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Published on: April 21, 2017
Health-related quality of life and upper-limb impairment in children with cerebral palsy: developing a mapping
Utsana Tonmukayakul1, Christine Imms2, Cathrine Mihalopoulos1
1Faculty of Health, Institute for Health Transformation, Deakin University, Geelong, Melbourne, Victoria, Australia.
Insights
This study explored how upper-limb impairments affect the quality of life for children with cerebral palsy. A new algorithm was developed to predict health utility scores using the Cerebral Palsy Quality of Life Questionnaire for Children.
Area of Science:
- Pediatric Rehabilitation
- Health Outcomes Research
- Biostatistics
Background:
- Children with cerebral palsy (CP) often experience upper-limb impairments affecting their health-related quality of life (HRQoL).
- Accurate HRQoL assessment is crucial for understanding the impact of CP and guiding interventions.
- Existing measures may not always align for comprehensive economic evaluations.
Purpose of the Study:
- To investigate the relationship between upper-limb impairment severity and HRQoL in children with CP.
- To develop a predictive algorithm mapping the Cerebral Palsy Quality of Life Questionnaire for Children (CPQoL-Child) to the Child Health Utility 9D (CHU9D) measure.
- To facilitate economic evaluations and cost-utility analyses in pediatric CP.
Main Methods:
- Assessed associations between physical/upper-limb classifications and HRQoL in 76 children (aged 6-15 years) with CP.
- Developed and tested five statistical techniques to predict CHU9D scores from CPQoL-Child scores, age, and sex.
- Utilized Manual Ability Classification System (MACS) and Neurological Hand Deformity Classification (NHDC) for impairment assessment.
Main Results:
- Manual Ability Classification System (MACS) and Neurological Hand Deformity Classification (NHDC) showed significant negative correlations with HRQoL measures.
- A generalized linear model incorporating participation, pain domain scores from CPQoL-Child, and age demonstrated the highest predictive accuracy for CHU9D.
- Most participants presented with mild upper-limb impairments, potentially influencing the strength of observed correlations.
Conclusions:
- MACS and NHDC are effective predictors of HRQoL in children with CP.
- The developed generalized linear model provides a reliable algorithm for mapping CPQoL-Child to CHU9D.
- This mapping algorithm supports the use of CPQoL-Child data for economic evaluations in pediatric cerebral palsy research.
Aim:
To: (1) investigate the relationship between upper-limb impairment and health-related quality of life (HRQoL) for children with cerebral palsy and (2) develop a mapping algorithm from the Cerebral Palsy Quality of Life Questionnaire for Children (CPQoL-Child) onto the Child Health Utility 9D (CHU9D) measure.
Method:
The associations between physical and upper-limb classifications and HRQoL of 76 children (40 females, 36 males) aged 6 to 15 years (mean age 9 years 7 months [SD 3y]) were assessed. Five statistical techniques were developed and tested, which predicted the CHU9D scores from the CPQoL-Child total/domain scores, age, and sex.
Results:
Most participants had mild impairments. The Manual Ability Classification System (MACS) level was significantly negatively correlated with CHU9D and CPQoL-Child (r=-0.388 and r=-0.464 respectively). There was a negative correlation between the Neurological Hand Deformity Classification (NHDC) and CPQoL-Child (r=-0.476, p<0.05). The generalized linear model with participation, pain domain, and age had the highest predictive accuracy.
Interpretation:
The weak negative correlations between classification levels and HRQoL measures may be explained by the restricted range of impairment levels of the participants. The MACS and NHDC explained the impact of upper-limb impairment on HRQoL better than the other classifications. The generalized linear model with participation, pain, and age is the suggested mapping algorithm. The suggested mapping algorithm will facilitate the use of CPQoL-Child for economic evaluation and can be used to conduct cost-utility analyses.
What This Paper Adds:
The Manual Ability Classification System and Neurological Hand Deformity Classification were the best predictors of health-related quality of life measures. Age and Cerebral Palsy Quality of Life Questionnaire for Children participation and pain domain scores can predict Child Health Utility 9D scores.
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