zscorer facilitates the calculation of z-scores (i.e. the number of standard deviations from the mean) for key anthropometric indices used to assess early childhood growth: weight-for-age (WFA), height-for-age (HFA), weight-for-height (WFH), BMI-for-age (BFA), arm circumference-for-age (ACFA), head circumference-for-age (HCFA), subscapular skinfold-for-age (SSFA) and triceps skinfold-for-age (TSFA). zscorer refers to the results of the WHO Multicentre Growth Reference Study as standard for calculating the z-scores hence it comes as a dataset in this package.
zscorer calculates the z-score for the corresponding anthropometric index given a dataset of children containing anthropometric measurements for height, weight, arm circumference, head circumference, subscapular skinfold, and triceps skinfold and additional data on age and sex to assess growth and nutritional status of the sample of children against the WHO Multicentre Growth Reference Study standard (WGS).
zscorer also contains functions that calculates the zscore for three commonly assessed anthropometric indices in nutrition surveys (weight-for-height, height-for-age, weight-for-age) given anthropometric measurements of individual children. These are legacy functions used in earlier versions of zscorer that will soon be deprecated.
You can install zscorer from CRAN:
or you can install the development version of zscorer from GitHub with:
if(!require(devtools) { install.packages("devtools") }
devtools::install_github("nutriverse/zscorer")then load 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:
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 2anthro3 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 -2.26The 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:
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.
| index | standing | age | height | Action |
|---|---|---|---|---|
| hfa or lfa | standing | < 731 days | index = lfa height = height + 0.7 cm | |
| hfa or lfa | supine | < 731 days | index = lfa | |
| hfa or lfa | unknown | < 731 days | index = lfa | |
| hfa or lfa | standing | ≥ 731 days | index = hfa | |
| hfa or lfa | supine | ≥ 731 days | index = hfa height = height - 0.7 cm | |
| hfa or lfa | unknown | ≥ 731 days | index = hfa | |
| wfh or wfl | standing | < 65 cm | index = wfl height = height + 0.7 cm | |
| wfh or wfl | standing | ≥ 65 cm | index = wfh | |
| wfh or wfl | supine | ≤ 110 cm | index = wfl | |
| wfh or wfl | supine | more than 110 cm | index = wfh height = height - 0.7 cm | |
| wfh or wfl | unknown | < 87 cm | index = wfl | |
| wfh or wfl | unknown | ≥ 87 cm | index = wfh | |
| bfa | standing | < 731 days | height = height + 0.7 cm | |
| bfa | standing | ≥ 731 days | height = height - 0.7 cm |
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.
| Error condition | Action |
|---|---|
Missing or nonsense value in standing parameter |
Set standing to 3 (unknown) and apply appropriate height or age rules. |
Unknown index specified |
Return NA for z-score. |
Missing sex |
Return NA for z-score. |
Missing firstPart |
Return NA for z-score. |
Missing secondPart |
Return NA for z-score. |
sex is not male (1) or female (2) |
Return NA for z-score. |
firstPart is not numeric |
Return NA for z-score. |
secondPart is not numeric |
Return NA for z-score. |
Missing thirdPart when index = "bfa" |
Return NA for z-score. |
thirdPart is not numeric when index = "bfa" |
Return NA for z-score. |
secondPart is out of range for specified index |
Return NA for z-score. |
We can see this error behaviour using the example data:
We can display the problem record:
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 Table Z1).
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$wfaz)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 3.450 7.692 9.840 9.684 11.430 15.900 1The odd range of values is due to age being recorded in months rather than days.
It is simple to convert all ages from months to days:
svy$age <- svy$age * (365.25 / 12)
head(svy)
#> psu age sex weight height muac oedema wfhz wfaz
#> 1 1 304.3750 1 5.7 64.2 125 2 -2.73 3.45
#> 2 1 304.3750 2 5.8 64.4 121 2 -2.04 3.95
#> 3 1 273.9375 2 6.5 62.2 139 2 0.13 5.12
#> 4 1 334.8125 9 6.5 64.9 129 2 NA NA
#> 5 1 730.5000 2 6.5 72.9 120 2 -3.44 3.82
#> 6 1 365.2500 2 6.6 69.4 126 2 -2.26 5.01before calculating and adding weight-for-age z-scores:
svy <- addWGSR(data = svy, sex = "sex", firstPart = "weight",
secondPart = "age", index = "wfa")
#> ===========================================================================
head(svy)
#> psu age sex weight height muac oedema wfhz wfaz
#> 1 1 304.3750 1 5.7 64.2 125 2 -2.73 -4.13
#> 2 1 304.3750 2 5.8 64.4 121 2 -2.04 -3.19
#> 3 1 273.9375 2 6.5 62.2 139 2 0.13 -1.97
#> 4 1 334.8125 9 6.5 64.9 129 2 NA NA
#> 5 1 730.5000 2 6.5 72.9 120 2 -3.44 -4.61
#> 6 1 365.2500 2 6.6 69.4 126 2 -2.26 -2.56
summary(svy$wfaz)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> -4.610 -1.873 -1.085 -1.154 -0.480 2.600 1The 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 -2.56before using the addWGS() 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 -1.46As 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 -2.0796For this example, we will use the getWGS() function and apply it to dummy data of a 52 month old male child with a weight of 14.6 kg and a height of 98.0 cm.
# weight-for-age z-score
waz <- getWGS(sexObserved = 1, # 1 = Male / 2 = Female
firstPart = 14.6, # Weight in kilograms up to 1 decimal place
secondPart = 52, # Age in whole months
index = "wfa") # Anthropometric index (weight-for-age)
waz
#> [1] -1.187651
# height-for-age z-score
haz <- getWGS(sexObserved = 1,
firstPart = 98, # Height in centimetres
secondPart = 52,
index = "hfa") # Anthropometric index (height-for-age)
haz
#> [1] -1.741175
# weight-for-height z-score
whz <- getWGS(sexObserved = 1,
firstPart = 14.6,
secondPart = 98,
index = "wfh") # Anthropometric index (weight-for-height)
whz
#> [1] -0.1790878Applying the getWGS() function results in a calculated z-score for one child.
For this example, we will use the getCohortWGS() function and apply it to sample data anthro1 that came with zscorer.
As you will see, this dataset has the 4 variables you will need to use with getCohortWGS() to calculate the z-score for the corresponding anthropometric index. These are age, sex, weight and height.
head(anthro1)
#> psu age sex weight height muac oedema haz waz whz flag
#> 1 1 6 1 7.3 65.0 146 2 -1.23 -0.76 0.06 0
#> 2 1 42 2 12.5 89.5 156 2 -2.35 -1.39 -0.02 0
#> 3 1 23 1 10.6 78.1 149 2 -2.95 -1.06 0.57 0
#> 4 1 18 1 12.8 81.5 160 2 -0.28 1.42 2.06 0
#> 5 1 52 1 12.1 87.3 152 2 -4.21 -2.68 -0.14 0
#> 6 1 36 2 16.9 93.0 190 2 -0.54 1.49 2.49 0To calculate the three anthropometric indices for all the children in the sample, we execute the following commands in R:
# weight-for-age z-score
waz <- getCohortWGS(data = anthro1,
sexObserved = "sex",
firstPart = "weight",
secondPart = "age",
index = "wfa")
head(waz, 50)
#> [1] -0.75605549 -1.39021503 -1.05597853 1.41575096 -2.67757242
#> [6] 1.49238050 -0.12987704 -0.02348159 -1.50647344 -1.54381630
#> [11] -2.87495712 -0.43497240 -1.03899540 -1.69281855 -1.31245898
#> [16] -2.21003260 -0.01189226 -0.90917762 -0.67839855 -0.94746695
#> [21] -2.49960425 -0.95659644 -1.65442686 -1.25052760 0.67335751
#> [26] 0.30156301 0.24261346 -2.78670709 -1.15820651 -1.15477183
#> [31] -1.35540820 -0.59134959 -4.14967218 -0.45748752 -0.74331669
#> [36] -1.69725836 -1.05745067 -0.18869508 -0.42095770 -2.21030414
#> [41] -1.30536715 -3.63778143 -0.60662526 -0.54360470 -1.59171780
#> [46] -1.74745738 -0.34803338 0.69896149 -0.74467130 0.18924572
# height-for-age z-score
haz <- getCohortWGS(data = anthro1,
sexObserved = "sex",
firstPart = "height",
secondPart = "age",
index = "hfa")
head(haz, 50)
#> [1] -1.2258169 -2.3475886 -2.9518041 -0.2812852 -4.2056663 -0.5387678
#> [7] -2.4020719 -1.0317699 -2.7410884 -4.7037571 -2.5670550 -2.1144960
#> [13] -2.2323505 -2.3155458 -2.7516165 -2.7930694 0.1121349 -1.9001797
#> [19] -2.9543730 -1.9671042 -3.8716522 0.8667206 -2.8252069 -2.1412285
#> [25] -2.7994643 0.5496459 -1.4372002 -3.7979410 -2.5661752 -1.8301183
#> [31] -1.6548589 -2.7110333 -3.6399642 -1.7955069 -1.6775100 -1.0317699
#> [37] -0.4356881 -1.2660152 0.4990326 -4.6085660 -3.1662351 -1.0695930
#> [43] -1.8477936 -2.5502314 -1.8301183 -2.2755493 -3.2816532 0.4876774
#> [49] -2.4396410 -0.4794744
# weight-for-height z-score
whz <- getCohortWGS(data = anthro1,
sexObserved = "sex",
firstPart = "weight",
secondPart = "height",
index = "wfh")
head(whz, 50)
#> [1] 0.05572347 -0.01974903 0.57469112 2.06231749 -0.14080044
#> [6] 2.49047246 1.83315197 0.93614891 0.18541943 2.11599287
#> [11] -1.96943887 1.06351047 0.35315830 -0.61151003 -0.01049441
#> [16] -0.75038993 -0.08000322 0.31277573 1.56456175 0.22152087
#> [21] -0.08798757 -2.14197877 -0.30804823 0.00778227 3.21041413
#> [26] 0.07434468 1.40966986 -0.81485050 0.63816647 -0.33540392
#> [31] -0.61955533 1.35716952 -2.77364671 1.00831095 0.32842063
#> [36] -1.66705281 -1.21157702 0.89024472 -0.89865037 0.82166393
#> [41] 0.64442137 -4.39847850 0.38411140 1.48299847 -0.93068495
#> [46] -0.88558228 1.69551410 0.65143649 0.61269397 0.59813891Applying the getCohortWGS() function results in a vector of calculated z-scores for all children in the cohort or sample.
For this example, we will use the getAllWGS() function and apply it to sample data anthro1 that came with zscorer.
# weight-for-age z-score
zScores <- getAllWGS(data = anthro1,
sex = "sex",
weight = "weight",
height = "height",
age = "age",
index = "all")
head(zScores, 20)
#> waz haz whz
#> 1 -0.75605549 -1.2258169 0.05572347
#> 2 -1.39021503 -2.3475886 -0.01974903
#> 3 -1.05597853 -2.9518041 0.57469112
#> 4 1.41575096 -0.2812852 2.06231749
#> 5 -2.67757242 -4.2056663 -0.14080044
#> 6 1.49238050 -0.5387678 2.49047246
#> 7 -0.12987704 -2.4020719 1.83315197
#> 8 -0.02348159 -1.0317699 0.93614891
#> 9 -1.50647344 -2.7410884 0.18541943
#> 10 -1.54381630 -4.7037571 2.11599287
#> 11 -2.87495712 -2.5670550 -1.96943887
#> 12 -0.43497240 -2.1144960 1.06351047
#> 13 -1.03899540 -2.2323505 0.35315830
#> 14 -1.69281855 -2.3155458 -0.61151003
#> 15 -1.31245898 -2.7516165 -0.01049441
#> 16 -2.21003260 -2.7930694 -0.75038993
#> 17 -0.01189226 0.1121349 -0.08000322
#> 18 -0.90917762 -1.9001797 0.31277573
#> 19 -0.67839855 -2.9543730 1.56456175
#> 20 -0.94746695 -1.9671042 0.22152087Applying the getAllWGS() function results in a data frame of calculated z-scores for all children in the cohort or sample for all the anthropometric indices.