J. Ross Maclean, MD

As value-based insurance design (VBID) programs proliferate, evidence is emerging on the impact of VBID. To date, studies have largely measured VBID impact on utilization, and a few studies have assessed its impact on quality, outcomes, and cost. In this commentary we discuss these domains, summarize evidence, and propose the extension of measurement of VBID impact into areas including workplace productivity and quality of life, employee and patient engagement, and talent attraction and retention. We contend that VBID evaluations should consider a broad variety of programmatic dividends on both humanistic and health-related outcomes.

Value-based insurance design (VBID) evaluations should consider a broad variety of both humanistic and health-related outcomes.

		Previous studies have explored the impact of VBID on utilization, and a few have included healthcare quality and outcomes.

		Additional domains of benefit include workplace productivity and quality of life, employee and patient engagement, and talent attraction and retention.

A decade ago, the concept of value-based insurance design (VBID) was conceived, based on the principle of aligning the clinical value of care for patients with consumer financial incentives, such as co-payments and co-insurance.1 In the VBID paradigm, high-value, evidence-based services have lower cost-sharing levels, and low-value services have higher cost-sharing levels.

In the years since its inception, various payers, firms, and government entities have implemented VBID programs.

For example, VBID has been incorporated into the Affordable Care Act via co-payment waivers for certain preventive services

 endorsed by the Medicare Payment Advisory Council in reports to Congress as an approach to improve value,

 and has been examined in the Institute of Medicine’s report to the secretary of Health and Human Services on essential health benefits.

VBID principles are also growing in prominence: a survey of large firms revealed 81% were planning to incorporate VBID into their benefits packages in the future.

Measuring the Impact of VBID Programs: A Spectrum of Dividends

As VBID programs proliferate, evidence is emerging on their impact. To date, studies have largely measured impact on utilization and a few have included quality, outcomes, and cost.8 Here, we discuss these domains as well as our belief that the effects are likely to extend into related areas including work productivity, quality of life, engagement, and talent.

Several VBID evaluations have focused on measuring the effects of VBID on healthcare utilization, which includes medication adherence, guideline adherence, and medical care utilization.

These studies focus on the central mechanism of VBID: the price to the consumer. In response to a change in price, utilization of these services is likely to change. Measuring the impact on utilization is essential as it provides information on the central question surrounding the effectiveness of the VBID program: is the core principle of the program working as intended?

A 2013 review found that VBID programs were associated with a 3% increase in medication adherence on average,

and a more recent study reported similar findings from a VBID program implemented within a large health plan, showing a 2.7% to 3.4% increase in adherence.

Two VBID studies analyzed medical service utilization, with the first study reporting a significant first year decrease in emergency department (ED) visits and physician office visits for patients in a diabetes VBID, and in the second year patients with diabetes experienced a significant decrease in ED visits.

Hospital admissions were unchanged in the first and second years. The second study reported reductions in ED visits, hospitalizations, and physician office visits for users of certain medications included in the VBID program.

Few studies have extended measurement of VBID effects to include healthcare quality and outcomes, incorporating clinical measures such as patient laboratory values, complications, and mortality into a VBID assessment.

 This approach focuses on outcomes of care and the quality measures most likely to be affected by utilization of VBID services. For example, reduced cost sharing for diabetes medications has been shown to increase adherence,

 and based on empirical evidence, patients with higher levels of adherence can experience better outcomes, including complication rates (eg, heart attack, stroke). Improvements to the process of care and to management of care could be ascertained through changes in quality measures related to high-value services, such as the Healthcare Effectiveness Data and Information Set measure assessing persistence of beta-blocker treatment after a heart attack.

In terms of evidence, a randomized VBID intervention waiving co-payments for preventive medications after heart attack (the “Post-Myocardial Infarction Free Rx Event and Economic Evaluation” [MI FREEE] trial)  reported a decline in total major vascular events and revascularizations, as well as in the rate of occurrence of initial major vascular events.

A secondary analysis from the MI FREEE trial also found a reduction in disparities in outcomes of care.

For subjects identifying as nonwhite, the co-payment waiver was associated with reductions in rates of revascularization and major vascular events, although not among subjects identifying as white. In addition, a prepost study of a combined pharmacist intervention model with a VBID diabetes medication copayment waiver showed a 17 percentage point increase in patients with a glycated hemoglobin value of ≤7.0; however, the study was uncontrolled.

A few studies have focused on cost impact based on changes in direct medical spending.8 In general, measurement of the economic impact is not straightforward and can vary depending on the perspective adopted (eg, patient, health plan, employer) and the extent of measurement (eg, direct medical costs, indirect costs, program costs). For example, if co-payments are lowered for high-value medications, then direct medical spending on these medications will decrease for the consumer. In contrast, the health plan is likely to experience higher direct spending on these medications due to higher payment rates and higher rates of utilization. At the same time, direct medical spending by the health plan could be reduced by short- or long-term savings in medical spending from any cost offsets resulting from lower complication rates. Additional programmatic costs to design and implement the VBID are likely to be incurred, as well as potential savings in indirect costs from changes in work productivity. Evidence to date shows that VBID programs are associated with a reduction in patient out-of-pocket costs for VBID services. Notably, existing VBID programs have not been associated with changes in total medical spending.

 is a natural extension due to the close ties among health, productivity, and functional status; because VBID is health-enhancing, work productivity in participants is likely to improve. For example, improved adherence to medications to treat diabetes, hypertension, hyperlipidemia, asthma, and chronic obstructive pulmonary disease has been associated with improvements in work productivity.

The theoretical relationship between VBID and productivity has been well addressed,

 although empirical verification has not yet occurred.

Also unexplored are the effects of VBID programs on patient engagement in health and wellness. Engaged employees are marked by characteristics such as commitment, vigor, dedication, and absorption.

Engagement in work and in one’s own health has been linked to better health outcomes because engaged employees are more likely to have better health outcomes and more importantly, better experiences with care provision.

It remains to be proven that when, for example, price incentives are applied to high-value services (eg, adherence to a regimen of care for a chronic condition), a patient utilizes more of these specific services and becomes more actively engaged in health management and wellness. In response to VBID, patients may gather relevant information, including data about their own health status (eg, biometric measures), health conditions, and appropriate treatments and actions. We contend that patients who are well informed and participate in their care in coordination with their provider are more likely to stay abreast of treatments that are considered high value or evidence-based, and are more likely to improve or maintain health.

 These patients are more likely to exhibit high health literacy, healthy behaviors, and greater productivity.

A final unexplored dimension of benefit of VBID for workplace programs is the impact on a firm’s talent. Offering enhanced or individualized benefits could improve recruitment or retention, as employees or their family members receive a package of benefits aligned with their needs.

However, this approach could also result in selection effects, as good potential employees might not choose to work at a firm with non-VBID plans. In addition, emphasizing the corporate value of health and wellness may also serve to attract and retain employees who place a great deal of value on health and wellness.

Additional Initiatives to Improve VBID Effects

VBID is not intended as a stand-alone lever to produce positive effects; it can be used in synergy with other initiatives such as wellness programs,

integrated health systems, and health information technology to enhance results. These activities may also include regular informationgathering from biometric screenings, assessment of results from health risk appraisals, and discussions of health risks and disease state(s) from well-structured patient-provider interactions.

At a minimum, these services could be used to identify diseases and risks to create an individualized list of high- and low-value services. For example, a patient with undiagnosed high blood pressure is not likely to respond to VBID financial incentives for treatment of hypertension. A likely responder is a well-informed, engaged hypertensive patient with a benefit program designed to provide information about blood pressure readings, cardiovascular risks, a medication management plan, a program of lifestyle change, and frequent interactions with care providers.

We provide this overarching framework to capture the spectrum of benefits that may result from VBID programs, extending beyond utilization and outcomes to positive impacts on work productivity, employee engagement, and talent management. VBID evaluations should consider a broad variety of programmatic dividends on humanistic as well as health-related outcomes.

Department of Health Care Policy, Harvard Medical School (TBG, MEC), Boston, MA; Truven Health Analytics (TBG), Ann Arbor, MI; Precision Health Economics (JRM), Santa Monica, CA; Schools of Medicine and Public Health, University of Michigan (AMF), Ann Arbor, MI; Bristol-Myers Squibb (CB), New York, NY.

 This commentary was prepared, in part, with funding from Bristol-Myers Squibb through an HMS grant. All opinions expressed are those of the authors.

 At the time the manuscript was being prepared, Drs Maclean and Baigel were employees and stockholders of Bristol-Myers Squibb. At the time the manuscript was being prepared, Dr Gibson was employed by Truven Health Analytics, and received funding from Bristol-Myers Squibb, Merck, and Pfizer. Dr Fendrick is a consultant for CMS, IMS Health, Janssen Global Services LLC, Kinetix, Merck and Co, Pfizer, Sanofi, Truven Health Analytics, and Zansors LLC; and does research for Gary and Mary West Health Policy Center Inc, National Pharmaceutical Council, and the RWJ Foundation. He is also on the Speaker’s Bureau for Merck & Co; the advisory boards of Janssen Global Services LLC and Pfizer; and is a partner of VBID Health LLC. Dr Chernew is a consultant for Genentech, BCBS, and Sanofi-aventis, and is a member of the board of PhRMA. He has received grants from CareFirst BCBS, Pfizer, PhRMA, AstraZeneca, and Universal American, and is a stockholder of VBID Health.

 Concept and design (CB, MEC, AMF, TBG, JRM); acquisition of data (JRM); analysis and interpretation of data (MEC, AMF, TBG, JRM); drafting of the manuscript (CB, AMF, TBG, JRM); critical revision of the manuscript for important intellectual content (MEC, AMF, TBG, JRM); obtaining funding (TBG, JRM); administrative, technical, or logistic support (CB, TBG, JRM); and supervision (CB).

Teresa B. Gibson, PhD, Lecturer, Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02135. E-mail: 

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Articles

The authors provide a framework to capture additional benefits that may result from VBID programs, extending beyond utilization and outcomes to productivity, engagement, and talent.

Value-Based Insurance Design: Benefits Beyond Cost and Utilization

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).

 The retrospective study covered the 13 years from 1997 to 2009.

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.

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.

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.

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.

 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.

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.

 In 2009, 96% of plans had a specialty tier, with virtually every plan imposing coinsurance rates of 25% to 33%.

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.

 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).

 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.

 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.

While these medications can dramatically alter the course of disease progression, they can cost $50,000 to $100,000 per year.

 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.

 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.

 Because complete response is not achieved or resistance is developed to first-line treatment in 20% to 30% of patients,

 the availability of multiple products in the class is clinically important.

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.

 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 

 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

 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 

 code) without a claim for the condition; for example, individuals with 2 

 codes for CML in 2004 would be considered newly diagnosed if they had no other 

 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.

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.

Estimating the impact of patient cost-sharing requires measurement of benefit generosity at the plan level. Based on prior work

 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.

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 

 is the number of days’ supply of TKIs by patient 

 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 

• Γ is a vector of sensitivity parameters to individual characteristics

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

 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.

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.

Our sample included 995 patients newly diagnosed with CML (

). 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 (

). 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.

 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,

 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.

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. 

 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.

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 al

 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 al

 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.

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.

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. 

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).

 This supplement was supported by Bristol-Myers Squibb.

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.

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).

 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: 

		Schickedanz A. Of value: a discussion of cost, communication, and  evidence to improve cancer care. 

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

Although clinical trial data have quantified patient survival gains associated with tyrosine kinase inhibitors in chronic myeloid leukemia, the overall value of these benefits is unknown.

To estimate the total value of survival gains associated with first- and second-line TKI therapy in chronic myeloid leukemia (CML) and the fraction of tyrosine kinase inhibitor (TKI)- related survival-gain value retained by patients and drug companies.

 This retrospective study identified CML patient data from the Surveillance, Epidemiology and End Results registry, dasatinib clinical trials, and insurance claims data sets.

 Multivariate Cox proportional hazard models were used to estimate improvements in CML survival associated with the introduction of first-line imatinib therapy. Survival gains associated with second-line dasatinib treatment were identified via retrospective analyses and published clinical outcomes. An economic model was developed to calculate the social value of survival gains derived from first- and second-line TKI treatment. TKI costs were used to estimate the fraction of survival gain value retained by patients and drug companies.

The introduction of TKIs in 2001 was associated with a hazard ratio of 0.833 (P <.01). Cost analyses indicate that the TKI drug class in CML therapy has created more than $143 billion in social value. Approximately 90% of this value is retained by patients and society, while approximately 10% is recouped by drug companies.

These estimates indicate that the introduction of TKI drugs to treat CML has generated significant social value as a result of survival gains, the vast majority of which has accrued to patients.

Cancer chemotherapy has long relied on generalized and nonspecific treatments that often involve substantial patient toxicity. One of the major chemotherapeutic advances over the past decade has been the discovery and deployment of targeted therapies directed at cancer-specific molecules and signaling pathways. In some cases this treatment specificity has proved very effective, appreciably improving survival rates for certain cancers.

Considered broadly, there have been substantial increases in cancer survival rates over the past several decades. The 5-year relative survival rate for all cancers diagnosed between 1996 and 2003 was 66%, up from 50% in the mid-1970s.

 Improvements in survival have been particularly dramatic for breast cancer, colon cancer, and non-Hodgkin lymphoma—conditions for which there have been important advances in therapy, screening, or both.

 It has, however, also been argued that some new cancer therapies only marginally extend life, and at a high cost.

 Hence, the cost-effectiveness of expensive new cancer therapies has been called into question, with recommendations made for more comprehensive economic evaluations, as well as innovative approaches that consider the unique economic impact of cancer treatments.

 The results of the recommended comprehensive economic analyses have important implications for the assessment of managed care, as they promote a better understanding of the total value of medical technologies.

The study described in this article examines the value of survival gains in chronic myeloid leukemia (CML) achieved by treatment with a new class of targeted therapy, tyrosine kinase inhibitors (TKIs), in first- and second-line therapy. In one multicenter, international, open-label, phase 3 randomized study, investigators compared the TKI imatinib with interferon as first-line treatment for CML, and demonstrated an 18-month survival rate of 97.2% for patients treated with imatinib.

 These results were durable; at 60-month follow-up, the overall survival (OS) rate for imatinib-treated patients was 89% (95% confidence interval, 86-92),

 and 7-year event-free survival and OS rates were 81% and 86%, respectively.

 Two newer TKIs, dasatinib and nilotinib, have also been approved for first-line CML treatment.

Studies of second-line TKI therapy in CML have also demonstrated efficacy. Patients receiving the newer TKIs (dasatinib and nilotinib) after imatinib failure experience significant cytogenetic response,

 and can expect substantial survival benefits. Indeed, in a randomized, international, multicenter, open-label phase 3 trial of dasatinib as second-line treatment following imatinib failure or intolerance, the overall 60-month survival rate was 78%.

Based on these clinical trial data, survival gains for CML patients treated with TKIs appear to be substantial. However, survival gains experienced in the community have not been studied, and the overall value of these survival gains—both in aggregate and relative to the cost of treatment&mdash;is unknown. In addition, the value of survival gains subsequently returned to patients and society has not been adequately evaluated. Prior research by Sun and colleagues

 revealed that, between 1988 and 2000, a substantial majority (~80%) of US cancerrelated survival gains were due to treatment improvements (as opposed to cancer detection strategies). Subsequently, Lakdawalla and colleagues

 evaluated, from a societal perspective, the economic gains due to cancer treatment associated with these improved overall survival rates; the study used an economic model developed by Becker et al,

 which was designed to assess patients’ willingness to pay for improved survival rates achieved by cancer treatment. Predicated on this model, the estimated average increase in life expectancy of 4 years between 1988 and 2000 was associated with 23 million life-years and $1.9 trillion gained in social value. The model also demonstrated that drug companies acquired 5% to 19% of this total value as revenue.

In this article we apply a similar approach to estimate the social value of TKIs in the treatment of CML. Observational data and clinical trial results were used to assess survival gains stemming from the introduction of both first- and second-line TKI therapies. Subsequently, an economic framework was employed to calculate the social value of improvements in survival gains due to TKI treatment. Lastly, to estimate the fraction of TKI treatment value retained by patients and society, as well as the proportion recouped by drug companies over time, the cost of TKI treatment was estimated from lifetime individual and population-level perspectives.

Multivariate Cox proportional hazard models were used to estimate survival improvements in CML associated with the 2001 introduction of first-line imatinib therapy. CML patient survival data were obtained from the Surveillance, Epidemiology, and End Results (SEER) registry. The SEER registry has tracked patients diagnosed with CML from 1973 to 2006 (the year of the most recent registry update). The key advantages of the SEER registry are that it is the only national cancer data base that: 1) follows patients over time in order to track survival, and 2) is large enough to contain sufficient sample sizes for survival analysis.

The sample for the survival analysis was restricted to CML patients diagnosed in 2000 or later. This ensured that the study captured the survival impact of the 2001 introduction of imatinib on the incident patient cohort, whose response to TKI therapy best represents the first-line survival effect of TKIs on present and future CML patients. Using multivariate hazard analysis, a rich set of demographic controls was applied, including age and squared age at diagnosis, gender, marital status, and separate indicator variables for white, black, Hispanic, Asian/Pacific Islander, and other races. To account for secular trends in CML survival unrelated to the introduction of imatinib, flexible controls for year and month of diagnosis were also included.

The key variable of interest in the proportional hazard model was the post-TKI indicator variable, which took the value of 1 in all years following 2000 and corresponded to the 2001 introduction of imatinib for first-line CML treatment. We report the estimated hazard ratio (HR) associated with the post-TKI variable, as well as the implied survival rate ratio.

The timing of second-line TKI approvals (2006 US Food and Drug Administration [FDA] approval for dasatinib; 2007 for nilotinib) precluded retrospective survival analysis of these drugs using the SEER registry. We estimated the community survival effect of new TKIs in second-line therapy by interpolation. First, we calculated the clinical survival effect of second-line relative to first-line treatment, which we obtained by dividing the 60-month OS rate in the second-line dasatinib clinical trial by the 60-month OS rate in the imatinib first-line clinical trial. We then applied this relative effectiveness rate of second-line therapy to the community-based survival effects estimated for imatinib in the retrospective SEER-based survival analysis. In performing this interpolation we assumed that the proportional difference in clinical and community survival rates for imatinib was the same as the proportional difference in clinical and community survival rates for dasatinib. Among newer TKIs we focused on dasatinib for this component of the analysis given the availability of recent 60-month survival results,

 which are not yet available for nilotinib.

The survival effects of imatinib were calculated as the average across all CML patients, regardless of whether they received imatinib; this was interpreted as the “intent-totreat” (ITT) effect. To estimate imatinib’s “treatment-onthe- treated” (TOT) effect, the ITT effect was scaled based on the percentage of CML patients who received treatment. The fraction of CML patients who received TKI treatment following its introduction was estimated using Ingenix Touchstone data,

 a database of private-sector healthcare claims, including prescription drug claims and inpatient emergency ambulatory medical claims from approximately 35 Fortune 500 firms. Individuals with CML were identified based on the presence of 2 or more International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) diagnoses (primary, secondary, or tertiary) with medical claims codes of 205.1x, or 1 ICD-9-CM code and TKI therapy use after diagnosis. Based on this data set, between 2001 and 2002, 40% of CML patients received treatment with imatinib; therefore, the TOT effect was calculated as the ITT survival effect divided by 0.4.

The economic model developed by Becker et al

 was employed to calculate the social value of survival gains due to first- and second-line TKI therapy. Clinical evidence suggests that, relative to TKI therapy, prevailing treatments are associated with relatively rapid mortality in CML, not slight increments in mortality risk. This economic model calculates patients’ willingness to pay (ie, value to patients) for longevity gains, accounting for discrete (rather than marginal) increases in survival probabilities. The technical details regarding the application of this methodological approach are presented in 

In keeping with this methodology, the benefit derived by CML patients was calculated based upon survival gains pertaining to TKI treatment and the average income level among CML patients. In particular, patients’ lifetime value was estimated using income data from the 2005 and 2006 Medical Expenditure Panels Study (MEPS). Due to the small number of CML patients in the MEPS, all MEPS cancer patients were used to estimate CML patient income. The estimated value was then aggregated across all CML cohorts to obtain the total social value of survival gains due to TKI treatment.

The Ingenix Touchstone Data database provided estimates of annual imatinib treatment costs, as well as information on drugs utilized, total drug costs, and patient costsharing information. Because claims-based costs for dasatinib were not available, these costs were estimated via imputation by applying the proportional difference between the manufacturers’ prices of imatinib and dasatinib to the claims-based cost estimates.

As with the lifetime value of TKI treatment, lifetime treatment cost was determined by calculating the present value of annual imatinib treatment costs given the life expectancy implied by the post-TKI survival curve. The total value of TKI therapy was estimated by summing lifetime costs for a CML patient across all CML patients in a cohort, and then summing present discounted lifetime costs across all present and future cohorts. This took into account the decreased costs associated with the patent expiration of imatinib (2015) and dasatinib (2020). It was assumed that current lifetime and total cohort costs prevailed until patent expiration, after which costs were assumed to be zero. In addition, MEPS and SEER data were used to estimate the size of each CML cohort (4,500), of which 30% were expected to be resistant or intolerant to imatinib based on prior evidence in the literature.

 shows the patient characteristics of all CML patients in the SEER registry and the SEER CML sample. Incident cohorts of CML patients diagnosed in 2000 or later were included in survival-gain estimations associated with the introduction of first-line imatinib treatment. Restricting the data to SEER CML registrants for whom key demographic controls were reported generated a final analytical sample of 5,523 patients. Those in the CML subsample were demographically similar to patients from the full SEER CML sample, with the exception of 2 variables; namely, survival time (which was expected given the greater probability of survival among CML patients diagnosed at later dates) and Hispanic race (which likely resulted from a broader definition of “Hispanic” in more recently diagnosed patients).

 shows the results from the multivariate Cox proportional hazard analysis of survival impact following the 2001 introduction of first-line TKI therapy. The independent variable of interest is the post-TKI variable, which takes the value of 1 in 2001 and thereafter. The coefficient of this variable can be interpreted as the HR corresponding to the introduction of imatinib. Table 2 also depicts the most flexible form of the secular effects of time trend and time since diagnosis; year-of-diagnosis indicators and quadratic trend for month-of-diagnosis were included as controls for the secular trend in survival. As shown, the post-TKI variable was associated with an HR of 0.833 (P <.01). The implied TOT survival ratio of 1.307 can be interpreted as imatinib causing an immediate ~30% increase in the probability of CML survival at 90 months. This effect can be seen in the 

, which shows the estimated pre-TKI and post-TKI survival curves for the treated patients. The survival ratio is equivalent to the vertical distance between curves at 90 months. Alternatively, the HR can be characterized by the median survival length (the life expectancy of a 50th percentile CML patient; ie, the horizontal distance between the curves). The HR of 0.833 corresponds to an estimated increase in median survival from 90 to 210 months.

J. Ross Maclean, MD

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