Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts

• Published in: The American journal of clinical nutrition Vol. 90; no. 5; pp. 1314 - 1320
• Main Authors: Flegal, Katherine M, Wei, Rong, Ogden, Cynthia L, Freedman, David S, Johnson, Clifford L, Curtin, Lester R
• Format: Journal Article
• Language: English
• Published: Bethesda, MD American Society for Clinical Nutrition 01.11.2009; American Society for Nutrition; American Society for Clinical Nutrition, Inc
• Subjects: 2000 Centers for Disease Control and Prevention growth charts; Adolescent; Biological and medical sciences; Body Mass Index; Centers for Disease Control and Prevention; Centers for Disease Control and Prevention, U.S; Child; child nutrition; Child, Preschool; children; Children & youth; data analysis; Disease control; disease detection; Disease prevention; estimation; Feeding. Feeding behavior; Female; Fundamental and applied biological sciences. Psychology; Growth - physiology; Human growth; Humans; Male; Nutrition Surveys; obesity; overweight; Overweight - epidemiology; Thinness - epidemiology; United States - epidemiology; Vertebrates: anatomy and physiology, studies on body, several organs or systems; Young Adult; United States; Human; Prevention; Body mass index; Growth; Age
• ISSN: 0002-9165; 1938-3207
• DOI: 10.3945/ajcn.2009.28335

BACKGROUND: The 2000 Centers for Disease Control and Prevention (CDC) growth charts included lambda-mu-sigma (LMS) parameters intended to calculate smoothed percentiles from only the 3rd to the 97th percentile. OBJECTIVE: The objective was to evaluate different approaches to describing more extreme values of body mass index (BMI)-for-age by using simple functions of the CDC growth charts. DESIGN: Empirical data for the 99th and the 1st percentiles of BMI-for-age were calculated from the data set used to construct the growth charts and were compared with estimates extrapolated from the CDC-supplied LMS parameters and to various functions of other smoothed percentiles. A set of reestimated LMS parameters that incorporated a smoothed 99th percentile were also evaluated. RESULTS: Extreme percentiles extrapolated from the CDC-supplied LMS parameters did not match well to the empirical data for the 99th percentile. A better fit to the empirical data was obtained by using 120% of the smoothed 95th percentile. The empirical first percentile was reasonably well approximated by extrapolations from the LMS values. The reestimated LMS parameters had several drawbacks and no clear advantages. CONCLUSIONS: Several approximations can be used to describe extreme high values of BMI-for-age with the use of the CDC growth charts. Extrapolation from the CDC-supplied LMS parameters does not provide a good fit to the empirical 99th percentile values. Simple approximations to high values as percentages of the existing smoothed percentiles have some practical advantages over imputation of very high percentiles. The expression of high BMI values as a percentage of the 95th percentile can provide a flexible approach to describing and tracking heavier children.