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

Area of Science:

• Pediatric Health
• Obesity Research
• Public Health

Background:

• Childhood overweight is a growing epidemic, posing significant health challenges.
• Early identification of at-risk children is essential for effective intervention strategies.

Purpose of the Study:

• To identify the most effective combined predictors for childhood overweight at school entry.
• To evaluate the predictive power of early risk factors for later overweight status.

Main Methods:

• Classification and regression trees analysis used on data from 4289 children aged 5-6 years.
• Parental questionnaires collected data on birth weight, weight at 2 years, breastfeeding, maternal smoking, parental education, parental overweight/obesity, nationality, and sibling count.
• Overweight defined using International Obesity Task Force (IOTF) BMI cut-points.

Main Results:

• Prevalence of overweight was 11% in the study population.
• High early weight gain (>10,000 grams) had a positive predictive value (PPV) of only 25%.
• The combination of high early weight gain and obese parents yielded the highest PPV (40%) but was present in only 4% of children.

Conclusions:

• Predictors available by age 2 can improve overweight prediction at school entry.
• The current predictive accuracy is insufficient for precise targeting of intervention programs for high-risk children.