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Exploring an algorithm to harmonize International Obesity Task Force and World Health Organization child overweight and obesity prevalence rates
BACKGROUND: The International Obesity Task Force (IOTF) and World Health Organization (WHO) body mass index (BMI) cut‐offs are widely used to assess child overweight, obesity and thinness prevalence, but the two references applied to the same children lead to different prevalence rates. OBJECTIVES:...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285550/ https://www.ncbi.nlm.nih.gov/pubmed/35193166 http://dx.doi.org/10.1111/ijpo.12905 |
Sumario: | BACKGROUND: The International Obesity Task Force (IOTF) and World Health Organization (WHO) body mass index (BMI) cut‐offs are widely used to assess child overweight, obesity and thinness prevalence, but the two references applied to the same children lead to different prevalence rates. OBJECTIVES: To develop an algorithm to harmonize prevalence rates based on the IOTF and WHO cut‐offs, to make them comparable. METHODS: The cut‐offs are defined as age‐sex‐specific BMI z‐scores, for example, WHO +1 SD for overweight. To convert an age‐sex‐specific prevalence rate based on reference cut‐off A to the corresponding prevalence based on reference cut‐off B, first back‐transform the z‐score cut‐offs [Formula: see text] and [Formula: see text] to age‐sex‐specific BMI cut‐offs, then transform the BMIs to z‐scores [Formula: see text] and [Formula: see text] using the opposite reference. These z‐scores together define the distance between the two cut‐offs as the z‐score difference [Formula: see text]. Prevalence in the target group based on cut‐off A is then transformed to a z‐score, adjusted up or down according to [Formula: see text] and back‐transformed, and this predicts prevalence based on cut‐off B. The algorithm's performance was tested on 74 groups of children from 14 European countries. RESULTS: The algorithm performed well. The standard deviation (SD) of the difference between pairs of prevalence rates was 6.6% (n = 604), while the residual SD, the difference between observed and predicted prevalence, was 2.3%, meaning that the algorithm explained 88% of the baseline variance. CONCLUSIONS: The algorithm goes some way to addressing the problem of harmonizing overweight and obesity prevalence rates for children aged 2–18. |
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