Implementing the Generalized Risk-Adjusted Cost-Effectiveness Model for Sickle Cell Disease: A Case Study

Abstract

Objectives

This study aims to quantify the societal value of curing sickle cell disease by implementing the generalized risk-adjusted cost-effectiveness (GRACE) method, thereby adjusting for factors such as disease severity, reduction of uncertainty in treatment outcomes, and trade-offs between quality of life (QoL) and life expectancy in a cost-effectiveness analysis (CEA).

Methods

Using GRACE, we recalibrated a recent health technology assessment in sickle cell disease for 2 gene therapies, lovo-cel and exa-cel. The GRACE framework modified existing CEA by adjusting willingness-to-pay thresholds based on untreated illness severity, incorporating treatment outcome uncertainty, and varying the substitution rates between life expectancy and QoL across health states.

Results

Implementing the GRACE framework resulted in a 6% reduction in both direct and societal incremental cost-effectiveness ratios for lovo-cel and exa-cel, demonstrating a decrease from $192 651 and $161 816 to $182 036 and $152 900 per quality-adjusted life year, respectively. Additionally, willingness-to-pay thresholds increased by approximately 50%, reflecting a higher valuation of treatments under GRACE. GRACE-adjusted estimates suggest that lovo-cel and exa-cel are cost-effective from both direct payer and societal perspectives.

Conclusions

The GRACE method offers a more comprehensive and precise estimation of societal value, leading to more efficient and equitable resource allocation. This study not only highlights the limitations of traditional CEA in capturing the total societal value of treatments for severe diseases but also provides a roadmap for incorporating GRACE model elements into health technology assessments, thereby facilitating a broader acceptance of innovative therapies that significantly enhance patient QoL.

Authors

Marlon Graf Joris Kleintjens Md Tahsin Hasan Natalie Land Jacquelyn W. Chou Karen Mulligan

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