Step 6: Examine the new dataset, _cdcdata, to verify that the z-scores and other variables have been created. Additional information on the extreme z-scores is given in the Extreme Values, Implausible Values, and Data Errors section. Step 5: The output dataset, _cdcdata, contains your original data and z-scores, percentiles, and flags for extreme values shown in Table 2. The %include will run your data through cdc-source-code.sas and create a dataset named _cdcdata. If necessary, change this statement to point at the folder/directory containing the downloaded cdc-source-code.sas file. %include ‘c:\sas_growth_charts\cdc-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 variables in your dataset, but you should avoid having variable names that begin with an underscore or with ‘mod_’ Z-scores and percentiles for the anthropometric variables not in mydata (or are that are missing) will be coded as missing (.) in the output dataset, _cdcdata. The program calculates BMI if it is not present in your data but will not overwrite BMI if present. If recumbent length was measured for children ≥ 24 months, subtract 0.8 cm.īMI. If standing height was measured for children under 24 months of age, you should add 0.8 cm to these values (see page 8 of ). Height is either standing height (for children ≥ 24 months of age or recumbent length (< 24 months). Sex must be coded as 1 for boys and 2 for girls. If age represents the completed years (e.g., 13 years), multiply by 12 and add 6. If only the completed number of months is known (as in NHANES), add 0.5 to the age so that the maximum error would be 15 days. However, if 49 months were used for all children between 49.0 and < 50 months of age, then most of the calculated z-scores would be too high because, on average, these children would be taller and heavier than children who are 49.0 months of age. In everyday usage, this child’s age would be 4 years or 49 months. For example, if a child were born on Oct 1, 2007, and examined on Nov 15, 2011, the child’s age would be 1506 days or 49.48 (1506 / 30.4375) months. Agemos must be in your dataset, and the program assumes that you know the number of months to the nearest day. Variableĭescription of variables and coding in the input dataset, mydata Instructions for SAS users (Step 3), guidance on renaming and coding variables in your dataset. If you’re not using SAS or R, you can download CDCref_d.csv and create a program based on cdc-source-code.sas. Note that the z-scores and percentiles calculated for children with obesity will differ from earlier (pre-2022) versions of this SAS program. The SAS program, cdc-source-code (files are below, in step #1), calculates these z-scores and percentiles for children in your data from the reference data in cdc_ref.sas7bdat for children without obesity and extended BMI percentiles and z-scores for children with obesity. See the section on the extended BMI percentiles and z-scores for more information. These extreme values, however, are not necessarily incorrect and could be reviewed for possible inclusion or exclusion.Īlthough the SAS program calculates z-scores and percentiles for children up to 20 years of age, the World Health Organization (WHO) growth charts are recommended for children = 95 th percentile (1.645 z-score)) changed on Dec 15, 2022, to use extended BMIz. The program also allows for the identification of outliers. In addition, weight-for-height z-scores and percentiles are also calculated. This SAS program calculates percentiles and z-scores (standard deviations) for a child’s sex and age for BMI, weight, height, and head circumference from the CDC growth charts (1). Note that the calculations for BMI z-scores and percentiles for 2- to 19-year-olds with obesity (BMI ≥ 95 th percentile for a child’s sex and age) have changed on Dec 15, 2022. Extreme values, Implausible Values, and Data Errors.
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