PATIENT PRIORITIZATION IN DISEASE-SPECIFIC-TREATMENT BUDGETS: THE CASE OF CHRONIC HEPATITIS C TREATMENT

Purpose: Payers face substantial affordability challenges when considering cost-effective but expensive treatments for common conditions. Given limited budgets, not all patients can receive treatment immediately. We formulate a framework to evaluate treatment prioritization policies, applying it to the example of hepatitis C treatment.

Method: We develop a multi-period treatment budget allocation model to evaluate the trade-offs of treatment prioritization guidelines including first-come first-served, priority to patients with most severe disease, priority to patients with most severe disease with age-stratification, priority to patients in order of the incremental cost effectiveness ratio (ICER) of treatment, and a priority sequence identified through optimization to maximize population lifetime discounted net monetary benefit (NMB). For the case of hepatitis C, we compare prioritization guidelines in terms of the number of individuals treated, the number of individuals with compensated cirrhosis, the number of individuals who progress to end-stage liver-disease (ESLD), population total quality-adjusted life-years (QALYs), and NMB.

Result: First-come first-served treats more people at lower near-term risk of disease progression or complications. When age-stratification is included, priority to younger patients (compared to older patients with the same disease severity) results in fewer cases of disease progression and/or disease-related complications because of higher competing mortality risks faced by older patients.  A guideline developed from maximizing the population lifetime discounted NMB in a multi-period framework explicitly accounts for the trade-offs in the timing of prioritizing subgroups including the consequence of requiring other specific subgroups to wait longer for treatment and the expected QALYs lost from potential disease progression. In contrast, prioritization based on ICER does not incorporate the relative consequences of waiting across subgroups. In the case of hepatitis C treatment prioritization, the optimization strategy yields the greatest population QALYs and NMB, though prioritization by disease severity prevents more cases of ESLD.

Conclusion: Explicit prioritization can improve population health outcomes. Differences in outcomes between prioritization guidelines increase when the available budget is smaller. Prioritizing on ICER does not necessarily maximize QALYs over multiple cohorts because its allocation of treatment resources to one group does not account for the optimal timing of resources for other groups. Determining the optimal prioritization guideline is important in terms of care delivery and for evaluating alternative prioritization guidelines.