PBMI Releases the 2011-2012 Prescription Drug Benefit Cost and Plan Design Report

PBMI 2009-2010 Drug Benefit Design Report Now Online

As employee benefit budgets remain tight, employers are adopting plan design changes that reduce drug benefit coverage and  improve pricing according to findings from the 2011-2012 Prescription Drug Benefit Cost and Plan Design Report published by the Pharmacy Benefit Management Institute (PBMI).    

 The 2011 survey was completed by 274 employers and other plans representing 5.2 million members.  Key survey results include:

  • Plans using a four-tier design increased from 17% to 25% between 2010 and 2011. Nearly 50% of large employers (more than 20,000 lives) now have a four-tier plan design.
  • Specialty copays increased by 37% in 2011.  The average specialty copay grew from $61 in 2010 to $84 in 2011.  Nearly 1 in 4 employers now place specialty drugs on a fourth tier.
  • PBM pricing pressure in mail is mounting as the average discount off AWP rose by 10 percentage points for generics dispensed through mail, increasing from 58% in 2010 to more than 68% in 2011.
  • Reduced coverage of specialty pharmaceuticals on the medical side is on the rise as 24% of employers now restrict coverage of specialty drugs under the medical benefit, up from 12% in 2010.

This report is a valuable reference tool for all industry stakeholders, covering the full spectrum of pharmacy benefits and continually changing market dynamics.  New to this year’s report are rebate figures for retail 90 and specialty prescription claims

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Where Should Copays be Set for Specialty Drugs?

A continually popular question that I receive is how high can specialty copays be set before patients stop taking their medication? 

Unfortunately, empirical data to guide the answer to this question is fairly limited.

 

Four studies of elasticity of specialty medications have been published, most examining data from the early to mid-2000s.  The first published study by Goldman et al. examined price elasticity for specialty medications used to treat cancer, kidney disease, rheumatoid arthritis (RA), and multiple sclerosis (MS). 

                                              Summary of Studies of Specialty Pharmaceutical Price Elasticity

Author Year Population Conditions* Key Dependent Variable
Karaca-Mandic (2010) 2000-2005 35 large private employers RA Medication initiation and continuation
Gleason (2009) 2006-2008 13 million commercially insured IBD, MS, RA, Psoriasis Prescription abandonment, defined as a reversal of a previously adjudicated claim
Curkendall (2008) 2002-2004 45 large employers RA Medication adherence and persistency
Goldman (2006) 2003-2004 15 large employers Cancer, Kidney disease, MS, RA Medication initiation and continuation

For specialty meds used to treat RA, a doubling of patient cost-sharing was correlated with a 21 percent reduction in patient spending.  Elasticity for MS medications was -.07, and kidney disease and cancer did not demonstrate any statistically significant relationship between patient cost-sharing and prescription demand.  Karaca-Mandic et al. found that for newly diagnosed RA patients doubling the average annual OOP cost under the pharmacy benefit (from $400 to $800) reduced the probability of initiating a biologic for RA by 9.3 percent.  For current users, doubling the average OOP cost under the pharmacy benefit reduced the probability of continuation by 3.8 percent (from 80 to 77 percent).  Interestingly, this study found that higher family OOP burden for medical expenditures, not just specialty, was also associated with decreased likelihood of initiation of a biologic.

In one of the most widely quoted studies, Gleason et al. (2009) examined abandonment of specialty prescriptions at the pharmacy for patients newly initiating a biologic for the treatment of MS or tumor necrosis factor (TNF) blocker for treatment of RA or other conditions. Prescription abandonment was defined as “reversal of an adjudicated claim with no evidence of a subsequent adjudicated paid claim in the ensuing 90 days.”  For MS, the abandonment rate for patients with OOP less than $100 was 5.7% compared to more than 25% for OOP expense exceeding $200.  For TNF, the abandonment rate increased most significantly at copays exceeding $500.

                                                                                                                                                               Results from Gleason et al.

 Out-of-Pocket Expense

Multiple Sclerosis (N=2,791)

TNF Factor (N=7,313)

Unadjusted Abandonment Rate Odds Ratio from Logistic Regression Unadjusted Abandonment Rate Odds Ratio from Logistic Regression
$0-$100 5.7 Reference Group 4.7 Reference Group
$101-$150 5.3 0.9 10.5 2.3*
$151-$200 10.6 2.0 14.6 3.7*
$201-$250 26.8 7.3* 10.6 2.1
$251-$350 25.8 6.5* 13.4 3.3*
$351-$500 26.2 6.1* 16.3 4.4*
>$500 28.5 6.7* 26.4 7.0*

*p<.05

Finally, Curkendall et al. examined the relationship between cost-sharing and compliance with two TNF blockers, adalimumab and etanercept.  A weekly OOP expense of $0-$40, which represented 95% of patients, was associated with a medication possession (MPR) of 0.53, compared to a MPR of 0.35 among individuals with a weekly OOP expense exceeding $40 (5% of the patients).  A separate analysis of medication persistency suggested that the poor adherence was primarily due to therapy discontinuation rather than patients missing doses. 

While these studies have provided important early insights on price sensitivity for specialty medication, their primary focus on rheumatoid arthritis and their examination of data from the early to mid-2000’s limit their application (exception being Gleason et al).  The specialty landscape has changed dramatically in recent years—annualized spend per commercial member has grown 50%, patient cost-sharing has grown rapidly with a fourth tier becoming the mainstay, and the majority of spend is now managed under the pharmacy benefit rather than the medical benefit.    Clearly more research is needed in this area.  In the meantime, Gleason’s work perhaps provides the greatest insight to this question, keeping in mind that the copay threshold for significantly greater non-adherence was different for the two therapy classes studied.  Even at $100, they observed a doubling of the abandonment rate for TNF factor so a $100 copay would be a conservative design.

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Do Cost-Effectiveness Models Need a Reality Check?

In a thoughtful commentary published in the British Medical Journal, clinical researchers from Europe question the claims of cost-effectiveness made for many commonly used pharmacological treatments.    The authors argue that “…although there are claims that important preventive drugs such as statins, antihypertensives, and bisphosphonates are cost effective,6 7 8 9 there are no valid data on the effectiveness, and particularly the cost effectiveness, in usual clinical care. Despite this dearth of data, the majority of clinical guidelines and recommendations for preventive drugs rest on these claims.”

The authors cite a 2009 study, which examined a cost-effectiveness model of selective cyclo-oxygenase-2 (COX 2) inhibitors, as evidence of the weak external validity of claims of cost-effectiveness.  The COX-2 evaluation, which was based on a clinical trial, found that the cost of avoiding one adverse gastrointestinal event by switching patients from conventional non-steroidal anti-inflammatory drugs to COX-2 inhibitors was approximately $20,000.  In contrast, when the same analysis was conducted using the UK’s General Practice Research Database, which includes patients’ medical records in routine care, the cost of preventing one bleed was over $100,000.2

These findings are similar to work myself and others conducted several years ago on the COX-2s.  While the original cost-effectiveness model for the U.S. reported a cost per year of life saved (YLS) of about $19,000 for COX-2s when compared to non-selective NSAIDs, our revised model, which was based on actual practice, found a cost per YLS of $107,000. 

In a different study, we examined the external validity of a cost-effectiveness model of treatment options for eradication of h. pylori.  The original decision-analytic model found that the lowest cost per effectively treated patient was for the dual combination of PPI and clarithromycin ($980), whereas we found that the lowest cost per treatment was for the triple combination of bismuth, metronidazole, and tetracycline at a cost of $852.  Why the disconnect?  In the original h. pylori model, the authors had made assumptions about medication compliance and the cost of recurrence that simply did not hold up in the real-world. 

In the case of the COX-2s, the recent commentary concluded that the published cost-effectiveness analyses of COX 2 inhibitors neither had external validity nor represented the patients treated in clinical practice. They emphasized that external validity should be an explicit requirement for cost effectiveness analyses that are used to guide treatment policies and practices.  At least one academic modeler vehemently disagrees with the requirement of external validity, arguing that “it is wrong to insist that models be ‘validated’ by events that have not yet occurred; after all, the modeler cannot anticipate advances in technology, or changes in human behavior or biology. All that can be expected is that the model reflects the current state of knowledge in a reasonable way, and that it is free of logical errors.”

It is true that right when a drug comes to market, the only available data will likely be from the original clinical trials used to seek FDA approval, and the modeler will be forced to make numerous assumptions about compliance, costs, concomitant medication use, etc.  The problem is that the extent to which these assumptions are made without bias is unclear.  Research has shown that sponsorship by the pharmaceutical industry affects the results of economic models.  In a review published in 2010, researchers found that 95 percent of studies sponsored by pharmaceutical manufacturers reported favorable conclusions compared to only 50 percent of nonsponsored studies.  While it could be argued that this reflects a publication bias, the validation studies that I have described above suggest otherwise.  In each of these cases, there were key assumptions that drove model outcomes which, from a plan sponsor perspective, we found highly questionable at the time the model was first published.

Surprisingly, the issue of model validity receives relatively little attention given the central role that these models play in the field of pharmacoeconomics, as for example, in the AMCP dossier process.  The commentary authors argue that real-world comparative studies are the key to producing cost-effectiveness models that possess external validity.  This certainly will help with the quality of models post-FDA approval.  However, for models used at the time a drug is launched, ultimately, I expect that plan sponsors will have to develop their own models to ensure systematic bias is removed.

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New Study Finds Reference Pricing Saves Big Money

Since the advent of the PBMs, the concept of reference pricing for pharmaceuticals has seen multiple waves of interest and policy discussion but only minimal uptake.  Reference pricing, sometimes referred to as a therapeutic MAC, requires patients to pay the full difference between the price charged at the pharmacy and a reference price reimbursed by the insurer.  The reference price is the price of a low-cost drug in a therapeutic cluster of drugs considered clinically equivalent in the treatment of a condition.

Over the years, reference pricing has been used successfully in many countries, including Canada and Britain, to manage prescription spending without reducing quality of care.  However, concerns over complexity and member satisfaction as well as the PBM industry’s historical reliance on rebates (prior to the growth in pass-thru models) have been barriers to the adoption of reference pricing in the U.S.

A study just published in The Journal of Managed Care Pharmacy, reports the results of Arkansas’ experience with reference pricing for proton pump inhibitors (PPIs) for its state employees.  Arkansas implemented reference pricing for PPIs including esomeprazole but excluding generic omeprazole, on September 1, 2005.  Beneficiary cost share for all PPIs except generic omeprazole was determined from comparison of the PPI actual price to the $0.90 omeprazole OTC reference price per unit.

Over 43 months of reference pricing, net plan costs for PPIs fell dramatically by 49.5% PMPM compared with the preperiod, despite an increase in the pharmacy dispensing fee.  In the first quarter of 2009, the net spend for PPIs was only $2.19, despite the state’s significantly higher than average utilization of PPIs.  While PPIs costs have been declining recently for most plan sponsors as more patients use generics, the state of Arkansas’ savings greatly exceeds those of other plan sponsors without reference pricing.  The authors estimated the net savings at $1.31 PMPM over the nearly four year study period relative to a very large and diverse comparison group.  As the authors point out, the savings would have been even greater had they included generic omeprazole in the reference pricing list.

Equally important given concerns that reference pricing is too complex for the average consumer, utilization of PPIs did not change yet beneficiary costs actually decreased by 6.7% due to a large movement away from branded PPIs to OTC and generic omeprazole.  Between 2004 and 2009, marketshare for omeprazole, generic and OTC combined, grew from 57 to 86%.  Given these results, it appears that the employer did a nice job of making beneficiaries aware of lower cost therapeutic alternatives, which patients took full advantage of over the course of the study. 

The study authors make little mention of the member “noise” resulting from this plan design change; but given the large uptake in omeprazole that was observed and the state’s long-term, continued adoption of the program, it is reasonable to conclude that any member noise was manageable and likely dissipated quickly with time, as I have repeatedly seen with other types of major plan design changes.  Bottom line: This evaluation provides solid evidence that reference pricing for PPIs can save real dollars without reducing utilization.  For plan sponsors looking to optimally manage their drug spend, referenced-based pricing is worth consideration.

 

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When Are Prescription Copay Waivers a Good Idea?

I recently received a question as to which drug class or population/disease state, if ever, would I recommend a zero dollar member cost share.   This is a great question, and there actually are some situations in which I would recommend use of a zero dollar copay.

First, let me briefly review why I do not recommend $0 copays as a standard part of the benefit design, even for classes such as diabetes and cardiovascular disease.

  • The primary reason is that most patients do not stop taking medication because of cost, particularly in a commercially insured population (see figure for common reasons for non-adherence).  Accordingly, copays are being waived without any possibility of benefit for the vast majority of patients.  This known fact is evidenced in the small improvements that are seen in compliance after implementing a copay waiver program—averaging 2-4 percentage points and representing 1-2 weeks of additional therapy.  Targeting copay waivers to patients at high risk for adverse events and who truly have cost as a financial barrier would be an ideal approach, but it raises HR and equity issues that are not easily resolved.
  • The second consideration to keep in mind is the potential for fraud.  I have heard from frustrated employers who implemented zero dollar copays for chronic conditions only to find that employees began sharing medications with family and friends with the same condition.  Quantity limits can help control this potential problem partially but not entirely.  I have not studied this phenomenon personally so I cannot speak to the magnitude of the problem.

I would however, recommend use of a zero dollar copay program under certain conditions.  First, zero dollar generic copays are a great tool for promoting use of lower cost, therapeutic alternatives for patients currently using brand medications. I would consider them for therapy classes for which you have step therapy in place as the two programs are complementary.  Step therapy will promote generic use for new users, and the $0 generic copay program is a carrot strategy for promoting generics with current brand users.  The $0 generic copay helps to grab the member’s attention and provides an extra little incentive for making the switch.  I would not make the copay waiver indefinite, however.  Six months of free generics is sufficient.  Based on my experience and rigorous evaluations, these programs have a solid ROI; and the patient saves money too of course.

Second, a population for which I MIGHT consider a $0 generic copay is hypertension and cholesterol, and other cardiovascular medications in seniors. The rate of adverse cardiovascular events (absent treatment) is much higher in the senior than in the commercial population; so IF price elasticity is at least as high as we see with commercial members, there is the potential to materially reduce the rate of adverse cardiovascular events and to achieve a net savings from reduced hospitalizations.  The key to this decision is determining the actual price elasticity of demand within your senior population.  As little contemporary public data is available on price elasticity within the senior population, individual vendors will have to assess the elasticity within their own data.  Once identified, a simple analytic tool, like the VBID calculator, can be used to determine the potential reduction in hospitalizations and medical spend that can be achieved.  Of course, implementing copay waivers in the Medicare Part D program is a greater administrative challenge than a commercial plan or retiree plan, which would be a relevant consideration.

A third population for which I would PILOT a $0 generic program is patients at HIGH risk for adverse cardiovascular events but who have NOT initiated pharmacotherapy. The classic example is the patient with a recent myocardial infarction who has not initiated a statin and/or beta-blocker.  While cost is not likely to be the reason for non-initiation for most patients, it might be a useful short-term incentive, when combined with the right intervention, for encouraging use.  I would say pilot first because it simply may not be effective and there is the risk that a zero price could actually deter use if it serves as a quality indicator for non-initiating patients.  Another growing challenge is that many patients will appear as non-users because of the $4 generic programs which do not always result in a claim being submitted.

For those of you looking for more information on copay waivers, see the recent Fairman editorial in JMCP and a paper Steve Melnick and I published last year on the potential financial savings from copay waivers.

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Does Makena Actually Have a Favorable Pharmacoeconomic Profile?

While there has been no shortage of press about the recent FDA approval of MakenaTM, made by K-V Pharmaceutical Company, I think the recent recently published letter in the New England Journal of Medicine about the pharmacoeconomics of the drug is quite useful.

Separate from the business and ethical questions of the $30,000 plus price tag for Makena (a drug that previously cost around $300 as a generic), one could argue that the pharmacoeconomics of the drug at the new price may still render the medication a cost-effective therapy.  A physician at Aetna examined just this question, evaluating the pharmacoeconomics of Makena based on the likely rate of treatment, the efficacy of the drug versus placebo, and the subsequent costs avoided due to treatment.

Based on published literature, Aetna estimated that about 139,000 women are candidates for Makena, of which about 22% (30,500) are likely to have a recurrent preterm birth absent medication.  With treatment, 33% (10,000) preterm births could be prevented, saving $334 million in direct medical costs and $519 million in indirect medical costs. The indirect medical costs include maternal care, special education, early intervention costs, etc.  The cost of treating the 139,900 women, at $29,000 per course of treatment (price before distribution mark-ups) would be $4.0 billion.  Accordingly, including both direct and indirect medical costs, Makena will cost $8 for every $1 saved, a strongly negative ROI.

However, one could argue that demonstration of net cost-savings should not be the criteria for coverage but rather payers should examine the drug’s cost-effectiveness, measured as cost per life-year saved (LYS).  To meet the threshold of $100,000 per LYS (inflation adjusted for the more commonly used $50,000 per quality-adjusted life year), the drug would need to prevent nearly 35,000 life years or 435 deaths, assuming an average lifespan of 80 years.  Previous studies suggest this is unlikely as the drug has shown a small (and not statistically significant) effect on reducing mortality.

Another coverage option is to identify opportunities to further target use of the drug to patients at the greatest risk for preterm birth.  However, even with an ability to target only patients with a 100% risk of preterm birth (highly implausible), the drug would still cost nearly double the amount it saves in direct and indirect medical costs given its effectiveness. Of course, these figures represent back-of-the-envelope pharmacoeconomic calculations for Makena; but absent very significant unmeasured benefits, the analysis highlights the difficulty this drug will have in demonstrating a favorable pharmacoeconomic profile.

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Persistency Is The Best Measure of Medication Adherence

In an earlier post, I discussed that while medication possession ratios (MPRs) tend to dominate the marketplace reporting tools for medication adherence programs, MPR is not, in fact, the best measure of medication adherence.  Reason being, the results of an MPR analysis depend heavily upon the methodological choices made in defining MPR and the quality of the days supply figured provided by the pharmacist.  Mostly importantly, MPRs allow for little to no clinical interpretation. 

Of all the measurement options that exist, I find persistency to be the most useful.  Persistency, which is a dichotomous yes/no measure that is based on the length of therapy, tells whether a patient’s length of therapy meets or exceeds a certain threshold.  For example, it is common for published studies to report the percent of newly treated patients who were persistent with their medication one year after starting therapy.  There are two key reasons why I prefer persistency over other adherence measures:

1.  It measures the most significant medication adherence problem, i.e., whether patients stop taking their medication altogether.  While small gaps in therapy are not desirable, they are far less prevalent or clinically impactful than discontinuing treatment altogether.  Studies have shown that between 40 and 60% of newly treated patients stop taking their chronic medications after one year.  The figure below, from a BMJ study of antihypertensive users, reports both persistency over time and non-adherence due to poor execution of the dosing regimen.  As the authors state, “non-execution of the dosing regimen created a shortfall in drug intake that is an order of magnitude smaller than the shortfall created by early discontinuation/short persistence.”  Discontinuation has to be the top priority, and organizations will manage what they measure–so measure persistency.

 

 2. The clinical interpretation is unequivocal.  As I discussed in the previous post, improvements in MPR are very difficult to interpret in terms of their clinical impact due to the lack of data on the relationship between differences in MPR or gaps and clinical outcomes.  However, when a patient discontinues their chronic medication altogether, the clinical impact is far clearer (as long as the patient was an appropriate candidate for the medication to begin with—a topic for another day).  The negative clinical and economic impact of discontinuation can be forecasted for a population based on published randomized trials.

Another advantage of a persistency measure is that it is less susceptible to errors in days supply figures provided by the pharmacist because the analysis typically provides a 30 or more day gap in therapy before labeling someone as non-persistent.  That said, persistency is still vulnerable, albeit less than MPR, to differences in methodological approaches.  The two key decisions in a persistency analysis are 1) how long to follow patients;  and 2) what gap in therapy will be considered non-persistence (e.g., 30-day, 60-day, or 90-day gap).  Obviously, the longer you follow patients, the lower the persistency rate will be and the larger the gap in therapy required to be labeled non-persistent, the higher the persistency rate will be.  Accordingly, if comparing vendors, make sure you have the same follow-up length and gap criteria.  In addition, persistency rates for new versus ongoing users look dramatically different so you should always ask that the two groups be reported separately to prevent changes in the mix of members from artificially influencing your results.

Perhaps the biggest reason persistency is not reported as frequently as MPR is because it requires member-level analysis over time, controlling for eligibility.  This involves more complex analytics and processing time for large groups of patients, but given the reasons I’ve outlined above, it is worth the effort.

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