All costs are independent of age and are discounted at 3.5%. We utilized published health state utility values based on a United States population analysis.18 Utility values are modeled independently of age. The health utilities used in this model are presented in Table 2. Future health benefits, as measured by QALYs, are discounted MG 132 at 3.5%. Our analysis focused on three key operational

areas that impact the cost-effectiveness of HCV testing and treatment: treatment eligibility, age and fibrosis stage treatment prioritization, and timing of treatment initiation. These are described in further detail: 1 The cost-effectiveness of testing for HCV is dependent upon the total number tested (which is a fixed cost for a given number tested), the number of HCV-infected individuals identified, and the numbers eligible for treatment. PD0332991 research buy We therefore assessed the relationship between the proportion of tested and diagnosed subjects treated (including subjects who die or progress before treatment initiation) and the cost-effectiveness

of birth cohort testing compared with risk-based testing. We presented the results of varying the proportion of tested and diagnosed people treated (15% to 100%) and compared two scenarios: a The base case birth cohort and risk-based populations, as reported in the CDC guidelines. Under the base model settings, the predicted cost-effectiveness of birth cohort testing compared with risk-based testing was $28,602. Figure 3 demonstrates the relationship between the percentage of the tested population being treated (including subjects who die or progress before treatment initiation) and the cost-effectiveness of birth cohort testing versus risk-based testing. At a willingness to pay threshold of $50,000, Fig. 3 shows that approximately 278,000 (26%) of the identified population need to be treated for birth cohort testing to be cost-effective when compared with current risk-based testing. By treating at least 143,000 more people than current risk-based

testing, enough benefit and cost offsets will be generated to warrant the extra costs related filipin to diagnosing 809,000 people on top of the current risk-based testing; an additional $1.44 billion in testing costs and $3.87 billion in chronic HCV care (assuming no treatment of the 809,000). Given the need to test, identify, and treat a large number of patients to ensure birth cohort testing is cost-effective, the impact of prioritizing treatment in those identified is illustrated in Fig. 4. This shows the effect on cost, QALYs, and number of complications associated with prioritizing treatment by age and fibrosis stage. In Fig. 4, the y axis at y = 0 represents the “no skew” scenario, with each plot showing differences in total lifetime costs, QALYs, and complications with treatment prioritized to those with less fibrosis (F0 skew) and those with advanced fibrosis (F4 skew).