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.
- Award ID(s):
- 1722665
- NSF-PAR ID:
- 10316214
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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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 R2on the normalized data.
Results 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.
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.
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Geographically-based screening policies for diabetic retinopathy (DR) can be effective in developing teleretinal imaging (TRI) guidelines while identifying patients with limited geographic access to eye care. This study conducts cost-effectiveness analysis of different screening policies for urban and rural diabetic patients in Western Pennsylvania. A Monte Carlo simulation model was used to evaluate the cost-effectiveness of 2 standardized screening policies (annual clinic-based screening (ACS) and annual TRI-based screening (ATRI)) and a personalized TRI-based screening policy (PTRI) for both urban and rural cohorts. PTRI was generated by a previously developed mathematical model that autonomously makes semi-annual screening recommendations based on each patient’s disease progression and compliance (Dorali et al. IOVS 2022; 63(7)). For each policy, hypothetical urban and rural cohorts of 50,000 patients were simulated and lifetime QALYs and costs were collected for each patient. TRI compliance rates were derived from electronic medical records. Compliance with clinic-based screening was selected from literature-based values (12-45% for rural patients and 50-65% for urban patients). For a base case urban cohort with an A1C level of 7% and entering age of 40, costs per QALY gain (CPQ) for ACS, ATRI, and PTRI were $744.93±1.57, $792.38±1.64, and $714.60±1.56, respectively; PTRI produced more cost saving than ACS with the same QALY gain (See Fig 1). For a base case rural cohort, CPQ for ACS, ATRI, and PTRI were $869.15±1.80, $819.24±1.88, and $761.51±1.42, respectively; both ATRI and PTRI dominated ACS in QALY gains and cost saving (Fig 1). PTRI recommended TRI more to rural patients (94.13±0.01%) than to urban patients (87.20±0.02%). For the rural cohort, the minimum average TRI compliance rate such that ATRI is more cost-effective than ACS was 56% (Fig 2). TRI-based screening was found more beneficial for rural patients. PTRI was found dominant in QALY gain and cost saving for both urban and rural cohorts against standardized policies. These findings suggest that TRI is best utilized when location-specific factors such as geographic access to care or TRI compliance are considered.more » « less
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Policy Points The World Health Organization has recommended sodium reduction as a “best buy” to prevent cardiovascular disease (CVD). Despite this, Congress has temporarily blocked the US Food and Drug Administration (FDA) from implementing voluntary industry targets for sodium reduction in processed foods, the implementation of which could cost the industry around $16 billion over 10 years.
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.
Findings Among food system ever workers, achieving long‐term sodium reduction targets could produce 20‐year health gains of approximately 180,000 QALYs (95% uncertainty interval [UI]: 150,000 to 209,000) and health cost savings of approximately $5.2 billion (95% UI: $3.5 billion to $8.3 billion), with an incremental cost‐effectiveness ratio (ICER) of $62,000 (95% UI: $1,000 to $171,000) per QALY gained. For the subset of processed food industry workers, health gains would be approximately 32,000 QALYs (95% UI: 27,000 to 37,000); cost savings, $1.0 billion (95% UI: $0.7bn to $1.6bn); and ICER, $486,000 (95% UI: $148,000 to $1,094,000) per QALY gained. Because many health benefits may occur in individuals older than 65 or the uninsured, these health savings would be shared among individuals, industry, and government.
Conclusions The benefits of implementing the FDA voluntary sodium targets extend to food companies and food system workers, with the value of health gains and health care cost savings outweighing the costs of reformulation, although not for the processed food industry.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-00362-y
