Estimating CHU-9D Utility Scores from the WAItE: A Mapping Algorithm for Economic Evaluation

• Published in: Value in health Vol. 22; no. 2; pp. 239 - 246
• Main Authors: Robinson, Tomos, Oluboyede, Yemi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2019; Elsevier Science Ltd
• Subjects: Adolescent; Adolescents; Algorithms; Body weight; Body Weight - physiology; Child; CHU-9D; Cost analysis; Cost-Benefit Analysis - methods; Economic indicators; Female; Health Status Indicators; Humans; Internal Medicine; Linear analysis; Male; Mapping; Obesity; Pediatric Obesity - diagnosis; Pediatric Obesity - economics; Pediatric Obesity - epidemiology; Predictions; Public Health; Quality adjusted life years; Regression analysis; Socioeconomic factors; Surveys and Questionnaires; Teenagers; Utility; Utility functions; WAItE; WAItE; Obesity; Utility; CHU-9D; Mapping
• ISSN: 1098-3015; 1524-4733; 1524-4733
• DOI: 10.1016/j.jval.2018.09.2839

The Weight-Specific Adolescent Instrument for Economic Evaluation (WAItE) is a new condition-specific patient reported outcome measure that incorporates the views of adolescents in assessing the impact of above healthy weight status on key aspects of their lives. Presently it is not possible to use the WAItE to calculate quality adjusted life years (QALYs) for cost-utility analysis (CUA), given that utility scores are not available for health states described by the WAItE. This paper examines different regression models for estimating Child Health Utility 9 Dimension (CHU-9D) utility scores from the WAItE for the purpose of calculating QALYs to inform CUA. The WAItE and CHU-9D were completed by a sample of 975 adolescents. Nine regression models were estimated: ordinary least squares, Tobit, censored least absolute deviations, two-part, generalized linear model, robust MM-estimator, beta-binomial, finite mixture models, and ordered logistic regression. The mean absolute error (MAE) and mean squared error (MSE) were used to assess the predictive ability of the models. The robust MM-estimator with stepwise-selected WAItE item scores as explanatory variables had the best predictive accuracy. Condition-specific tools have been shown to be more sensitive to changes that are important to the population for which they have been developed for. The mapping algorithm developed in this study facilitates the estimation of health-state utilities necessary for undertaking CUA within clinical studies that have only collected the WAItE.