Australian researchers have compared two health assessment tools, CHU9D and PedsQL, to measure the quality of life in children aged 0-7 diagnosed with anorectal malformations or Hirschsprung disease. Conducted through telephone interviews with parents, the study analyzed data collected over two years to determine the effectiveness and validity of these instruments.
Study Methodology and Participant Demographics
The study encompassed 145 children diagnosed with ARM or HD, identified from a patient database managed by a tertiary pediatric hospital over two decades. Since 2020, parents have been administered the CHU9D and PedsQL proxy report versions via telephone interviews. The research focused on assessing the feasibility, ceiling and floor effects, known-group validity, and convergent validity of both tools across different conditions and age groups.
Key Findings and Instrument Performance
Results indicated that 13.1% of participants had missing values on the CHU9D schoolwork dimension, while 20.7% had missing values on the PedsQL school functioning domain for the 2-4 year old version. Neither CHU9D nor PedsQL exhibited ceiling effects. Importantly, CHU9D demonstrated a stronger effect size in distinguishing children with ARM (ES = 0.32) or HD (ES = 0.90) from healthy peers compared to PedsQL. No significant score differences were observed between ARM and HD groups. Furthermore, moderate to strong correlations were found in most dimensions that are theoretically related between the two instruments.
Inference insights include:
- CHU9D may offer greater sensitivity in differentiating affected children’s quality of life compared to PedsQL.
- Both CHU9D and PedsQL are suitable for assessing children aged two and above with ARM or HD.
- Additional validation is necessary for using CHU9D in children under two years old.
The comparison highlights that while both assessment tools are reliable, CHU9D has a slight edge in certain aspects, particularly in distinguishing between health conditions and healthy controls. The absence of ceiling effects suggests that both tools are effective in capturing a wide range of quality of life scores without bunching at the higher end.
Future research should focus on expanding the age range applicability of CHU9D, ensuring that it provides accurate assessments for even younger children. Moreover, exploring the reasons behind missing values in specific dimensions could enhance the robustness of these tools.
Australian healthcare providers and researchers can utilize these findings to select appropriate quality of life measurement instruments tailored to specific pediatric conditions. Understanding the strengths and limitations of CHU9D and PedsQL will aid in better clinical assessments and potentially improve patient care strategies.
The study underscores the importance of selecting the right tools for measuring pediatric quality of life, particularly in children with congenital conditions like ARM and HD. CHU9D’s superior effect size in certain areas suggests it may be more adept at capturing nuanced differences in health status among young children. However, the necessity for further validation in infants highlights a gap that future studies must address. By adopting the most effective assessment instruments, healthcare professionals can better understand and support the wellbeing of children with these conditions, ultimately leading to more tailored and effective interventions. The comparative analysis between CHU9D and PedsQL not only informs clinical practice but also sets the stage for ongoing improvements in pediatric health measurement.
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