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Beyerlein, A. ; Kusian, D. ; Ziegler, A.-G. ; Schaffrath-Rosario, A.* ; von Kries, R.*

Classification tree analyses reveal limited potential for early targeted prevention against childhood overweight.

Obesity 22, 512–517 (2014)

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	Open Access Green

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OBJECTIVE: Whether specific combinations of risk factors in very early life might allow identification of high-risk target groups for overweight prevention programs was examined. DESIGN AND METHODS: Data of n = 8981 children from the German KiGGS study were analyzed. Using a classification tree approach, predictive risk factor combinations were assessed for overweight in 3-6, 7-10, and 11-17-year-old children. RESULTS: In preschool children, the subgroup with the highest overweight risk were migrant children with at least one obese parent, with a prevalence of 36.6 (95% confidence interval or CI: 22.9, 50.4)%, compared to an overall prevalence of 10.0 (8.9, 11.2)%. The prevalence of overweight increased from 18.3 (16.8, 19.8)% to 57.9 (46.6, 69.3)% in 7-10-year-old children, if at least one parent was obese and the child had been born large-for-gestational-age. In 11-17-year-olds, the overweight risk increased from 20.1 (18.9, 21.3)% to 63.0 (46.4, 79.7)% in the highest risk group. However, high prevalence ratios were found only in small subgroups, containing <10% of all overweight cases in the respective age group. CONCLUSIONS: Our results indicate only a limited potential for early targeted preventions against overweight in children and adolescents.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Body-mass Index; German Health Interview; Risk-factors; Preschool-children; Adolescents Kiggs; Remission Rates; Obesity; Prevalence; Trends; Worldwide

ISSN (print) / ISBN 1930-7381

e-ISSN 1930-739X

Journal Obesity

Quellenangaben Volume: 22, Issue: 1, Pages: 512–517

Publisher Wiley

Publishing Place Hoboken

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Diabetes Research (IDF)