# Coverage and Use of Cancer Therapies in the Treatment of Chronic Myeloid Leukemia | Page 2

Published Online: November 30, 2012
Theodore Darkow, PharmD; J. Ross Maclean, MD; Geoffrey F. Joyce, PhD; Dana Goldman, PhD; and Darius N. Lakdawalla, PhD
Empirical Approach: Estimating the Demand for TKI Therapy

We estimated the price sensitivity of TKI therapy using multivariate regression to correlate the utilization of therapy to the price of drug therapy in the plan. Specifically, we used the following linear probability model for individual j and year t:

Yjt = α + δt + β1pricejt + ΓXjtjt

In the above equation,

• Yjt is the number of days’ supply of TKIs by patient j in year t

• α is a constant parameter

• δt is a year fixed-effect

• β1 is the price sensitivity parameter

• pricejt is the average price per-claim for TKI therapies, which is calculated as the average OOP amount per claim among all persons using a TKI in individual j’s plan in year t

• Γ is a vector of sensitivity parameters to individual characteristics

• Xjt is a vector of individual characteristics (including age, comorbidities, etc)

• εjt is the error term, which we cluster at the employer level, since the variation in price is at the plan level and plans are nested within employers.

We first estimated the probability of a patient initiating TKI therapy as a function of plan generosity, as well as adherence to TKI therapy, conditional on use. We measured adherence as the number of days supplied (or proportion of days covered, or cumulative medication gap in a time frame), conditional on initiating therapy. This structure imposed the restriction that the price sensitivity of TKI therapy is similar across the different medications. This seemed reasonable given that all 3 TKIs currently on the market are used to treat advanced forms of CML, and imatinib was the sole TKI until 2006. Moreover, pooling the data across TKIs allowed us to obtain more precise estimates than if we had focused on each medication alone—an important consideration given that some of the medications in our sample were used by only a small number of patients. We also estimated the impact of TKI therapy on medical spending and use. Estimates from the multivariate models were used to predict medical and prescription spending for TKI users and TKI non-users, controlling for individual and plan heterogeneity. More specifically, estimates from the models were used to predict annual spending by type of service for each person in the study sample. The models included controls for patient demographics (age, gender, marital status, employment status), comorbid conditions, time since diagnosis, geographic and socioeconomic measures (census region or state, urban residence, median household income in the zip code), and annual time dummies. In all models we adjusted our variance estimates to allow for correlation (or “clustering”) of individuals within plans.

Sensitivity Analysis

To provide some context for our results, we looked at the impact of cost-sharing on the use of another oral cancer agent. Erlotinib is a kinase inhibitor used to treat non-smallcell lung cancer (NSCLC) and is also used as part of a combination regimen for pancreatic cancer. Erlotinib is widely used in the United States and costs \$30,000 to \$40,000 for a year of treatment. We looked at the impact of copayments on utilization of erlotinib to provide a point of comparison.

Finally, we examined the sensitivity of our results against more stringent definitions of CML by excluding individuals with CML diagnosis codes who were not taking any medications for CML.

Results

Our sample included 995 patients newly diagnosed with CML (Table 1). Since data were available for these individuals for multiple years after diagnosis, the number of person-years (number of individuals multiplied by number of years in the data post-diagnosis) is considerably larger (N = 3,765). More than 80% (812) of patients in the study sample were diagnosed with CML after the introduction of imatinib in 2001, with 42% (415) having filled at least 1 prescription for a TKI over the study period. The majority of patients were observed for 3 or more years. The demographic characteristics of CML patients who initiated use of a TKI were similar to those of non-users, although non-users were more likely to be working than were users (34.7% vs 29.0%). This suggests that differences in health outcomes between the 2 groups are more likely to result from TKI use than from preexisting demographic differences. Both groups frequently utilized healthcare services, with the average CML patient making 26 outpatient visits per year and accruing total annual expenditures in excess of \$50,000. Even so, the distribution of spending varied substantially. TKI users incurred higher total healthcare costs than non-users; specifically, these patients spent considerably more on pharmaceuticals and less on medical services than non-users. Non-users were enrolled in health plans with less generous coverage of TKIs but, overall, average coinsurance rates were low.

Although TKI therapies are expensive, they were generously covered in most of the employer-sponsored plans in our sample (Table 2). A 30-day supply of a TKI costs \$4,000 to \$8,000, yet the median copayment in our sample was just \$25. The least generous plans, with copayments of \$100 or more per 30-day supply, had an average coinsurance rate of less than 5%. However, even generous cost-sharing at the plan level can translate into significant OOP expenses for CML patients, given the cost of these treatments. For example, mean annual OOP costs in the sample were \$2,779, with \$785 attributable to medications. Further, 1 in 4 CML patients had total OOP costs in excess of \$7,000.

Given the efficacy of these therapies and the low level of patient cost-sharing, it is not surprising that adherence was high. The average medication possession ratio for TKIs was 85.7%, which suggests that CML patients had the TKI on hand roughly 7 out of 8 days. More importantly, we found no statistically significant relationship between generosity of coverage and use of TKI therapies, measured either as initiation of TKI therapy or adherence conditional on use. CML patients with less generous coverage were more likely to stop therapy. This effect was statistically significant, although quite modest in size. It is notable here that no plans charged copayments in excess of \$200, and only a small number charged copays higher than \$100. The lack of extreme costsharing is likely to mute the effect of cost-sharing on initiation of treatment.

Table 3 shows adjusted estimates of annual spending for TKI users and non-users based on multivariate models that control for individual and plan heterogeneity. Pharmaceutical spending was considerably higher among TKI users. CML patients using TKIs spent \$26,452 more on medications than similar patients who did not begin treatment with a TKI (in 2009 dollars). However, average annual medical expenditures were \$11,788 lower, offsetting nearly 45% of the incremental costs in medication spending. In sum, the use of TKIs increased total expenditures at a relatively modest cost per life-year and shifted the distribution of healthcare spending. Specifically, as discussed in the second article in this supplement by Yin et al,15 the introduction of TKIs led to a 10-year gain in median survival, from 7.5 years to 17.5 years. The incremental annual cost of TKI use is just under \$15,000. Over a 17.5-year horizon, with a 3% real rate of discount, an annual flow of \$15,000 in net costs has a net present value of \$206,000. Thus, this rough “back-of-theenvelope” calculation suggests that the incremental costeffectiveness ratio is less than \$20,600 per life-year gained, which would be considered cost-effective by conventional standards.

Sensitivity Analyses

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