Abstract
BackgroundMost cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate HPV infection, cervical disease and prevention strategies. However, it is common for professionals who would need to perform these analyses to not be familiar with the models. This work introduces the Online Cost-Effectiveness ANalysis tool, featuring an easy-to-use web interface providing health professionals, researchers and decision makers involved in cervical cancer prevention programmes with a useful instrument to conduct complex cost-effectiveness analyses, which are becoming an essential tool as an approach for supporting decision-making that involves important trade-offs.ResultsThe users can run cost-effectiveness evaluations of cervical cancer prevention strategies without deep knowledge of the underlying mathematical model or any programming language, obtaining the most relevant costs and health outcomes in a user-friendly format. The results provided by the tool are consistent with the existing literature.ConclusionsHaving such a tool will be an asset to the cervical cancer prevention community, providing researchers with an easy-to-use instrument to conduct cost-effectiveness analyses.
Highlights
Most cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate human papillomavirus (HPV) infection, cervical disease and prevention strategies
HPV infections are asymptomatic in most cases, some can lead to the formation of cervical abnormalities called cervical intraepithelial neoplasia (CIN), which can lead to cervical cancer
The goal is to mimic some of the cervical cancer prevention strategies available in Spain, comparing conventional cytology to HPV testing, with and without vaccination
Summary
This section reproduces the cost-effectiveness analysis reported in [7] using the OCEAN tool. We can use the calibration part of the tool to check whether the input matrix we use fits the Spanish registered data in a reasonable manner (Tables S2 and S3 show the considered HPV infection prevalence and cervical cancer incidence). Once a -or multiple- calibrated transition probability matrix that feeds the Markov model has been generated in the calibration part, it is time for the costeffectiveness analysis. In this example, we will consider the following prevention strategies: Natural history: The first scenario considers no prevention strategy. A figure reproducing Fig. 3 from [7] is available as supplementary material (Fig. S2), leading to the expected results
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