Classifying Childhood Obesity Using Long-Term Data
Author Information
Author(s): Thaker Vidhu, Ebrahim Nia, Khadegi Apurva, Deng Shuliang, Qian Kun, Yao Zonghui, Thaker Shaleen, May Benjamin, Patibandala Nandan, Lopez-Pintado Sara
Hypothesis
Can a childhood obesity classification system using longitudinal clinical data provide better insights than traditional cross-sectional BMI measurements?
Conclusion
The Longitudinal BMI classification may better reflect long-term cardiometabolic risk in children.
Supporting Evidence
- Obesity was observed in 24.1% and severe obesity in 10.6% of the children studied.
- Children with early onset obesity tended to remain in the same or higher obesity class.
- Higher socioeconomic status was associated with lower odds of obesity.
- The AUC for cardiometabolic risk by longitudinal BMI class was higher than that for cross-sectional BMI.
Takeaway
This study looks at how tracking children's weight over time can help us understand their health better than just looking at their weight at one point.
Methodology
This observational study used electronic health record data from a tertiary care hospital and analyzed individuals with at least three BMI measurements.
Participant Demographics
Children from a tertiary care hospital, with data from 2014-2018.
Statistical Information
P-Value
0.01 - < 0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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