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      Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature

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          Abstract

          Background

          Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used.

          Methods

          In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed.

          Results

          We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme.

          Conclusions

          Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s10198-015-0753-2) contains supplementary material, which is available to authorized users.

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          Most cited references33

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          2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults

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            Cardiovascular disease risk profiles.

            This article presents prediction equations for several cardiovascular disease endpoints, which are based on measurements of several known risk factors. Subjects (n = 5573) were original and offspring subjects in the Framingham Heart Study, aged 30 to 74 years, and initially free of cardiovascular disease. Equations to predict risk for the following were developed: myocardial infarction, coronary heart disease (CHD), death from CHD, stroke, cardiovascular disease, and death from cardiovascular disease. The equations demonstrated the potential importance of controlling multiple risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, glucose intolerance, and left ventricular hypertrophy) as opposed to focusing on one single risk factor. The parametric model used was seen to have several advantages over existing standard regression models. Unlike logistic regression, it can provide predictions for different lengths of time, and probabilities can be expressed in a more straightforward way than the Cox proportional hazards model.
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              Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6.

              A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.
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                Author and article information

                Contributors
                davidepstein@ugr.es
                Journal
                Eur J Health Econ
                Eur J Health Econ
                The European Journal of Health Economics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1618-7598
                1618-7601
                18 December 2015
                18 December 2015
                2016
                : 17
                : 8
                : 1041-1053
                Affiliations
                [1 ]Department of Applied Economics, University of Granada, Campus de la Cartuja, 18071 Granada, Spain
                [2 ]Escuela Andaluza de Salud Pública, Granada, Spain
                [3 ]Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
                Article
                753
                10.1007/s10198-015-0753-2
                5047941
                26682549
                1971a504-e710-429d-8cee-14c80807874d
                © The Author(s) 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 5 March 2015
                : 25 November 2015
                Funding
                Funded by: European Commision
                Award ID: 279233
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2016

                Economics of health & social care
                cost-effectiveness analysis,screening,cardiovascular disease,primary prevention,statins,literature review,i180,h510

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