Gareth Furber1, Leonie Segal, Matthew Leach, Jane Cocks. 1. Health Economics and Social Policy Group, University of South Australia, Playford Building, City East Campus, North Terrace, Adelaide, SA, 5000, Australia, Gareth.furber@unisa.edu.au.
Abstract
PURPOSE: Quality of life mapping methods such as "Transfer to Utility" can be used to translate scores on disease-specific measures to utility values, when traditional utility measurement methods (e.g. standard gamble, time trade-off, preference-based multi-attribute instruments) have not been used. The aim of this study was to generate preliminary ordinary least squares (OLS) regression-based algorithms to transform scores from the Strengths and Difficulties Questionnaires (SDQ), a widely used measure of mental health in children and adolescents, to utility values obtained using the preference-based Child Health Utility (CHU9D) instrument. METHODS: Two hundred caregivers of children receiving community mental health services completed the SDQ and CHU9D during a telephone interview. Two OLS regressions were run with the CHU9D utility value as the dependent variable and SDQ subscales as predictors. Resulting algorithms were validated by comparing predicted and observed group mean utility values in randomly selected subsamples. RESULTS: Preliminary validation was obtained for two algorithms, utilising five and three subscales of the SDQ, respectively. Root mean square error values (.124) for both models suggested poor fit at an individual level, but both algorithms performed well in predicting mean group observed utility values. CONCLUSION: This research generated algorithms for translating SDQ scores to utility values and providing researchers with an additional tool for conducting health economic evaluations with child and adolescent mental health data.
PURPOSE: Quality of life mapping methods such as "Transfer to Utility" can be used to translate scores on disease-specific measures to utility values, when traditional utility measurement methods (e.g. standard gamble, time trade-off, preference-based multi-attribute instruments) have not been used. The aim of this study was to generate preliminary ordinary least squares (OLS) regression-based algorithms to transform scores from the Strengths and Difficulties Questionnaires (SDQ), a widely used measure of mental health in children and adolescents, to utility values obtained using the preference-based Child Health Utility (CHU9D) instrument. METHODS: Two hundred caregivers of children receiving community mental health services completed the SDQ and CHU9D during a telephone interview. Two OLS regressions were run with the CHU9D utility value as the dependent variable and SDQ subscales as predictors. Resulting algorithms were validated by comparing predicted and observed group mean utility values in randomly selected subsamples. RESULTS: Preliminary validation was obtained for two algorithms, utilising five and three subscales of the SDQ, respectively. Root mean square error values (.124) for both models suggested poor fit at an individual level, but both algorithms performed well in predicting mean group observed utility values. CONCLUSION: This research generated algorithms for translating SDQ scores to utility values and providing researchers with an additional tool for conducting health economic evaluations with child and adolescent mental health data.
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