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From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation.

Gang Chen¹, Katherine Stevens, Donna Rowen

¹Flinders Health Economics Group, School of Medicine, Flinders University, Adelaide, Australia. gang.chen@flinders.edu.au.

Health and Quality of Life Outcomes

|August 30, 2014

Summary

This summary is machine-generated.

This study developed a mapping algorithm to predict Child Health Utility 9D (CHU9D) scores from the KIDSCREEN-10 index in adolescents. This enables cost-utility analyses when only KIDSCREEN-10 data is available.

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Area of Science:

Pediatric Health Outcomes Research
Health Economics and Outcomes Research (HEOR)

Background:

The KIDSCREEN-10 index and Child Health Utility 9D (CHU9D) are generic instruments for measuring pediatric health-related quality of life.
The CHU9D is suitable for cost-utility analyses, but the KIDSCREEN-10 is not.
A mapping algorithm is needed to utilize KIDSCREEN-10 data in cost-utility analyses.

Purpose of the Study:

To develop and validate an algorithm for mapping KIDSCREEN-10 index scores to CHU9D utility scores.
To enable the use of KIDSCREEN-10 data in cost-utility analyses for children and adolescents.

Main Methods:

A sample of 590 Australian adolescents (aged 11-17) completed both KIDSCREEN-10 and CHU9D.
Various econometric models were estimated, including MM-estimator and OLS, using KIDSCREEN-10 item scores as predictors.
Model predictive accuracy was assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Main Results:

The MM-estimator model with stepwise-selected KIDSCREEN-10 items demonstrated the highest predictive accuracy based on MAE.
The ordinary least squares (OLS) model showed the best predictive accuracy based on RMSE.
The MM-estimator model was identified as the preferred algorithm for predicting CHU9D utility.

Conclusions:

A validated mapping algorithm using the MM-estimator and KIDSCREEN-10 item scores accurately predicts CHU9D utility.
This algorithm facilitates cost-utility analyses by generating cost per quality-adjusted life year estimates from KIDSCREEN-10 data alone.