Coverage and Use of Cancer Therapies in the Treatment of Chronic Myeloid Leukemia | Page 3

Published Online: November 30, 2012
Theodore Darkow, PharmD; J. Ross Maclean, MD; Geoffrey F. Joyce, PhD; Dana Goldman, PhD; and Darius N. Lakdawalla, PhD
Given the generosity of coverage for TKIs and the lack of variation across plans, we looked at the coverage of other oral cancer agents to assess the impact of high patient costsharing on use. As previously noted, the kinase inhibitor erlotinib is used for the treatment of NSCLC and as part of a combination regimen for the treatment of pancreatic cancer and costs $30,000 to $40,000 for a year of treatment. The mean copayment in our sample was $124, with the least generous plans (top decile) charging members $224 or more for a 30-day supply. Table 4 shows the adjusted impact of copayments on the annual days of supply for erlotinib. For lung cancer patients using erlotinib, annual days supplied was 15% lower for those paying $200 or more, relative to similar patients with copayments less than $100 per 30-day prescription.

Some individuals in our sample were classified as having CML based on 2 or more ICD-9-CM diagnosis codes, but were not taking TKIs or other medication for the condition. We re-estimated the models excluding these patients to assess the extent of this misclassification bias. Although the overall results were substantively unchanged, the magnitude of effect increased in some specifications. For example, 58% of CML patients were taking a TKI, which was more in line with current levels of treatment. In addition, TKI use was associated with even larger reductions in medical spending (38%) when potentially misclassified CML patients were excluded.

Discussion

Cost-sharing has been viewed as a way to protect against moral hazard and discourage overconsumption of services with little social value. One difficulty, of course, is identifying the services that are cost-saving or cost-effective and then ensuring that cost-sharing policies are sufficiently generous to encourage their use.

We found that use of TKIs was associated with lower spending on other types of healthcare services. CML patients on TKI therapy had roughly $12,000 less in nonpharmaceutical medical costs than did patients on alternative forms of therapy. This translated into a decline of more than 30% in medical spending and offset roughly 40% of the cost of TKIs. This result is consistent with prior work that suggested changing generosity for one healthcare service has both short- and long-term implications for spending in other areas. Chandra et al16 found substantial off-setting increases in hospitalizations when copayments for outpatient and pharmaceutical use were increased. In this case, the cost of hospitalizations was borne by Medicare while the benefits of the reduction in prescription use accrued to the state’s supplemental retiree plan. Similarly, Gaynor et al17 found that while increased copayments for prescription medications reduced prescription utilization and spending, consumers spent more on other outpatient care.

Current TKI coverage policies appear to support the continued diffusion and use of these products. While the medications cost approximately $4,000 per month, the median copayment for a 30-day supply of TKIs was $25, and only a few plans charged members more than $100 per prescription. Further, neither initiation nor use of TKIs was related to cost-sharing. Patient adherence to prescribed medication regimens was high, perhaps reflecting the generosity of insurance coverage and the efficacy of these agents. By contrast, other oral cancer agents are not covered as generously and can serve as cautionary examples of the demand response if cost-sharing increases beyond certain thresholds. Copayments of $200 or more per month for erlotinib, an analogous oral agent used to treat lung cancer, were associated with a 15% reduction in use relative to those with copayments less than $100. Thus far, copay burdens of this magnitude are absent for TKIs. If they were to appear and exert downward pressure on adherence of the sort observed in the case of erlotinib, the evidence suggests that adverse health outcomes would result.18

A key challenge in this type of analysis is that disease severity cannot be measured directly, and patients who are more severely ill may use different types of therapies. Ideally, we would want to instrument for TKI use with the generosity of plan coverage, but, as noted earlier, there was limited variation in cost-sharing across plans in our sample. We attempted to minimize bias by selecting a more homogenous cohort of patients newly diagnosed with CML. Although this reduced our sample size by nearly 40%, we had full information on the history of the condition and the treatments received by these patients. Further, we found no significant differences between TKI users and non-users with regard to number of comorbid conditions or the probability of being hospitalized or admitted to the ED during their first year in the database. An additional limitation of our study was that the sample of CML patients, although large for this condition, was not representative of all privately insured patients or CML patients nationally. Lastly, our measure of medication utilization, days supplied, may have overstated actual adherence to TKI therapies, although this measurement error should not vary systematically across health plans.

Conclusions

The continued introduction of effective, high-cost, novel cancer therapeutics and diagnostics is likely to exert increasing financial pressure on patients, oncologists, payers, businesses, and society, while simultaneously improving outcomes and expanding treatment options. Payers will have more latitude with which to vary the benefit designs offered to patients with cancer, and these decisions are likely to have significant health consequences. The solution is a more evidence-based approach to the design of insurance benefits. It will become critical to identify the value to patients of new therapies themselves, and of generous coverage for these therapies. Greater importance will thus be placed on evidence of value, and pressure will grow for payers to connect reimbursement and cost-sharing more directly to this evidence.

TKIs in particular have generated substantial health improvements for patients with CML; for instance, the evidence presented in this study suggests that their use has decreased medical care utilization by more than 30%. Payers appear to have recognized this value insofar as they have insulated TKIs from the more extreme cost-sharing requirements that have been placed on other oncology agents. This decision likely explains why adherence to TKIs is relatively high compared with other oncology agents. Taken together, these facts suggest that TKI coverage represents an important and instructive success story, in which benefit design is connected to value.

Author affiliations: Bristol-Myers Squibb, Plainsboro, NJ (TD, JRM); Leonard D. Schaeffer Center for Health Policy and Economics, and School of Pharmacy, University of Southern California, Los Angeles, CA (GFJ); Leonard D. Schaeffer Center for Health Policy and Economics, Sol Price School of Public Policy, and School of Pharmacy, University of Southern California, Los Angeles, CA (DG); Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA (DNL).

Funding source: This supplement was supported by Bristol-Myers Squibb.

Author disclosures: Dr Darkow and Dr Maclean report employment with and stock ownership in Bristol-Myers Squibb. Dr Goldman and Dr Lakdawalla report consultancy with Bristol-Myers Squibb and partnership in Precision Health Economics. Dr Joyce reports consultancy with Precision Health Economics.

Authorship information: Concept and design (TD, DG, GFJ, DNL, JRM); acquisition of data (DG, GFJ, DNL); analysis and interpretation of data (TD, DG, GFJ, DNL, JRM); drafting of the manuscript (TD, DG, GFJ, DNL, JRM); critical revision of the manuscript for important intellectual content (TD, DG, GFJ, DNL, JRM); statistical analysis (GFJ); obtaining funding (TD, GFJ, JRM); and supervision (JRM).

Address correspondence to: Dana Goldman, PhD, Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, 3335 S Figueroa St, Unit A, Los Angeles, CA 90089-7273. E-mail: dpgoldma@healthpolicy.usc.edu.
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