Hsin-Chi Wua, b, Chen-Chin Hsuc, Bin-Han Huangd, Shu-Hui Wene
a Department of Physical Medicine and Rehabilitation, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan
b Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
c Department of Psychiatry, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan
d Department of Community Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan
e Department of Public Health, College of Medicine, Tzu Chi University, Hualien, Taiwan
Abstract
Objective
The Chinese Child Developmental Inventory (CCDI) is a convenient screening tool to identify children with possible developmental delays. The purpose of this study was to update the CCDI norms using a contemporary sample of children, and to compare it with existing CCDI normative data.
Materials and methods
Five hundred fifty-two children, 36.5–75.5 months old, from 30 kindergartens located in three districts (Xindian, Jhonghe, and Yonghe) of New Taipei City, Taiwan were assessed using the CCDI. The updated normative data were compared with existing CCDI norms using a quadratic linear regression model. In addition, smoothed percentile curves (5th–95th) were estimated using the lambda-mu-sigma method.
Results
Among the eight CCDI developmental dimensions, the average scores for general development, comprehension-conceptual, fine motor, situation-comprehension and expressive language (at <50 months old) were higher than the scores of the existing norms that are based on data from 1978; however, the score of the gross motor dimension was slightly lower. No differences in the average scores for self-help existed between the updated and previous norms.
Conclusion
The updated CCDI normative data will provide valuable information for physicians and other professionals working to identify developmental delays at early stages.
Keywords
Child development; Chinese Child Developmental Inventory; Lambda-mu-sigma; Norm; Polynomial regression