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dc.contributor.authorLeal, Jose
dc.contributor.authorKhurshid, Waqar
dc.contributor.authorPagano, Eva
dc.contributor.authorFeenstra, Talitha
dc.date.accessioned2018-03-08T10:53:35Z
dc.date.available2018-03-08T10:53:35Z
dc.date.issued2017-10-05
dc.identifier.citationComputer simulation models of pre-diabetes populations: a systematic review protocol. 2017, 7 (10):e014954 BMJ Openen
dc.identifier.issn2044-6055
dc.identifier.pmid28982807
dc.identifier.doi10.1136/bmjopen-2016-014954
dc.identifier.urihttp://hdl.handle.net/10029/621553
dc.description.abstractDiabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations.
dc.language.isoenen
dc.rightsArchived with thanks to BMJ openen
dc.titleComputer simulation models of pre-diabetes populations: a systematic review protocol.en
dc.typeArticleen
dc.identifier.journalBMJ Open 2017; 7(10):e0114954en
refterms.dateFOA2018-12-18T14:05:26Z
html.description.abstractDiabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations.


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