Jalpa A. Doshi, PhD

Medicare Part D specialty drug users not qualifying for low-income subsidies (non-LIS beneficiaries) face high and variable cost sharing during the calendar year. We examined their out-of-pocket (OOP) cost patterns under the existing Part D cost-sharing policies and proposed changes to these policies.

Using 100% Medicare claims data from 2012, we examined mean annual and monthly OOP drug costs for Medicare Part D patients who were full-year users of Part D specialty drugs for rheumatoid arthritis (RA) (n = 1063), multiple sclerosis (MS) (n = 2256), or chronic myeloid leukemia (CML) (n = 1135) under existing policy. Using the same data, we simulated costs under both proposed Medicare Payment Advisory Commission (MedPAC) policy recommendations and our own recommendations.

 In 2012, our sample faced mean annual cumulative OOP drug costs (for all medications) of $3949 (RA), $5238 (MS), and $6322 (CML). Mean OOP costs were $977 (RA), $1613 (MS), and $2456 (CML) in January alone. A substantial proportion of total annual OOP prescription spending also occurred during the catastrophic coverage phase (RA: $1229 [31%]; MS: $2456 [47%]; CML: $3546 [56%]). Under proposed MedPAC changes, patients would have faced maximum annual OOP spending of $4700, but mean OOP costs in January and February would have been higher compared with the existing policy. Under our proposed strategy, OOP costs would have been spread evenly over 12 months (≤$392 per month). The potential incremental costs of our proposed strategy would have been $23.55 per non-LIS Part D beneficiary per year.

The existing Part D cost-sharing structure creates a substantial financial burden for specialty drug users, especially early in the year. Implementing both annual and monthly OOP maximum spending limits would result in lower, more consistent OOP costs, potentially increasing patients’ ability to access treatments for life-threatening, chronic, and rare diseases.

Am J Manag Care. 2017;23(3 Suppl):S39-S45

Recent pharmaceutical advances hold the promise of transforming the medical care of serious life-threatening, chronic, and/or rare diseases and significantly improving the lives of those afflicted. Yet, these advances may be out of reach for many Americans because of high out-of-pocket (OOP) costs.

 Financial barriers to treatment access are particularly acute for Medicare Part D patients requiring specialty drug treatments. Since Part D’s creation in 2006, patients who do not receive low-income subsidies (non-LIS beneficiaries) have been subject to highcoinsurance requirements for specialty drugs, which are placed on a “specialty tier.” Unlike co-payments of a fixed dollar amount, coinsurance payments vary based on the cost of the medication. Furthermore, under Part D, the required coinsurance percentage fluctuates across the coverage year (

), with the highest costs concentrated at the beginning of the year. The cycle resets on January 1 of the following year.

 In addition, because Medicare Part D plans are not required to have an annual maximum OOP spending limit, patients who are prescribed continuous specialty drug treatment continue to face substantial OOP costs, even during the catastrophic coverage phase.

 These fluctuating costs have been characterized as a “cost-sharing rollercoaster.”

A growing body of evidence links higher cost sharing with reduced utilization of specialty drugs.

 In the Medicare population, studies of several specialty drug classes have documented that beneficiaries not receiving LIS (ie, those responsible for high cost sharing) have lower rates of treatment initiation, greater incidence of gaps in treatment, and more frequent interruptions in treatment compared with counterparts who receive full LIS (and thus face low, relatively stable costs across the coverage year).

 Although further research is needed, it is likely that the high cost sharing for specialty drugs under Medicare Part D may place patients at risk for compromised treatment outcomes due to reduced or delayed initiation, poor adherence, and higher discontinuation rates.

Strategies to address financial barriers must balance access to treatment with very real financial constraints. Based on the accumulating data, and in keeping with our prior recommendations,

 we propose the introduction of 2 key changes to the Part D cost-sharing requirements that would provide additional protection for Medicare beneficiaries. First, the introduction of an annual OOP maximum spending limit—which is commonplace in private insurance plans and in health insurance exchange plans&mdash;would protect beneficiaries from unmanageable cumulative OOP costs .Indeed, the Medicare Payment Advisory Commission (MedPAC) recently recommended policy changes that would effectively establish an annual OOP maximum for Medicare Part D beneficiaries.

 Second, more stable and consistent timing of OOP spending requirements—effectively introducing a monthly OOP maximum as well&mdash;would avoid the major fluctuations in monthly expenses that characterize the current cost-sharing structure and would be more appropriate for seniors, many of whom are on a fixed income. Previously, we have cited programs that offset the burden of high winter heating bills by distributing energy costs across the calendar year as a model for this approach.

In order to test the impact of our proposed strategies on real-world OOP costs for non-LIS Medicare Part D beneficiaries, we undertook a 3-part investigation. First, using actual 100% Medicare claims from 2012, we illustrated the real-world OOP cost patterns and burden under the existing Part D cost-sharing structure for continuous users of specialty drugs for 3 chronic conditions that rank among the top spending categories for specialty drugs.

 Second, we used the same Medicare claims data on drug utilization and costs to simulate the OOP cost patterns that these specialty drug users would face if recent MedPAC recommendations related to Part D cost sharing were to be implemented. Third, we simulated OOP costs for these patients under our proposed strategies and compared them with those under existing policy and the MedPAC recommendations. Finally, we discuss the incremental costs of our proposed strategies and how they could be funded.

Estimation of OOP Cost Patterns Under the Existing Part D Cost-Sharing Structure

To estimate the impact of current policy, we used a data extract from the 2012 Chronic Conditions Data Warehouse 100% Medicare claims files, which contain data on all fee-for-service (FFS) Medicare beneficiaries. (Part D’s cost-sharing structure has not changed since 2012.) We included non-LIS beneficiaries who had: 1) continuous FFS Medicare and stand-alone Part D plan coverage throughout 2012; 2) a diagnosis of rheumatoid arthritis (RA), multiple sclerosis (MS), or chronic myeloid leukemia (CML); and 3) prescription claims reflecting continuous use of a disease-specific Part D-covered specialty drug (listed in the 

]) over the course of the coverage year (ie, a prescription fill for the specialty drug in January and total days’ supply for any disease-specific specialty drug ≥360 days in 2012). For example, if an individual switched from one disease-specific specialty drug to another but had no gap in treatment (eg, a CML patient had 180 days’ supply of bosutinib and 180 days’ supply of dasatinib), then he or she was classified as a continuous user. After these criteria were applied, the resulting sample consisted of 1063 patients with RA, 2256 patients with MS, and 1135 patients with CML. Using actual 2012 claims data for these disease samples, we calculated annual cumulative OOP drug costs and OOP costs by calendar month and benefit phase for patients in each disease group.

We opted to restrict our sample to continuous users for several reasons. First, in the absence of specific reasons for discontinuation (eg, intolerable side effects, poor response), consistent treatment is typically recommended for each of the conditions we examined.

 Thus, we wished to examine the OOP costs associated with optimal treatment patterns. Second, inclusion of individuals who used the specialty drugs during only part of the year could have artificially depressed our mean estimates of annual and monthly costs. Third, high OOP costs are associated with interruptions in treatment and discontinuation of treatment; thus, including individuals who may have used medications inconsistently due to financial burden would have led to further underestimation of the true OOP costs associated with optimal use of these treatments.

Simulation of OOP Cost Patterns Under Proposed MedPAC Changes to Part D Cost Sharing

Next, using the same drug utilization and cost information from the 2012 Medicare claims data, we simulated the OOP cost patterns for the 3 disease samples if recent MedPAC recommendations were to be applied to the 2012 Part D benefit. This involved simulating the impact of 2 key changes proposed by MedPAC.

 The first involved the elimination of the 5% cost-sharing requirement during the catastrophic coverage phase, resulting in an effective annual OOP maximum equal to the annual true OOP (TrOOP) spending limit that triggers catastrophic coverage. In 2012, the TrOOP was $4700. Second, under existing Part D policies, brand name prescription drugs purchased by Part D beneficiaries during the coverage gap phase include a 50% manufacturer discount that is credited toward beneficiaries’ TrOOP spending. Under the MedPAC proposal, these manufacturer discounts would no longer be credited toward patients’ TrOOP spending. Given that movement through the coverage gap is based on TrOOP costs, elimination of the manufacturer discount credit under the MedPAC proposal would effectively extend the time patients spend in the coverage gap and increase the amount they must spend OOP before reaching catastrophic coverage. Then, only patients whose spending was sufficient to trigger entry into the catastrophic coverage phase would benefit from the elimination of cost sharing during that phase.

Simulation of OOP Cost Patterns Under Our Proposed Changes to Part D Cost Sharing

We estimated OOP costs under our proposed policy changes following the same procedure used for the MedPAC simulation, with 2 key differences. First, our proposal does not include a change to the current policy of crediting manufacturer discounts toward patients’ TrOOP spending; we maintained the $4700 spending threshold that triggers catastrophic coverage as our proposed annual OOP spending limit, counting both beneficiary OOP spending and manufacturer discounts. (As in the MedPAC proposal, this spending limit would mean elimination of 5% cost sharing during Part D’s catastrophic coverage phase.) Second, to spread the OOP costs more evenly across the year, we divided the annual OOP maximum spending limit number (ie, $4700) by 12 months to derive a maximum monthly OOP spending limit. This monthly limit also includes both beneficiary OOP spending and manufacturer discounts, meaning that actual beneficiary spending may be lower than the monthly OOP spending limit during the month(s) of the coverage gap.

Estimating the Incremental Cost of Our Proposed Changes and Financing Strategies

To estimate the incremental costs of our proposed policy changes, we identified publicly available data from the year closest to our 2012 utilization data (ie, 2013) to identify: 1) estimates of the total number of non-LIS beneficiaries who entered catastrophic coverage during that year and 2) the average OOP spending among these beneficiaries during the catastrophic coverage period.

 The product of the 2 estimates provides the total OOP costs borne by these beneficiaries that would be foregone (ie, the incremental costs of our proposed policy of instituting an annual OOP spending maximum at the catastrophic coverage threshold level). We then divided the incremental cost of our proposed changes by the total number of non-LIS beneficiaries enrolled in the Part D program in 2013 to identify the per-beneficiary cost of the proposed policy change.

 The per-beneficiary cost is approximate because, whereas the additional cost would be priced out through the Part D plan bidding process, our proposed financing strategy estimates the cost as if it were passed through to beneficiaries directly.

OOP Cost Patterns Under the Existing Part D Cost-Sharing Structure

In 2012, specialty drug users in the RA group faced mean annual cumulative OOP drug costs of $3949 for all their Part D medications, whereas those in the MS and CML groups paid an average of $5238 and $6322, respectively. The majority of these costs were due to specialty drug spending (ie, 88% for the RA group, 91% for the MS group, and 95% for the CML group) (

). A significant proportion of the MS (84%) and CML (87%) groups had annual specialty drug OOP costs of $5000 or more (data not shown). Because OOP spending pushed specialty drug users through the deductible, initial coverage phase, and coverage gap quite quickly in 2012, patients continued to have substantial OOP spending requirements during the catastrophic coverage phase, despite the fact that their coinsurance obligation dropped to 5%. Across disease areas, a substantial proportion of total annual OOP prescription spending occurred during the catastrophic coverage phase (RA: $1229 [31% of annual OOP costs]; MS: $2456 [47%]; CML: $3546 [56%]) (

). At the same time, specialty drug users had to bear large mean OOP costs in January alone (RA: $977; MS: $1613; CML: $2456). Once again, specialty drug costs were driving this OOP spending (see 

). These January OOP costs represented a substantial portion of spending for the entire year (RA: 25% of annual spending; MS: 31%; CML: 40%) (

), and about half of total OOP costs were paid out by February.

OOP Cost Patterns Under Proposed MedPAC Changes to Part D Cost Sharing and Under Our Proposed Strategies

Simulated analyses examining the impact of proposed MedPAC recommendations showed that the policy changes would have mixed effects on beneficiaries' OOP burden (

). Because manufacturer discounts for brand name drugs during the coverage gap phase would no longer be credited toward patients’ TrOOP spending, patients using brand name specialty drugs would have taken longer to reach the TrOOP spending limit, leading them to remain in the coverage gap phase for a greater period of time. Because of this widened coverage gap, they would have been subject to 50% cost sharing until they reached the TrOOP spending limit that triggers entry into the catastrophic coverage phase. As such, only patients whose cumulative annual spending (excluding manufacturer discounts) would have exceeded the designated spending limit ($4700 in 2012) would have seen overall savings from the elimination of 5% cost sharing during the catastrophic coverage phase. Under current policy, manufacturer discounts can effectively reduce OOP spending in the coverage gap by up to half, depending on an individual beneficiary’s drug utilization patterns. This differential impact was apparent across our 3 diagnostic subgroups. Although patients in the MS and CML groups would have seen a decrease in cumulative mean annual OOP costs under the proposed changes, the RA group would have experienced an increase in costs ($4540 under MedPAC vs $3949 in 2012; data not shown) due to fewer beneficiaries reaching the OOP limit.

Given that patients in all disease samples had OOP costs that pushed them into the coverage gap early in the coverage year (often with the first fill of their disease-specific specialty drug), and they would have faced a higher threshold to exit the coverage gap under the MedPAC recommendations, the proposed policy changes would ultimately subject patients to even higher, more concentrated OOP costs during these early months (Figure 5). Patients would have faced maximum annual OOP spending of $4700 in 2012, but this entire OOP cost burden would have to be borne by most patients in the first 3 to 4 months of the calendar year. Furthermore, under the MedPAC changes, the mean OOP costs in January and February would be even higher than under existing policy (eg, $994 under MedPAC vs $977 in January 2012 for RA; $1685 under MedPAC vs $1613 for MS; $2814 under MedPAC vs $2452 for CML) (Figures 4 and 5).

Instituting a monthly OOP maximum based on an annual OOP maximum of $4700—which also counts the 50% manufacturer discounts for brand name drugs in the coverage gap&mdash;under our proposed strategies, in contrast, would result in a maximum of $392 in OOP costs to be borne by the patient in any given calendar month, as indicated by the dotted line in Figure 5.

Incremental Cost of Our Proposed Changes and Financing Strategies

Based on Medicare utilization numbers,18 approximately 700,000 (2.8%) of the 24.2 million non-LIS beneficiaries enrolled in Part D plans reached the catastrophic coverage phase in 2013. They paid mean OOP costs of approximately $814 during that phase, for a total of approximately $569.8 million in patient OOP spending during the catastrophic coverage phase. Dividing that cost among all non-LIS beneficiaries, implementation of our proposal would have cost an additional $23.55 for each non-LIS beneficiary per year (ie, $1.96 per month).

 In practice, the increased costs of $23.55 per beneficiary per year would not be straight pass-through costs via premiums, but would instead be borne out through the Part D plan bidding process, ultimately reducing the amount by which beneficiaries are directly impacted. Nevertheless, the incremental costs of our proposed changes are minimal when averaged over all non-LIS beneficiaries, regardless of the ultimate financing mechanism.

Our analyses demonstrate that Medicare beneficiaries using high-cost specialty drugs face variable and high OOP cost obligations under the existing Part D cost-sharing structure, with a majority of these expenditures concentrated at the beginning of the calendar year. In 2 of our 3 disease samples (MS and CML), the average OOP cost of the first disease-specific specialty drug fill for the calendar year nearly equaled or exceeded the average monthly Social Security benefit.

 This benefit provides a substantial portion of income for many Medicare beneficiaries.

 In all 3 disease areas we examined—RA, MS, and CML&mdash;the required 5% coinsurance payments during the catastrophic coverage phase also resulted in considerable cumulative OOP spending during the remainder of the year, consistent with findings reported elsewhere.

 Our simulation found that changes proposed by MedPAC, which effectively introduce an annual OOP spending maximum via elimination of cost sharing during the catastrophic coverage phase, would provide some relief later in the year for patients facing the highest specialty drug costs. Yet, the changes would actually exacerbate the problem of front-loaded costs at the beginning of the coverage year and fewer beneficiaries would likely benefit from the annual OOP spending limit, as many patients would remain in the coverage gap longer under the proposal to exclude manufacturer discounts for brand name drugs from TrOOP spending. This would exacerbate their total OOP costs and the timing of these costs. Excluding the discounts would also effectively double OOP costs paid by these beneficiaries in the coverage gap by widening the gap; this undermines the goal of better managing the timing and magnitude of OOP costs and ensuring appropriate access to care.

Such high and variable spending requirements are disruptive to monthly budgets, and our data highlight the fact that evaluation of the financial burden related to OOP spending should pay attention to both the magnitude and the timing of OOP expenditures. Our proposed strategies would institute both an annual and a monthly OOP maximum spending limit, which would spread OOP costs more evenly across the year. This would have resulted in OOP costs closer to $400 per month in our 2012 sample (inclusive of manufacturer discounts in the coverage gap); although the amount would increase slightly in 2017 due to an increase in the TrOOP spending limit, it would remain more manageable than existing OOP obligations. This would most likely improve patients’ ability to meet their OOP cost-sharing obligations, especially early in the year. In addition, maintaining the current policy to count manufacturer discounts toward TrOOP would effectively lower overall OOP spending below the $4700 limit for beneficiaries requiring brand name medications. The proposed OOP spending limit would also increase the predictability of monthly OOP obligations, much like how payment options provided by energy companies enable more consistent budgeting despite seasonal variability in consumption. In light of a growing body of evidence that links high cost sharing among Medicare Part D beneficiaries with delayed initiation of treatment, increased gaps in treatment, and reduced adherence (compared with beneficiaries who receive LIS and face more stable, low cost sharing), reducing the OOP burden associated with specialty drug use would likely improve access to and optimal use of these treatments.

It should be noted that our analyses provide a snapshot of the impact of both existing and proposed policies utilizing a sample year of prescription fill data from 2012. This is in line with MedPAC’s own analysis of their proposed changes, which also used a single year of data (from 2013). However, the Part D benefit is dynamic; each year, there are changes to the designated spending limits that trigger entry into each benefit phase, and additional changes related to provisions of the Affordable Care Act (ACA) are scheduled to be implemented over the next 5 years, further impacting OOP costs. Most importantly, the catastrophic coverage threshold is scheduled to increase significantly by 2020, and 2 main factors will lead to additional widening of the coverage gap phase, making it harder for beneficiaries to reach the OOP threshold for catastrophic coverage (and thus, the proposed annual OOP spending maximum).

First, Part D plans will offer more generous plan coverage, which will displace previous beneficiary OOP spending during the coverage gap with payments that will not count toward patients’ TrOOP—thereby indirectly widening the gap. Second, the growth rate of the TrOOP threshold that triggers catastrophic coverage was slowed from 2014 through 2019 under the ACA. As the coverage gap closes, however, the TrOOP threshold for 2020 will be set as if growth had not been artificially slowed.

 This will lead to a $1200 increase in the TrOOP threshold from 2019 to 2020, a phenomenon known as the “OOP cliff.”

 Consequently, the beneficiary OOP burden documented in our analyses will intensify over time. Furthermore, a larger number of beneficiaries are likely to be affected over time. Ongoing developments in pharmaceutical treatments are likely to increase the number of non-LIS beneficiaries who are prescribed medications associated with the highest levels of OOP spending; for example, the past few years have seen a dramatic increase in the use of new treatments for hepatitis C, a trend that began after our 2012 data. This underscores the urgent need for strategies to alleviate OOP costs and burden.

Our analysis had several limitations. First, as with all administrative databases, Medicare claims may be subject to errors or omissions. Second, we examined 2012 data and applied policy changes that took effect under the ACA in that year. As noted above, if the ACA remains in effect, the coverage gap will be phased out by 2020 and the catastrophic coverage limit will be higher; thus, the catastrophic coverage limit (and our proposed annual OOP spending maximum) will increase and the figures presented here will represent an underestimate of patient OOP spending. Our analysis is not able to account for future changes to the ACA that may impact Medicare policy. Third, given the availability of claims data, we limited our sample to FFS Medicare Part D beneficiaries; to illustrate the real-world OOP cost burden in patients prescribed continual treatment throughout the year, we further limited our sample to full-year users of specialty drugs. However, our proposed strategies are recommended for, and our incremental cost and financing calculations apply to, all Medicare Part D beneficiaries (FFS and Medicare Advantage) regardless of whether they were full- or part-year users of specialty drugs. Our calculations do not account for any increases in specialty drug utilization and spending that would occur among part-year users in response to lower and more stable monthly cost sharing under our proposed strategies. Similarly, as the number of beneficiaries eligible for and requiring specialty drug treatments increases over the years, the incremental cost of our proposed strategies would further increase. It is notable that our estimated costs begin at a relatively modest amount (less than $2 per month per beneficiary), however.

As with most aspects of healthcare, the fine-grained logistical details of implementing our proposed strategies will be straightforward in some cases and more complex in others. Redistribution of annual OOP costs in the form of a stable monthly payment is least complicated for individuals who are prescribed medications for continuous use throughout the coverage year (as in our sample), whereas protocols would have to be developed for those who initiate treatment later in the year. For example, if a patient fills a prescription in September that would have carried a $1500 coinsurance payment, he or she may need to continue to pay the remaining balance on that prescription into the following year. Although this does increase the complexity somewhat, much of the rest of the healthcare system bills patients for remaining OOP costs after a service is rendered (eg, imaging tests, surgeries, hospitalizations). Developing adjustments in pharmacy and insurance procedures that are more in step with the advances in pharmaceutical treatments is a worthwhile goal.

Indeed, the expanding role of self-administered pharmaceutical treatments in the management of serious life-threatening, chronic, and/or rare diseases also argues for a less siloed approach and greater attention to overall OOP costs for patients. Our analysis addresses prescription drug OOP costs only, yet patients are also responsible for OOP costs related to premiums, medical deductibles, and medical co-pays and coinsurance. Unlike most employer and health insurance exchange plans, which integrate medical and prescription drug expenses into a combined annual OOP maximum spending limit,

 all Medicare beneficiaries currently lack an annual OOP maximum limit for their Part D prescription drug spending and the majority have no annual maximum for other OOP medical spending. Our proposal represents an important first step toward addressing these issues.

Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine (JAD, PL, VPL), Leonard Davis Institute of Health Economics (JAD, PL), Center for Public Health Initiatives (ARP), University of Pennsylvania, Philadelphia, PA; PhRMA (JSD, AF), Washington, DC.

At the time of the study, Dr Doshi reported serving as a consultant for Alkermes, Inc, Forest Laboratories (now Allergan), Ironwood Pharmaceuticals, Shire, and Vertex Pharmaceuticals; and had received research funding from AbbVie Inc, Biogen, Humana, Inc, Janssen, PhRMA, Pfizer Inc, Regeneron, Sanofi, and the National Pharmaceutical

Council. Dr Doshi’s spouse holds stock in Merck & Co, Inc, and Pfizer Inc. Dr Dougherty and Ms Flint are employees of PhRMA. Drs Li and Pettit and Mr Ladage have no conflicts to report.

Concept and design (JAD, PL, ARP, JSD, AF); acquisition of data (JAD, PL, VPL); analysis and interpretation of data (JAD, PL, ARP, VPL); drafting of the manuscript (JAD, PL, ARP, JSD, AF); critical revision of the manuscript for important intellectual content (JAD, PL, ARP, JSD, AF, VPL); statistical analysis (PL); obtaining funding (JAD); administrative, technical, or logistic support (VPL); and supervision (JAD, PL).

Jalpa A. Doshi, PhD, University of Pennsylvania, 1223 Blockley Hall, Philadelphia, PA 19104. E-mail: jdoshi@mail.med.upenn.edu.

Specialty drugs represent vital treatments for patients who often have few or no effective alternatives available, and consistent use can often help to prevent disease progression and other costly complications. Yet, these treatments can only be effective if patients can afford to utilize them. Thus, Medicare Part D policies that support access and adherence are critically important. Our analyses indicate that efforts to alleviate financial barriers to specialty drug adherence should include attention to both the amount and timing of OOP costs.

1. Doshi JA, Li P, Ladage VP, Pettit AR, Taylor EA. Impact of cost sharing on specialty drug utilization and outcomes: a review of the evidence and future directions. 

2. Stuart B, Briesacher BA, Shea DG, Cooper B, Baysac FS, Limcangco MR. Riding the rollercoaster: the ups and downs in out-of-pocket spending under the standard Medicare drug benefit. 

3. Hoadley J, Cubanski J, Neuman T. It pays to shop: variation in out-of-pocket costs for Medicare Part D enrollees in 2016. Kaiser Family Foundation website. http://kff.org/report-section/it-pays-to-shop-variationin-out-of-pocket-costs-for-medicare-part-d-enrollees-in-2016-findings/. Published December 2, 2015. Accessed December 16, 2016.

4. Winn AN, Keating NL, Dusetzina SB. Factors associated with tyrosine kinase inhibitor initiation and adherence among Medicare beneficiaries with chronic myeloid leukemia. 

. 2016;34(36):4323-4328. doi: 10.1200/JCO.2016.67.4184.

5. Doshi JA, Li P, Huo H, et al. High cost sharing and specialty drug initiation under Medicare Part D: a case study in patients with newly diagnosed chronic myeloid leukemia. 

6. Streeter SB, Schwartzberg L, Husain N, Johnsrud M. Patient and plan characteristics affecting abandonment of oral oncolytic prescriptions. 

. 2011;7(suppl 3):46s-51s. doi: 10.1200/JOP.2011.000316.

7. Shen C, Zhao B, Zhou S, et al. Low income subsidy status outweighs insurance cost-sharing in predicting adherence to tyrosine kinase inhibitors among Medicare Part D beneficiaries with chronic myeloid leukemia [abstract #6531]. Presented at: 2016 ASCO Annual Meeting; June 4, 2016; Chicago, IL. http://www.ashclinicalnews.org/in-chronic-myeloid-leukemia-subsidies-and-out-of-pocket-costs-predict-adherence-totki-inhibitors/. Accessed January 31, 2017.

8. Kaisaeng N, Harpe SE, Carroll NV. Out-of-pocket costs and oral cancer medication discontinuation in the elderly. 

9. Doshi JA, Hu T, Li P, Pettit AR, Yu X, Blum M. Specialty tier-level cost sharing and biologic agent use in the Medicare Part D initial coverage period among beneficiaries with rheumatoid arthritis. 

. 2016;68(11):1624-1630. doi: 10.1002/acr.22880.

10. Doshi JA, Takeshita J, Pinto L, et al. Biologic therapy adherence, discontinuation, switching, and restarting among patients with psoriasis in the US Medicare population. 

. 2016;74(6):1057-1065.e4. doi: 10.1016/j.jaad.2016.01.048.

11. Takeshita J, Gelfand JM, Li P, et al. Psoriasis in the US Medicare population: prevalence, treatment, and factors associated with biologic use. 

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12. Improving Medicare Part D. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/chapter-6-improving-medicare-part-d-june-2016-report-.pdf?sfvrsn=0. Published June 2016. Accessed December 16, 2016.

13. Holcomb K, Harris J. Commercial specialty medication research: 2016 benchmark projections. Milliman website. http://us.milliman.com/uploadedFiles/insight/2016/commercial-specialty-medication-research.pdf. Published December 28, 2015. Accessed December 16, 2016.

Articles

Medicare claims analyses offer insight into how proposed policy changes would affect out-of-pocket prescription costs for Part D beneficiaries requiring specialty drugs.

Reducing Out-of-Pocket Cost Barriers to Specialty Drug Use Under Medicare Part D: Addressing the Problem of "Too Much Too Soon"

Medication adherence is often suboptimal, especially among patients on multiple chronic medications. We examined the association between synchronized medication refill schedules—which typically reduce organizational effort and logistical demands&mdash;and adherence.

Retrospective study among patients enrolled in Medicare Advantage prescription drug plans. 

 We used 2012 pharmacy, medical, and enrollment data linked with consumer meta-data for Medicare patients filling 2 or more maintenance prescriptions for antihypertensives, lipid-lowering agents, antidiabetic agents, antidepressants, and/or antiosteoporotic agents. Medication adherence for the year was measured using the proportion of days covered (PDC) at the drug class level. Patients were deemed adherent if drug class PDC was ≥0.80. Outcomes were compared between 1:1 propensity score-matched patients on synchronized versus nonsynchronized refill schedules for maintenance medications. 

The synchronized refill group showed better adherence than the control group, although the magnitude of effects varied by drug class and specific outcome measure. Mean PDC scores ranged from 0.02 higher for antihypertensives to 0.07 higher for antidepressants in the synchronized refill group relative to the control group (P <.01). Further, compared with the control group, a larger proportion of synchronized refill group members were deemed adherent, ranging from 6 percentage points higher for antihypertensives to 15 percentage points higher for lipid-lowering agents (P <.01). Differences between the synchronized and control groups were larger among exclusive users of retail versus mail order pharmacies for maintenance medications.

Synchronized medication refill schedules were associated with better medication adherence, particularly for patients filling maintenance medications exclusively at retail pharmacies.

	Prescription synchronization programs are popular, but evidence regarding their utility in improving adherence is limited. Our retrospective claims analysis of Medicare Advantage patients receiving multiple maintenance medications (ie, antihypertensives, lipid-lowering drugs, antidiabetic agents, antidepressants, and/or antiosteoporotic agents) compared patients with synced refill schedules to propensity score-matched patients without synced schedules.

		Compared with the nonsynced group, the synced group showed better adherence as measured by proportion of days covered. The magnitude varied by drug class.

		Differences between the synced and nonsynced groups were larger in patients exclusively using retail pharmacies compared with patients exclusively using mail order pharmacies.

		Synchronized prescription refills may facilitate adherence.

Poor medication adherence is a common problem among patients taking medications for a chronic condition.

 For patients receiving multiple medications for the treatment of more than 1 chronic condition, the resulting complexity can be particularly challenging.

 To address some of the practical burdens associated with long-term medication regimens, there has been increasing interest in whether synchronizing medication refill schedules—whereby all of a patient’s maintenance medications are refilled at the same time&mdash;may be a promising, scalable approach to improving adherence.

A recent review of the synchronization literature revealed few major peer-reviewed studies of synchronization.

 Holdford and Inocencio (2013) examined a convenience sample of patients at an independent retail pharmacy chain in the rural midwestern United States who were receiving new prescription medications, from 1 of 6 drug classes, to treat cardiovascular disease and diabetes.

 Intervention patients (ie, those who were participating in an appointment-based medication synchronization program that included synchronization of refills) had higher adherence across all drug classes than a control group of patients receiving usual care. A second study by Holdford (2015) among commercially insured retail pharmacy patients in Ohio who had been using medications for 6 months or more found similar results.

 Further, Choudhry et al (2011) used retrospective claims data from a large pharmacy benefit manager to examine therapeutic complexity in patients receiving refills for cardiovascular medications from a chain of retail and/or mail order pharmacies and found that less consolidated (ie, less synchronized) refill schedules were an independent predictor of worse medication adherence.

 Although valuable, the generalizability of these studies may be limited due to their focus on either specific geographic regions, retail pharmacy users, commercially insured patients, and/or a limited set of drug classes. 

Despite the absence of more generalizable evidence, there has been increasing adoption of medication synchronization programs across the nation.

 According to the American Pharmacists Association Foundation, more than 23 pharmacy chains and approximately 2000 independent pharmacies in the United States have implemented medication synchronization programs.

 An increasing number of states have proposed or recently passed legislation to remove barriers to medication synchronization, such as requiring insurers to pro-rate co-payments for the partial refills that are often needed to synchronize existing prescriptions.

 According to a recent national survey of 1000 adults 40 years or older who were receiving 1 or more medications for long-term medical conditions, about 1 in 8 patients were enrolled in a prescription synchronization program.

 In light of the growing adoption of, and policy interest in, synchronization programs, there is a pressing need for additional evaluations of the effectiveness of synchronizing refill schedules.

In this study, we utilized existing claims data for a sample of individuals enrolled in Medicare Advantage prescription drug plans (MA-PDs) who were receiving antihypertensives, lipid-lowering drugs, antidiabetic agents, antidepressants, and/or antiosteoporotic agents, to examine adherence outcomes in patients with synchronized medications compared with those whose medications were not synchronized. We focused on the Medicare population for several reasons. First, an estimated 85% of US adults 65 years or older have at least 1 chronic health condition, and 60% have at least 2.

 Second, given that forgetfulness and difficulties with transportation may increase with age, Medicare patients may be a particularly appropriate target population for interventions that reduce trips to the pharmacy and make it easier to manage prescription refills. Third, medication adherence for seniors has garnered increasing attention due to the CMS Five-Star Quality Ratings System program, which provides public ratings of Medicare Part D plans and ties performance to bonus payments.

 The ratings incorporate several medication adherence measures and award an increasing number of stars based on the percentage of a Part D plan’s members that meet adherence benchmarks.

Using 2012 pharmacy, medical, and enrollment data from Humana Inc’s top 50 (ie, highest enrollment) MA-PD plans linked with consumer meta-data files from the AmeriLINK Consumer Database,

 we took a 20% random sample of individuals meeting the following study criteria: 1) continuous eligibility for pharmacy and medical benefits in the same plan throughout 2012; 2) filling 2 or more prescriptions for antihypertensives, lipid-lowering drugs, antidiabetic agents, antidepressants, and/or antiosteoporotic agents; 3) a record of at least 2 different prescription fill dates for those medications in calendar year (CY) 2012; 4) exclusive use of either retail or mail order pharmacies to refill maintenance medications during the study year (ie, all prescriptions filled via 1 delivery channel); and 5) an equal number of unique maintenance drug classes in both the first half of the year and in the entire year (as a proxy for no major disruption in the maintenance drug regimen) to ensure that it would have been possible to  maintain synced refill schedules throughout the year.

The primary study group consisted of individuals who had a synchronized refill schedule for all of their maintenance medications for the entire study year—defined as having refills for all medications at every refill date during the year. For example, a patient taking 4 maintenance medications would need to show refills for all 4 medications at each refill date in order to be classified in the synchronized medication refill group.

In sensitivity analyses, we applied alternate eligibility criteria for the study group, representing less restrictive definitions of synchronization. In the first sensitivity analysis, patients were classified in the synchronized refill group if they refilled all maintenance drugs at the same time at the first 2 fills in the year (ie, started as synced, but did not necessarily remain synced). In the second sensitivity analysis, the synchronized refill group consisted of patients who refilled all maintenance drugs at once at any 2 consecutive fill dates during the year, regardless of when in the year this occurred.

The control group, which remained the same for all analyses, included patients who did not have any 2 consecutive fill dates during the year where they refilled all their maintenance drugs at the same time. The study protocol was approved by the University of Pennsylvania Institutional Review Board.

Our primary outcome was medication adherence at the drug class level, as measured by the proportion of days covered (PDC) for CY2012.

 In keeping with CMS’s technical guidance on measuring adherence to Star Rating medications, PDC was calculated as the proportion of days in the observation period (ie, 365 days in our study) with a day’s supply of a medication available from at least 1 prescription in that drug class.

 A continuous PDC measure was calculated for each of the 5 drug classes of interest among patients filling drugs for that class. We also created indicators for whether a PDC measure was ≥0.80, as this is a common cutoff for classifying a patient as adherent and also used by CMS for Part D Plan Star Ratings calculations.

It should be noted that although some mail order pharmacies offer patients the option to receive automatic refills, CMS regulations require pharmacies to obtain consent from Medicare beneficiaries prior to shipping every refill. Thus, patients must take an action (eg, via initiating a refill order or responding to a call) before medication is sent. CMS has noted that this requirement would be expected to increase the value (ie, accuracy) of refill-related adherence measures.

We applied propensity score matching to balance the synchronized refill group members with control members across a variety of characteristics. As a first step, we stratified the sample by 4 key variables likely to affect medication adherence in order to ensure that matched pairs were identical with respect to the following parameters: 1) type of pharmacy where maintenance medications were filled (exclusive use of retail pharmacy vs exclusive use of mail order pharmacy); 2) insurance plan identifier, to account for differences in insurance benefits structure, co-payment requirements, drug formulary and utilization management tools, and other plan-specific features; 3) total number of maintenance medications; and 4) combination of maintenance drug classes being used (eg, a synchronized refill group member taking only antihypertensives and lipid-lowering medications would be matched to a control group member taking only those 2 classes of maintenance drugs).

Only strata with at least 1 synchronized refill group member and 1 potential control were considered in the matching algorithm. Within each stratum, we matched along a wide range of 310 variables, including: 1) sociodemographic variables (eg, age, gender, marital status, Medicare Part D low-income subsidy status, disability status, geographic region, county-level percent on Medicare Advantage); 2) Census tract-level measures for variables including home ownership, education, household income, and home value; 3) clinical variables (ie, medical condition indicators and the CMS Hierarchical Condition Categories (CMS-HCC) score)

; 4) prescription cost variables, including total prescription costs for the year, total prescription costs for maintenance medications only, and total out-of-pocket costs; and 5) consumer meta-data variables used to characterize plan members, including estimated income, interests, hobbies, consumer personas (eg, young urban singles, struggling seniors), occupation (including retirement status), attitudes (eg, positive outlook, control seekers), and tendency to drink alcohol and/or smoke. These variables were obtained from and/or predicted based on demographics, purchasing behavior, public records, Census data, consumer surveys, and other primary sources.

The success of propensity score matching was evaluated by comparing the means of matched synchronized and control group members along the 310 variables used. We calculated Cohen’s d to estimate the size of any differences between the matched groups across all variables; any sample characteristic with a Cohen’s d <0.10 was considered insignificant as per standard practice.

Using the matched sample, we performed paired 

 tests for continuous outcomes and McNemar’s tests for categorical outcomes. We also conducted subgroup analyses by type of pharmacy used to fill all maintenance medications (exclusively retail vs exclusively mail order). We then tested—via regression analyses with interaction terms for the continuous PDC scores and Mantel-Haenszel tests for the dichotomous adherence measure (PDC ≥0.80)&mdash;whether the association between synchronized refills and medication adherence differed for exclusively mail order pharmacy users compared with exclusively retail pharmacy users.

In sensitivity analyses, we repeated the main analysis twice using the 2 alternate eligibility criteria for defining the study group. A final sensitivity analysis repeated the main analysis after excluding the consumer meta-data variables during the propensity-matching step. Statistical significance is reported at the alpha = 0.05 level and all hypothesis tests were 2-sided. Propensity score matching was conducted in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina), whereas statistical analyses were performed in Stata version 13.1 (StataCorp LP, College Station, Texas).

Our final sampling frame consisted of 42,162 individuals, of which 6544 (15.5%) had synchronized refill schedules (eAppendix Figure A [

]). After applying the propensity score-matching algorithm, 6458 synchronized refill group members (99%) were successfully matched to 6458 control group members.

 provides key characteristics for the synchronized refill and control group members. Prior to propensity score matching, the 2 groups were similar with respect to demographics but differed on a number of clinical and treatment characteristics. For example, synchronized refill group members tended to have fewer medications, correspondingly lower prescription costs, and lower CMS-HCC scores. The matching process successfully reduced observable differences between groups, as demonstrated by Cohen’s d <0.10 for each of the 310 variables used in the propensity score matching (eAppendix Table A).

The matched sample had a mean age of 75 years and just under half were female. A majority of the matched sample used antihypertensives (89.9%) and lipid-lowering drugs (60.5%), whereas fewer than 10% used antidiabetic agents, antidepressants, and antiosteoporotic agents. Approximately 82% were exclusive mail order pharmacy users.

Both adherence outcome measures showed higher adherence in the synchronized refill group compared with the control group, although the magnitude and significance of the association varied by the drug class and specific outcome measure (

). Absolute differences in mean PDC scores ranged from 0.02 higher for antihypertensives to 0.07 higher for antidepressants in the synchronized refill group relative to the control group. Similarly, a larger proportion of synchronized refill group members were classified as adherent (PDC ≥0.80) compared with the control group, with absolute differences ranging from 6 percentage points higher among patients taking antihypertensives to 15 percentage points higher among patients taking lipid-lowering agents.

Examining subgroups by type of pharmacy revealed that the magnitude of the absolute differences in adherence between synced and nonsynced schedules were greater in patients utilizing retail pharmacies (

eAppendix Figure B). Mean PDC score differences between the synchronized refill group and the control group ranged from 0.06 higher for antihypertensives to 0.15 higher for lipid-lowering agents in the retail pharmacy users, whereas they were 0.02 and 0.05 higher, respectively, for the mail order pharmacy users. Similarly, the absolute differences in the proportion of individuals who were adherent (PDC ≥0.80) between the synchronized refill group and the control group ranged from 12 percentage points for antihypertensives to 30 percentage points for lipid-lowering agents in the retail pharmacy subgroup, whereas the differences were 4 percentage points to 13 percentage points, respectively, for the mail order pharmacy subgroup. The magnitude of the association between synchronization and adherence was significantly larger in retail users for 3 of the 5 drug classes we examined (eAppendix Table B). All sensitivity analyses showed findings similar to those of our main analyses (eAppendix Table C).

To our knowledge, this is the first retrospective analysis to examine the adherence benefits associated with synchronized medication refill schedules in a sample of individuals enrolled in MA-PDs and receiving a wide range of common maintenance medications. Our analyses revealed higher levels of adherence among those patients with synchronized refill schedules than those with unsynchronized schedules for both of our outcome measures: mean drug class PDC and percentage of patients meeting an adherence threshold of PDC ≥0.80. The magnitude of adherence advantages associated with prescription synchronization varied by drug class, with the largest differences seen among lipid-lowering and antidepressant agents and the smallest differences seen among antihypertensives, where adherence was high even in the control group. Differences for antiosteoporotic agents did not reach statistical significance, likely due to the smaller sample size in that category.

Overall, the differences we observed were of sufficient magnitude to affect CMS Part D Plan Five-Star Rating criteria, particularly in regard to the percentage of patients in the plan meeting adherence criteria (ie, PDC ≥0.80) for lipid-lowering drugs and antidiabetic agents.

 Further research is needed to determine whether some of the more modest adherence differences we observed would translate into improved health outcomes or economic savings, especially for patients with good baseline adherence. Nonetheless, it has been widely documented that sustaining long-term adherence is a complex behavior that poses challenges for most patients

; therefore, increased convenience may help to support maintenance of good adherence and reduce the risk of “adherence fatigue,” even in those who are already doing well.

In addition, we found that patients with synchronized refill schedules who were filling their maintenance medications exclusively through retail pharmacies had higher adherence than their nonsynced retail pharmacy counterparts. In comparison, smaller differences in adherence were seen for synced versus nonsynced schedules among patients exclusively using mail order pharmacies. This is not surprising given the greater time and travel demands associated with multiple trips to a retail store; reducing the number of pharmacy visits likely alleviates some of the burdens associated with obtaining refills.

It is notable that less than 20% of our final study population consisted of customers who exclusively used retail pharmacies for maintenance medications, perhaps reflecting the increasing use of mail order pharmacies for long-term prescriptions. Exclusively mail order patients were characterized by higher adherence overall compared with exclusively retail patients, which is consistent with other studies.

 This may be because mail order pharmacies typically offer a 90-day supply of medication—often with lower co-pays than required in the retail setting&mdash;as well as the fact that home delivery reduces logistical demands. Nevertheless, the fact that we still observed higher adherence associated with synchronized refill schedules, despite high adherence levels among all mail order patients, highlights that multiple approaches to increasing adherence could have additive effects. Even when patients have consistent medication regimens and use a consistent type of pharmacy, synchronization may be further reducing prescription refill management burdens and facilitating improved adherence.

Although our analyses controlled for baseline differences using propensity score matching, inspection of our study sample prior to matching offers insight into systematic differences between patients with synchronized maintenance medication refill schedules and those without them. For example, nonsynced patients had a higher mean number of both maintenance and nonmaintenance medications and higher CMS-HCC scores than patients with synced medications, suggesting that patients who might benefit most from simplified refill schedules are not necessarily on synchronized schedules. Synchronized refill schedules were also less common among patients using only retail pharmacies for their maintenance medications, despite the fact that having to pick up prescriptions in multiple trips is typically less convenient than having medication delivered to the home and that many existing synchronization programs are aimed at retail patients. 

Several caveats deserve mention. First, given our cross-sectional design, our study documents associations but does not establish causal relationships between prescription synchronization and improved adherence. Second, although PDC has been shown to correlate with other indicators of medication adherence and is a widely used and generally accepted adherence measurement method,

 it does not reflect whether a medication was actually taken as prescribed. Third, the potential for confounding, inherent in all observational studies, applies here. In particular, systematic unobserved patient-related differences could have confounded our findings; for example, it is possible that more highly organized patients would be both more likely to initiate synchronized schedules and to be adherent. We sought to offset these limitations by propensity score matching on a variety of patient demographic and clinical characteristics and also on an extensive series of consumer meta-data variables, including attitudes, lifestyles, and other health behaviors that are typically unobserved confounders in most retrospective claims analyses. Fourth, we observed high levels of adherence in our control group, perhaps due to our sample selection criteria and propensity-matching approach, which may have led to a ceiling effect in our analyses. Some analyses of individual medication classes (eg, antiosteoporotic agents, antidepressants) were also based on small sample sizes; hence, we should be cautious about estimates in those cases.

Finally, we also did not have access to information regarding how patients came to have synchronized refill schedules. Patients may have been participating in a medication synchronization program available at their retail pharmacy, have chosen to synchronize their medication of their own accord or on the advice of a healthcare provider or pharmacist (outside of a formal medication synchronization program), or ended up with a naturally synchronized schedule if new prescriptions were required after switching physicians, health plans, or type of pharmacy (retail to mail order pharmacy or vice versa). Although this lack of detail is a limitation in some respects, it also allowed us to assess whether synchronized schedules in general are associated with improved adherence. Medication synchronization programs typically involve both synchronization and multiple touchpoints, where patients have contact with a pharmacy staff member who may also provide additional adherence support, such as early detection of adherence problems, problem-solving strategies, or encouragement. Because our sample would be expected to include individuals exposed to various medication synchronization programs in diverse contexts, our results can be seen as a general estimate of synchronization impact. As noted by others, future intervention research that permits disentangling of potentially active treatment components is needed to further understand the potential benefits of synchronization and associated support programs.

Our analyses demonstrate that propensity score matching can be applied to existing data sets to generate valuable insights into differences in medication adherence outcomes between patients on synchronized refill schedules and those whose refills are not synchronized. Thus, secondary datasets can serve as another source of information to inform policy debates while we wait for additional data to become available from prospective evaluations of prescription synchronization programs being implemented in the current marketplace. Several states have already passed legislation requiring insurance plans to permit partial, prorated refills for patients wishing to synchronize multiple prescriptions,

 and CMS’s beneficiary handbook now makes this option available to all Medicare beneficiaries.

 More than a dozen states have introduced similar legislation.

 Our findings add to the conversation regarding the utility of large-scale implementation of medication synchronization programs.

The authors wish to thank Michael Relish, MS, Vicki L. Vogel, BA, Brian M. Lefeber, MS, MBA, George J. Spurlock, MBA, Brian Minichillo, BS, and Lara Wimmer, MBA, all of Humana Inc, for their support with this study. The authors also thank Amy R. Pettit, PhD, consultant and adjunct fellow, University of Pennsylvania Center for Public Health Initiatives, for her feedback on the manuscript and assistance with editing.

 Department of Medicine (JAD, RL, PL, KGV), Department of Medical Ethics and Health Policy (RL, KGV), and Department of Health Care Management (KGV), University of Pennsylvania, Philadelphia, PA; Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics (JAD, ABT, KGV), University of Pennsylvania, Philadelphia, PA; Fuse Innovation Lab of Cardinal Health (PPY), Dublin, OH; Humana, Inc (VFL), Louisville, KY; Department of Population Health (ABT), New York University, New York, NY; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center (KGV), Philadelphia, PA.

A retrospective claims analysis showed that synchronized refill schedules were associated with better medication adherence among Medicare Advantage patients taking multiple maintenance medications.

Synchronized Prescription Refills and Medication Adherence: A Retrospective Claims Analysis

Specialty drugs often offer medical advances but are frequently subject to high cost sharing. This is particularly true with Medicare Part D, where after meeting a deductible, patients without low-income subsidies (non-LIS) typically face 25% to 33% coinsurance (initial coverage phase with “specialty tier” cost sharing), followed by ~50% coinsurance (coverage gap phase), and then 5% coinsurance (catastrophic phase). Yet, no studies have examined the impact of such high cost sharing on specialty drug initiation under Part D. Oral tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of chronic myeloid leukemia (CML), making it an apt case study.

 A retrospective claims-based analysis utilizing 2011 to 2013 100% Medicare claims.

TKI initiation rates and time to initiation were compared between fee-for-service non-LIS Part D patients newly diagnosed with CML and their LIS counterparts who faced nominal cost sharing of ≤$5.

The first 30-day TKI fill “straddled” benefit phases, for a mean out-of-pocket cost of $2600 or more for non-LIS patients. Non-LIS patients were less likely than LIS patients to have a TKI claim within 6 months of diagnosis (45.3% vs 66.9%; 

<.001) and those initiating a TKI took twice as long to fill it (mean = 50.9 vs 23.7 days; 

<.001). Cox regressions controlling for sociodemographic, clinical, and plan characteristics confirmed descriptive findings (hazard ratio, 0.59; 95% CI, 0.45-0.76). Extensive sensitivity analyses confirmed the robustness of our findings.

 High cost sharing was associated with reduced and/or delayed initiation of TKIs. We discuss policy strategies to reduce current financial barriers that adversely impact access to critical therapies under Medicare Part D.

Am J Manag Care. 2016;22(4 Suppl):S78-S86

	We used 100% Medicare claims data to examine initiation of tyrosine kinase inhibitors (TKIs) in patients newly diagnosed with chronic myeloid leukemia.

		Patients not eligible for low-income subsidies, who faced mean out-of-pocket costs of $2600 or more for the first 30-day fill, were less likely to have an initial TKI claim within 6 months of diagnosis and took twice as long to initiate TKI treatment compared with patients receiving low-income subsidies who faced nominal cost sharing (≤$5).

		High cost sharing was associated with reduced and/or delayed initiation of TKIs under Medicare Part D. 

		Changes in Part D policies have the potential to increase access to lifesaving cancer treatments.

Specialty drugs commonly offer significant medical advances for chronic and/or life-threatening diseases, but they frequently carry high out-of-pocket costs that may impede patients’ access to treatment.

 The relationship between high out-of-pocket costs and specialty drug treatment access is particularly relevant for Medicare beneficiaries, who face complex cost-sharing arrangements under Medicare Part D’s prescription drug benefit. Per CMS regulations, Part D plans may place any drug that exceeds a designated cost threshold ($600 per month from 2011-2015) on a “specialty tier,” which typically requires patients to pay 25% to 33% coinsurance during each calendar year’s initial coverage phase.

 Once patients’ total drug spending exceeds an initial coverage limit ($2840-$2960 from 2011-2015),

 they enter a coverage gap phase, which requires even higher cost sharing (45%-50% coinsurance from 2011-2015)

 until their total out-of-pocket Part D spending reaches a certain threshold ($4550-$4700 from 2011-2015). After patients reach that catastrophic coverage limit, they pay 5% coinsurance for the remainder of that calendar year.

 Today, virtually all Part D plans using tiered benefit structures have a specialty tier.

Initial access to specialty drug treatment is particularly important for those who are newly diagnosed with cancer, where prompt treatment is often essential. This is an increasingly common scenario given that oncology drugs represent one of the largest areas of specialty drug growth in terms of both innovation and spending.

 Although the majority of Medicare patients experience high cost sharing for specialty drugs under Part D, patients who qualify for full low-income subsidies (LIS) face nominal cost sharing (≤$5) throughout the year. Thus, cancer treatment under Part D provides an ideal case study to explore the relationship between high cost sharing for specialty drugs and treatment initiation, with LIS patients serving as a natural control group.

We focused on newly diagnosed patients with chronic myeloid leukemia (CML) for several reasons. First, nearly half of new CML diagnoses occur in individuals 65 years or older.

 Second, the treatment of CML has been revolutionized by tyrosine kinase inhibitors (TKIs), a class of “targeted” specialty drugs that act on a unique oncogenic protein in CML cells known as the BCR-ABL fusion protein. These highly active oral medications limit the growth and progression of CML, transforming it into a chronic condition. With continuous and typically lifelong treatment, TKIs allow most patients with CML to enjoy a near-normal life expectancy compared with a median survival of less than 3 years in the pre-TKI era.

 Third, all TKIs are covered under Medicare Part D. Unlike conditions for which some treatment options with lower out-of-pocket costs may be available (eg, infused drugs covered under Medicare’s Part B benefit), elderly patients with CML do not have an equivalent lower-cost option. In this study, we examined the association between high cost sharing and TKI treatment initiation in a sample of patients with Medicare Part D who had been newly diagnosed with CML.

This retrospective claims-based study examined TKI initiation among patients covered by Medicare Part D, newly diagnosed with CML. We compared initiation of TKIs among non-LIS Medicare beneficiaries subject to high levels of cost sharing under Part D at the time of initial CML diagnosis (non-LIS group), against a contemporaneous comparison group of newly diagnosed full-LIS patients who faced only nominal cost sharing (LIS group).

Although both the initial coverage phase and the coverage gap phase involve high out-of-pocket costs, our primary analysis focused on patients with a new CML diagnosis during the initial coverage phase only, for 2 key reasons. First, since exiting the initial coverage phase is triggered by reaching an out-of-pocket spending threshold, individuals who had already reached the coverage gap phase at the time of CML diagnosis would have done so because of substantial out-of-pocket spending on other prescriptions, potentially biasing our selection process toward patients with significant comorbidity (ie, requiring multiple and/or expensive medications). Second, those patients would likely be balancing other substantial medical/prescription expenses and health conditions that might influence their decisions to initiate a TKI, thereby limiting our ability to interpret the degree to which initiation decisions may have been related specifically to the out-of-pocket expense for the TKI.

We used a data extract of the 2011 to 2013 100% Medicare Chronic Condition Data Warehouse files, which contain data on all fee-for-service Medicare beneficiaries in the United States. We specifically extracted the Medicare inpatient (Part A), outpatient (Part B), and prescription drug (Part D) data files linked with beneficiary summary files and Part D prescription drug plan characteristics files for patients with at least 1 diagnosis of CML (

International Classification of Diseases, Ninth Revision, Clinical Modification

We applied several selection criteria to capture our main sample of Medicare patients with newly diagnosed CML. Patients were included if they had: a) at least 1 inpatient or outpatient claim with a diagnosis of CML (

 code 205.1) between July 1, 2011, and June 30, 2013 (the first of which represented the “index date”); b) continuous enrollment in both fee-for-service Medicare and a stand-alone Part D prescription plan in the 180 days (6 months) before and after the index date (pre-index period and postindex period, respectively); c) ≥2 CML claims occurring at least 30 days apart (ie, the index claim and at least 1 other claim during the postindex period); d) a claim for a molecular oncogene diagnostic test during the 30 days before or the 30 days after the index date; and e) an index date that fell within the beneficiary’s Part D initial coverage phase.

Patients were excluded if they had: a) any CML claims during the pre-index period; b) any claim for a TKI during the pre-index period; c) any diagnosis of acute lymphocytic leukemia (ALL), defined as ≥2 ALL claims 30 days apart during the pre- or postindex period; or d) any change in LIS status during the pre- or postindex period.

Our main outcome variable was time to TKI initiation, defined as the number of days elapsed between the index date (first CML diagnosis claim during the study period) and the date that the first TKI prescription was filled during the 6-month postindex period. Patients who did not have a TKI claim during the 6-month postindex period were considered censored. We also measured the percentage of patients filling a TKI prescription within 6 months of diagnosis and time to TKI initiation among TKI users. All TKIs approved for CML and available during our 2011 to 2013 study period (imatinib [Gleevec], dasatinib [Sprycel], nilotinib [Tasigna], bosutinib [Bosulif], and ponatinib [Iclusig]) were included in the outcome definition.

Descriptive statistics were generated for the main sample. Multivariable Cox regressions were used to examine the difference in time to TKI initiation between non-LIS and LIS patients. Model covariates included sociodemographic characteristics capturing patient age, sex, race, Census region of residence (Northeast, Midwest, South, West), and county-level per capita income. Clinical characteristics included CML disease complexity (mild, moderate, or severe; classified according to the algorithm used by Darkow et al [2007]

), number of drug classes filled during the pre-index period, Charlson comorbidity score as adapted by the National Cancer Institute,

 diagnosis of end-stage renal disease (yes/no), and whether the index CML claim was an inpatient claim. Plan characteristics included the Part D drug benefit type (defined standard benefit, actuarially equivalent standard, enhanced alternative, other), TKI formulary coverage (the proportion of TKI drugs available on the market that were covered by the plan during the index claim year), and TKI utilization management tools (the proportion of covered TKIs requiring prior authorization, quantity limits, or step therapy). Finally, covariates for the year of the index CML diagnosis were included to control for any temporal trends.

We conducted extensive sensitivity analyses to test the robustness of our results. First, we examined an alternative way of measuring time to TKI initiation. Since physicians may assign a working diagnosis of CML but delay TKI initiation until the diagnosis is confirmed via molecular oncogene testing, we examined time to initiation based on the days elapsed between the date of the second (rather than the first) CML claim and the date of the first TKI claim. Second, we examined an alternative model specification by using plan formulary-level fixed effects variables, including indicators of the plan formulary identifier for the plans in which patients were enrolled. This approach enabled us to compare non-LIS and LIS patients facing the same formulary, while controlling for a series of patient confounders, in order to isolate the effects of cost-sharing differences and to rule out the influence of other formulary restrictions (eg, formulary coverage, prior authorization, quantity limits, and step therapy requirements for TKIs and other medications). Third, we removed the sample selection requirement that a patient’s index date fall within the initial coverage phase and allowed inclusion of all patients meeting our other primary sample selection criteria, regardless of the Part D coverage phase during which they were first diagnosed with CML.

Finally, we also conducted extensive sensitivity analyses to test the degree to which our sampling criteria were effective in achieving our goal of capturing patients newly diagnosed with CML. That is, we sought to maximize sensitivity (identifying all patients newly diagnosed with CML) while also maximizing specificity (excluding patients who may have had a CML claim prior to receiving a revised final diagnosis of a different form of cancer, as well as patients who may have received a TKI for another condition).

To examine how changes in our selection criteria would influence the resulting sample, we began with the most relaxed criterion (identifying new CML patients by requiring 2 or more CML claims, including the index claim) and then applied additional criteria in sequence to examine the impact: a) requiring ≥2 CML claims 30 days apart; b) requiring ≥2 CML claims 30 days apart and requiring a claim for a molecular oncogene diagnostic test (during the 30 days before or the 30 days after the index date); c) requiring ≥2 CML claims and excluding ALL patients (defined as ≥2 ALL claims during the pre- or postindex period); d) requiring ≥2 CML claims 30 days apart and excluding ALL patients (defined as ≥2 ALL claims 30 days apart during the pre- or postindex period); e) requiring ≥2 CML claims 30 days apart, excluding ALL patients (defined as ≥2 ALL claims 30 days apart during the pre- or postindex period), and requiring a molecular oncogene diagnostic test (as defined above); and f) excluding patients whose index CML claim occurred during an inpatient stay, since a lengthy stay may have included an initial course of a TKI during the hospitalization. (Oral drugs administered in inpatient settings are not captured in inpatient claims data.)

All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, North Carolina) and STATA/MP version 13 (StataCorp LP, College Station, Texas). The University of Pennsylvania Institutional Review Board deemed the study exempt from informed consent procedures, as no data were collected directly from human subjects.

Our main sample selection criteria identified 1053 patients with newly diagnosed CML between 2011 and 2013. Baseline characteristics for this sample are reported in 

. The non-LIS group was older and had a higher percentage of males and white patients, compared with the LIS group. The non-LIS group also had a lower mean Charlson comorbidity score but a larger proportion of patients with moderate to severe CML severity at diagnosis, as well as a greater percentage of patients who faced prior authorization requirements for TKIs, compared with the LIS group.

All non-LIS patients were subject to 25% to 33% coinsurance for TKIs during the initial coverage phase, regardless of whether their plan had a distinct specialty tier. Further, given mean total costs of ~$6800 per 30-day TKI prescription, the first drug fill “straddled” Part D benefit phases (pushing beneficiaries out of the initial coverage phase and into the coverage gap phase) and generated an out-of-pocket cost for the initial TKI fill of approximately $2600 or more for non-LIS patients (data not shown). On the other hand, the full LIS patients faced out-of-pocket costs of $5 or less for the initial TKI fill (data not shown).

 shows Kaplan-Meier curves for TKI initiation, stratified by LIS status. The time to TKI initiation was significantly longer in the non-LIS group compared with the LIS group (log-rank 

 <.001). A significantly lower proportion of the non-LIS group initiated a TKI within 1 month (21% vs 53%; 

 <.001) and within 3 months (36% vs 65%; 

 <.001) of initial CML diagnosis compared with the LIS group. Six months after the first CML claim, the non-LIS group was still less likely than the LIS group to have initiated a TKI (45% vs 67%; 

On average, non-LIS patients who did fill a TKI took twice as long to do so (mean = 50.9 vs 23.7 days; 

A Medicare claims analysis of patients newly diagnosed with chronic myeloid leukemia revealed that high cost sharing was associated with reduced and/or delayed tyrosine kinase inhibitor initiation under Part D.

Author | Jalpa A. Doshi, PhD

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