Cost-effectiveness of sofosbuvir- ledipasvir for Hepatitis C treatment: a systematic review.

INTRODUCTION: Hepatitis C virus (HCV) infection remains a major cause of liver-related morbidity and mortality, affecting about 50 million people worldwide. Given its global priority, high treatment costs, and equity concerns, evaluating the cost-effectiveness of HCV therapies is crucial for informing policy and resource allocation. This systematic review synthesizes the cost-effectiveness of sofosbuvir/ledipasvir (SOF/LDV) compared with alternative regimens in patients with HCV. METHODS: A systematic search was conducted across major databases and additional sources following PRISMA 2020 guidelines. Eligible studies were full economic evaluations comparing SOF/LDV with other treatments. Methodological quality was assessed using the Quality of Health Economic Studies (QHES) checklist. Incremental cost-effectiveness ratios (ICERs) were adjusted to 2024 U.S. dollars, and findings were synthesized qualitatively. RESULTS: Of 864 retrieved records, 15 met inclusion criteria, with a mean QHES score of 88.6, reflecting high methodological quality. Seven studies identified SOF/LDV as cost-effective or dominant across various healthcare systems, perspectives, and time horizons, particularly for genotype 1 and early stages liver disease. However, results varied by genotype, fibrosis stage, comparator, and national healthcare settings. Sensitivity analyses showed that ICERs were mainly influenced by patient age, utility values, treatment costs, dosage, treatment duration, discount rate, sustained virologic response (SVR) rate, fibrosis progression probabilities, and mortality rates. CONCLUSION: SOF-based regimens play a pivotal role in HCV management. However, contextual factors—such as healthcare infrastructure, willingness-to-pay thresholds, and model assumptions— significantly influence cost-effectiveness. Future research should address geographic inequities, incorporate societal perspectives, reduce methodological heterogeneity, and integrate real-world data to enhance generalizability.