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 (euro891/QALY). Reflex testing (same sample-single visit) vs two patients' visits algorithms, yielded the highest QALYs and high cost-effective ICERs (euro566 and euro635/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.