OBJECTIVE: To develop a simulation model to assess the impact of type 1 diabetes and its associated complications on health-related quality of life of a population. RESEARCH DESIGN AND METHODS: The methodology builds upon 1) an existing population model of type 1 diabetes progression, 2) an empirical study designed to measure state- and age-specific health statuses of type 1 diabetes, and 3) existing literature to quantify quality of life of the corresponding health status. Health statuses were measured in a group of type 1 diabetic patients using the Medical Outcomes Study short form 36 (SF-36). A published empirical regression equation was then used to predict corresponding Quality of Well-Being Index (QWB) scores from these assessments. The QWB scores were incorporated into a previously developed type 1 diabetes progression and cost simulation model. Sensitivity analyses on key parameters were performed, and the model was found to be robust. RESULTS: The augmented model can estimate quality-adjusted life years (QALYs) as well as costs associated with type 1 diabetes on any population of interest over any period of time. The model is used to compare intensive versus conventional treatment strategies using a simplified set of assumptions regarding the relative effects of these alternative treatments. With these assumptions, intensive strategy produces more QALYs than does conventional strategy and is cost-beneficial after 5 years. CONCLUSIONS: The model enables health planners to perform cost-effectiveness analyses to compare alternative treatment strategies for type 1 diabetes and support subsequent decision making.
OBJECTIVE: To develop a simulation model to assess the impact of type 1 diabetes and its associated complications on health-related quality of life of a population. RESEARCH DESIGN AND METHODS: The methodology builds upon 1) an existing population model of type 1 diabetes progression, 2) an empirical study designed to measure state- and age-specific health statuses of type 1 diabetes, and 3) existing literature to quantify quality of life of the corresponding health status. Health statuses were measured in a group of type 1 diabeticpatients using the Medical Outcomes Study short form 36 (SF-36). A published empirical regression equation was then used to predict corresponding Quality of Well-Being Index (QWB) scores from these assessments. The QWB scores were incorporated into a previously developed type 1 diabetes progression and cost simulation model. Sensitivity analyses on key parameters were performed, and the model was found to be robust. RESULTS: The augmented model can estimate quality-adjusted life years (QALYs) as well as costs associated with type 1 diabetes on any population of interest over any period of time. The model is used to compare intensive versus conventional treatment strategies using a simplified set of assumptions regarding the relative effects of these alternative treatments. With these assumptions, intensive strategy produces more QALYs than does conventional strategy and is cost-beneficial after 5 years. CONCLUSIONS: The model enables health planners to perform cost-effectiveness analyses to compare alternative treatment strategies for type 1 diabetes and support subsequent decision making.
Authors: C J Currie; C D Poole; A Woehl; C Ll Morgan; S Cawley; M D Rousculp; M T Covington; J R Peters Journal: Diabetologia Date: 2006-08-30 Impact factor: 10.122
Authors: R Brett McQueen; Samuel L Ellis; David M Maahs; Heather D Anderson; Kavita V Nair; Anne M Libby; Jonathan D Campbell Journal: Patient Date: 2014 Impact factor: 3.883
Authors: Robert Brett McQueen; Marc D Breton; Joyce Craig; Hayden Holmes; Melanie D Whittington; Markus A Ott; Jonathan D Campbell Journal: J Diabetes Sci Technol Date: 2018-04-21