Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts
National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD 20782, USA. American Journal of Clinical Nutrition
(Impact Factor: 6.77).
09/2009; 90(5):1314-20. DOI: 10.3945/ajcn.2009.28335
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.
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.
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.
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.
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.
Available from: Louise L Hardy
- "Weight (kg) and height (cm) were measured by study nurses at baseline, 3, 6 and 12 months using standard procedures as previously described . Body mass index (BMI; kg m −2 ) was calculated from age and sex specific reference values and expressed as a percentage of the 95th centile (BMI % 95th centile) . Change in BMI z-score was not used as >86% of the adolescents had a BMI > 97th centile which is beyond the scope of the CDC 2000 reference data . "
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ABSTRACT: A 12 week exercise program was evaluated for its effect on aerobic fitness, anaerobic threshold, physical activity and sedentary behavior levels in obese insulin resistant adolescents post intervention and at follow up. 111 obese insulin resistant 10-17 year olds were recruited to a 12 month lifestyle intervention, known as RESIST. From months 4 to 6, adolescents participated in supervised exercise sessions twice per week (45-60min/session). Aerobic fitness and anaerobic threshold were measured by gas analysis at baseline, 6 months (post intervention) and 12 months (follow up). Self-reported physical activity and sedentary behavior was measured using the CLASS questionnaire. At 6 months aerobic fitness and time to reach the anaerobic threshold had improved by 5.8% [95% CI: 0.8-11.3] and 19.7% [95% CI: 10.4-29.0], respectively compared with baseline. These improvements were maintained at 12 months. Compared to baseline, 6 month physical activity levels increased by 19min/day [95% CI: 5-33] and screen time decreased by 49min/day [95% CI: 23-74] but returned to baseline levels by 12 months. Improved fitness and anaerobic threshold can be sustained up to 6 months following completion of an exercise program possibly enhancing capacity to perform daily functional tasks.
Copyright © 2015 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Obesity Research & Clinical Practice 05/2015; DOI:10.1016/j.orcp.2015.04.001 · 1.18 Impact Factor
Available from: Lauren M Rossen
- "There are also racial/ethnic disparities in the prevalence of extreme obesity among children [9,21e23]; these disparities are evident as young as preschool and observed within limited socioeconomic strata such as low-income samples  . Most prior studies have examined differences in the proportion of children falling above various body mass index (BMI) cutoffs (e.g., 97th or 99th percentile   , 1.2 times the 95th percentile  ). National estimates for the United States suggest that non-Hispanic black children are nearly twice as likely to fall above the 97th BMI percentile for age and sex compared with non-Hispanic white children (18.6% vs. 9.8%, respectively), and rates are also high among Hispanic children (15.6% ). "
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To explore whether contextual variables attenuate disparities in weight among 18,639 US children and adolescents aged 2 to 18 years participating in the National Health and Nutrition Examination Survey, 2001 to 2010.
Disparities were assessed using the Symmetrized Rényi Index, a new measure that summarizes disparities in the severity of a disease, as well as the prevalence, across multiple population groups. Propensity score subclassification was used to ensure covariate balance between racial and ethnic subgroups and account for individual-level and contextual covariates.
Before propensity score subclassification, significant disparities were evident in the prevalence of overweight and/or obesity and the degree of excess weight among overweight/obese children and adolescents. After propensity score subclassification, racial/ethnic disparities in the prevalence and severity of excess weight were completely attenuated within matched groups, indicating that racial and ethnic differences were explained by social determinants such as neighborhood socioeconomic and demographic factors.
The limited overlap in covariate distributions between various racial/ethnic subgroups warrants further attention in disparities research. The attenuation of disparities within matched groups suggests that social determinants such as neighborhood socioeconomic factors may engender disparities in weight among US children and adolescents.
Annals of Epidemiology 08/2014; 24(10). DOI:10.1016/j.annepidem.2014.07.010 · 2.00 Impact Factor
Available from: Lisa Bailey-Davis
- "Body Mass Index (BMI) and BMI z-scores and percentiles based on age and gender were calculated for each student based on CDC 2000 growth charts . Weight status category was defined based on BMI percentile: underweight (<5th percentile); healthy (≥5th and <85th percentile); overweight (≥85th and < 95th percentile); obese (≥100% to ≤ 120% of the 95th percentile), and severely obese (≥120% of the 95th percentile) [16,17]. "
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ABSTRACT: Increasing school breakfast participation has been advocated as a method to prevent childhood obesity. However, little is known about children’s breakfast patterns outside of school (e.g., home, corner store). Policies that increase school breakfast participation without an understanding of children’s breakfast habits outside of school may result in children consuming multiple breakfasts and may undermine efforts to prevent obesity. The aim of the current study was to describe morning food and drink consumption patterns among low-income, urban children and their associations with relative weight.
A cross-sectional analysis was conducted of data obtained from 651 4th-6th graders (51.7% female, 61.2% African American, 10.7 years) in 2012. Students completed surveys at school that included all foods eaten and their locations that morning. Height and weight were measured by trained research staff.
On the day surveyed, 12.4% of youth reported not eating breakfast, 49.8% reported eating one breakfast, 25.5% reported eating two breakfasts, and 12.3% reported eating three or more breakfasts. The number of breakfasts consumed and BMI percentile showed a significant curvilinear relationship, with higher mean BMI percentiles observed among children who did not consume any breakfast and those who consumed ≥ 3 breakfasts. Sixth graders were significantly less likely to have consumed breakfast compared to younger children. A greater proportion of obese youth had no breakfast (18.0%) compared to healthy weight (10.1%) and overweight youth (10.7%, p = .01).
When promoting school breakfast, policies will need to be mindful of both over- and under-consumption to effectively address childhood obesity and food insecurity.
Clinical trial registration
NCT01924130 from http://clinicaltrials.gov/.
BMC Public Health 06/2014; 14(1):604. DOI:10.1186/1471-2458-14-604 · 2.26 Impact Factor
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