TY - JOUR
T1 - Mapping scores from the Strengths and Difficulties Questionnaire (SDQ) to preference-based utility values
AU - Furber, Gareth
AU - Segal, Leonie
AU - Leach, Matthew
AU - Cocks, Jane
N1 - Funding Information:
Acknowledgements The authors would like to acknowledge the School of Nursing and Midwifery at the University of South Australia for the research development grant that enabled this study to take place as well as Southern Mental Health—Child and Adolescent Mental Health Service (SMH-CAMHS) for their support in hosting ongoing health economic projects within their service.
PY - 2014/3
Y1 - 2014/3
N2 - 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.
AB - 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.
KW - Child and adolescent
KW - Mapping
KW - Mental health
KW - Utility
UR - http://www.scopus.com/inward/record.url?scp=84900380295&partnerID=8YFLogxK
U2 - 10.1007/s11136-013-0494-6
DO - 10.1007/s11136-013-0494-6
M3 - Article
C2 - 23943259
AN - SCOPUS:84900380295
SN - 0962-9343
VL - 23
SP - 403
EP - 411
JO - Quality of Life Research
JF - Quality of Life Research
IS - 2
ER -