Background: Chronic hepatitis C (HCV) is a significant public health problem affecting more than three million Americans. The US health care systems are ramping up costly HCV screening and treatment efforts with limited budget. We determine the optimal implementation of HCV birth-cohort screening and treatment strategies under budget constraints and health care payer's perspective. Methods: Markov model and scenario-based simulation optimization. The target population is birth cohort born between 1945 and 1975. The interventions are allocating annual budget to screen a proportion of the target population and treat a proportion of the identified chronic HCV-positive patients over 10 years. Outcomes measure is to maximize lifetime discounted quality-adjusted life-years. Results: Allocate a percentage of the annual budget to screening, then treat patients with the remaining budget and prioritize the sickest patients. When the budget is $1 billion/year, the best strategy is to allocate the entire budget to treatment. When the budget is $5 billion/year, it is optimal to allocate 60% of the budget to screening in the first 2 years and 0% thereafter for age cohort 40 to 49; and allocate 20% of the budget to screening starting in year 3 for age cohorts 50 to 59 and 60 to 69. Health benefits are sensitive to budget in the first 2 years. Results are not sensitive to distribution of fibrosis stages by awareness of HCV. Conclusion: When budget is limited, all efforts should be focused on early treatment. With higher budget, better population health outcomes are achieved by reserving some budget for HCV screening while implementing a priority-based treatment strategy. This work has broad applicability to diverse health care systems and helps determine how much effort should be devoted to screening versus treatment under resource limitations.
Keywords: Markov models; decision analysis; economics (health); national health services; operations research; resource allocation; simulation.