Abstracts of the 2024 Annual Meeting of the ALEH
More infoYes, Coalition For Global Hepatitis Elimination support the project
Introduction and ObjectivesBackground: WHO aims for HCV elimination by 2030, targeting a 80% reduction in incidence and a 65% reduction in mortality, with 90% diagnosed and 80% treatment coverage compared to 2015. Uruguay, with a population of 3.4 million, has low HCV prevalence and universal treatment access, but testing and treatment rates are low. Objective: To assess the feasibility of HCV elimination and compare the burden and budget impacts of various testing strategies in Uruguay.
Patients / Materials and MethodsMethods: Disease burden and budget impact projections were generated using a decision-analytic model, The Hep C Elimination Tool, developed by Massachusetts General Hospital with support from the Coalition for Global Hepatitis Elimination and calibrated with Uruguayan parameters.
Results and DiscussionWith 100% follow-up for confirmatory testing and treatment initiation, 42 strategies meet three elimination goals by 2030.
The strategy with the greatest death reductionuses a 30% annual screening rate and 80% treatment rate, requiring 3,220,000 people to be tested (800,000/annual from 2024-2026) and 20,000 treated (5,000/annual from 2024-2026) by 2030. This achieves 91% diagnosis and treatment coverage, with reductions in incidence of 89%, prevalence of 91%, decompensated cirrhosis of 74%, HCC of 46% and mortality of 56%, costing $121.63 million from 2022-2050.
The most gradual strategy uses a 15% annual screening rate and 70% treatment rate, requiring 3,190,000 people to be tested (400,000/annual from 2023-2029) and 19,035 treated (2,500/annual from 2024-2029) by 2030. This achieves 90% diagnosis and 85% treatment coverage, with reductions in incidence of 82%, prevalence of 85%, decompensated cirrhosis of 66%, HCC of 34% and mortality of 30%, costing $132.92 million from 2022-2050.
ConclusionsUruguay can achieve WHO HCV elimination incidence goal and diagnosis and treatment targets by 2030. Mathematical modeling can inform policymakers about the impact of different interventions on HCV burden, supporting informed and cost-effective decision-making.