(19) Estimating the Budget Impact of Prevalence-Adjusted Willingness to Pay Thresholds Using the Generalized Dynamic Prevalence (GDP) Theory

Background: : The Generalized Dynamic Prevalence (GDP) theory, proposed by Padula and Pontinha, offers a novel framework that links willingness to pay (WTP) thresholds with disease prevalence and relative risk, addressing inherent cost-effectiveness biases—particularly those disadvantaging individuals with rarer medical conditions. By incorporating time as a fixed effect, GDP enables modeling of prevalence dynamics influenced by natural disease progression or curative interventions such as gene therapies. Despite its conceptual utility, the real-world budget impact of applying GDP-informed, prevalence-adjusted WTP thresholds remains unclear.

Objective: : This study projects the 5-year budget impact of adopting varying WTP thresholds across seven medical conditions with diverse prevalence—ranging from hypercholesterolemia to Duchenne Muscular Dystrophy (DMD) —within a commercial health plan covering one million lives.

Methods: : GDP theory was applied using the mathematical relationship between willingness to pay (WTP), relative risk, and time-dependent disease prevalence: K(t) = M / [1 – (prevalenceₜ²)²]. Prevalence estimates for seven medical conditions were derived from the 2016–2022 Medical Expenditure Panel Survey (MEPS) and standardized using Box-Cox transformations to address skewness. WTP thresholds were calibrated as a function of standardized disease prevalence factors. A fixed annual reduction in prevalence was assumed over a 5-year time horizon to simulate curative or disease-modifying treatment effects. Each condition was linked to a representative reference technology (e.g., PCSK9 inhibitors for hypercholesterolemia, gene therapy for Duchenne Muscular Dystrophy). Budget impact was calculated from the perspective of a U.S. health plan covering one million lives and reported as per member per month (PMPM) costs for each condition.

Results: : U.S. WTP could vary between $104,000/QALY for common conditions and $990,049/QALY for rare conditions. For example: Sickle Cell Disease, with a prevalence of 0.03% and a standardized prevalence factor of 0.825, K=$326,233/QALY; Duchenne Muscular Dystrophy, with a prevalence of 0.015% and a standardized prevalence factor of 0.912, K=$621,247/QALY. PMPM budget impact was found to decrease with decreasing prevalence.

Conclusions: : GDP offers a solution to incorporating dynamic prevalence in estimations of WTP thresholds and helping define resource allocation across rare disease cohorts for commercial payers. Additionally, GDP application could improve access to revolutionary health technologies for rare conditions with significant unmet medical needs.

Sponsorship:: None