Generalized Cost-Effectiveness Analysis with Dynamic Prevalence for Improving Health Equity Among Rare Diseases
Author(s)
William V. Padula, PhD1, Vasco M. Pontinha, MA, MSc, PhD2;
1University of Southern California, Assistant Professor, Rancho Palos Verdes, CA, USA, 2Virginia Commonwealth University, Richmond, VA, USA
1University of Southern California, Assistant Professor, Rancho Palos Verdes, CA, USA, 2Virginia Commonwealth University, Richmond, VA, USA
OBJECTIVES: Generalized risk-adjusted cost-effectiveness (GRACE) theory links relative risk (r) in health to willingness-to-pay (WTP, or “K”) scaled by gross domestic product (M). GRACE addresses cost-effectiveness biases against patients with disabilities, among other health equity concerns. Disease rarity further exacerbates inequitable patient access, biasing resource allocation to larger subgroups under fixed WTP: K=M/[1-r2]. Our objective is to establish a relationship between WTP in GRACE theory to relative risk using epidemiologic foundations to draw a correlation between WTP and disease prevalence.
METHODS: Using GRACE theory, we derived a mathematical relationship between WTP and relative risk as a function of dynamic prevalence of disease over time: K(t)=M/[1-(prevalencet2)2]. Given Expected Utility Theory, WTP increases exponentially as diseases become rarer based on standardized prevalence factors using Box-Cox transformation of prevalence. We linked U.S. Medical Expenditures Panel Survey (MEPS) data with U.S. Centers for Disease Control and Prevention (CDC) disease prevalences to linearly transform standardized prevalence factors. We calibrated WTP as a function of disease prevalence to two points: ($0/QALY, 100% prevalence); and ($104,000/QALY, 33% prevalence), according to Vanness et al. estimation of U.S. WTP based on opportunity costs of average care for Medicare patients’ statin use for hypercholesterolemia. Finally, evaluated the relationship between disease prevalence and WTP for two important rare diseases: Sickle Cell Disease and Duchenne Muscular Dystrophy.
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.
CONCLUSIONS: This novel approach referred to as “Generalized Dynamic Prevalence” offers a solution to empirical incorporation of the rarity of disease with dynamic prevalence in GRACE analysis, and may help inform priorities for resource allocation across rare disease cohorts.
METHODS: Using GRACE theory, we derived a mathematical relationship between WTP and relative risk as a function of dynamic prevalence of disease over time: K(t)=M/[1-(prevalencet2)2]. Given Expected Utility Theory, WTP increases exponentially as diseases become rarer based on standardized prevalence factors using Box-Cox transformation of prevalence. We linked U.S. Medical Expenditures Panel Survey (MEPS) data with U.S. Centers for Disease Control and Prevention (CDC) disease prevalences to linearly transform standardized prevalence factors. We calibrated WTP as a function of disease prevalence to two points: ($0/QALY, 100% prevalence); and ($104,000/QALY, 33% prevalence), according to Vanness et al. estimation of U.S. WTP based on opportunity costs of average care for Medicare patients’ statin use for hypercholesterolemia. Finally, evaluated the relationship between disease prevalence and WTP for two important rare diseases: Sickle Cell Disease and Duchenne Muscular Dystrophy.
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.
CONCLUSIONS: This novel approach referred to as “Generalized Dynamic Prevalence” offers a solution to empirical incorporation of the rarity of disease with dynamic prevalence in GRACE analysis, and may help inform priorities for resource allocation across rare disease cohorts.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
EE521
Topic
Economic Evaluation
Topic Subcategory
Novel & Social Elements of Value, Thresholds & Opportunity Cost
Disease
SDC: Oncology, SDC: Rare & Orphan Diseases, STA: Genetic, Regenerative & Curative Therapies, STA: Multiple/Other Specialized Treatments, STA: Personalized & Precision Medicine