title: Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39.
abstract: OBJECTIVE
  To compare the Diabetes-39 (D-39) with six multi-attribute utility (MAU) instruments (15D, AQoL-8D, EQ-5D, HUI3, QWB, and SF-6D), and to develop mapping algorithms which could be used to transform the D-39 scores into the MAU scores.
  
  
  METHODS
  Self-reported diabetes sufferers (N=924) and members of the healthy public (N=1760), aged 18 years and over, were recruited from 6 countries (Australia 18%, USA 18%, UK 17%, Canada 16%, Norway 16%, and Germany 15%). Apart from the QWB which was distributed normally, non-parametric rank tests were used to compare subgroup utilities and D-39 scores. Mapping algorithms were estimated using ordinary least squares (OLS) and generalised linear models (GLM).
  
  
  RESULTS
  MAU instruments discriminated between diabetes patients and the healthy public; however, utilities varied between instruments. The 15D, SF-6D, AQoL-8D had the strongest correlations with the D-39. Except for the HUI3, there were significant differences by gender. Mapping algorithms based on the OLS estimator consistently gave better goodness-of-fit results. The mean absolute error (MAE) values ranged from 0.061 to 0.147, the root mean square error (RMSE) values 0.083 to 0.198, and the R-square statistics 0.428 and 0.610. Based on MAE and RMSE values the preferred mapping is D-39 into 15D. R-square statistics and the range of predicted utilities indicate the preferred mapping is D-39 into AQoL-8D.
  
  
  CONCLUSIONS
  Utilities estimated from different MAU instruments differ significantly and the outcome of a study could depend upon the instrument used. The algorithms reported in this paper enable D-39 data to be mapped into utilities predicted from any of six instruments. This provides choice for those conducting cost-utility analyses.
authors.affiliation: Flinders Health Economics Group, Flinders University, Adelaide, Australia.
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.affiliation: Centre for Health Economics, Monash University, Melbourne, Australia. Electronic address: angelo.iezzi@monash.edu.
authors.forename: Angelo
authors.initials: A
authors.lastname: Iezzi
authors.affiliation: Centre for Health Economics, Monash University, Melbourne, Australia.
authors.forename: John
authors.initials: J
authors.lastname: McKie
authors.affiliation: Centre for Health Economics, Monash University, Melbourne, Australia.
authors.forename: Munir A
authors.initials: MA
authors.lastname: Khan
authors.affiliation: Centre for Health Economics, Monash University, Melbourne, Australia.
authors.forename: Jeff
authors.initials: J
authors.lastname: Richardson
mesh_terms: D000328:Adult; D000465:Algorithms; D003362:Cost-Benefit Analysis; D003920:Diabetes Mellitus / Q000523:psychology*; D005260:Female; D014943:Global Health; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011788:Quality of Life; D015203:Reproducibility of Results; D011795:Surveys and Questionnaires
keywords: Diabetes-39; Mapping; Multi attribute utility; Quality of life