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

Area of Science:

• Hepatology
• Public Health
• Health Economics

Background:

• Hepatitis C virus (HCV) elimination requires efficient diagnostic strategies.
• Current diagnostic pathways for active HCV infection involve antibody detection followed by confirmatory RNA or core antigen testing.
• High endemicity settings necessitate cost-effective and sustainable diagnostic solutions.

Purpose of the Study:

• To evaluate the cost-effectiveness and sustainability of various diagnostic algorithms for active HCV infection.
• To compare different HCV diagnostic strategies within a high endemicity context.
• To inform clinical practice guidelines for optimal HCV diagnosis.

Main Methods:

• A Markov disease progression model simulated six diagnostic algorithms over a 10-year period.
• The model incorporated factors like assay false negatives, patient drop-off, age, liver disease staging, and treatment costs.
• Quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated from a healthcare perspective.

Main Results:

• HCV-RNA reflex testing yielded the highest health gains (974,458 QALYs) with a cost-effective ICER of €891/QALY.
• Same-sample reflex testing (HCV-Ag and HCV-RNA) provided the highest QALYs and cost-effective ICERs (€566 and €635/QALY, respectively).
• These findings were robust, confirmed in 99.9% of 5,000 simulations, outperforming two-visit algorithms.

Conclusions:

• HCV reflex testing is the most cost-effective diagnostic option compared to other pathways.
• The study supports EASL and WHO clinical practice guidelines recommending HCV reflex testing.
• Optimized diagnostic algorithms are crucial for achieving HCV elimination goals efficiently.