AIMS: To develop age and sex-specific risk equations for predicting mortality following major complications of diabetes, using a large linked administrative dataset from Western Australia (WA) and to incorporate these into an existing diabetes simulation model. METHODS: The study uses linked hospital and mortality records on 13,884 patients following a major diabetes-related complication with a mean (SD) duration of 2.62 (2.25) years. Risk equations for predicting mortality were derived and integrated into the UKPDS Outcomes Model. Estimates of life expectancy and incremental QALYs gained as a result of two theoretical therapies (a reduction of HbA1c of 1%, and reduction of systolic blood pressure of 10 mmHg) were determined using the original and adapted models. RESULTS: The two versions of the model generated differences in life expectancy following specific events; however there was little impact of using alternative mortality equations on incremental QALYs gained as a result of reducing HbA(1c) or systolic blood pressure, or on outcomes of life expectancy for a cohort initially free of complications. CONCLUSIONS: Mortality following complications varies across diabetic populations and can impact on estimates of life expectancy, but appears to have less impact on incremental benefits of interventions that are commonly used in pharmoeconomic analyses.
AIMS: To develop age and sex-specific risk equations for predicting mortality following major complications of diabetes, using a large linked administrative dataset from Western Australia (WA) and to incorporate these into an existing diabetes simulation model. METHODS: The study uses linked hospital and mortality records on 13,884 patients following a major diabetes-related complication with a mean (SD) duration of 2.62 (2.25) years. Risk equations for predicting mortality were derived and integrated into the UKPDS Outcomes Model. Estimates of life expectancy and incremental QALYs gained as a result of two theoretical therapies (a reduction of HbA1c of 1%, and reduction of systolic blood pressure of 10 mmHg) were determined using the original and adapted models. RESULTS: The two versions of the model generated differences in life expectancy following specific events; however there was little impact of using alternative mortality equations on incremental QALYs gained as a result of reducing HbA(1c) or systolic blood pressure, or on outcomes of life expectancy for a cohort initially free of complications. CONCLUSIONS: Mortality following complications varies across diabetic populations and can impact on estimates of life expectancy, but appears to have less impact on incremental benefits of interventions that are commonly used in pharmoeconomic analyses.
Authors: M C Weinstein; E L Toy; E A Sandberg; P J Neumann; J S Evans; K M Kuntz; J D Graham; J K Hammitt Journal: Value Health Date: 2001 Sep-Oct Impact factor: 5.725
Authors: May Song; Charles M Alexander; Panagiotis Mavros; Victor A Lopez; Shaista Malik; Hemant M Phatak; Nathan D Wong Journal: Diabetes Res Clin Pract Date: 2010-11-11 Impact factor: 5.602
Authors: Ali Abbasi; Eva Corpeleijn; Linda M Peelen; Ron T Gansevoort; Paul E de Jong; Rijk O B Gans; Wolfgang Rathmann; Bernd Kowall; Christine Meisinger; Hans L Hillege; Ronald P Stolk; Gerjan Navis; Joline W J Beulens; Stephan J L Bakker Journal: Eur J Epidemiol Date: 2012-01-04 Impact factor: 8.082