Disease burden of hepatitis C in the Austrian state of Tyrol - Epidemiological data and model analysis to achieve elimination by 2030

奥地利蒂罗尔州丙型肝炎疾病负担——流行病学数据和模型分析,旨在到2030年实现消除丙型肝炎的目标

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Abstract

BACKGROUND: In 2016, the World Health Organization (WHO) and 69th World Health Assembly approved the first global health sector strategy (GHSS) on viral hepatitis with the goal to eliminate hepatitis C virus (HCV) infections worldwide. The aim is a 90% reduction of new infections and 65% reduction of HCV-related deaths by 2030. AIM: This study reports on the epidemiology of HCV infections in the Austrian state of Tyrol (total population 750,000) and uses a predictive model to identify how the WHO strategy for elimination of HCV can be achieved. METHODS: We developed a regional disease burden model based on observed local diagnosis data from 2001 to 2016. Scenarios were developed to evaluate the impact of diagnosis and treatment on HCV-related outcomes (viremic prevalence, decompensated cirrhosis, hepatocellular carcinoma, and liver-related deaths) from 2015 through 2030. RESULTS: In the last 15 years, 1,721 patients living in Tyrol have been diagnosed with chronic HCV infection. When ageing, mortality and treatment were factored in, there were an estimated 2,043 viremic HCV infections in 2016, of which 1,136 cases had been diagnosed. A baseline model predicts a decrease of 588 HCV cases from 2015 to 2030, which would not translate into the significant reduction of infections needed to achieve WHO global health recommendations. A total of 1,843 infected individuals need to be identified and treated to achieve the WHO goals by 2030 (1,254 averted cases as compared to baseline model). Implementation of this strategy would avoid 523 new HCV infections and decreases HCV-related mortality by 73%. CONCLUSION: HCV elimination and >65% reduction of associated mortality are possible for Tyrol, but requires a significant increase in new diagnoses and treatment rate. The model presented in this study could serve as an example for other regions to reliably predict regional disease burden and estimate how WHO goals can be met in the future.

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