Exploring an algorithm to harmonize International Obesity Task Force and World Health Organization child overweight and obesity prevalence rates

Abstract

Summary 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 zA and zB to age‐sex‐specific BMI cut‐offs, then transform the BMIs to z‐scores zB,A and zA,B using the opposite reference. These z‐scores together define the distance between the two cut‐offs as the z‐score difference dzA,B=12()zB−zA+zA,B−zB,A. Prevalence in the target group based on cut‐off A is then transformed to a z‐score, adjusted up or down according to dzA,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.

Differences in Classification Standards For the Prevalence of Overweight and Obesity in Children. A Systematic Review and Meta-Analysis

“…International standards are insufficient for working with the current population. Further studies similar to that described above by Cole and Lobstein 61 comparing or constructing national or local references using BMI as a primary measure are needed or a working group should be created to address this issue and agree on the unification of a gold standard to address the current epidemic of childhood overweight and obesity, taking into account the geographical region, the ethnic groups and the age groups of the child and youth population and above all, the secular growth to stop working with a standard that has been very useful in the absence of others, but that should be updated.…”

“…Due to this disparity between the prevalence of childhood overweight and obesity according to the classification used, we must highlight the recent publication by Cole and Lobstein 61 where they developed an algorithm to harmonise the cut-off points according to BMI for age and sex of the WHO, CDC and IOTF and make them comparable. This tool allows the researcher to choose any of these three classifications and compare them with the others, making the results more comparable.…”

An improved algorithm to harmonize child overweight and obesity prevalence rates

“…This improved algorithm explains 96.7% of the baseline variance in prevalence, as compared to 88.2% achieved by the original algorithm. 7 So improving the algorithm has reduced the residual variance by over four-fifths, from 11.8% to 3.3%. To apply • In addition the distribution has generally become more skew, with a relatively heavier upper tail, and this has caused the QQ plot to fall more at the upper than the lower end of the range, making the line shallower.…”

“…The original algorithm assumed implicitly that the QQ plot for each target group could be summarized as the line , that is, with 7 . This corresponds to a downward shift in the QQ plot as measured by the intercept .…”

“…This means that the two sets of results cannot be compared or combined, a silo effect which represents a major waste of research effort. This is an issue of data harmonization, and our previous paper 7 introduced an algorithm to address it. The algorithm takes prevalence rates based on one reference, for example, WHO, and estimates what they would be if based on another reference, for example, IOTF.…”