Calculating anthropometric z-scores using zscorer

The main function in the zscorer package is addWGSR().

To demonstrate its usage, we will use the accompanying dataset in zscorer called anthro3. We inspect the dataset as follows:

anthro3

which returns:

#>   psu age sex weight height muac oedema
#> 1   1  10   1    5.7   64.2  125      2
#> 2   1  10   2    5.8   64.4  121      2
#> 3   1   9   2    6.5   62.2  139      2
#> 4   1  11   9    6.5   64.9  129      2
#> 5   1  24   2    6.5   72.9  120      2
#> 6   1  12   2    6.6   69.4  126      2

anthro3 contains anthropometric data from a Rapid Assessment Method (RAM) survey from Burundi.

Anthropometric indices (e.g. weight-for-height z-scores) have not been calculated and added to the data.

We will use the addWGSR() function to add weight-for-height (wfh) z-scores to the example data:

svy <- addWGSR(data = anthro3, sex = "sex", firstPart = "weight",
               secondPart = "height", index = "wfh")
#> ===========================================================================

A new column named wfhz has been added to the dataset:

#>   psu age sex weight height muac oedema  wfhz
#> 1   1  10   1    5.7   64.2  125      2 -2.73
#> 2   1  10   2    5.8   64.4  121      2 -2.04
#> 3   1   9   2    6.5   62.2  139      2  0.13
#> 4   1  11   9    6.5   64.9  129      2    NA
#> 5   1  24   2    6.5   72.9  120      2 -3.44
#> 6   1  12   2    6.6   69.4  126      2 -2.26

The wfhz column contains the weight-for-height (wfh) z-scores calculated from the sex, weight, and height columns in the anthro3 dataset. The calculated z-scores are rounded to two decimals places unless the digits option is used to specify a different precision (run ?addWGSR to see description of various parameters that can be specified in the addWGSR() function).

The addWGSR() function takes up to nine parameters to calculate each index separately, depending on the index required. These are described in the Help files of the zscorer package which can be accessed as follows:

?addWGSR

The standing parameter specifies how “stature” (i.e. length or height) was measured. If this is not specified, and in some special circumstances, height and age rules will be applied when calculating z-scores. These rules are described in the table below.

The addWGSR() function will not produce error messages unless there is something very wrong with the data or the specified parameters. If an error is encountered in a record then the value NA is returned. Error conditions are listed in the table below.

We can see this error behaviour using the example data:

table(is.na(svy$wfhz))
#> 
#> FALSE  TRUE 
#>   220     1

We can display the problem record:

svy[is.na(svy$wfhz), ]
#>   psu age sex weight height muac oedema wfhz
#> 4   1  11   9    6.5   64.9  129      2   NA

The problem is due to the value 9 in the sex column, which should be coded 1 (for male) and 2 (for female). Z-scores are only calculated for records with sex specified as either 1 (male) or 2 (female). All other values, including NA, will return NA.

The addWGSR() function requires that data are recorded using the required units or required codes (see ?addWGSR to check units required by the different function parameters).

The addWGSR() function will return incorrect values if the data are not recorded using the required units. For example, this attempt to add weight-for-age z-scores to the example data:

svy <- addWGSR(data = svy, sex = "sex", firstPart = "weight", 
               secondPart = "age", index = "wfa")
#> ===========================================================================

will give incorrect results:

summary(svy$wfaz)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   3.450   7.692   9.840   9.684  11.430  15.900       1

The odd range of values is due to age being recorded in months rather than days.

It is simple to convert all ages from months to days:

svy$age <- svy$age * (365.25 / 12)
head(svy)
#>   psu      age sex weight height muac oedema  wfhz wfaz
#> 1   1 304.3750   1    5.7   64.2  125      2 -2.73 3.45
#> 2   1 304.3750   2    5.8   64.4  121      2 -2.04 3.95
#> 3   1 273.9375   2    6.5   62.2  139      2  0.13 5.12
#> 4   1 334.8125   9    6.5   64.9  129      2    NA   NA
#> 5   1 730.5000   2    6.5   72.9  120      2 -3.44 3.82
#> 6   1 365.2500   2    6.6   69.4  126      2 -2.26 5.01

before calculating and adding weight-for-age z-scores:

svy <- addWGSR(data = svy, sex = "sex", firstPart = "weight", 
               secondPart = "age", index = "wfa")
#> ===========================================================================
head(svy)
#>   psu      age sex weight height muac oedema  wfhz  wfaz
#> 1   1 304.3750   1    5.7   64.2  125      2 -2.73 -4.13
#> 2   1 304.3750   2    5.8   64.4  121      2 -2.04 -3.19
#> 3   1 273.9375   2    6.5   62.2  139      2  0.13 -1.97
#> 4   1 334.8125   9    6.5   64.9  129      2    NA    NA
#> 5   1 730.5000   2    6.5   72.9  120      2 -3.44 -4.61
#> 6   1 365.2500   2    6.6   69.4  126      2 -2.26 -2.56
summary(svy$wfaz)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>  -4.610  -1.873  -1.085  -1.154  -0.480   2.600       1

The muac column in the example dataset is recorded in millimetres (mm). We need to convert this to centimetres (cm):

svy$muac <- svy$muac / 10
head(svy)
#>   psu      age sex weight height muac oedema  wfhz  wfaz
#> 1   1 304.3750   1    5.7   64.2 12.5      2 -2.73 -4.13
#> 2   1 304.3750   2    5.8   64.4 12.1      2 -2.04 -3.19
#> 3   1 273.9375   2    6.5   62.2 13.9      2  0.13 -1.97
#> 4   1 334.8125   9    6.5   64.9 12.9      2    NA    NA
#> 5   1 730.5000   2    6.5   72.9 12.0      2 -3.44 -4.61
#> 6   1 365.2500   2    6.6   69.4 12.6      2 -2.26 -2.56

before using the addWGSR() function to calculate MUAC-for-age z-scores:

svy <- addWGSR(svy, sex = "sex", firstPart = "muac",    
               secondPart = "age", index = "mfa")
#> ===========================================================================
head(svy)
#>   psu      age sex weight height muac oedema  wfhz  wfaz  mfaz
#> 1   1 304.3750   1    5.7   64.2 12.5      2 -2.73 -4.13 -1.97
#> 2   1 304.3750   2    5.8   64.4 12.1      2 -2.04 -3.19 -1.88
#> 3   1 273.9375   2    6.5   62.2 13.9      2  0.13 -1.97 -0.14
#> 4   1 334.8125   9    6.5   64.9 12.9      2    NA    NA    NA
#> 5   1 730.5000   2    6.5   72.9 12.0      2 -3.44 -4.61 -2.70
#> 6   1 365.2500   2    6.6   69.4 12.6      2 -2.26 -2.56 -1.46

As a last example we will use the addWGSR() function to add body mass index-for-age (bfa) z-scores to the data to create a new variable called bmiAgeZ with a precision of 4 decimal places as:

svy <- addWGSR(data = svy, sex = "sex", firstPart = "weight", 
               secondPart = "height", thirdPart = "age", index = "bfa", 
               output = "bmiAgeZ", digits = 4)
#> ===========================================================================
head(svy)
#>   psu      age sex weight height muac oedema  wfhz  wfaz  mfaz bmiAgeZ
#> 1   1 304.3750   1    5.7   64.2 12.5      2 -2.73 -4.13 -1.97 -2.6928
#> 2   1 304.3750   2    5.8   64.4 12.1      2 -2.04 -3.19 -1.88 -2.0005
#> 3   1 273.9375   2    6.5   62.2 13.9      2  0.13 -1.97 -0.14  0.0405
#> 4   1 334.8125   9    6.5   64.9 12.9      2    NA    NA    NA      NA
#> 5   1 730.5000   2    6.5   72.9 12.0      2 -3.44 -4.61 -2.70 -2.8958
#> 6   1 365.2500   2    6.6   69.4 12.6      2 -2.26 -2.56 -1.46 -2.0796