Background The recently approved drugs, sofosbuvir and ledipasvir, for chronic hepatitis C virus (HCV) treatment are more efficacious and safer but are substantially more expensive than the old standard-of-care (oSOC). It remains unclear whether and in which patients their improved efficacy justifies their increased cost. Objective To evaluate the cost-effectiveness and budget impact of sofosbuvir- and ledipasvir-based therapies. Design Simulation model of the natural history of HCV. Data Sources Published literature. Target population Treatment-naive and treatment-experienced HCV population defined on the basis of HCV genotype, age and fibrosis distribution in the United States. Time Horizon Lifetime. Perspective Third-party payer. Interventions Simulation of sofosbuvir/ledipasvir-based therapies compared with the oSOC that consisted of interferon-based therapies. Outcomes Measures Quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), and 5-year spending on antiviral drugs. Results of Base-Case Analysis Sofosbuvir-based therapies added 0.56 QALY relative to the oSOC, at an incremental cost of $55 400 per additional QALY. The ICERs ranged from $9700 to $284 300 per QALY depending on the patient’s status with respect to prior treatment, HCV genotype, and the presence of cirrhosis. At $100 000 willingness-to-pay per QALY, sofosbuivr-based therapies were cost-effective in 83% of treatment-naive and 81% of treatment-experienced patients. Compared with the oSOC, new drugs would cost an additional $65 billion in the next 5 years to treat eligible HCV-infected people in the United States, whereas the resulting cost offsets would be $16 billion. Results of Sensitivity Analysis Results were sensitive to the drug price, drug efficacy and quality-of-life after a successful treatment. Limitation Data on real world effectiveness of new antivirals is lacking. Conclusions HCV treatment is cost-effective in the majority of patients, but additional resource and value-based patient prioritization are needed to manage HCV patients.

Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. Methods Beginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. Results Analysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. Conclusions This analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.