Amy R. Pettit, 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. 

. 2015;135(12):2955-2963. doi: 10.1038/jid.2015.

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"

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; 

 <.001). After controlling for sociodemographic, clinical, and plan characteristics in Cox regression analysis, LIS status remained highly associated with TKI initiation, with the non-LIS group having a lower hazard of TKI initiation compared with the LIS group (hazard ratio, 0.59; 95% CI, 0.45-0.76; 

]). Subgroup analyses in elderly versus disabled Medicare beneficiaries showed consistent findings, with non-LIS patients having a lower hazard of TKI initiation than LIS patients (data not shown). Extensive sensitivity analyses using varying outcome definitions, analytic techniques, and sample selection criteria showed findings consistent with the main results (Table 2).

Our study offers new insights into the relationship between high cost sharing and cancer treatment initiation in Medicare Part D beneficiaries. We found significantly lower fill rates and significantly longer time to initiation of TKIs among beneficiaries newly diagnosed with CML who were responsible for high out-of-pocket costs compared with their counterparts who faced minimal out-of-pocket costs due to receipt of LIS. The robustness of these results was confirmed via a wide range of sensitivity analyses, suggesting that patient out-of-pocket burden is associated with delayed and/or reduced initiation of critical treatments under Medicare Part D.

To our knowledge, this is the first study to utilize a 100% data extract of national Medicare Part A, B, and D claims linked with Part D plan and formulary characteristics information to explore the relationship between high cost sharing and access to specialty cancer drugs. Use of this data set allowed us to extend prior findings on this topic in a number of ways. First, most prior evidence in this area comes from data on privately insured populations from 2009 or earlier, wherein specialty drugs were subject to substantially lower levels of cost sharing.

 In fact, the specialty drugs examined in all of these studies had median monthly out-of-pocket payments of $30 or less.

 As a result, those findings have limited generalizability to the complicated cost-sharing structure and high out-of-pocket costs currently present under Medicare Part D. Two prior studies that included data on Medicare patients using specialty cancer drugs either did not conduct subgroup analyses in Medicare patients

 or averaged cost-sharing amounts across the high and low cost-sharing coverage phases under Part D,

 and thus, the specific impact of out-of-pocket costs on treatment during periods of high cost sharing was obscured. Furthermore, both studies lacked medical claims data and were unable to control for important clinical characteristics.

Second, all prior studies lacked information on plan formulary characteristics and, hence were unable to account for the confounding effect of utilization management tools, such as prior authorization, quantity limits, and step therapy, which are increasingly applied to specialty drugs as a complement or substitute to cost-sharing strategies.

 Our study is the first to use detailed information on Part D plan formularies to control for the extent of formulary coverage and utilization management for the studied cancer drug class. Finally, although the literature in general suggests a stronger association between cost sharing and treatment initiation than treatment adherence and/or discontinuation,

 prior studies examining specialty cancer drug treatment initiation have lacked a large sample size (given their data source) and/or a more clinically nuanced approach to identify patients newly diagnosed with cancer, or to examine treatment history in a way that would reveal whether patients may have delayed treatment initiation.

The magnitude of the discrepancies in the outcomes we measured—double-digit differences in the percentage of patients initiating TKI treatments within 6 months of initial diagnosis and, on average, those filling Part D TKI prescriptions within 6 months taking roughly twice as long to start treatment&mdash;are striking. This is especially notable given that, unlike some cancer treatments that extend survival by months, TKIs can extend survival by many years and allow most patients to live with CML as a chronic disease.

 Further, oral TKIs do not carry additional burdens associated with many specialty drugs that require self-injection or infusions (eg, needle anxiety, pain with administration, need for refrigeration or special handling, travel to an infusion center), so there may be fewer potential barriers beyond out-of-pocket burden for patients to consider when deciding to initiate treatment promptly or at all. In these ways, TKIs represent particularly high-value medications from a patient perspective. Although the relatively short duration of follow-up in our observational analyses did not allow us to assess the impact of TKI delay on long-term clinical outcomes, recent data suggest that prolonged delays in TKI initiation can have a deleterious effect on survival in patients with CML.

Several study limitations should be noted. First, this was a cross-sectional analysis that used LIS patients as a control group and, hence, the study documents associations and is not able to establish causal relationships between high cost sharing and treatment initiation. As with all observational studies, there is the potential for unobserved confounding (eg, related to additional clinical or treatment history variables not available in our claims data) to contribute to observed differences in initiation. Of note, many of the issues that are relevant for other oncology studies, such as differences in patient perceptions of the benefits and costs of an expensive treatment that may have more limited potential to improve quality of life or extend survival, would be expected to play less of a role in decisions about whether to initiate a TKI. In addition, whereas patient-level demographic and clinical differences could have contributed to the observed differences in TKI initiation, evidence suggests that age and comorbidities have little effect on the success of TKI treatment for CML.

 Thus, all patients would be expected to be similarly eligible for TKI treatment. Further, we sought to offset the limitations of our observational design by employing multivariable regression to control for a variety of sociodemographic, clinical, and plan-level characteristics that could influence treatment decisions. We also conducted extensive sensitivity analyses, with consistent findings that suggest our results are robust.

Second, although 100% sample Medicare claims should include data for all beneficiaries who accessed TKIs through their Part D benefit (barring coding errors or omissions), we note 2 circumstances that may not be reflected in the claims data. On one hand, our data set did not capture patients who may have been receiving medication outside of their Part D benefit, such as through a manufacturer program that provides free or reduced-cost prescription drugs. Thus, it is possible that some patients who were classified as not initiating treatment, or as delaying initiation, may have been receiving medication via other means that would not have resulted in a Medicare Part D prescription claim.

 Regardless, our results are an accurate reflection of access issues under the Medicare Part D program, which was specifically created to improve access to drug therapy. On the other hand, some non-LIS patients in our sample who accessed TKIs under Medicare Part D may have been able to do so only because of supplemental cost-sharing help from patient assistance programs sponsored by nonprofit foundations. (Federal law constrains manufacturer-sponsored patient assistance programs from offering cost-sharing assistance to Part D beneficiaries.

) In those cases, our results would underestimate the true adverse impact of high cost sharing. In both circumstances, the need to seek additional assistance has the potential to add stress at an overwhelming time, when individuals and families are coping with the impact of a new cancer diagnosis.

Third, in order to isolate a newly diagnosed population that would be subject to high cost sharing, we required that our sample be diagnosed during Medicare Part D’s initial coverage period. As a result, our patient population may have been healthier overall than patients newly diagnosed with CML who did not meet this criterion. That is, patients who had already reached the initial coverage limit through spending on other pharmaceutical treatment (presumably due to the number and/or severity of other medical conditions) were excluded from our sample. A sensitivity analysis examining patients who received a new diagnosis of CML at any point during the coverage year showed consistent results, however, suggesting that this sampling decision did not significantly limit the generalizability of our findings. Finally, since Medicare claims available from CMS only include data on Medicare fee-for-service patients, our results may not be generalizable to Medicare Advantage patients.

Insurance coverage, as per economic theory, is intended to help protect patients against the risk of catastrophic loss, where the loss itself is a relatively rare and low-likelihood event, and beneficiaries of that coverage are not likely to alter their behavior in the presence of insurance.

 The oncology context is an excellent example of how insurance coverage can be used to reduce the risk of high spending for patients: many specialty drugs are very expensive, and they are often used to treat conditions that have a low probability of occurring. With 1% to 5% of patients using specialty drugs,

 insurance can serve its intended purpose to spread the risk of such occasional losses over a large insured population so that sick patients are not burdened with inordinately high costs for potentially life-saving or life-extending treatments. The potential for such burden is particularly of concern under Medicare Part D, where beneficiaries who do not qualify for low-income subsidies face specialty tier cost sharing of 25% to 33% in the initial coverage phase followed by 45% cost sharing in the coverage gap phase. Individuals currently need to spend up to $4700 out of pocket before their cost sharing drops to 5% during the catastrophic coverage period.

 Although the 2010 Affordable Care Act is scheduled to gradually close the Part D coverage gap by 2020, patients will still be responsible for 25% to 33% coinsurance during the coverage gap phase, effectively extending the high cost sharing that is currently in place for specialty drugs during the initial coverage phase. Thus, patients will continue to face financial barriers that may inadvertently discourage use of high-value treatments.

Our results point toward the critical need for regulators to consider approaches to provide Medicare Part D patients with additional protection against extremely high cost sharing for these medications. Several policy solutions may be considered. First, CMS should reconsider its policies permitting assignment of drugs to Part D specialty tiers based solely on the fact that their monthly acquisition cost exceeds a certain threshold, as well as the application of across-the-board high levels of coinsurance for all specialty drugs and for all patients requiring these drugs. Specialty drug cost sharing that accounts for medication value, as is the case with value-based insurance design approaches, may be more appropriate than these current one-size-fits-all Part D policies, wherein cost sharing is directly a function of the medication cost.

 That is, it makes sense to reduce or limit cost sharing to modest amounts so as to remove it as a barrier to patient initiation of and adherence to high-value specialty medications. Policy changes to lower cost sharing for high-value specialty drugs may be financially feasible, given a recent actuarial analysis that indicated that the cost of eliminating Part D specialty tiers could be offset by implementing relatively minor increases in traditional 3-tiered co-payments.

Second, creating greater consistency in out-of-pocket costs throughout the benefit year has the potential to reduce burden on patients who are currently subject to front-loaded out-of-pocket costs during the Part D initial coverage period and coverage gap. The complex and variable cost sharing required by the current Medicare Part D benefit structure likely poses challenges, particularly for elderly beneficiaries who are on a fixed income. Our study documented a mean out-of-pocket payment for the first TKI prescription fill of ~$2600 or more; this amount far exceeds the average monthly Social Security benefit (<$1350), which provides a substantial portion of income for many Medicare beneficiaries.

To help preserve the stability of monthly budgets for Medicare beneficiaries, approaches allowing patients to distribute total out-of-pocket costs more evenly throughout the benefit year should be considered. This would be analogous to strategies used to help individuals and families manage energy bills by distributing high winter heating costs throughout the calendar year. Finally, given that entry into the catastrophic coverage period still leaves Medicare Part D beneficiaries responsible for 5% cost sharing for the remainder of the calendar year—resulting in sums that can be substantial for specialty medications (eg, ~$350 per monthly TKI fill)&mdash;CMS should consider annual out-of-pocket maximums akin to those in the health insurance exchange plans and many private insurance plans. A combination of these approaches, which would distribute out-of-pocket costs as well as limit annual out-of-pocket liabilities for beneficiaries, has the potential to not only reduce the risk of initiation delays, but also to reduce cost-related adherence and persistence problems.

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.

High Cost Sharing and Specialty Drug Initiation Under Medicare Part D: A Case Study in Patients With Newly Diagnosed Chronic Myeloid Leukemia

Specialty drugs often represent major medical advances for patients with few other effective options available, but high costs have attracted the attention of both payers and policy makers. We reviewed the evidence regarding the impact of cost sharing on utilization of specialty drugs indicated for rheumatoid arthritis (RA), multiple sclerosis (MS), and cancer, and on the use of nondrug medical services, health outcomes, and spending. 

Systematic review of Medline-indexed studies identified via an OVID search for articles published in English from 1995 to 2014, using combinations of terms for cost sharing and specialty drugs, and/or our 3 conditions of interest. We identified additional studies from reference lists.   

We identified 19 articles focusing on specialty drugs indicated for MS (n = 9), cancer (n = 8), and RA (n = 8). Studies examined prescription abandonment (n = 3), initiation or any utilization (n = 8), adherence (n = 9), persistence/discontinuation (n = 7), number of claims (n = 1), and drug spending (n = 1). Findings varied by disease, but generally indicated stronger effects for noninitiation or abandonment of a prescription at the pharmacy and somewhat smaller effects for refill behavior and drug spending once patients initiated therapy. Studies have not examined specialty tier cost sharing seen under Medicare Part D or health insurance exchanges, nor effects on medical utilization, spending, or health outcomes. 

Evidence to date generally indicates reductions in specialty drug utilization associated with higher cost sharing; effects have varied by type of disease and specialty drug use outcome. We draw upon our findings and the gaps in evidence to summarize future directions for research and policy.

	Current evidence indicates that higher specialty drug cost sharing is associated with reduced specialty drug utilization. Research examining broader health outcomes and spending is unavailable. 

		Cost-sharing effects varied by type of disease and specialty drug utilization outcome examined. 

		There is a critical need for methodologically rigorous research to further evaluate whether the aggressive cost-sharing arrangements found in the current marketplace may cause patients to forego, delay, or decrease adherence to specialty drugs, and whether that results in poor health outcomes and higher total spending.

Specialty drugs typically represent significant medical advances, either in the form of novel approaches for the treatment of complex chronic conditions (eg, rheumatoid arthritis [RA], multiple sclerosis [MS]) or because they target diseases with few prior treatment options (eg, rare diseases or advanced cancers). Although the definition of specialty drug varies by payer, these medications share certain key characteristics.

 Typically, they are complex molecules and may be derived from biologic sources; require highly involved manufacturing processes; require special distribution and handling, such as refrigeration; involve intensive patient education and/or follow-up monitoring to ensure appropriate use; and are high cost. Many specialty medications require injection or infusion in clinical settings and are usually covered under the medical benefit. The remainder are administered at home by self-injection, or oral or inhaled administration, and are almost always covered under the pharmacy benefit.

Despite the therapeutic advances offered by many of these agents, specialty drugs have attracted payer attention because they are often accompanied by higher costs than traditional medications. Although the percentage of patients using specialty drugs is quite small—ranging from 1% to 5%

—a recent report estimated specialty drug spending in the United States at $95 billion in 2013, or about 29% of total prescription drug spending.

 Although published estimates vary depending on which agents are included, about two-thirds of specialty drug spending growth in 2013 was accounted for by agents to treat cancers, autoimmune conditions like RA, and MS.

Since about half of specialty drug spending is on self-administered agents covered by pharmacy benefits, these have been the focus of payer efforts to control spending.

 Insurers and pharmacy benefit managers seeking to manage costs have been unable to use the traditional 3-tiered cost-sharing design to encourage utilization of lower-cost drugs, however, given the fact that specialty drugs often have few close, less-expensive substitutes. As a result, utilization management (UM) tools, such as prior authorization and quantity limits, have been applied instead.

 Nevertheless, growing pressure to control spending has led insurers to increasingly place self-administered specialty drugs on new, separate “specialty tiers.” Whereas tiered cost-sharing designs typically have fixed co-payments of increasing amounts for generics, preferred brands, and nonpreferred brands, respectively, newer specialty tiers usually impose a coinsurance requirement which can be as high as 30% to 50% of the cost of the drug. Although specialty tiers were first broadly implemented with Medicare Part D, use of this strategy (and associated high cost sharing) has grown significantly in other markets, including the employer-sponsored insurance market and the new health insurance exchange plans created under the Affordable Care Act.

 Further, many plans—particularly those under Medicare Part D&mdash;place medications meeting a designated cost threshold (eg, $600 a month or more) on the specialty tier, regardless of whether those medications meet other common criteria for the specialty drug designation.

Despite the market trends in increasing patient out-of-pocket (OOP) costs for specialty drugs, the impact of patient cost sharing on specialty drug utilization and outcomes is unclear. Numerous reviews have summarized the relationship between cost sharing and utilization of traditional oral drugs, but none have evaluated the evidence as it relates to specialty pharmaceuticals.

 Such a review is needed for several reasons. First, patients may have strong demand for specialty drugs if they do not have other treatment options or if they perceive alternative, less expensive treatments to be ineffective or undesirable. In this context, they may be willing to pay a much higher cost share compared with traditional pharmaceuticals. On the other hand, the magnitude of OOP costs may exceed patients’ ability to pay, particularly in the context of overall medical expenses and other basic needs. More information is needed to determine the degree to which patient cost sharing is associated with changes in specialty drug utilization, as well as with health outcomes, use of nondrug medical services, and overall healthcare spending.

We sought to address this gap with a systematic review of the published evidence. In light of the highly variable definitions of specialty drugs across insurers, we focused our review on cost-sharing studies of specialty drug classes with indications for 3 key disease areas: RA, MS, and cancer. These represented the top 3 drivers of specialty drug spending in the United States at the time of our review.

 We examined associations between cost sharing and specialty drug utilization for these indications and draw upon our findings, including the gaps in evidence in light of current market trends, to highlight future directions for specialty drug research and policy.

We conducted an OVID search for all Medline-indexed articles published in English between 1995 and 2014. The primary search was based on various combinations of 2 sets of search terms. The first set included cost sharing and related terms: co-pay, coinsurance, benefit design, tiered benefit, specialty tier, and out-of-pocket cost. The second set included terms for specialty medications and our indications of interest: specialty drug, specialty pharmaceutical, biologic, rheumatoid arthritis, multiple sclerosis, cancer, oncology, carcinoma, leukemia, oncolytics, myeloma, and tumor. Additional studies were obtained from reference lists of identified studies. Identified articles were then independently screened and reviewed by 2 members of the research team to determine whether they met our criteria for inclusion (

These inclusion criteria, applied in the following order, were: 1) published as a peer-reviewed original research article (ie, review articles, editorials, and letters were not eligible), 2) examined the effects of cost sharing (co-payments, coinsurance, or OOP costs) for specialty drugs on at least 1 of our chosen outcomes (specialty drug utilization or spending, medical utilization or spending, or health outcomes), and 3) examined drugs or drug classes that were indicated for RA, MS, or cancer and met the aforementioned high cost criterion for specialty drugs, along with at least 1 additional specialty drug criterion (eg, complex molecule or dispensed through specialty pharmacy). Because anti-inflammatory biologics used to treat RA are also approved for other indications (eg, psoriatic arthritis, ankylosing spondylitis), we did not exclude studies that sampled patients based on use of such drugs rather than diagnosis, even though they may have included patents with conditions other than RA. In light of the limited number of studies available, we also did not impose additional restrictions based on study quality. Nineteen articles met our criteria.

We extracted relevant information from each eligible study, including whether they reported cost sharing to have evidence of effects (ie, statistically significant nonzero effect) or no evidence of effects (ie, no statistically significant effect). When price elasticity of demand estimates were reported or could be derived, we captured this information as the expected relative change in the studied outcome if cost sharing were to double (ie, increase by 100%). Given the heterogeneity present in the cost-sharing measures, reported outcomes, study designs, and analytic approaches employed across the identified studies, our summary of the findings and conclusions are largely qualitative in nature.

Detailed characteristics of the 19 studies included in this review are available in the 

). Several studies examined 1 or more of our indications of interest, resulting in 8 studies for RA, 9 studies for MS, and 8 studies for cancer.

 More than half of the studies (n = 10) examined the effect of cost sharing on specialty drugs covered under the pharmacy benefit only, whereas the remainder examined agents covered under either the pharmacy or medical benefit. The specific specialty drugs examined in each study varied, particularly for cancer. All studies were observational, and a majority (n = 13) used cross-sectional study designs to examine specialty drug use among patients in plans with lower cost sharing compared with patients in plans with higher cost sharing for the specialty drug(s) being studied.

Two studies examined data from individuals enrolled in Medicare Part D,

 whereas all other studies used administrative claims data on privately insured patients in commercial plans or employer-sponsored health plans for current or retired employees. No study directly examined the effect of specialty tier—related cost sharing. In fact, databases used in the vast majority (84%) of these studies were from 2009 or earlier, during which time few private insurers were employing the use of specialty tiers and aggressive cost sharing for specialty drugs.

Studies used heterogeneous methods to measure specialty drug cost sharing; some used continuous measures of either co-payments or coinsurance, or an overall plan cost-sharing generosity index, whereas others created categories of cost-sharing groups based on either arbitrary cutoffs, medians, or quartiles for OOP payments observed in the data. A majority of patients were facing relatively low cost sharing, in keeping with the time frame of the data sources. For instance, 7 (64%) of 11 studies with information on mean OOP costs per month had mean co-payments less than $110 per month, and all 7 studies with median OOP information had median co-payments less than $50 per month. The OOP costs were substantially higher in the 2 studies examining Medicare Part D patients, yet these data are difficult to interpret due to a lack of detail. Medicare beneficiaries faced different levels of cost sharing during the calendar year (eg, 25%-33% coinsurance during an initial coverage phase vs 100% cost sharing during the “donut hole” coverage gap), but the studies did not examine changes during or between specific coverage phases. 

A wide variety of specialty drug use outcomes were reported across the studies, including prescription abandonment (n = 3), initiation or any utilization (n = 8), adherence (n = 9), persistence/discontinuation (n = 7), number of claims (n = 1), and drug spending (n = 1). We found no studies examining the impact of specialty drug cost sharing on medical utilization, spending, or health outcomes. The 

 summarizes key study findings by the type of outcome and indication(s) examined.

 In the 3 eligible studies, prescription abandonment was defined as a reversal of an adjudicated claim for a newly prescribed specialty drug (ie, the prescription was submitted and approved by the insurer but not obtained by the patient, so the pharmacy withdrew [reversed] the claim), with no subsequent paid claim within 90 days of the reversed claim.

 Prescription abandonment may be thought of as a subcategory of primary nonadherence, which is when a physician prescribes a medication but the patient does not submit, or does not pick up, the prescription from the pharmacy.

All studies reported a strong association of higher cost sharing with abandonment (vs initiation) of specialty drug prescriptions, for all indications examined. In one study among privately insured patients, approximately 25% abandoned their specialty medication when the OOP cost on the claim was greater than $200 for MS specialty drugs and greater than $500 for anti-inflammatory biologics, compared with less than 5% to 6% abandonment rates when OOP  costs on the claim were less than $100.

 A follow-up study using similar, but more recent, data reported that 50% to 60% of privately insured patients abandoned anti-inflammatory biologics and MS specialty drugs when faced with $2000 or more in monthly OOP costs (ie, due to high deductibles) compared with 5% to 6% abandonment rates among patients facing less than $50 in monthly costs; however, it should be noted that only a small percentage (2%-4%) of patients were subject to such high cost-sharing levels.

 Even for oral cancer agents, approximately one-fourth of privately insured and Medicare patients abandoned their specialty medication when OOP cost on the claim was greater than $500 compared with less than 5% to 6% abandonment rates with less than $100 in OOP costs.

 Notably, the cost-sharing burden in this study was higher for Medicare patients relative to privately insured patients (46% vs 11% facing greater than $500 OOP costs), but subgroup analyses were not conducted.

Initiation was typically defined as the first-time use of a specialty drug within a study period, among patients with a given disease. We grouped studies that examined any utilization (first time or repeated use) during the study period into this category as well. All studies examining initiation in patients with RA and MS reported a negative association with higher cost sharing. The magnitude of the effect varied across studies, with demand elasticities ranging from —0.03 to –0.33. For example, a 100% increase (ie, doubling) in cost sharing for MS drugs was associated with a 3% to 4% reduction in the probability of initiating these agents in 2 studies,

 but was associated with a 33% reduction in initiation in another study.

The authors review published evidence regarding associations between high cost sharing for specialty pharmaceuticals indicated for rheumatoid arthritis, multiple sclerosis, and cancer, and their utilization.

Amy R. Pettit, PhD

Articles

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

High Cost Sharing and Specialty Drug Initiation Under Medicare Part D: A Case Study in Patients With Newly Diagnosed Chronic Myeloid Leukemia

Impact of Cost Sharing on Specialty Drug Utilization and Outcomes: A Review of the Evidence and Future Directions