Assessing the cost-effectiveness of interventions to expand hepatitis C testing to help achieve elimination targets in the United Kingdom

Introduction: In 2016, the UK adopted World Health Organization goals to eliminate the hepatitis C virus (HCV) as a public health burden by 2030. Testing is currently recommended for those most at risk. However, in order to diagnose and treat the estimated 118,000 cases of HCV in the UK, testing needs to be expanded to people who are currently unlikely to receive testing. Research aims and objectives: To identify and evaluate the cost-effectiveness of three novel HCV testing strategies which seek to test those who are unlikely to be offered HCV testing under the current testing guidance, in the UK. This research also sought to explore issues of heterogeneity, and the impact of this upon cost-effectiveness estimates. Findings: Three economic evaluations were performed. Two HCV testing interventions in primary care (general practice) were considered. An algorithm to identify those at elevated risk of HCV was highly likely to be cost-effective. In contrast, the cost-effectiveness of HCV screening for birth cohorts, when offered to everyone attending the NHS health check for those aged 40-74 years old in primary care, was highly uncertain, with additional empirical evidence required. HCV testing in Emergency Departments (ED) was also evaluated, since the prevalence tends to be higher than in the general population, and this may be the only healthcare setting that some people engage with. Testing was cost-effective, and highly likely to be cost-effective when the chronic HCV prevalence was 0.5%. Finally, a methodological analysis found that assuming homogeneity amongst the testing population can lead to considerable inaccuracies in on economic model estimates. Conclusions: HCV testing in primary care and the ED can be cost-effective, and testing guidelines and policies should be updated to reflect this. Future economic evaluations should consider the heterogeneity amongst the testing population to accurately capture the impact of new testing interventions.