Katherine M. Flegal

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

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.

Issues in the Long-Term Evaluation of Diet in Longitudinal Studies

Longitudinal studies are very useful for studying diet/disease relationships. The fundamental components of a longitudinal study are that: 1) data are collected for two or more distinct time periods; 2) the subjects are the same or comparable from one time period to the next; and 3) data are compared between or among time periods in the analysis. A longitudinal study is often assumed to be synonymous with a cohort study, but there are at least four possible definitions for a longitudinal study. While focusing on cohort studies, the paper describes the nature of longitudinal studies, including a discussion of how the different definitions differ from a cohort study and a set of important assumptions necessary to cohort studies. It also highlights some of the major issues associated with such studies, including the selection of a dietary survey methodology; data collection issues in multicultural, multilingual societies; the importance of nutrient databases; measurement error and misclassification in nutrient intake and energy adjustment.

Effects of trimming weight-for-height data on growth-chart percentiles

BACKGROUND Before estimating smoothed percentiles of weight-for-height and BMI-for-age to construct the WHO growth charts, WHO excluded observations that were considered to represent unhealthy weights for height. OBJECTIVE The objective was to estimate the effects of similar data trimming on empirical percentiles from the CDC growth-chart data set relative to the smoothed WHO percentiles for ages 24-59 mo. DESIGN We used the nationally representative US weight and height data from 1971 to 1994, which was the source data for the 2000 CDC growth charts. Trimming cutoffs were calculated on the basis of weight-for-height for 9722 children aged 24-71 mo. Empirical percentiles for 7315 children aged 24-59 mo were compared with the corresponding smoothed WHO percentiles. RESULTS Before trimming, the mean empirical percentiles for weight-for-height in the CDC data set were higher than the corresponding smoothed WHO percentiles. After trimming, the mean empirical 95th and 97th percentiles of weight-for-height were lower than the WHO percentiles, and the proportion of children in the CDC data set above the WHO 95th percentile decreased from 7% to 5%. The findings were similar for BMI-for-age. However, for weight-for-age, which had not been trimmed by the WHO, the empirical percentiles before trimming agreed closely with the upper percentiles from the WHO charts. CONCLUSION WHO data-trimming procedures may account for some of the differences between the WHO growth charts and the 2000 CDC growth charts.

Diabetes, impaired fasting glucose, and elevated HbA1c in U.S. adolescents: The third National Health and Nutrition Examination Survey

OBJECTIVE Using population-based data, we estimated the prevalence of diabetes, impaired fasting glucose, and elevated HbA1c (>6%) levels in U.S. adolescents. RESEARCH DESIGN AND METHODS The Third National Health and Nutrition Examination Survey (1988-1994) examined a representative sample of the U.S. population, which included 2,867 adolescents aged 12-19 years who had serum glucose measured. RESULTS A total of 13 adolescents in the sample were considered to have diabetes; 9 reported using insulin, 2 reported using oral agents only, and 2 did not report any treatment but had high glucose levels (> or = 11.1 mmol/l regardless of length of fast or > or = 7.0 mmol/l after an 8-h fast). Four of these cases (31% of the sample with diabetes) were considered to have type 2 diabetes. The estimated prevalence of diabetes (all types) per 100 adolescents ages 12-19 years was 0.41% (95% confidence interval 0-0.86). The prevalence of impaired fasting glucose (> or = 6.1 mmol/l) among adolescents without diabetes who had fasted for at least 8 h was 1.76% (0.02-3.50). The prevalence of elevated HbA1c (>6%) was 0.39% (0.04-0.74). CONCLUSIONS National data reflect the presence of type 2 diabetes in U.S. adolescents, but the survey sample size was not large enough to obtain precise prevalence estimates because of the relatively low prevalence.