Features of the CDC Growth Charts
The data used to construct
the 2000 CDC charts included a nationally representative reference
population of children and teens from 2 years to 20 years of age.
State-of-the-art statistical smoothing methods were used to fit the data
from national surveys to create smooth curves.
Read more about statistical smoothing procedures
Read more about
the construction of LMS parameters for the CDC 2000 growth charts.
There are several clinically significant features of the 2000 CDC
growth charts that include:
- BMI-for-age charts for children and teens aged 2 years to 20
- Stature-for-age and weight-for-age charts for children and teens
aged 2 years to 20 years.
- Set 1 of the clinical growth charts has the 5th, 25th, 50th,
75th, 90th, and 95th percentile lines. In addition, the BMI-for-age
growth chart includes the 85th percentile line to identify
overweight. These BMI charts are the most commonly used in the
United States to screen for underweight, healthy weight, overweight,
- Set 2 of the clinical growth charts has the 3rd and 97th
percentile lines on specific charts for selected applications.
Pediatric endocrinologists and others who assess the growth of
children with special health care requirements may wish to use these
- Between the 3rd and 97th percentiles the smoothed percentile
curves have corresponding
- Smoothed percentile curves and z-scores are used to evaluate
the growth of children.
- Percentiles are the most commonly used clinical indicator to
assess the size and growth patterns of individual children in
the United States. Percentiles rank the position of an
individual by indicating what percent of the reference
population the individual would equal or exceed. For example, on
the weight-for-age growth charts, a 5-year-old girl whose weight
is at the 25th percentile, weighs the same or more than 25
percent of the reference population of 5-year-old girls, and
weighs less than 75 percent of the 5-year-old girls in the
reference population (Kuczmarski et al., 2002).
- Because z-scores have a direct relationship with
percentiles, a conversion can occur in either direction using a
standard normal distribution table. For every z-score there is a
corresponding percentile and vice versa population (Kuczmarski
et al., 2002).
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