighted approach counts each plan offering in proportion to its enrollment levels. This reflects actual beneficiary selection from the available plan offerings. 

 illustrates the number of study drugs on a straight-average and enrollment-weighted basis at 2006 and 2008 by plan type. Enrollees in the nonbenchmark plans exhibited the strongest preference for less restrictive formularies, as measured by the difference in the count of unrestricted study drugs on a straight-average basis and an enrollment-weighted basis. Enrollees in MA-PD plans showed almost no preference for less restrictive formularies using the same metric. Figure 2 also suggests the preference for PDPs with unrestricted coverage was stronger at program inception (as observed by larger differentials in 2006 than in 2008). These results hold true at the region level as well. 

Enrollees in the nonbenchmark PDPs were specifically choosing prescription drug coverage (compared with MAPD enrollees who had to consider medical providers as well as benefit designs). All the individuals in the nonbenchmark plans in 2006 elected that coverage; none were auto-assigned. The higher count of unrestricted drugs on an enrollment-weighted basis reflects their selection process. The enrollment in the benchmark plans, however, reflects a mix of beneficiaries who elected the plan as well as those who were auto-assigned to the plan. The auto-assigned individuals would be expected to have coverage that is most similar to the straight-average basis (as this is reflective of the random assignment process).

Figure 1 indicates that the average number of unrestricted study drugs remained fairly stable from year to year. However, the averages shown in Figure 1 mask some significant changes in formulary composition at the plan level. The extent of plan-level changes was quantified by isolating the PDPs that maintained the same plan number in 2006, 2007, and 2008. Plans that were available for all 3 years account for approximately 96% of the 2006 and 2007 PDP enrollment and approximately 88% of the 2008 PDP enrollment.

 stratifies the renewing plans by the change in the number of unrestricted drugs between 2006 and 2008. Table 2 indicates that 39.3% of the PDPs that were available in all 3 plan years added or removed 7 or more drugs to or from unrestricted access. Of the renewing PDPs that removed 7 or more products from unrestricted status, the average number of drugs removed was 18.4 (from 145.2 to 126.8). Of the renewing PDPs that added 7 or more drugs to unrestricted status, the average number of drugs added was 16.1 (from 123.4 to 139.5). This high volume of renewing plans with significant formulary changes suggests that at open enrollment beneficiaries would need to review their current plan to determine whether it or a competitor plan would better meet their needs.

Beneficiaries associated with the plans that removed 3 or more products from unrestricted status constituted 27% of PDP enrollment in 2006 and 21% of PDP enrollment in 2008. Beneficiaries associated with the plans that added 7 or more products to unrestricted status constituted 22% of PDP enrollment in 2006 and 23% of PDP enrollment in 2008. 

The changes in formulary composition from year to year, whether positive or negative, suggest that beneficiaries should review their plan options carefully each year. Beneficiaries cannot assume their formulary coverage will be maintained from one year to another. Their current plan may become more restrictive, or a competitor plan may become more attractive. Beneficiaries must balance formulary access, premium costs, and cost-sharing levels to make the most appropriate selection.

Analysis of Medicare Part D formulary composition since program inception suggests beneficiaries may not be using their open-enrollment periods to reevaluate available plan offerings.

November 2008 - Special Issue

How Similar Are States' Medicaid Preferred Drug Lists?

Systematic Analysis of Outcomes Evaluations Applied to Drug Management Programs

Medicare Part D Formulary Coverage Since Program Inception: Are Beneficiaries Choosing Wisely?

Impact of Therapeutic Switching in Long-Term Care

Medicare Part D's Effects on Elderly Patients' Drug Costs and Utilization

Introduction

Primary Care Physicians' Views of Medicare Part D