Faculty of Health, Institute for Health Transformation, Deakin University, Geelong, Melbourne, Victoria, Australia.
Centre for Disability and Development Research, Australian Catholic University, Fitzroy, Melbourne, Victoria, Australia.
Dev Med Child Neurol. 2020 Jul;62(7):854-860. doi: 10.1111/dmcn.14488. Epub 2020 Feb 16.
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.
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.
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.
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.
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.
(1)研究脑瘫儿童上肢功能障碍与健康相关生活质量(HRQoL)之间的关系,(2)制定从脑瘫儿童生活质量问卷(CPQoL-Child)到儿童健康效用 9 维度(CHU9D)的映射算法。
评估了 76 名 6 至 15 岁(平均年龄 9 岁 7 个月[SD 3y])儿童的身体和上肢分类与 HRQoL 的相关性。开发并测试了 5 种统计技术,这些技术从 CPQoL-Child 总分/领域得分、年龄和性别预测 CHU9D 得分。
大多数参与者的损伤程度较轻。手动能力分类系统(MACS)水平与 CHU9D 和 CPQoL-Child 呈显著负相关(r=-0.388 和 r=-0.464)。神经手畸形分类(NHDC)与 CPQoL-Child 呈负相关(r=-0.476,p<0.05)。包含参与度、疼痛域和年龄的广义线性模型具有最高的预测准确性。
分类水平与 HRQoL 指标之间的弱负相关可能是由于参与者的损伤程度范围有限所致。MACS 和 NHDC 比其他分类更能解释上肢功能障碍对 HRQoL 的影响。包含参与度、疼痛和年龄的广义线性模型是建议的映射算法。建议的映射算法将促进 CPQoL-Child 在经济评估中的使用,并可用于进行成本效用分析。
手动能力分类系统和神经手畸形分类是健康相关生活质量指标的最佳预测因子。年龄、脑瘫儿童生活质量问卷参与度和疼痛域评分可以预测儿童健康效用 9 维度评分。