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Quality of Life Research
Gepubliceerd in: Quality of Life Research 9/2019

01-06-2019

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

Auteurs: Rajan Sharma, Yuanyuan Gu, Kompal Sinha, Mona Aghdaee, Bonny Parkinson

Gepubliceerd in: Quality of Life Research | Uitgave 9/2019

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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.
vorige artikel Health-related quality of life in adult males with lower urinary tract symptoms
volgende artikel The role of family functioning and self-esteem in the quality of life of adolescents referred for psychiatric services: a 3-year follow-up
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Voetnoten
1
Utilities are measured on a cardinal scale, anchored at 0 (death) and 1 (full health) [1, 5, 9]. Utilities less than zero are possible if a health state is considered to be worse than death [1].
 
2
MAUIs are a health-related quality of life questionnaires which are associated with an algorithm to convert responses to the included questions into utilities.
 
3
Mapping is the process of establishing a statistical relationship between a non-PBM and a PBM using regression techniques.
 
4
It can certainly be updated using the new version of the CHU9D instrument but to our best knowledge such an updated algorithm is not publicly available. In this study we obtained a subsample of the original data used in Furber et al. [24] from the lead author and updated their algorithm to be used as a comparator of our new algorithm in the external validation.
 
5
For GLM and EEE the disutility was used as the outcome variable to avoid non-negative values.
 
6
Brant test was used to check proportional odds assumption to determine the ordered nature of CHU9D domains. Parallel assumption or proportional odds assumption is an assumption of an ordered logit model. This assumption assumes that coefficients between different categories of the dependent variable are equal.
 
7
The MAE was calculated as mean of the absolute values of the difference between the observed and predicted CHU9D utilities.
 
8
The MSE were computed as the mean squared differences between the predicted and observed CHU9D utilities.
 
9
Formula: \(\% \; {\text{Error}}\; {\text{for}}\;{\text{GMAE}} = 100 \times \frac{\text{MAE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right)} \right)}}.\)
 
10
Formula: \(\% \;{\text{Error}}\; {\text{for}}\; {\text{GMSE}} = 100 \times \frac{\text{MSE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D }}\;{\text{utilities}}} \right)} \right)}}.\)
Note: \(\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = 1\;\;{\text{and}}\;\;\left( {{\text{Min}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = - \,0.1059\)
 
11
We used new CHU9D tariff to obtain observed CHU9D variable in the CAMHS dataset, ran an OLS regression with SDQ subscales as predictors and obtained coefficients to be used as new or modified mapping algorithm for Furber et al.
 
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Metagegevens
Titel
Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children
Auteurs
Rajan Sharma
Yuanyuan Gu
Kompal Sinha
Mona Aghdaee
Bonny Parkinson
Publicatiedatum
01-06-2019
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 9/2019
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-019-02220-x

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