Hepatitis C virus (HCV) is 15 times more prevalent among persons in Spain’s prisons than in the community. Recently, Spain initiated a pilot program, JAILFREE-C, to treat HCV in prisons using direct-acting antivirals (DAAs). Our aim was to identify a cost-effective strategy to scale-up HCV treatment in all prisons. Using a validated agent-based model, we simulated the HCV landscape in Spain’s prisons considering disease transmission, screening, treatment, and prison-community dynamics. Costs and disease outcomes under status quo were compared with strategies to scale-up treatment in prisons considering prioritization (HCV fibrosis stage vs. HCV prevalence of prisons), treatment capacity (2,000/year vs. unlimited) and treatment initiation based on sentence lengths (>6 months vs. any). Scaling-up treatment by treating all incarcerated persons irrespective of their sentence length provided maximum health benefits–preventing 10,200 new cases of HCV, and 8,300 HCV-related deaths between 2019–2050; 90% deaths prevented would have occurred in the community. Compared with status quo, this strategy increased quality-adjusted life year (QALYs) by 69,700 and costs by €670 million, yielding an incremental cost-effectiveness ratio of €9,600/QALY. Scaling-up HCV treatment with DAAs for the entire Spanish prison population, irrespective of sentence length, is cost-effective and would reduce HCV burden.
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.
- NSF-PAR ID:
- 10371514
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Medical Decision Making
- Volume:
- 43
- Issue:
- 1
- ISSN:
- 0272-989X
- Format(s):
- Medium: X Size: p. 68-77
- Size(s):
- p. 68-77
- Sponsoring Org:
- National Science Foundation
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We modeled the health and economic impact of meeting the two‐year and ten‐year FDA targets, from the perspective of people working in the food system itself, over 20 years, from 2017 to 2036.
Benefits of implementing the FDA voluntary sodium targets extend to food companies and food system workers, and the value of CVD‐related health gains and cost savings are together greater than the government and industry costs of reformulation.
Context The US Food and Drug Administration (FDA) set draft voluntary targets to reduce sodium levels in processed foods. We aimed to determine cost effectiveness of meeting these draft sodium targets, from the perspective of US food system workers.
Methods We employed a microsimulation cost‐effectiveness analysis using the US IMPACT Food Policy model with two scenarios: (1) short term, achieving two‐year FDA reformulation targets only, and (2) long term, achieving 10‐year FDA reformulation targets.
We modeled four close‐to‐reality populations: food system “ever” workers; food system “current” workers in 2017; and subsets of processed food “ever” and “current” workers. Outcomes included cardiovascular disease cases prevented and postponed as well as incremental cost‐effectiveness ratio per quality‐adjusted life year (QALY) gained from 2017 to 2036.
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Methods We used a previously validated mathematical model to simulate the landscape of HCV in the United States from 2011 onwards, accounting for HCV screening policy updates, newer HCV treatments and rising HCV incidence.
Results By the end of 2018, of 4.29 million HCV persons alive, 2.71 million (63%) were actively viremic, 2.24 million (52%) aware and 1.58 million (37%) cured. By 2030, under the status quo, of 3.65 million HCV persons alive, 1.88 million (51%) would be viremic, 2.25 million (62%) aware and 1.77 million (49%) cured. The HCV care cascade in 2018 differed substantially by subpopulation: of 1.34 million incarcerated HCV persons, 96% were viremic, 36% aware and 4% cured; of 0.87 million HCV persons in Medicare, 31% were viremic, 72% aware and 69% cured; and of 0.37 million HCV persons in Medicaid, 49% were viremic, 54% aware and 51% cured. Implementing universal screening, providing unrestricted treatment and controlling HCV incidence were factors found to have the largest effect on improving the HCV care cascade.
Conclusions Since the launch of DAAs, the HCV care cascade has shifted towards higher awareness and treatment rates; however, additional interventions are needed to move towards HCV elimination.
