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A SAS Program for the CDC Growth Charts

The purpose of this SAS program is to easily generate a dataset that contains indices of the anthropometric status of children from birth to 20 years of age based on the 2000 CDC growth charts. This updated version of the SAS program includes identifying outlier observations or observations that are considered to be "biologically implausible values (BIVs). It is intended to be easy to use for someone with basic SAS programming skills.

Step by step instructions (Please print this file for later reference):

Step One: Save the SAS programs to your PC.

Windows Internet Explorer users, right-click on each file below and select the Save Target As... option.

Windows Netscape users, right-click on each file below and select the Save Link As... option.

In either case, keep the file names and file extensions as they are — and Save them in a location where you can access them from your SAS program. (1k) (227k)

Note: some browsers may not allow the downloading of .sas files. Try downloading the zip file instead. This contains both files above:

Step Two: Prepare your existing dataset.

Your dataset must include the following variables with the same exact name and the given data format before you run the SAS programs:

AGEMOS Child's age in month. Numeric variable (you may need to calculate this from the date of measurement and the date of birth).
SEX Child's gender. Numeric variable. 1 for male and 2 for female.
HEIGHT Child's recumbent length or standing height in centimeters. Numeric variable.
RECUMBNT Indicator of child's height measurement. 1 for recumbent length and 0 for standing height. Numeric variable.
WEIGHT Child's weight in kilograms. Numeric variable.
HEADCIR Child's head circumference in centimeters (may be set to missing if not collected).

Note: Avoid variable names starting with underscore "_" in your dataset, otherwise your data maybe replaced by some data from the SAS program.

Step Three: Modify the program.

Open the program using your PC SAS PROGRAM EDITOR. Then modify the program as following:

%let datalib='x:\xxxxxx\'; Insert the subdirectory for your existing input dataset (e.g., 'c:\project1\').
%let datain=xxxx; Insert the name of your existing input dataset (e.g., data1).
%let dataout=xxxx; Insert the name of the file you wish to put the results into. If the file name is the same as the input file name, the original file will be overwritten. All data from the input file will be placed in the output file, with the anthropometric indices (z-scores and percentiles) added to the end of the record.
%let saspgm='x:\xxxxxx\'; Insert the subdirectory where the downloaded program was saved (e.g., 'c:\growthcharts\').

No changes are needed in the program file.

Step Four: Run the modified program.

Submit the modified program. The newly created SAS dataset will contain all the original variables as well as the following variables for percentiles and z-scores.

HTPCT: percentile for length-for-age or stature-for-age
HAZ: z-score for length-for-age or stature-for-age
WTPCT: percentile for weight-for-age
WAZ: z-score for weight-for-age
WHPCT: percentile for weight-for-length or weight-for-stature
WHZ: z-score for weight-for-length or weight-for-stature
BMIPCT: percentile for body mass index-for-age
BMIZ: z-score for body mass index-for-age
BMI: calculated body mass index value
[weight(kg)/height(m)2 ]
HCPCT: percentile for head circumference-for-age
HCZ: z-score for head circumference-for-age
_BIVHT: outlier variable for height-for-age (0 – acceptable normal range; 1 – too low; 2 – too high)
_BIVWT: outlier variable for weight-for-age (0 – acceptable normal range; 1 – too low; 2 – too high)
_BIVWHT: outlier variable for weight-for-height (0 – acceptable normal range; 1 – too low; 2 – too high)
_BIVBMI: outlier variable for body mass index-for-age (0 – acceptable normal range; 1 – too low; 2 – too high)

Note: all the outliers defined in this SAS program as too low or too high were based on the World Health Organization fixed exclusion ranges. If researchers prefer to use their own cutoffs to clean their anthropometric data, they can modify the file and output all the flag variables.

For more, see the BIV cutoffs documentation.PDF file (PDF-26k)

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Page last reviewed: May 22, 2007
Page last updated: May 22, 2007
Content Source: Division of Nutrition, Physical Activity and Obesity, National Center for Chronic Disease Prevention and Health Promotion