Valuing gene therapies for orphan pediatric disease: An economics perspective

Rena Conti, Jonathan Gruber, Daniel Ollendorf, Peter Neumann 23 December 2020



Gene therapy aims to modify the underlying etiology of a disease by altering part of a patient’s genome. The first commercially available gene therapies, including voretigene neparvovec (Luxturna®, Spark Therapeutics) (US FDA 2017), were approved in 2017. As of writing, there are fewer than five available gene therapies, including onasemnogene abeparvovec-xioi (marketed as Zolgensma®) for spinal muscular atrophy, but there are hundreds more in development to treat cancers, neuromuscular disorders among other conditions (Quinn et al. 2019). While still in their infancy, gene therapies offer patients the promise of improved health, longevity, and hope.

The results of one recent simulation (Wong et al. 2020) suggest that 1.1 million patients are expected to be treated by gene therapy in the US between January 2020 and December 2034. The majority of these therapies will treat ‘orphan’ disease, i.e. diseases or sub-sets of common disease impacting relatively few patients, and at least a third of these therapies will treat children. They are also expected to come with very high price tags: Luxturna’s price is $425,000 per treated eye and Zolgensma’s price is $2.1 million per patient.  

How much money are we talking about in aggregate? According to Wong et al. (2020), the expected peak annual spending on these therapies is $25.3 billion, and the total spending from January 2020 to December 2034 will amount to $306 billion. While substantial in magnitude, the US health care system already absorbs such annual costs for the treatment of cancer (Howard et al. 2015) and neurological disorders.  

It might be tempting to think it will do the same for gene therapy. However, the nature of gene therapy spending is different from other examples: according to Wong et al. (2020) approximately half of expected spending (53%) will be by US payers facing fixed annual budgets, including employer-offered and state-sponsored plans. 

In a recent paper (Gruber et al. 2020), we argue that the infancy of the technology and its sizable impact on budget conscious payers raise fundamental questions regarding gene therapy’s value. Payers will rely on value assessments to decide whether to provide access to these technologies, and innovators will use such assessments to set prices. One independent body in the US conducting value assessments for payers, the Institute for Clinical and Economic Review (ICER), is already active in the evaluation of new gene therapies (ICER 2016, ICER 2019b).    

Problems in practice

In theory, the value of a new gene therapy is a readily measurable concept measured using standard methods of cost-effectiveness analysis (CEA). The value is the improvement it delivers to the quantity and quality of life for patients and the associated benefits to others relative to the next best alternative. 

In practice, measurement of the value of a new gene therapy applied to treat pediatric orphan disease faces a number of challenges (ICER 2016). Some of these are technical, as there is so much we don’t know about these therapies’ benefits in non-trial populations (ICER March 2019), as well as their durability and potential harms (ICER March 2018). These challenges are similar to those faced when analysts evaluate new therapies generally but may be particularly acute when assessing technologies that embody highly novel approaches to treating disease. 

The other set of challenges are related to quality adjusted life-years or ‘QALYs’. The use of the QALY in CEA is intended to bring a comparable numerical value to different states of health, with the extreme benchmarks being value of zero for dead and a value of one for good health. The total QALYs gained from an intervention relative to the alternative is a measure of therapeutic benefit to the individual. 

There are a set of issues raised by QALY measurement that are particularly difficult for therapies used to treat pediatric orphan disease. The first is common to all rare diseases and arises from inferring patient preferences for patients and their families based on community preferences from surveys of the general population on the value of health states. In theory, QALYs are meant to express the personal utility of health outcomes as judged ‘on average’ by the general public from behind a veil of ignorance about future health (Nord et al. 2009). This approach has the advantage of reflecting social views on the value of health improvement, however, it should not be expected to reflect the specific views and lived experiences of the impacted individuals living with rare disease. 

In addition, the benefits of treatment are attributable to the child and their family – so whose opinions should be valued in establishing QALY weights? Clearly, for diseases affecting very young children, it is impossible to obtain a valid self-reported measure of quality of life impacts. But just as clearly, when someone achieves a mature age, we would want to consider their own self-reported quality of life. 

Finally, it is common for analysts to add up QALYs and compare them across populations or therapies. Yet this practice is particularly problematic when applied to pediatric rare disease (Ollendorfet et al. 2018). It is inappropriate to sum QALYs if individuals value large changes in health improvements to one person (e.g. an extra 10 years of life) differently to small changes in health improvements to many persons (one extra month of life for 120 persons). It could be argued that the vulnerability and lack of effective medical treatments for pediatric orphan disease should tip the scales of weighting towards the former valuation.

Addressing these challenges 

Two important initiatives would help address these challenges. The first is to be more forward looking in our data collection on the consequences for well-being of disease and its treatment. This starts with a horizon-scanning exercise on a disease-by-disease basis to understand what is known and what is not about the elements of the valuation expression. This would be followed by data collection initiatives to fill those missing pieces. 

Some of this would require new survey and data collection from patients, since general population databases are unlikely to be very helpful when dealing with specific rare illnesses.  Samples can be readily identified by taking advantage of registries created by patient advocacy groups and hospitals. A complementary effort would involve taking better advantage of data that already exists in the health care ecosystem–such as insurance claims data.  

Second, we need to further invest in the science of drug evaluation. For example, the randomised controlled trial is the gold standard for evaluating therapeutic efficacy. But this is a taxing and unrealistic standard for rare treatments: RCTs are expensive to run, can be difficult to recruit for, have small samples, and are often ended abruptly for ethical reasons. We can improve on this model through the use of adaptive trial design, real world evidence, and other ‘silver standard’ data such as the those being collected by PCORI or piloted by the NEWDIGS initiative at MIT.1

More analytic work should focus on methods for assessing and weighting QALYs for pediatric disease across children and their families. More work should also systematically collect the personal utility associated with health outcomes among patients and their families living with orphan disease, and assess the use of these ex-post-derived QALYs in cost effectiveness analysis applied to orphan disease.

Finally, new methods are needed to address the distributional challenges in using QALYs to value treatments that aim to relieve the suffering of vulnerable populations including those living with orphan disease. Some have proposed using alternative thresholds (Garrison et al. 2019) for assessing cost effectiveness analysis of select therapeutic approaches in populations afflicted by rare disease, while ICER has introduced ‘contextual consideration assessments’ into their voting criteria. Both approaches are difficult to operationalise without a priori judgements about ‘worthy’ populations and neither has yet been empirically justified using the traditional extra-welfarist perspective grounding CEA.


Garrison LP, T Jackson , D Paul and M Kenston (2019), “Value-Based Pricing for Emerging Gene Therapies: The Economic Case for a Higher Cost-Effectiveness Threshold”, J Manag Care Spec Pharm 25(7):793-799. 

Gruber J, R M Conti, D Ollendorf and P Neumann (2020), “Valuing Rare Pediatric Drugs: An Economics Perspective”, NBER working paper 27978.

Howard D H, P B Bach, E R Berndt and R M Conti (2015), “Pricing in the market for anticancer drugs", Journal of Economic Perspectives 29 (1): 139–162.

Institute for Clinical and Economic Review (2016), “Gene Therapy: Understanding the Science, Assessing the Evidence, and Paying for Value”.

Institute for Clinical and Economic Review (2018), “Leukemia and Lymphoma: An assessment of CAR-T Therapies (Tisagenlecleucel and Axicabtagene Ciloleucel) for Leukemia and Lymphoma”.

Institute for Clinical and Economic Review (2019a), “Methods Update: Valuing a cure”.

Institute for Clinical and Economic Review (2019b), Institute for Clinical and Economic Review (ICER), “Spinal Muscular Atrophy: An assessment of Onasemnogene Abeparvovec and Nusinersen for Spinal Muscular Atrophy (SMA)”. 

Nord E, N Daniels and M Kamlet (2009), “QALYs: Some Challenges”, Value in Health 12(1). 

Quinn C, C Young, J Thomas and M Trusheim (2019), “Estimating the Clinical Pipeline of Cell and Gene Therapies and Their Potential Economic Impact on the US Healthcare System”, Value in Health 22(6): 621-626. 

United States Food and Drug Administration (2017), “FDA approves novel gene therapy to treat patients with a rare form of inherited vision loss”.

Wong CH, D Li, N Wang, J Gruber, R M Conti and A W Lo (2020), ‘Estimating the Financial Impact of Gene Therapy’, medRxiv 2020.  




Topics:  Health economics

Tags:  gene therapy, orphan disease, pediatric disease, quality adjusted life-years, QALY

Assistant Professor of Health Policy, University of Chicago

Ford Professor of Economics, MIT

Director of Value Measurement and Global Health Initiatives, Center for the Evaluation of Value and Risk in Health, Tufts University Medical Center

Director, Center for the Evaluation of Value and Risk, Tufts Medical Center; Professor of Medicine, Tufts University School of Medicine


CEPR Policy Research