![]() ![]() Step 6: Examine the new dataset, _whodata, with PROC MEANS or some other procedure to verify that the z-scores and other variables have been created. Triceps skinfold thickness-for-age for children between 91 and 731 days of age Subscapular skinfold thickness-for-age for children between 91 and 731 days of age Head circumference-for-age for children between 0 and 731 days of ageĪrm circumference-for-age for children between 91 and 731 days of age Note that for children under 2 y of age, weight-for-height, not BMI-for-age, is recommended. Weight-for-height for children with heights between 45 and 110 cmīMI-for-age for children between 1 and 731 days of age. Height-for-age for children between 1 and 731 days of age Weight-for-age for children between 1 and 731 (inclusive) days of age Z-Scores, percentiles, and extreme (biologically implausible, BIV) values in output dataset. Table 2: Z-Scores, percentiles, and extreme values (biologically implausible, BIV) in output dataset, _ whodata The names and descriptions of these new variables in _whodata are in Table 2. Step 5: Submit the %include statement. This will create a dataset, named _whodata, which contains all of your original variables along with z-scores, percentiles, and flags for extreme values. If necessary, change this statement to point at the folder containing the downloaded ‘WHO-source-code.sas’ file. This tells your SAS program to run the statements in ‘WHO-source-code.sas’. %include ‘c:\sas\growth charts\who\data\WHO-source-code.sas’ run Step 4: Copy and paste the following line into your SAS program after the line (or lines) in step #3. It’s unlikely that the SAS code will overwrite other variables in your dataset, but you should avoid having variable names that begin with an underscore, such as _bmi. Z-scores and percentiles for variables that are not in mydata will be coded as missing (.) in the output dataset (named _whodata). Sex (coded as 1 for boys and 2 for girls) and agedays must be in mydata. īMI (weight (kg) / height (m) 2). If your data doesn’t contain BMI, the program calculates it. If BMI is present in your data, the program will not overwrite it. Recumbent length in cm. If standing height (rather than recumbent length) was recorded, add 0.7 cm to the values (see. If age is known only to the completed number of weeks (e.g., 5 weeks of age would represent any number of days between 35 and 41), multiply by 7 and consider adding 4 (median number of days in a week). If age is known only to the completed number of months, multiply by 365.25/12, and consider adding 15. VariableĬhild’s age in days must be present. If this value is not an integer, the program rounds to the nearest whole number. Instructions for SAS users (step 3), guidance on renaming and coding variables in your dataset. If you’re not using SAS, you can download WHOref_d.cvs, and create a program based on who-source-code.sas to do the necessary calculations. ![]() This reference data set combines values from several WHO datasets. The SAS program, WHO-source-code.sas (files are below, in step #1), calculates these z-scores and percentiles based on reference values in WHOref_d.sas7bdat. Although WHO provides several macros and a PC program for these calculations, this SAS program follows the same steps as does the SAS program for the CDC growth charts. Additional details about the ages for which the various z-scores and percentiles are calculated are given in Table 2 (below). Observations that contain extreme values ( absolute z-scores above 5 or 6 ) are flagged as being biologically implausible. Weight-for-height z-scores and percentiles are also calculated. The purpose of this SAS program is to calculate the percentiles and Z-scores (standard deviations) for a child’s sex and age from birth up to 2 years of age for BMI, weight, height, skinfold thicknesses (triceps and subscapular), arm circumference, and head circumference based on the WHO Growth Charts. ![]()
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