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The American Journal of Managed Care November 2016
Referrals and the PCMH: How Well Do We Know Our Neighborhood?
Andrew Schreiner, MD; Patrick Mauldin, PhD, Jingwen Zhang, MS; Justin Marsden, BS; and William Moran, MD, MS
Does Medicare Advantage Enrollment Affect Home Healthcare Use?
Daniel A. Waxman, MD, PhD; Lillian Min, MD, MSHS; Claude M. Setodji, PhD; Mark Hanson, PhD; Neil S. Wenger, MD, MPH; and David A. Ganz, MD, PhD
A New Chapter in Health Reform
Michael E. Chernew, PhD, and A. Mark Fendrick, MD
Prescribers' Perceptions of Medication Discontinuation: Survey Instrument Development and Validation
Amy Linsky, MD, MSc; Steven R. Simon, MD, MPH; Kelly Stolzmann, MS; Barbara G. Bokhour, PhD; and Mark Meterko, PhD
Enhancing Patient and Family Engagement Through Meaningful Use Stage 3: Opportunities and Barriers to Implementation
Jaclyn Rappaport, MPP, MBA; Sara Galantowicz, MPH; Andrea Hassol, MSPH; Anisha Illa, BS; Sid Thornton, PhD; Shan He, PhD; Jean Adams, RN, ACIO; and Charlie Sawyer, MD, FACP
Integrated Care Organizations: Medicare Financing for Care at Home
Karen Davis, PhD; Amber Willink, PhD; and Cathy Schoen, MS
Currently Reading
Reconsidering the Economic Value of Multiple Sclerosis Therapies
Tiffany Shih, PhD; Craig Wakeford, MA; Dennis Meletiche, PharmD; Jesse Sussell, PhD; Adrienne Chung, PhD; Yanmei Liu, MS; Jin Joo Shim, MS; and Darius Lakdawalla, PhD
Maternal Mental Health and Infant Mortality for Healthy-Weight Infants
Susan E. White, PhD, RHIA, CHDA, and Robert W. Gladden, MA, BS
The Role of Internal Medicine Subspecialists in Patient Care Management
Jonathan L. Vandergrift, MS; Bradley M. Gray, PhD; James D. Reschovsky, PhD; Eric S. Holmboe, MD; and Rebecca S. Lipner, PhD
Medical Home Transformation and Breast Cancer Screening
Amy W. Baughman, MD, MPH; Phyllis Brawarsky, MPH; Tracy Onega, PhD, MS; Tor D. Tosteson, ScD; Qianfei Wang, MS; Anna N.A. Tosteson, ScD; and Jennifer S. Haas, MD, MSc

Reconsidering the Economic Value of Multiple Sclerosis Therapies

Tiffany Shih, PhD; Craig Wakeford, MA; Dennis Meletiche, PharmD; Jesse Sussell, PhD; Adrienne Chung, PhD; Yanmei Liu, MS; Jin Joo Shim, MS; and Darius Lakdawalla, PhD
Availability of multiple sclerosis (MS) therapies provides substantial value to the currently healthy (who may contract MS in the future), particularly when treatment is fully covered by insurance.
To complete the economic model, we used established estimates from the literature for the health and risk-aversion parameters. The Table summarizes the baseline and sensitivity values used for the MS epidemiological parameters,28,29 the QALY impacts of therapy,16,30,31 and the economic parameters for the value of health.6,32 In addition, the Table displays values used in sensitivity analyses that account for insurance loading and, separately, the cost of drug development borne by manufacturers.33-35

Estimates of the Value of MS Therapies

Figure 2 provides baseline estimates of value for all 3 drugs combined, aggregated over all years from 2002 through 2013. Aggregate value to the sick, when bearing the full cost of therapy, is estimated to be $11.1 billion. When actuarially fair insurance is available—so that the healthy and sick share the cost of treatment—value to the sick almost triples, to $31.8 billion. Conversely, value to the healthy without insurance is estimated to be $8.9 billion. When insurance is available, value to the healthy rises to $14.4 billion. This increase demonstrates the value of financial risk reduction that is obtained with insurance coverage.

Based on an 80% national average insurance coverage rate, the total value of the 3 therapies is estimated to be $40.9 billion.36 Overall, these results suggest that estimates of the value of medical technologies which ignore either the benefits that accrue to the healthy or the role of health insurance may be biased downward—perhaps severely so.

Impact of Disease Severity on Value to an Individual Insurance Enrollee

Conceptually, the value to the healthy should be higher when considering treatments for more severe diseases. For instance, an effective new treatment for a highly fatal disease provides significantly more peace of mind to the healthy than one for a mild skin condition. Our analysis confirms this intuition by re-estimating the value of 1 therapy (Tysabri) to a healthy individual with insurance, while incrementally varying the assumed severity of MS, holding other factors (including the absolute treatment effect) constant. The results are depicted in Figure 3.

If MS was not a severe disease, the value of Tysabri to a healthy individual would be small. This is evident on the left side of Figure 3: as the assumed QALY of untreated MS approaches 1, the value of treatment to a healthy individual approaches 0. However, MS is a debilitating disease, with an estimated untreated QALY value of 0.584 for those patients who might be treated by Tysabri.30 At this level, our model estimates the monthly value of Tysabri to a healthy individual to be $6.26. By contrast, we calculate that the actuarially fair per-member-per-month cost of insurance coverage of Tysabri is an order of magnitude smaller—about $0.48. This suggests that individual insurance enrollees gain more value from access to coverage than they lose due to the associated incremental insurance premium.

Significantly, the value of the treatment varies with disease severity, even when clinical effectiveness is held constant. Intuitively, a given improvement in clinical status is worth more to a patient suffering from a more severe disease. Therefore, singular focus on efficacy and/or effectiveness may ignore an important additional determinant of value.

Distribution of Surplus

Figure 4 portrays the relative share of lifetime value accruing to all consumers (both healthy and sick) and manufacturers, aggregated across the 3 therapies. When no insurance is available, an estimated 49% of value accrues to consumers ($20.0 billion—the “population-wide value” previously described), and 51% accrues to manufacturers as revenues ($21.2 billion). Because most individuals in the United States had health insurance during the time period of the study,37 the values under full insurance are empirically relevant. When full insurance is assumed, the share of value accruing to consumers rises to 69% ($46.2 billion), with the remaining 31% accruing to manufacturers. We conservatively assumed that all revenues ($21.2 billion) accrue to the manufacturer as profits. In reality, costs are not 0, and as a result, the true share of value accruing to consumers will be larger than what we have estimated here. In the sensitivity analyses, we provide a revised estimate of the distribution of surplus that incorporates estimates of the opportunity cost of research and development.

Sensitivity Analyses

Our model relies on both epidemiological (eg, incidence rate) and economic (eg, risk aversion) inputs, obtained from the literature and from our original data analysis. Varying these inputs moderately alters the results presented above. For example, assuming the availability of health insurance, estimates of the share of overall value accruing to consumers range from 59% (when the relative value of health is reduced) to 75% (when the treatment prevalence rate is reduced). When using the lower bound for risk aversion (0.15), the share to consumers (assuming insurance coverage) is 62%. These results are presented in the eAppendix (exhibits A12 and A13).

In addition to relying on parameters retrieved from the literature, our model takes as inputs parameters obtained via novel data analysis—specifically the income and medical cost effects of MS (relative to no MS) and of therapy (conditional on MS). These parameters have associated error distributions, and we accounted for these distributions using bootstrap methods. We created 1000 weighted bootstrap samples from the Medical Expenditure Panel Survey and claims datasets and estimated the parameter of interest (population-wide value—the sum of aggregate value to the sick and aggregate value to the healthy) from each set of regression results. The results are qualitatively robust to the introduction of these error distributions: 95% of the resampled estimates show more aggregate value accruing to consumers than to the manufacturer. The distribution of bootstrapped estimates is presented in the eAppendix (exhibit A14).

At baseline, our model assumes that actuarially fair insurance is available; however, in reality, insurance always involves some loading cost to cover administrative overheads.38 We therefore conducted a sensitivity analysis using an administrative load parameter of 16% (the median of the values reported by Karaca-Mandic et al [2011]).35 Finally, our baseline estimates of manufacturer surplus do not take into account the costs of drug development, and therefore overestimate the percent of surplus accruing to the manufacturer. We calculate the annualized costs of new drug development, based on recent work by DiMasi et al (2016)34 and recalculate the distribution of surplus. When subtracting these costs from manufacturer surplus, the consumer share of surplus increases from 49% to 51% and from 69% to 71% in the cases without and with insurance, respectively.


Severe diseases like MS reduce the health of the sick and inspire fear among the healthy who may be susceptible. Thus, it is important to understand the value that treating such diseases produces for each group. Although some recent economic research has described and estimated this “peace of mind” value to the healthy,4 the concept has not yet been widely presented to the payer or health policy communities. The importance of insurance coverage in expanding the value of medical technology has been similarly neglected.

Our study demonstrates the empirical relevance of value to the healthy in the case of 1 severe illness—MS. When consumers are covered under actuarially fair health insurance, we estimate the aggregate value to the sick of the 3 therapies for MS to be $31.8 billion. Adding value to the healthy (with insurance) leads to a $46.2 billion estimate of population-wide value. The healthy therefore accrue 31.1% of the total consumer value from the 3 therapies. In this scenario, consumers derive 69% of the total value generated by the technology, while the manufacturer retains 31%.

The results of this study also illustrate the unique and complementary relationship between health insurance and medical technology. More generous insurance boosts the value of medical technology, and helps society extract greater value from new innovations. For sick patients, the introduction of actuarially fair health insurance increases the value of therapy to $31.8 billion compared with $11.1 billion when patients bear the full cost of treatment.

Note that the size of the additional value provided by insurance coverage varies depending on the efficiency of insurance. Our baseline model assumes that insurance allocates treatments efficiently. If, on the other hand, insurance leads to overuse or underuse of therapies, then the value of insurance would be lower. By similar logic, if there are other inefficiencies in the market apart from insurance (eg, agency problems that result in physicians failing to maximize the well-being of patients), the value of medical technology would fall in both the insured and uninsured cases. These points represent the more general observation that the value of medical technology is intimately linked to the efficiency of the institutions allocating it to patients.

Our estimates of consumer value and the consumer share of value are conservative in that they do not incorporate all sources of consumer value (eg, alleviated caregiver burden), nor do they consider manufacturer costs of production. Regardless, other severe diseases may display similar patterns, and this analysis may inform value assessments for technologies that treat them.

At the same time, some other severe diseases might also feature known risk factors— asbestosis is an extreme example, which occurs only for individuals occupationally or environmentally exposed to asbestos. In such cases, the healthy can be clearly divided into populations at risk, and populations not at risk. The “at-risk” group derives insurance value, while the “not-at-risk” group cross-subsidizes the value enjoyed by both the sick and the at-risk healthy. This pattern is worth exploring in future research.


This study has several important limitations. First, it emphasizes 3 therapies for the treatment of MS (Avonex, Tysabri, and Tecfidera); the generalizability of our results to other MS treatments or to other disease areas is not yet clear. Second, although efforts were taken to minimize bias, the estimated cost and income effects were obtained through observational data analysis; if bias persisted in these estimates, it would extend to the main study findings as well. Third, owing to small sample sizes, we were unable to directly estimate the cost offset and income effects for Tecfidera or the income effects for Tysabri; we conservatively assumed these to be equal to the Avonex effects. Finally, the estimated QALY benefits of the 3 therapies were obtained from 3 different sources, rather than from a single head-to-head analysis.


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