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OBJECTIVE: To measure the impact of complications on summary measures of health-related quality of life among people with type 2 diabetes. METHODS: Patients participating in the Action in Diabetes and Vascular Disease:Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial were administered a health-related quality-of-life questionnaire, the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), on four occasions over a 5-year period. We used two-way fixed-effects longitudinal regression models to investigate the impact of incident diabetes complications (stroke, heart failure, myocardial infarction, ischemic heart disease, renal failure, blindness, and amputation) on EQ-5D-3L utility score (where 1 = perfect health), while controlling for characteristics of individuals that do not vary over time. RESULTS: The effect of having any one of the seven complications was to reduce the EQ-5D-3L utility score by 0.054 (95% confidence interval 0.044-0.064), and this was not significantly affected by baseline age, sex, economic region, or the value set used to derive utilities. The complication with the largest disutility was amputation (0.122), followed by stroke (0.099), blindness (0.083), renal failure (0.049), heart failure (0.045), and myocardial infarction (0.026). Ischemic heart disease did not significantly reduce the utility score. Quality of life also declined with elapsed time-by an average of 0.006 per year, in addition to the effect of complications. CONCLUSIONS: Common complications significantly reduce health-related quality of life. Utility scores derived from the EQ-5D-3L provide a potential measure that can be used to summarize patient-reported outcomes and inform health economic models. Prevention of complications is critical to reduce the progressive burden of declining quality of life for people with diabetes.

Original publication

DOI

10.1016/j.jval.2015.10.010

Type

Journal article

Journal

Value Health

Publication Date

01/2016

Volume

19

Pages

36 - 41

Keywords

complications, longitudinal analysis, quality of life, type 2 diabetes, Age Factors, Aged, Diabetes Mellitus, Type 2, Female, Global Health, Health Status Indicators, Humans, Longitudinal Studies, Male, Middle Aged, Quality of Life, Regression Analysis, Sex Factors, Time Factors