Published Online: November 30, 2012
Theodore Darkow, PharmD; J. Ross Maclean, MD; Geoffrey F. Joyce, PhD; Dana Goldman, PhD; and Darius N. Lakdawalla, PhD
Objectives: This study was designed to assess the effect of tyrosine kinase inhibitor (TKI) use on nonpharmaceutical medical spending for patients with chronic myeloid leukemia (CML), and estimate the association between cost-sharing and the TKI medication possession ratio (MPR).
Study Design: The retrospective study covered the 13 years from 1997 to 2009.
Methods: Analyses were conducted using a large administrative health insurance claims database covering 45 large employers. From this database, 995 unique patients with CML were identified, with 3,765 patient-years; of these patients, 415 (or 1,689 patientyears) were TKI users. We estimated the association of TKI use with total pharmaceutical spending and total non-pharmaceutical medical spending. In addition, we characterized plan-level cost-sharing rules for TKIs and assessed whether these were associated with the MPR for TKI therapy among CML patients.
Results: TKI users averaged $26,406 in annual non-pharmaceutical medical spending, compared with $38,194 for non-users; this was a difference of approximately 30%, which was statistically significant at the 5% level. The median patient out-ofpocket payment was $25, which increased to $63 at the 75th percentile and to $122 at the 95th percentile. MPRs were 94.8 at the median cost-sharing level and 100.0 at the 75th percentile and higher. There was no statistically significant association between cost-sharing and MPR.
Conclusions: Use of TKIs was associated with a 30% reduction in non-pharmaceutical medical spending for CML patients. This difference is approximately equal to 40% of the incremental pharmaceutical cost associated with using TKI therapy. The net annual cost of TKI therapy is roughly $15,000. An informal calculation suggests that this is well within the range of conventional cost-effectiveness thresholds. On balance, coverage of TKIs is relatively generous, with the vast majority of patients exhibiting high levels of adherence to therapy.
(Am J Manag Care. 2012;18:S272-S278)
Advances in biotechnology have produced many effective new treatments for serious and debilitating diseases. This is evidenced by the growth in use of specialty products and by the number of specialty products in the pipeline, most strikingly in the field of oncology. The number of investigational new drug applications for cancer treatment rose from 925 in 2003 to 1,440 in 2008.1 In addition, the price of oncologics has been increasing over time: the most expensive chemotherapy agent in the early 1990s was paclitaxel, which sold for $4,000 per year, while today, the cost of bevacizumab is 10 times higher.2
As utilization of and spending on cancer medications increase, both public and private insurers have adopted a more aggressive approach to managing reimbursement, distribution, and benefit designs. For example, most Medicare Part D plans place medications costing $600 or more per month (primarily cancer agents) on a special tier with higher cost-sharing requirements.3 In 2009, 96% of plans had a specialty tier, with virtually every plan imposing coinsurance rates of 25% to 33%.4
While lack of insurance is almost certainly a barrier to accessing specialty cancer medications, it is unclear whether demand for these products is affected by the cost-sharing burden imposed on patients by insurance benefit design. The tradeoff between risk-sharing and appropriate incentives raises the issue of how and to what extent payers should cover these products. Benefit-design decisions regarding cancer agents are particularly challenging given the severity of the illnesses they are designed to treat, the diversity of patient responsiveness to therapy, and widespread off-label use within the field of oncology.5 The principal challenge facing society is balancing the need to ensure access to a wide range of therapeutic alternatives and the need to constrain the rapid growth in healthcare expenditures. In order to do this effectively, more information is needed on clinical efficacy, price sensitivity, and overall value.
In this article, we estimate coverage and demand for a specific class of high-cost cancer therapies; namely, tyrosine kinase inhibitors (TKIs). Imatinib was introduced in 2001 and quickly became the standard of care for patients with newly diagnosed chronic myeloid leukemia (CML).6 Since then, dasatinib and nilotinib have been introduced and are associated with significantly higher and faster rates of complete cytogenetic response, and better long-term, progression-free survival versus imatinib.7 Dasatinib and nilotinib were originally introduced as second-line therapies in 2006 and 2007, respectively; both agents were approved for first-line use in 2010, given their demonstrated superiority to imatinib in this setting.7,8
While these medications can dramatically alter the course of disease progression, they can cost $50,000 to $100,000 per year.9 For this reason, we examined the ways in which these products are covered in employer-sponsored plans, the degree of demand sensitivity to cost-sharing, and the extent to which spending on these products affects use of other healthcare services.
Chronic Myeloid Leukemia and the Role of TKIs
CML is a slowly progressing blood and bone marrow disease that is usually diagnosed during or after middle age, and is rarely diagnosed in children. Prognosis and treatment options depend upon the patient’s age, the stage at diagnosis, the load of blasts in the blood (or bone marrow), and the patient’s general health.
TKIs work by targeting a specific protein that promotes cancer cell growth.10 While it is not possible to eliminate all leukemia cells, treatment can help achieve long-term remission of the disease. The prognosis for patients with CML has improved dramatically over the past decade. Prior to the introduction of TKIs, fewer than 20% of patients survived 10 years after diagnosis. First-line treatment with a TKI agent increased 10-year survival rates to 85% or more.11 Because complete response is not achieved or resistance is developed to first-line treatment in 20% to 30% of patients,12 the availability of multiple products in the class is clinically important.
Data and Methods
We assembled a data set of all pharmacy and medical claims from 1997 to 2009 for 45 large, geographically diverse US employers. These data, obtained from a benefits consulting firm, have been used previously to explore the association of pharmacy benefit design with medication use by the chronically ill.13,14 Each employer offered 1 or more health plans to its active or retired employees and their dependents. Each pharmacy claim included the medication name, dosage, days supplied, date and place of purchase (retail or mail order), patient out-of-pocket (OOP) expense, and health plan payment. Medical claims included information on the service type (eg, inpatient or emergency department [ED]) and date, billed charges, patient OOP expense, health plan payments, and associated diagnosis codes. Demographic variables included age, gender, the first 3 digits of the individual’s zip code of residence, relationship to the primary beneficiary, and employment status of the primary beneficiary.
The study sample included a cohort of adult patients (aged >18 years) with an initial diagnosis of CML during 1997 to 2009. We identified individuals with CML based on the presence of 2 or more International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) diagnosis codes (primary, secondary, or tertiary) of 205.1x in the medical claims, or 1 ICD-9-CM code and use of TKI therapy after diagnosis. The requirement for a second CML diagnosis or the use of a TKI serves to filter out patients with “rule-out” diagnoses. We excluded patients with less than 1 year of follow- up data and with just 1 ICD-9-CM diagnosis for CML, as well as those with no claims for TKI therapy over their entire claims history. Because firms entered and exited the database over time, we did not have a complete panel on all individuals. The majority of members were observed for 2 to 4 years, with fewer than 10% of the sample followed for 7 years.
We defined 2 distinct study samples: 1) those newly diagnosed with CML, and 2) CML patients in whom use of a TKI was initiated. Patients were considered newly diagnosed if they had at least 1 year of data prior to the index date (date of their first ICD-9-CM code) without a claim for the condition; for example, individuals with 2 ICD-9-CM codes for CML in 2004 would be considered newly diagnosed if they had no other ICD-9-CM codes for the condition in prior years (eg, 1997-2003) if continuously enrolled. Similarly, we defined the TKI-initiation sample based on the absence of any TKI use in prior years. We focused on newly diagnosed patients because we had full information on the history of their condition and the treatments they had received for it.
Use of and Adherence to TKI Therapies
The aims of the study were to examine the ways in which TKIs are covered in private health plans, how demand responds to cost-sharing, and whether use of TKIs affects the use of other healthcare services. We measured adherence to TKI treatment as the proportion of days in which therapy was supplied; for example, if a patient filled 9 TKI prescriptions, each providing a 30-day supply, utilization was determined to be 74% (270/365). Not all CML patients who began a TKI regimen remained on therapy; nearly 3% of TKI users stopped therapy in the first year of treatment and more than 5% stopped at some point during the study period. For these patients utilization was measured from the date of initiation to their last day of supply. This approach yields a higher estimate of adherence but minimizes measurement error due to discontinuation of therapy.
Because the data were derived from health claims and enrollment data, they contained a limited set of demographic covariates, including age, gender, marital status, and sponsorship status (employee or dependent type). Measures of health status were derived from the claims directly as a sequence of binary indicators for whether the patient had 2 or more claims for a specific condition during the year; 2 or more claims were required to avoid rule-out diagnoses. One virtue of such large data sets is that many comorbid conditions can be entered simultaneously as control variables. We included the following 30 comorbid conditions in the analyses: essential hypertension, congestive heart failure, diabetes, asthma, hypercholesterolemia, ulcer, depression, chronic obstructive pulmonary disease, allergic rhinitis, migraine, arthritis, chronic sinusitis, anxiety disorder, cardiac disease, vascular disease, epilepsy, gastric acid disorder, glaucoma, gout, hyperlipidemia, irritable bowel syndrome, malignancies other than CME, psychotic illness, thyroid disorder, rheumatoid arthritis, tuberculosis, angina, human immunodeficiency virus, anemia, and stroke.
Plan-Level Index of Generosity
Estimating the impact of patient cost-sharing requires measurement of benefit generosity at the plan level. Based on prior work13 we characterized the OOP cost of TKI therapy by computing the mean OOP cost within a plan for the weighted basket of TKI medications available in that year, where the weights reflect the market share of TKIs across all plans in the sample year. This allowed for a direct comparison of the OOP costs associated with purchase of these medications across plans. We excluded plans with small numbers of TKI users and adjusted prices to constant dollars using the medical services consumer price index.
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