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Prior studies demonstrate that eliminating hepatitis C virus (HCV) in the United States (US) heavily depends on treating incarcerated persons. Knowing the scope of the carceral HCV epidemic by state will help guide national elimination efforts.

Between 2019 and 2023, all state prison systems received surveys requesting data on hepatitis C antibody and viremic prevalence. We supplemented survey information with publicly available HCV data to corroborate responses and fill in data gaps.

Weighting HCV prevalence by state prison population size, we estimate that 15.2% of the US prison population is HCV seropositive and 8.7% is viremic; 54.9% of seropositive persons have detectable RNA. Applying prevalence estimates to the total prison population at year-end 2021, 91 090 persons with HCV infection resided in a state prison.

With updated and more complete HCV data from all 50 states, HCV prevalence in state prisons is nearly 9-fold higher than the US general population. The heterogeneity in HCV prevalence by state prison system may reflect variable exposure before arrest and/or differences in treatment availability during incarceration. Elimination of HCV in the country depends on addressing the carceral epidemic, and one of the first steps is understanding the size of the problem.

Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)–based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model.

We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson’s R²on the normalized data.

The R²values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R²values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel.

ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best.

Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools. Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation. The machine learning–based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools.