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The American Journal of Managed Care September 2017
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In-Gap Discounts in Medicare Part D and Specialty Drug Use
Jeah Jung, PhD; Wendy Yi Xu, PhD; and Chelim Cheong, PhD
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In-Gap Discounts in Medicare Part D and Specialty Drug Use

Jeah Jung, PhD; Wendy Yi Xu, PhD; and Chelim Cheong, PhD
This study examined the early impacts of closing the donut hole in Medicare Part D.
Our primary analysis included use of any specialty cancer drugs not limited to those approved to treat the patient’s cancer type. This captures off-label drug use, which is common in cancer treatments,18 and allowed us to examine the total demand for specialty cancer drugs in the study population. To check whether the results were sensitive to the selection of cancer-type specific drugs, we performed 2 additional analyses. First, we selected patients with CML and examined the use of tyrosine kinase inhibitors (ie, imatinib, dasatinib, and nilotinib), which are top Part D specialty cancer drugs (Table 1). Second, we limited the sample to patients with pancreatic cancer and examined use of erlotinib or sunitinib. 

We used logit estimation for the analysis of specialty drug use and calculated marginal effects. For the interaction term between non-LIS and post indicators, the variable of interest, we obtained the average marginal effects.19 For other dependent variables (eg, the number of fills and spending), we limited the analysis to specialty cancer drug users. Among users, the residuals from the regressions were approximately normally distributed. We then applied linear estimations to analyze those outcomes. Error terms were accounted for clustering within a plan in all analyses. 

RESULTS

Table 2 presents the summary statistics of all study variables by LIS status. The table indicates that non-LIS beneficiaries were likely to be white, have fewer chronic conditions, and live in areas with relatively high income and education levels compared with LIS enrollees. 

Approximately 3% of non-LIS patients used a specialty cancer drug in a given year, and each user spent an average of $4838 OOP per year; the average total annual spending for specialty cancer drugs was $44,460 per user. Most specialty cancer drug users reached the coverage gap (99%) and catastrophic coverage level (94%) within a given year. In contrast, the LIS group had a higher specialty drug use rate (5.7%) than the non-LIS group, yet the average OOP spending for specialty cancer drugs among users was only $44. 

Table 3 describes trends in specialty cancer drug use and OOP spending. The data indicate that the utilization rates of specialty cancer drugs increased over years, but to a small degree, and there was no differential increase following the in-gap discount. Among users, the number of specialty cancer drug fills remained stable. Despite little changes in utilization among users, total spending for specialty cancer drugs per user increased substantially over years in both groups—possibly due to rises in drug prices (Table 1). We found that non-LIS beneficiaries’ OOP spending for specialty cancer drugs significantly decreased after the in-gap discount. Annual OOP spending of non-LIS beneficiaries for specialty cancer drugs sharply dropped between 2010 and 2011, from $5721 to $4254 (a 26% reduction). The average annual OOP spending in the postdiscount period (2011 to 2013) was $4494, which corresponds to a 19% decrease, from $5533. 

The Figure depicts changes in OOP spending for specialty cancer drugs separately for the gap and catastrophic coverage phases in the non-LIS group. A large reduction in OOP spending occurred in the gap phase following the ACA discount (from $3725 to $2151, or a 42% decrease). However, OOP spending in the catastrophic coverage phase increased from $1363 to $1919 between pre- and postdiscount periods. 

Table 4 presents results on key variables from regression analyses. We reported average marginal effects of the in-gap discount for specialty cancer drug use and OOP spending. Despite a seemingly large discount provided to beneficiaries, the decrease in the in-gap cost sharing had no significant effect on the use of specialty cancer drugs, either for use or the number of fills, among non-LIS beneficiaries with uncommon cancers. However, it significantly decreased OOP spending for specialty cancer among non-LIS patients by $1114. The results of all other covariates are reported in the eAppendix Table (eAppendices available at ajmc.com). 

The results from the subgroup analyses were consistent with the primary analysis. In both CML and pancreatic cancer groups, specialty drug utilization did not change after the in-gap discount. Among non-LIS patients with CML, annual OOP spending for tyrosine kinase inhibitors declined by $970 after the in-gap discount. Non-LIS beneficiaries with pancreatic cancer also had a reduction in OOP spending on erlotinib or sunitinib, but this effect was not statistically significant possibly due to the small sample size (n = 246). 

DISCUSSION

We found that the ACA in-gap discount decreased patients’ OOP spending for specialty cancer drugs but did not increase specialty cancer drug use in its early years. It is encouraging that the ACA’s initiative to close the coverage gap in Part D mitigated the patients’ financial burdens, to some extent. A $1114 decrease in annual OOP spending on specialty antineoplastic drugs (–19%) is not a small reduction; however, some patients with cancer without subsidies face a large financial burden even with the in-gap discount. The average annual budget of Medicare beneficiaries was reported to be $33,000 in 2012.20 Thus, the mean OOP spending of $4494 implies that patients with cancer spent 15% of their budget for specialty cancer drugs. Further, about 14% of (non-LIS) these patients spent more than $6600—20% of their budget—for specialty cancer drugs. This suggests that the in-gap discount does not offer sufficient financial protection to certain patients using specialty cancer drugs without subsidies. 

Our finding that the in-gap discount did not increase specialty cancer drug use might be because patients are not responsive to cost sharing in specialty drug use. Alternatively, it may be because the cost-sharing reduction kicks in after relatively high cost sharing for specialty drugs in the pregap phase.12 Beneficiaries would not use specialty drugs regardless of the in-gap discount if they cannot afford cost sharing in initial coverage. Or some patients may not begin a specialty drug treatment due to high annual OOP spending required to complete a course of treatment. To these patients, the in-gap discount is irrelevant and unlikely to lead them to use specialty drugs. 

In Part D, catastrophic coverage is a stop-loss mechanism. However, because of high prices of specialty drugs, even the 5% coinsurance in catastrophic coverage can bring a financial pressure on patients. Our data showed that the average OOP spending for specialty cancer drugs in catastrophic coverage increased from $1363 to $1919. This change reflects increases in drug prices given no change in drug fills among users. This high level of OOP spending even in the catastrophic coverage phase may have deterred patients from starting a specialty drug. 

Demand for specialty drugs is expected to increase as more drugs become available.4,21 Because specialty drugs do not usually have generic substitutes or alternative treatments,3 high cost sharing for specialty drugs can create financial difficulties to patients, which can lead them to forgo specialty drugs. Some patients may use specialty drugs despite high cost sharing; however, high cost sharing puts these patients at financial risk. It will thus be critical to identify high-value specialty drugs and ensure patients’ access to those medications. Reducing financial stress on beneficiaries who need expensive but effective drugs can help improve patients’ access to needed drugs. Expanding eligibility for low-income cost-sharing subsidies for certain costly yet effective specialty drugs might be an option to explore. Setting an OOP spending maximum for Part D drugs, either a fixed amount or as a certain percentage of household income, could be another possibility to consider. 

Limitations

Several limitations of this study should be noted. First, we do not have detailed clinical information, such as the stage of diseases, which may predict patterns of specialty drug use. However, this limitation would result in any bias only if temporal changes in the distribution of cancer stages systematically differed by LIS status, which is unlikely. Second, we focused on the impact of a change only in the in-gap benefit. Our findings do not apply to patients’ responsiveness to changes in overall or initial cost sharing in specialty cancer drug use, which is an important topic to pursue in future research. However, we were able to avoid selection in drug benefit choice, which could potentially lead to biased estimates, by exploiting an exogenous change in benefits. Finally, our study is limited to PDP enrollees with 6 uncommon cancers, and the results may not be generalizable to MA-PD enrollees or patients with other conditions.

CONCLUSIONS

The Part D in-gap discount decreased patients’ OOP spending for specialty cancer drugs; however, even with the in-gap discount, the financial burdens of specialty cancer drug users without subsidies remain high. Approaches to reduce financial burdens for high-value specialty drugs may improve patients’ access to needed drugs.

Author Affiliations: Department of Health Policy and Administration, College of Health and Human Development (JJ, CC), The Pennsylvania State University, University Park, PA; Division of Health Services Management and Policy, College of Public Health, The Ohio State University (WYX), Columbus, OH.

Source of Funding: This work was supported by NIH/NIA grant number 1R01AG047934-01, and NIH grant number R24 HD041025. No conflicts of interest exist.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (JJ); acquisition of data (JJ); analysis and interpretation of data (JJ, WYX, CC); drafting of the manuscript (JJ, WYX); critical revision of the manuscript for important intellectual content (JJ, WYX, CC); statistical analysis (CC); obtaining funding (JJ); administrative, technical, or logistic support (CC). 

Address Correspondence to: Jeah Jung, PhD, Department of Health Policy and Administration, College of Health and Human Development, The Pennsylvania State University, 601E Ford Building, University Park, PA 16801. E-mail: kuj11@psu.edu.
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