Hepatitis C Elimination in Rwanda: Progress, Feasibility, and Economic Evaluation.

OBJECTIVES: In 2018, Rwanda launched a national program to eliminate the hepatitis C virus (HCV). We aim to assess the impact of the program to date and identify strategies to achieve the World Health Organization's HCV elimination goals by 2030. METHODS: We developed a microsimulation model to simulate Rwanda's HCV epidemic from 2015 through 2050 and evaluated temporal trends in HCV infection, prevalence, mortality, and the total cost of care for scenarios that could achieve HCV elimination by 2030. RESULTS: Between 2018 and 2022, over 7 million people were screened for HCV, and 60 000 were treated. The study projected that Rwanda could achieve HCV elimination as early as 2027. A feasible strategy of an annual screening rate of 15% and a treatment rate of 100% would achieve all World Health Organization elimination goals by 2028, requiring screening an additional 4 million people and treating 23 900 patients by 2030. The elimination strategy costs $25 million for screening and diagnosis and $21 million for treatment from 2015 to 2050. The national program would avert 4900 hepatocellular carcinoma cases and 6700 HCV-related deaths and save the health system $25.33 million from 2015 to 2050. CONCLUSIONS: Rwanda is poised to become one of the first countries in the world to eliminate HCV. Rwanda's program serves as a blueprint for other countries in the African region. By rapid screening and treatment scale-up (eg, by leveraging HIV platforms) and by drug price negotiations, HCV elimination is not only feasible but can be cost-saving in low-income settings.

Hepatitis C Elimination in Moldova Is Feasible and Cost-Saving: A Modeling Study.

BACKGROUND: Moldova, an upper-middle-income country in Eastern Europe, is facing a high burden of hepatitis C virus (HCV). Our objective was to assist the National Agency of Public Health of Moldova in planning to achieve the World Health Organization's HCV elimination goals by 2030. METHODS: This study adapted a previously developed microsimulation model to simulate the HCV epidemic in Moldova from 2004 to 2050. Model outcomes included temporal trends in HCV infection, prevalence, mortality, and total cost of care, including screening and treatment. We evaluated scenarios that could eliminate HCV by 2030. RESULTS: Multiple strategies could lead to HCV elimination in Moldova by 2030. A realistic scenario of a 20% annual screening and 80% treatment rate would require 2.75 million individuals to be screened and 65 000 treated by 2030. Compared to 2015, this program will reduce HCV incidence by 98% and HCV-related deaths by 72% in 2030. Between 2022 and 2030, this strategy would cost $17.5 million for HCV screening and treatment. However, by 2050, the health system would save >$85 million compared to no investment in elimination efforts. CONCLUSIONS: HCV elimination in Moldova is feasible and can be cost saving, but requires resources to scale HCV screening and treatment.

Quantile Markov Decision Processes.

The goal of a traditional Markov decision process (MDP) is to maximize expected cumulative reward over a defined horizon (possibly infinite). In many applications, however, a decision maker may be interested in optimizing a specific quantile of the cumulative reward instead of its expectation. In this paper we consider the problem of optimizing the quantiles of the cumulative rewards of a Markov decision process (MDP), which we refer to as a quantile Markov decision process (QMDP). We provide analytical results characterizing the optimal QMDP value function and present a dynamic programming-based algorithm to solve for the optimal policy. The algorithm also extends to the MDP problem with a conditional value-at-risk (CVaR) objective. We illustrate the practical relevance of our model by evaluating it on an HIV treatment initiation problem, where patients aim to balance the potential benefits and risks of the treatment.

Metamodeling for Policy Simulations with Multivariate Outcomes.

PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. METHODS: We combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings. RESULTS: Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models. CONCLUSIONS: In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.

Cost-Effectiveness of a Core Antigen-Based Rapid Diagnostic Test for Hepatitis C.

OBJECTIVES: Hepatitis C virus (HCV) affects 58 million worldwide and > 79% of people remain undiagnosed. Rapid diagnostic tests (RDTs) for HCV can help improve diagnosis and treatment rates. Nevertheless, the high price and infrastructure needed to use current molecular HCV RDT options present a barrier to widespread use-particularly in low- and middle-income countries. We evaluated the performance and cost-effectiveness of a theoretical core antigen (cAg) RDT for HCV viremia confirmation, which requires fewer resources. METHODS: We adapted a previously validated microsimulation model to simulate HCV disease progression and outcomes under different HCV testing algorithms in Georgia and Malaysia. We compared standard of care testing with laboratory-based ribonucleic acid HCV to a cAg-based RDT for HCV confirmation. We simulated a cohort of 10 000 adults in each country, with an HCV-ribonucleic acid prevalence of 5.40% in Georgia and 1.54% in Malaysia. We projected the cumulative healthcare costs, quality-adjusted life-years, and diagnosis coverage rates over a lifetime horizon. RESULTS: Compared with the standard of care testing, the cAg-based RDT would increase quality-adjusted life-years by 270 in Georgia and 259 in Malaysia per 10 000 people. The high diagnosis rate and treatment rate of the cAg-based RDT result in substantial cost savings because of averted HCV sequelae management costs. Cost savings are $281 000 for Georgia and $781 000 for Malaysia. CONCLUSIONS: We found that a cAg-based RDT for HCV could improve the diagnosis rate and result in cost savings. Such a test could have a substantial impact on the feasibility and cost of HCV elimination.

Assessing cost-effectiveness of hepatitis C testing pathways in Georgia using the Hep C Testing Calculator.

The cost of testing can be a substantial contributor to hepatitis C virus (HCV) elimination program costs in many low- and middle-income countries such as Georgia, resulting in the need for innovative and cost-effective strategies for testing. Our objective was to investigate the most cost-effective testing pathways for scaling-up HCV testing in Georgia. We developed a Markov-based model with a lifetime horizon that simulates the natural history of HCV, and the cost of detection and treatment of HCV. We then created an interactive online tool that uses results from the Markov-based model to evaluate the cost-effectiveness of different HCV testing pathways. We compared the current standard-of-care (SoC) testing pathway and four innovative testing pathways for Georgia. The SoC testing was cost-saving compared to no testing, but all four new HCV testing pathways further increased QALYs and decreased costs. The pathway with the highest patient follow-up, due to on-site testing, resulted in the highest discounted QALYs (123 QALY more than the SoC) and lowest costs ($127,052 less than the SoC) per 10,000 persons screened. The current testing algorithm in Georgia can be replaced with a new pathway that is more effective while being cost-saving.

Health outcomes and cost-effectiveness of treating depression in people with HIV in Sub-Saharan Africa: a model-based analysis.

High prevalence of depression among people living with HIV (PLHIV) impedes antiretroviral therapy (ART) adherence and viral suppression. We estimate the effectiveness and cost-effectiveness of strategies to treat depression among PLHIV in Sub-Saharan Africa (SSA). We developed a microsimulation model of HIV disease and care in Uganda which captured individuals' depression status and the relationship between depression and HIV behaviors. We consider a strategy of screening for depression and providing antidepressant therapy with fluoxetine at ART initiation or re-initiation (if a patient has dropped out). We estimate that over 10 years this strategy would reduce prevalence of depression among PLHIV by 16.0% [95% uncertainty bounds 15.8%, 16.1%] from a baseline prevalence of 28%, increase adherence to ART by 1.0% [1.0%, 1.0%], and decrease rates of loss to followup by 3.7% [3.4%, 4.1%]. This would decrease first-line ART failure rates by 2.5% [2.3%, 2.8%] and increase viral suppression rates by 1.0% [1.0%, 1.0%]. This strategy costs $15/QALY compared to the status quo, and was highly cost-effective over a broad range of sensitivity analyses. We conclude that screening for and treating depression among PLHIV in SSA with fluoxetine would be effective in improving HIV treatment outcomes and would be highly cost-effective.