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Updated: Aug 31, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
An improved algorithm to harmonize child overweight and obesity prevalence rates
Tim J Cole1, Tim Lobstein2,3
1University College London Great Ormond Street Institute of Child Health, London, UK.
Insights
An improved algorithm harmonizes child overweight and obesity prevalence rates using different BMI references. This new method significantly enhances accuracy in estimating prevalence across diverse populations.
Area of Science:
- Pediatrics
- Public Health
- Biostatistics
Background:
- Child overweight and obesity prevalence varies with Body Mass Index (BMI) references and cut-offs.
- Previous algorithms could convert prevalence rates between references but required improvement.
Purpose of the Study:
- To enhance an existing algorithm for converting child overweight and obesity prevalence rates.
- To improve accuracy by incorporating both overweight and obesity prevalence data.
Main Methods:
- Developed a revised algorithm using paired prevalence rates of overweight and obesity.
- The algorithm estimates a group-specific z-score adjustment factor.
- Prevalence is transformed to z-score, adjusted, and back-transformed to predict prevalence using a different cut-off.
Main Results:
- The revised algorithm demonstrated superior performance compared to the original.
- Reduced the standard deviation of residuals to 0.8% (from 4.3% for original prevalence pairs).
- Explained 96.7% of baseline variance, a significant improvement over the original algorithm's 88.2%.
Conclusions:
- The enhanced algorithm effectively harmonizes child overweight and obesity prevalence rates across different references.
- Provides a more accurate and reliable method for comparing global child obesity data.
Background:
Prevalence rates of child overweight and obesity for a group of children vary depending on the BMI reference and cut-off used. Previously we developed an algorithm to convert prevalence rates based on one reference to those based on another.
Objective:
To improve the algorithm by combining information on overweight and obesity prevalence.
Methods:
The original algorithm assumed that prevalence according to two different cut-offs A and B differed by a constant amount on the z-score scale. However the results showed that the z-score difference tended to be greater in the upper tail of the distribution and was better represented by , where was a constant that varied by group. The improved algorithm uses paired prevalence rates of overweight and obesity to estimate for each group. Prevalence based on cut-off A is then transformed to a z-score, adjusted up or down according to and back-transformed, and this predicts prevalence based on cut-off B. The algorithm's performance was tested on 228 groups of children aged 6-17 years from 20 countries.
Results:
The revised algorithm performed much better than the original. The standard deviation (SD) of residuals, the difference between observed and predicted prevalence, was 0.8% (n = 2320 comparisons), while the SD of the difference between pairs of the original prevalence rates was 4.3%, meaning that the algorithm explained 96.7% of the baseline variance (88.2% with original algorithm).
Conclusions:
The improved algorithm appears to be effective at harmonizing prevalence rates of child overweight and obesity based on different references.
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