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Abstract Background and aimsAddressing the opioid crisis requires opioid use disorder (OUD) prevalence estimates at the community level for targeted interventions. This study presents a new method that utilizes Medicaid claims data and publicly available data to estimate OUD prevalence at the United States (US) county level and compares it with other existing estimates. Design, setting and participantsThis study utilized data on OUD diagnoses among Medicaid beneficiaries in combination with national survey data, Census data and published literature to estimate the prevalence of OUD (including undiagnosed OUD) in each of the US state of Ohio's 88 counties for the years 2019 to 2021 among the population aged 12 years and older. Prevalence estimates were adjusted for misclassification in claims data and for variation in healthcare utilization among individuals with OUD. MeasurementsCounts and proportions of the population aged 12 and older with OUD at the US state and county level. FindingsOUD prevalence for Ohioans aged 12 years and older was relatively stable at 3.6% [95% uncertainty interval (UI) = 3.5%–3.8%] in 2019 and 3.7% (95% UI = 3.5%–3.9%) in 2021. County‐level prevalence estimates ranged from 0.7% to 14.2% in 2021. Southern counties generally had higher OUD prevalence than northern counties. The prevalence estimates were strongly correlated (Pearson's r = 0.88) with prevalence estimates for 19 Ohio counties from a previous study. Compared with the previous estimates, estimates from the current study tended to be lower for most communities but remained within the 95% credible intervals of previous estimates. ConclusionsThis approach for estimating opioid use disorder prevalence within United States communities by using Medicaid claims data and publicly available data is a robust alternative to methods relying on individual‐level data or multiple linked datasets. 

Background 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. Methods 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 R 2 on the normalized data. Results The R 2 values 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 R 2 values 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. Conclusions ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best. Highlights 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. 

Abstract BackgroundPrior 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. MethodsBetween 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. ResultsWeighting 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. ConclusionsWith 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. 

Abstract 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.