Children at high risk for overweight: a classification and regression trees analysis approach

Obes Res. 2005 Jul;13(7):1270-4. doi: 10.1038/oby.2005.151.

Abstract

Objective: Early identification of children at high risk for childhood overweight is a major challenge in fighting the obesity epidemic. We tried to identify the most powerful set of combined predictors for childhood overweight at school entry.

Research methods and procedures: A classification and regression trees analysis on risk factors for childhood overweight in 4289 children 5 to 6 years of age participating in the obligatory school entry health examination 2001/2002 in Bavaria, Germany, was performed. Parental questionnaires asked for children's weight at birth and 2 years, breastfeeding history, maternal smoking in pregnancy, parental education, parental overweight/obesity, nationality, and number of older siblings. Overweight was defined according to sex- and age-specific BMI cut-points proposed by the International Obesity Task Force.

Results: Prevalence of overweight was 11% among the entire study population. Although high early weight gain >10,000 grams was found in about one-half of the overweight children, its positive predictive value reached only 25%, indicating that one of four children with a high early weight gain is overweight at school entry. The best reliable set of predictors included high early weight gain and obese parents and accounted for a likelihood ratio of 3.6, with a corresponding positive predictive value of 40%, and was found in 4% of all children.

Discussion: A combination of predictors available at 2 years of age could improve predictability of overweight at school entry. However, corresponding low positive predictive values indicate a precision of the prediction that might be insufficient for targeting intervention programs for identified high-risk children.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Birth Weight
  • Body Mass Index
  • Child
  • Child, Preschool
  • Educational Status
  • Female
  • Germany / epidemiology
  • Health Surveys
  • Humans
  • Male
  • Obesity / classification
  • Obesity / epidemiology*
  • Obesity / etiology
  • Parents*
  • Predictive Value of Tests
  • Regression Analysis
  • Risk Assessment
  • Risk Factors
  • Smoking
  • Surveys and Questionnaires
  • Weight Gain*