Reconsidering the Economic Value of Multiple Sclerosis Therapies

ABSTRACT

Objectives: To illustrate a more comprehensive view of value associated with medicines treating a highly severe illness and to apply these insights to estimate the costs and benefits of 3 treatments for multiple sclerosis (MS): Avonex, Tysabri, and Tecfidera.

Study Design: Retrospective study spanning 2002 to 2013. We used economic theory to derive the value of therapy to patients with MS and to individuals who face the risk of contracting MS in the future, under the alternative assumptions that therapies were fully insured or paid for out of pocket.

Methods: Models were parameterized through secondary data analysis and targeted literature review. Estimates of individual value were aggregated to the societal level using therapy-specific treatment prevalence rates. Aggregate consumer value was compared with manufacturer revenue.

Results: In the baseline model, Avonex, Tysabri, and Tecfidera generated $46.2 billion of total value to consumers, almost one-third of which accrued to those without MS. The total value to consumers was double manufacturer revenue. Results were qualitatively robust to the use of alternate epidemiological and economic parameters. We found that value to the healthy is positively related to disease severity, and that value to both the sick and the healthy are larger when costs are shared via health insurance.

Conclusions: Theory predicts that treatments for severe disease provide “peace of mind” value to the healthy. Avonex, Tysabri, and Tecfidera have generated significant social value, a large majority of which accrues to consumers. Future economic valuations of medical technology should consider both the potential value to the healthy and the effects of insurance.

Am J Manag Care. 2016;22(11):e368-e374

Take-Away Points

Although many studies have assessed the social value of medical care to the sick, the value to the healthy who may use treatment if they become sick has been largely ignored. We used empirical estimations to parameterize an economic model that describes the value of 3 multiple sclerosis treatments to those who are healthy but face the risk of contracting MS in the future, as well as to the sick.

• When patients bear the full cost of treatment, the value of the 3 treatments to the sick totals $11.1 billion, while the value to the healthy is $8.9 billion.
• The value of therapy increases with the severity of the disease being treated.
• Insurance coverage has a complementary effect on the value of therapy: the total populationwide value of the 3 treatments increases to $46.2 billion when actuarially fair insurance is assumed.

The rising cost of healthcare in recent decades has been accompanied by an increasing interest in quantifying the value of medicine.^1,2 The cost of healthcare—unlike the costs of other goods—is often borne primarily by healthy consumers who are not currently using it. For example, premiums for private health insurance and taxes for public health insurance schemes are paid by the entire population, not just the patients who happen to be sick at a point in time. This raises a key question: What is the value of healthcare to the sick and to the healthy consumers who are paying for it?

The value of medical care to the sick is readily apparent, whereas the value to the healthy manifests in at least 2 ways. First, healthy individuals value medical technology because it will be available to them if they become sick in the future. The higher the likelihood of becoming sick with a particular disease, the more a healthy person values treatments for that disease. Second, new technology can provide peace of mind in the present, even to those who may never end up using it. The worse the disease, the more a healthy person values this peace of mind.^3-5

To illustrate the “peace of mind” value, consider the analogy of a fire extinguisher in one’s home: it provides value should the house catch fire by reducing the damage the fire would cause, and this value increases with the size of the potential loss. Awareness of this potential benefit provides immediate and continuous peace of mind from the protection against fire damage. This value is realized even if a fire never breaks out. Moreover, this peace of mind value increases with the size of the potential loss. A renter with few possessions may worry little about the risk of fire and thus derive few benefits from a fire extinguisher. In contrast, the owner of a home filled with priceless heirlooms might worry more and thus place higher value on the fire extinguisher. The “peace of mind” value is likely to be quantitatively meaningful, because evidence suggests that most consumers dislike risk and value reducing it.^6-10

Medical technology provides peace of mind similar to that in the fire extinguisher example. To illustrate, consider a healthy individual today and another in 1990 who are concerned about the prospect of a human immunodeficiency virus (HIV) infection. Both would experience anxiety, but the first individual would be anxious about the risk of complications and the inconvenience of a lifetime of medical treatment; the second individual would be anxious about death. The very real difference between these 2 levels of anxiety contributes to the value that healthy individuals have obtained from modern HIV and AIDS therapies. In other disease areas, the value of a new therapy to a healthy individual can be similarly characterized by decreased anxiety or fear of a diagnosis due to the therapy’s ability to reduce the harm from a disease. This example illustrates that the peace of mind afforded by new medical technologies will be especially valuable for treatments that mitigate the consequences of the most severe diseases.^4,5

Multiple sclerosis (MS) provides a useful case study of a severe disease, as it is the leading cause of nontraumatic neurologic disability among young adults.^11,12 In MS, the body’s immune system attacks the central nervous system, creating brain lesions. During relapses, symptoms dramatically worsen and the disease can transition into a stage of progressive disability. MS patients suffer from fatigue and pain, as well as mobility and sensory problems.^13-15 Peak onset occurs between the ages of 20 and 40, often affecting healthy individuals in their prime years of productivity. Thus, MS onset imposes high medical costs and has severe consequences for quality of life and productivity, such as lost income.^13-15

MS therapies also highlight the wider debate over the value of new medical technology. Some question the value of innovative drugs that help manage—but do not cure—debilitating and progressive diseases. Skepticism about the value of such drugs has been fueled by cost-effectiveness studies and a recent United Kingdom risk-sharing scheme.^16,17

Our study uses an economic model developed by Lakdawalla, Malani, and Reif (2015)⁴ to estimate the value of MS therapies to both healthy and sick individuals. We focus on 3 currently available MS therapies, incorporating their specific dates of introduction, magnitudes of health benefit, and prices: Avonex (interferon beta-1a intramuscular, introduced 1996), Tysabri (natalizumab, introduced 2004), and Tecfidera (dimethyl fumarate, introduced 2013).

METHODS

From an economic perspective, the value of a good is measured as the amount of other consumption that an individual is willing to sacrifice in exchange for it. These trade-offs are conventionally estimated in the framework of a “utility” model that explicitly estimates the value that consumers assign to different goods. A plethora of studies measure the value consumers assign to health relative to other goods,^18-23 and these measurements provide the empirical basis for utility models that estimate the trade-off between health improvements and other consumption. We followed this approach and estimated the value of the MS therapies of interest by constructing an economic model of the trade-off between consumption and health. Following the economic literature, we assumed that the quantity of consumption is determined by the income that remains after medical costs.

As we describe above, value may accrue not only to those suffering from an illness, but also to those who are currently healthy but still susceptible to future illness. We refer to these constructs as “value to the sick” and “value to the healthy.” Both of these depend, in turn, on how the costs of therapy are incurred. Unlike standard goods, a portion of healthcare is often paid for by nonusers, via insurance. Insurance may increase the value of therapy for both the sick (by replacing direct costs with less costly insurance premiums) and the healthy (by reducing financial risk). Our study thus estimates the value of MS therapy from 4 perspectives: value to the sick and value to the healthy, first under the assumption that costs are fully borne by consumers (without insurance) and then assuming actuarially fair insurance, in which therapy costs are distributed across the entire risk pool (with insurance).^2,8 These perspectives are summarized in Figure 1.

Prior efforts to estimate the value of new medical technologies have typically emphasized only 1 of these 4 perspectives: the value to the sick, without consideration of insurance.

The eAppendix (available at www.ajmc.com) formally describes the economic model developed by Lakdawalla, Malani, and Reif (2015),⁴ which we used to measure value from each of these perspectives (utility model—based willingness-to-pay estimation). The value to the sick depends on: 1) health benefits of the therapy for those who are diagnosed with MS, as measured by incremental quality-adjusted life-years (QALYs); 2) the health costs of MS, as measured by QALYs for individuals with and without MS; 3) the costs of therapy; 4) other medical costs with and without therapy; and 5) differences in consumer income, which determine the value of money to a consumer. We measured these both for MS patients receiving best supportive care (BSC) and for MS patients utilizing 1 of the 3 qualified drugs.

The value to the healthy depends on all 5 factors above, along with: 6) the incidence of MS, which measures the risk that healthy people will acquire the disease in any given year; and 7) the degree of consumer risk-aversion, which measures the value of risk-reduction to healthy consumers. In reality, some individuals are not materially at risk for MS, meaning that the population could consist of 3 groups: those with MS, healthy individuals at risk for MS, and healthy individuals not at risk for MS. This third group may never derive benefit from the actual use of the therapies. However, as the causes of MS are still not well understood,^24-26 healthy individuals cannot easily ascertain whether they fall into the second or third groups. Thus, for the purposes of our analysis, we pooled these groups together.

We used parameters in these 7 areas to construct separate economic utility models for each of the 4 perspectives described in Figure 1. Our models assume that the health state and drug utilization choice are constant for an individual within each year. These models are then used to estimate the annual value to consumers of using 1 of our 3 drugs of interest relative to BSC. The incremental value of the 3 drugs is given by the difference in value between using the drug and using BSC.

Finally, we aggregated the different estimates of incremental per-patient value to the societal level using disease prevalence and drug utilization rates. We added up the individual annual values of treatment over the period 2002 to 2013 (the years for which data on the therapies are available) to obtain the aggregate value of the 3 therapies. These aggregate values have been compared with manufacturer revenue to determine the share of value that returns to consumers. Complete details on economic model specification, parameterization, and sensitivity analyses are provided in the eAppendix.

RESULTS

Economic Model and Parameters

The Table summarizes the parameters obtained from our literature review and data analysis, which were used to calculate the social value of therapies. As detailed in the eAppendix, our analysis suggests that MS patients earn 37.1% less income than their non-MS counterparts; Avonex users earn 41.4% more income than their MS patient counterparts who are not using disease-modifying therapies (DMTs). Because of data limitations, we were unable to estimate income effects for Tysabri and Tecfidera directly. Instead, we assumed that the effects for those therapies were equal to that of Avonex. This conservative assumption likely understates the income effect of those therapies, because both of those products reduce relapse rates and disability progression more than Avonex does.

An MS diagnosis was also associated with a significant increase in non-DMT medical costs (87.0%), while the use of Avonex and Tysabri reduced annual medical costs by 11.2% and 16.4%, respectively. There were too few cases of Tecfidera usage in the claims data to identify an effect on medical costs (Tecfidera had a sample size of 137 compared with 9272 and 1223 for Avonex and Tysabri, respectively). As a result, we elected to use the Avonex cost offset parameter (—11.2%) for Tecfidera. This is a conservative approach, as Tecfidera was shown to reduce disability progression and relapse frequency more compared with interferons (including Avonex).²⁷ As a result of using this conservative estimate, our models likely underestimate the social value of Tecfidera. In the eAppendix, we describe a sensitivity analysis in which this value is set equal to the midpoint of the Avonex and Tysabri estimates (—13.8%).

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.

DISCUSSION

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.

Limitations

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.

CONCLUSIONS

This paper brings tools of economic analysis to bear on the question of value in healthcare. Our approach resolves 2 key omissions in prior valuations of MS therapies. First, this study quantified the role of insurance coverage in enhancing the value of therapy. Second, this study examined how MS therapies improve the outlook of those who face the risk of future MS onset, in addition to providing benefits to those who are already sick.⁴ We found that accounting for these 2 factors more accurately depicts the estimated overall value of the therapies considered here.

Acknowledgments

The authors thank Sarah Beers, Oliver Diaz, Melissa Frasco, Barney Hartman-Glaser, Sarah Green, and Anshu Shrestha for excellent research assistance and technical support. They also thank Dr Lawrence Steinman for clinical advice.

Author Affiliations: Precision Health Economics (TS, JS, AC, YL, JJS), Los Angeles, CA; Biogen (CW, DM), Cambridge, MA; Schaeffer Center for Health Policy and Economics, University of Southern California (DL), Los Angeles, CA.

Source of Funding: Biogen.

Author Disclosures: Drs Shih and Sussell, and Ms Liu, and Ms Shim are employees of Precision Health Economics (PHE), a consulting firm that received research funding from Biogen. Dr Chung was an employee of PHE at the time this research was performed. Dr Meletiche and Mr Wakeford are employees of Biogen, which funded this research project. Dr Lakdawalla is chief strategy officer and holds equity at PHE.

Authorship Information: Concept and design (TS, CW, DM, DL); acquisition of data (YL); analysis and interpretation of data (TS, CW, JS, YL, JJS, DL); drafting of the manuscript (TS, JS, AC); critical revision of the manuscript for important intellectual content (TS, CW, DM, JS, AC, YL, JJS, DL); statistical analysis (TS, JS, YL, JJS); obtaining funding (CW, DM); administrative, technical, or logistic support (AC); and supervision (TS, CW, DM, JS, DL).

Address Correspondence to: Jesse Sussell, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. E-mail: jesse.sussell@PHEconomics.com.

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