Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children

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Abstract

Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. 
Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.
Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.
Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.
LanguageEnglish
Pages2429-2441
Number of pages13
JournalQuality of Life Research
Volume28
Issue number9
Early online date1 Jun 2019
DOIs
Publication statusPublished - Sep 2019

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Statistical Factor Analysis
Adolescent Health Services
Mental Health Services
Nonparametric Statistics
Cost-Benefit Analysis
Longitudinal Studies
Child Health
Surveys and Questionnaires
Linear Models
Health
Research

Keywords

  • CHU9D
  • Mapping
  • SDQ
  • Utility

Cite this

@article{51ebeaf8671f4d2c9fc12f50f8971807,
title = "Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children",
abstract = "Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.",
keywords = "CHU9D, Mapping, SDQ, Utility",
author = "Rajan Sharma and Yuanyuan Gu and Kompal Sinha and Mona Aghdaee and Bonny Parkinson",
year = "2019",
month = "9",
doi = "10.1007/s11136-019-02220-x",
language = "English",
volume = "28",
pages = "2429--2441",
journal = "Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation",
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number = "9",

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T1 - Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children

AU - Sharma, Rajan

AU - Gu, Yuanyuan

AU - Sinha, Kompal

AU - Aghdaee, Mona

AU - Parkinson, Bonny

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N2 - Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.

AB - Purpose: Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods: We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.Results: SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.Conclusions: The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.

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