An improved algorithm to harmonize child overweight and obesity prevalence rates.
Summary: 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 b...
| Publicado en: | Pediatric Obesity Vol. 18; no. 1; pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160784461&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160784461 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20476302 ETV7 jtl: Pediatric Obesity issn: 20476302 maglogo: Y pubinfo: dt: Jan2023 vid: 18 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 160784461 158639338 160784461 160784461 10.1111/ijpo.12970 160784461 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: An improved algorithm to harmonize child overweight and obesity prevalence rates. aug: au: Cole, Tim J. Lobstein, Tim affil: University College London Great Ormond Street Institute of Child Health, London, UK sug: subj: Algorithms Pediatric Obesity Epidemiology Health Information Human Child Descriptive Statistics Comparative Studies World Health Organization Child: 6-12 years ab: Summary: 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 dz 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 b×dz, where b was a constant that varied by group. The improved algorithm uses paired prevalence rates of overweight and obesity to estimate b for each group. Prevalence based on cut‐off A is then transformed to a z‐score, adjusted up or down according to b×dz 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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