2002 Library of Congress Cataloging-in-Publication Data 2000 CDC growth charts for the United States: methods and development.. Series 11, Number 246 2000 CDC Growth Charts for the Uni
Trang 12000 CDC Growth Charts for the United States: Methods and Development
Trang 2All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated
Suggested Citation
Kuczmarski RJ, Ogden CL, Guo SS, et al 2000 CDC growth charts for the United States: Methods and development National Center for Health Statistics Vital Health Stat 11(246) 2002
Library of Congress Cataloging-in-Publication Data
2000 CDC growth charts for the United States: methods and development
p cm — (DHHS publication ; no (PHS) 2002-1696) (Vital and health statistics Series 11, Data from the National Health Survey ; no 246)
‘‘May, 2002.’’
ISBN 0-8406-0575-7
1 Children—Anthropometry—United States—Statistics 2 Children— United States—Growth—Statistics 3 United States—Statistics, Vital
Trang 3Series 11, Number 246
2000 CDC Growth Charts for the United States: Methods
and Development
Data From the National Health
Examination Surveys and the National Health and Nutrition Examination
Surveys
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Disease Control and Prevention
National Center for Health Statistics
Trang 4National Center for Health Statistics
Edward J Sondik, Ph.D., Director
Jack R Anderson, Deputy Director
Jack R Anderson, Acting Associate Director for
International Statistics
Jennifer H Madans, Ph.D., Associate Director for Science Lawrence H Cox, Ph.D., Associate Director for Research
and Methodology
Jennifer H Madans, Ph.D., Acting Associate Director for
Analysis, Epidemiology, and Health Promotion
Edward L Hunter, Associate Director for Planning, Budget,
and Legislation
Jennifer H Madans, Ph.D., Acting Associate Director for
Vital and Health Statistics Systems
Douglas L Zinn, Acting Associate Director for
Management
Charles J Rothwell, Associate Director for Information
Technology and Services
Division of Health Examination Statistics
Trang 5Acknowledgments
acknowledge the contributions
of many individuals who had
identified in appendix I
iii
Trang 6Acknowledgments iii
Abstract 1
Introduction 1
Historical Background 1
Concerns Surrounding the 1977 Charts 2
The Revision 3
Methods 3
Data Sources 3
Data Exclusions 5
Statistical Curve Smoothing Procedures 5
Results 10
Observed and Smoothed Percentiles 10
Evaluation of the Revised Growth Curves 10
Differences Between the 1977 NCHS and the 2000 CDC Growth Curves 11
Discussion 11
Revision Process 11
Growth Chart Workshops 12
Major Features of the 2000 CDC Growth Charts for the United States 12
Using the Revised Growth Charts 12
Specialized Charts 13
General Growth Chart Principles 14
Conclusions 15
References 16
Appendix I Description of Growth Chart Workshops 187
Workshop 1 187
Workshop 2 188
Workshop 3 189
Workshop 4 189
Appendix Table I Participants in the NCHS growth chart workshops 187
Figures 1 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 97th percentiles, birth to 36 months: Boys weight-for-age 19
2 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 97th percentiles, birth to 36 months: Girls weight-for-age 20
3 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 97th percentiles, birth to 36 months: Boys length-for-age 21
4 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 97th percentiles, birth to 36 months: Girls length-for-age 22
v
Trang 7Boys weight-for-length
6 Girls weight-for-length
7 Boys head circumference-for-age
8 Girls head circumference-for-age
9 Boys weight-for-age
10 Girls weight-for-age
11 Boys stature-for-age
12 Girls stature-for-age
13 Boys body mass index-for-age
14 Girls body mass index-for-age
15 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, 97th percentiles: Boys weight-for-stature
16 Individual growth chart 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, 97th percentiles: Girls weight-for-stature
17 and weight-for-age percentiles
18 and weight-for-age percentiles
19 circumference-for-age and weight-for-length
20 circumference-for-age and weight-for-length
21 weight-for-age
22 weight-for-age
23 index-for-age
24 index-for-age
25 Clinical growth chart 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th percentiles: Boys weight-for-stature
26 Clinical growth chart 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th percentiles: Girls weight-for-stature
27 Smoothed percentile curves, 22–39 months: Boys length-for-age and stature-for-age
28 Smoothed percentile curves, 22–39 months: Girls length-for-age and stature-for-age
29 Smoothed percentile curves, 75–106 cm: Boys weight-for-length and weight-for-stature
30 Smoothed percentile curves, 75–106 cm: Girls weight-for-length and weight-for-stature
31 Boys weight-for-age
32 Boys weight-for-age
33 Girls weight-for-age
34 Girls weight-for-age
35 Boys recumbent length-for-age
36 Boys recumbent length-for-age
37 Girls recumbent length-for-age
vi
Trang 8Girls recumbent length-for-age
39
Boys weight-for-length
40
Boys weight-for-length
41
Girls weight-for-length
42
Girls weight-for-length
43
Boys head circumference-for-age
44
Boys head circumference-for-age
45
Girls head circumference-for-age
46
Girls head circumference-for-age
47
Boys weight-for-stature
48
Boys weight-for-stature
49
Girls weight-for-stature
50
Girls weight-for-stature
51
Boys weight-for-age
52
Boys weight-for-age
53
Girls weight-for-age
54
Girls weight-for-age
55
Boys stature-for-age
56
Boys stature-for-age
57
Girls stature-for-age
58
Girls stature-for-age
59
Boys body mass index-for-age
60
24–237 months: Boys body mass index-for-age
61
Girls body mass index-for-age
62
Girls body mass index-for-age
63
Boys weight-for-age
64
Boys weight-for-age
65
Girls weight-for-age
66
Girls weight-for-age
vii
Trang 9Boys length-for-age
68
Boys length-for-age
69
Girls length-for-age
70
Girls length-for-age
71
for-length
72
for-length
73
for-length
74
for-length
75
head circumference-for-age
76
Boys head circumference-for-age
77
Girls head circumference-for-age
78
Girls head circumference-for-age
79
for-stature
80
for-stature
81
for-stature
82
83
84
85
86
87
88
89
90
91
92
93
94
95
viii
Trang 1097
98
99
100
101
102
103
104
105
106
107
108
Text Tables
A Charts included in the 1977 NCHS Growth Charts and the 2000 CDC Growth Charts
B Source of data for each growth chart
C Primary and supplemental data sources
D Summary of curve smoothing procedures
Detailed Tables
1
2
3
4
5
6
7
8
9
10
11
12
Trang 1114
15
16 Observed mean, standard deviation, and selected percentiles for body mass index (kilograms/meter2
17
18
19
20
21
22
23
24 L, M, and S parameters and selected smoothed percentiles for body mass index (BMI, kilograms/meter2
x
Trang 12Series 11, No 246 [ Page 7
parametric form with estimated
parameters specific for each selected
major percentile
The parameters of the linear
regressions were estimated using the
SAS procedure REG, and the parameters
in the nonlinear regression were
estimated using the SAS procedure
NLIN (39) The fit of the models was
evaluated using root mean square error
(RMSE), R2, and CV (40)
The Transformation Stage
In order to estimate any percentile
and allow calculation of standard
deviation units and z-scores, a modified
LMS statistical procedure was applied to
the smoothed percentile curves The
LMS method does not change the
distribution of percentile curves in a
growth chart; rather it provides a way to
estimate percentiles in a continuous
manner
The distribution of some
anthropometric data used in the growth
charts are skewed To remove skewness,
a power transformation can be used to
stretch one tail of the distribution while
the other tail is shrunk A Box-Cox
transformation can make the distribution
nearly normal (41) The assumption is
that, after the appropriate power
transformation, the data are closely
approximated by a normal
distribution (42) The transformation
does not adjust for kurtosis, which is a
less important contributor to
nonnormality than skewness (43)
In the LMS technique, three
parameters are estimated: the median
(M), the generalized coefficient of
variation (S), and the power in the
Box-Cox transformation (L) The L
reflects the degree of skewness The
LMS transformation equation is:
X = M (1 + LSZ) 1/L L ≠ 0
or
X = M exp(SZ) L = 0
where X is the physical measurement
and Z is the z-score that corresponds to
the percentile
The key task of the transformation
was to estimate parameters L, M, and S
With estimates of L, M, and S, values of
X are connected to the values of Z
through the above equation The
percentile is obtained from a normal distribution table where the z-score corresponds to the percentile of interest
For example, a z-score of 0.2019 corresponds to the 58th percentile In
the case of growth charts, with the L, M, and S parameters, it is possible to
evaluate any single measure in a population as an exact z-score or percentile
To generate age-specific estimates
of L, M, and S, Cole (42,44) has
recommended applying a penalized likelihood estimation procedure to the raw data In this approach smoothed
curves of L, M, and S are generated
first, and then smoothed percentile curves, or an individual standardized score, can be obtained from the values
of L, M, and S
In contrast to the original LMS procedure, a modified LMS estimation procedure was created and used to generate the 2000 CDC Growth Charts
In the modified LMS approach, empirical percentile curves were initially smoothed and parametric models were generated, as described above Then, at each age or length/stature interval, a group of 9 equations (10 for BMI charts) was generated by specifying the LMS transformation equations for the previously smoothed major percentiles
A simultaneous solution for the three
parameters of L, M, and S from the
group of specified equations was generated using the SAS procedure NLIN (39) By minimizing the sum of
squared errors, the set of L, M, and S
parameters was obtained as the best solution to a system of equations rather than as likelihood-based estimates from empirical data This approach is similar
to the method used by Cole to estimate LMS parameters from published percentile curves (45–47)
This modified LMS procedure produced final curves that are extremely close to the smoothed percentile curves obtained from the first stage of
smoothing The net result is that the close fit of the smoothed curves from the first stage of smoothing to the empirical data is retained In addition, the modified LMS method allows z-scores to be obtained in a continuous manner The LMS values were
calculated by solving equations that
used the values for percentiles ranging from the 3rd to the 97th Percentiles less than the 3rd or greater than the 97th are beyond the range of the data from which the LMS parameters were calculated As in any statistical procedure, extrapolation beyond the range of the data should be done with caution
The final set of percentile curves for the CDC growth charts presented in this report was produced using this modified LMS estimation procedure In the transformation stage, percentiles were developed at 1-month or 1-centimeter intervals in the infant and
child charts Estimates of L, M, and S
parameters in these intervals were calculated to provide the necessary tools for determining additional percentiles Generally, 1-month or 1-centimeter intervals will be adequate for estimation
or evaluation To obtain percentiles at
finer intervals, the L, M, and S values
could be interpolated
Detailed Procedures by Chart
Weight-for-Age
Combining infant and child/adolescent weight-for-age—After the infant
weight-for-age and child/adolescent weight-for-age curves were smoothed using a 3-parameter linear model and LWR, the results were combined and refit from birth to age 20 years using a single regression model for each sex The smoothed weight-for-age curves for infants and for older children were combined to obtain a seamless transition between the curves Ultimately the combined weight-for-age curve was separated into infant and child/
adolescent curves to facilitate use in clinical settings
In order to combine the infant and child/adolescent weight-for-age curves, weighted averages of overlapping empirical percentiles from infant and child charts at 2.25 years (24.0–29.9 months) and 2.75 years (30.0–35.9 months) were calculated using the combined infant and child/adolescent data The empirical percentiles were not identical at ages 24–36.9 months because VLBW infants (<1,500 grams) were excluded from the infant