TY  - INPR
Y1  - 2022/02/22/
TI  - Exploring an algorithm to harmonize International Obesity Task Force and World Health Organization child overweight and obesity prevalence rates
N1  - © 2022 The Authors. Pediatric Obesity published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PB  - WILEY
KW  - Science & Technology
KW  -  Life Sciences & Biomedicine
KW  -  Pediatrics
KW  -  harmonization
KW  -  IOTF
KW  -  obesity
KW  -  overweight
KW  -  prevalence
KW  -  WHO
KW  -  BODY-MASS INDEX
KW  -  THINNESS
KW  -  HEIGHT
ID  - discovery10144938
N2  - 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 z A  and z B  to age-sex-specific BMI cut-offs, then transform the BMIs to z-scores z B , A   and z A , B   using the opposite reference. These z-scores together define the distance between the two cut-offs as the z-score difference dz A , B  = 1 2  z B - z A + z A , B  - z B , A     . Prevalence in the target group based on cut-off A is then transformed to a z-score, adjusted up or down according to dz A , B   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.
AV  - public
JF  - Pediatric Obesity
EP  - 10
A1  - Cole, Tim J
A1  - Lobstein, Tim
UR  - https://doi.org/10.1111/ijpo.12905
ER  -