Optimizing diagnostic algorithms to advance Hepatitis C elimination in Italy: a cost effectiveness evaluation

Objectives: Optimized diagnostic algorithms to detect active infections are crucial to achieving HCV elimination. We evaluated the cost effectiveness and sustainability of different algorithms for HCV active infection diagnosis, in a context of a high endemic country for HCV infection. Methods: A Markov disease progression model, simulating six diagnostic algorithms in the birth cohort 1969-1989 over a 10-year horizon from a healthcare perspective was used. Conventionally diagnosis of active HCV infection is through detection of antibodies (HCV-Ab) detection followed by HCV-RNA or HCV core antigen (HCV-Ag) confirmatory testing either on a second sample or by same sample reflex testing. The undiagnosed and unconfirmed rates were evaluated by assays false negative estimates and each algorithm patients' drop-off. Age, liver disease stages distribution, liver disease stage costs, treatment effectiveness and costs were used to evaluate the quality-adjusted life-years (QALYs) and the incremental cost-effectiveness ratios (ICER). Results: The reference option was Rapid HCV-Ab followed by second sample HCV-Ag testing which produced the lowest QALYs (866,835 QALYs). The highest gains in health (QALYs=974,458) was obtained by HCV-RNA reflex testing which produced a high cost-effective ICER (€891/QALY). Reflex testing (same sample-single visit) vs two patients' visits algorithms, yielded the highest QALYs and high cost-effective ICERs (€566 and €635/QALY for HCV-Ag and HCV-RNA, respectively), confirmed in 99.9% of the 5,000 probabilistic simulations. Conclusions: Our data confirm, by a cost effectiveness point of view, the EASL and WHO clinical practice guidelines recommending HCV reflex testing as most cost effective diagnostic option vs other diagnostic pathways.