University College London Great Ormond Street Institute of Child Health, London, UK.
World Obesity Federation, London, UK.
Pediatr Obes. 2023 Jan;18(1):e12970. doi: 10.1111/ijpo.12970. Epub 2022 Aug 23.
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.
To improve the algorithm by combining information on overweight and obesity prevalence.
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.
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).
The improved algorithm appears to be effective at harmonizing prevalence rates of child overweight and obesity based on different references.
根据所使用的 BMI 参考值和截断值，一组儿童的超重和肥胖患病率会有所不同。此前，我们开发了一种算法，可将基于一种参考值的患病率转换为基于另一种参考值的患病率。
通过合并超重和肥胖患病率信息来改进该算法。
原始算法假设根据两个不同的截断值 A 和 B 的患病率在 z 分数尺度上相差一个常数。然而，结果表明，分布的上尾处 z 分数差异往往更大，更好地用 表示，其中 是一个随组而异的常数。改进的算法使用超重和肥胖的配对患病率来估计每个组的 。然后，基于截断值 A 的患病率转换为 z 分数，根据 进行上下调整，并进行反转换，从而预测基于截断值 B 的患病率。该算法在来自 20 个国家的 228 组 6-17 岁儿童中进行了测试。
修订后的算法的性能明显优于原始算法。观察到的和预测的患病率之间的残差标准差（SD）为 0.8%（n=2320 次比较），而原始患病率对之间的 SD 差异为 4.3%，这意味着该算法解释了基线方差的 96.7%（原始算法为 88.2%）。
改进后的算法似乎能够有效地协调基于不同参考值的儿童超重和肥胖患病率。
