Objective: Several risk prediction algorithms have been developed to guide antiviral therapy initiation among patients with chronic hepatitis B (CHB). This study assessed the cost-effectiveness and budget impact of three risk prediction algorithms among patients with CHB in Thailand.
Methods: A decision tree with a Markov model was constructed. Three risk prediction algorithms were compared with current practices including HePAA, TREAT-B and REACH-B. PubMed was searched from its inception to December 2022 to identify inputs. Tenofovir alafenamide and best supportive care were selected for antiviral-eligible patients, and incremental cost-effectiveness ratios per quality-adjusted life year (QALY) were calculated.
Results: Our base case analysis showed that HePAA and REACH-B could provide better QALY (0.098 for HePAA and 0.921 for REACH-B) with decreased total healthcare costs (-10909 THB for HePAA and -8,637 THB for REACH-B). TREAT-B provided worse QALY (-0.144) with increased total healthcare costs (10,435 THB). The budget impacts for HePAA and REACH-B were 387 million THB and 3,653 million THB, respectively.
Conclusion: HePAA and REACH-B algorithms are cost-effective in guiding antiviral therapy initiation. REACH-B is the most cost-effective option, but has a high budget impact. Policymakers should consider both cost-effectiveness and budget impact findings when deciding which algorithm should be implemented.
Keywords: Budget impact analysis; chronic hepatitis b; economic evaluation; hepatocellular carcinoma; risk prediction algorithms.