| Title: | Computation of the WHO Child Growth Standards |
|---|---|
| Description: | Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/child-growth-standards>. |
| Authors: | Dirk Schumacher [aut, cre], Elaine Borghi [ctb], Jonathan Polonsky [ctb], World Health Organization [cph] |
| Maintainer: | Dirk Schumacher <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.1.9000 |
| Built: | 2024-11-01 11:17:14 UTC |
| Source: | https://github.com/worldhealthorganization/anthro |
Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/child-growth-standards>.
Maintainer: Dirk Schumacher [email protected]
Other contributors:
Elaine Borghi [email protected] [contributor]
Jonathan Polonsky [email protected] [contributor]
World Health Organization [copyright holder]
Useful links:
Report bugs at https://github.com/worldhealthorganization/anthro/issues
Compute prevalence of zscores
anthro_api_compute_prevalence(data, zscores_to_compute, survey_subsets)anthro_api_compute_prevalence(data, zscores_to_compute, survey_subsets)
data |
a data frame containing the underlying data |
zscores_to_compute |
a list of zscore indicators that should be computed. The list needs to have 4 named values:
|
survey_subsets |
subsets for which the prevalence values should be computed. It is a named list of characters, where the values correspond to columns and the names to labels in the output. |
This function is meant to be used by other anthro related packages. It is not advised to use this in you own packages or analysis. If you must use it, prepare for potential breaking changes in the future.
Helper function to compute zscores
anthro_api_compute_zscore(y, m, l, s)anthro_api_compute_zscore(y, m, l, s)
y |
a numeric vector |
m |
a numeric vector |
l |
a numeric vector |
s |
a numeric vector |
This function is meant to be used by other anthro related packages. It is not advised to use this in you own packages or analysis. If you must use it, prepare for potential breaking changes in the future.
http://www.who.int/childgrowth/standards/Chap_7.pdf
Helper function to compute the adjusted zscore
anthro_api_compute_zscore_adjusted(y, m, l, s)anthro_api_compute_zscore_adjusted(y, m, l, s)
y |
a numeric vector |
m |
a numeric vector |
l |
a numeric vector |
s |
a numeric vector |
This function is meant to be used by other anthro related packages. It is not advised to use this in you own packages or analysis. If you must use it, prepare for potential breaking changes in the future.
http://www.who.int/childgrowth/standards/Chap_7.pdf
Standardize the Oedema input values
anthro_api_standardize_oedema_var(oedema)anthro_api_standardize_oedema_var(oedema)
oedema |
a vector of values |
This function is meant to be used by other anthro related packages. It is not advised to use this in you own packages or analysis. If you must use it, prepare for potential breaking changes in the future.
Standardize the Sex input values
anthro_api_standardize_sex_var(sex)anthro_api_standardize_sex_var(sex)
sex |
a vector of values |
This function is meant to be used by other anthro related packages. It is not advised to use this in you own packages or analysis. If you must use it, prepare for potential breaking changes in the future.
Prevalence estimates according to the WHO recommended standard analysis: includes prevalence estimates with corresponding standard errors and confidence intervals, and z-score summary statistics (mean and standard deviation) with most common cut-offs describing the full index distribution (-3, -2, -1, +1, +2, +3), and at disaggregated levels for all available factors (age, sex, type of residence, geographical regions, wealth quintiles, mother education and one additional factor the user is interested in and for which data are available).
anthro_prevalence( sex, age = NA_real_, is_age_in_month = FALSE, weight = NA_real_, lenhei = NA_real_, measure = NA_character_, oedema = "n", sw = NULL, cluster = NULL, strata = NULL, typeres = NA_character_, gregion = NA_character_, wealthq = NA_character_, mothered = NA_character_, othergr = NA_character_ )anthro_prevalence( sex, age = NA_real_, is_age_in_month = FALSE, weight = NA_real_, lenhei = NA_real_, measure = NA_character_, oedema = "n", sw = NULL, cluster = NULL, strata = NULL, typeres = NA_character_, gregion = NA_character_, wealthq = NA_character_, mothered = NA_character_, othergr = NA_character_ )
sex |
A numeric or text variable containing gender information. If it is numeric, its values must be: 1 for males and 2 for females. If it is character, it must be "m" or "M" for males and "f" or "F" for females. No z-scores will be calculated if sex is missing. |
age |
A numeric variable containing age information; age can be in either days or months (if optional argument is_age_in_month is set to TRUE). An exact age in days is expected and should not be rounded if age is in months. Age-related z-scores will NOT be calculated if age is missing (NA). |
is_age_in_month |
A logical flag; if TRUE, variable age unit will be treated as months. The function converts it to days by dividing age by 30.4375 and rounding it to integer so that reference tables can be used. When unspecified, the default value FALSE is used and age unit is treated as days. |
weight |
A numeric variable containing body weight information, which must be in kilograms. Weight-related z-scores are not calculated if body weight is missing. |
lenhei |
A numeric variable containing length (recumbent length) or height (standing height) information, which must be in centimeters. Length/height-related z-scores will not be calculated if lenhei is missing. For children with age below 24 months (i.e. below 731 days) and standing height measured, the function converts it to recumbent length by adding 0.7 cm; and for children with age equal and above 24 months and measured in recumbent length, the function converts it to standing height by subtracting 0.7 cm. This way all the z-scores calculated by this function are length-based for children below 24 months, and height-based otherwise. This converted length/height according to age is assigned to the variable clenhei in the resulting data.frame. |
measure |
A character variable indicating whether recumbent length or standing height was measured for each observation. The values of this variable must be "L" or "l" for recumbent length, and "H" or "h" for standing height. Although it is highly recommended that this variable is provided according to the measurements taken in the survey, it is possible to run the analysis without specifying this variable. If unspecified, the default vector with all missing values is used. The function imputes the missing values according to the following algorithm:
|
oedema |
The values of this character variable must be "n", "N" or "2"
for non-oedema, and "y", "Y", "1" for oedema. Although it is highly
recommended that this variable is provided by the survey,
it is possible to run
the analysis without specifying this variable. If unspecified,
the default vector of all "n" with values considered as
non-oedema is used. Missing values will be
treated as non-oedema. For oedema, weight related z-scores
(zwei, zwfl and zbmi) are NOT calculated (set to missing),
BUT they are treated as being < -3 SD in the weight-related
indicator prevalence ( |
sw |
An optional numeric vector containing the sampling weights. If NULL, no sampling weights are used. |
cluster |
An optional integer vector representing clusters. If the value is NULL this is treated as a survey without a cluster. This is also the case if all values are equal, then we assume there is also no cluster. |
strata |
An optional integer vector representing strata. Pass NULL to indicate that there are no strata. |
typeres |
An optional integer or character vector representing a type of residence. Any values are accepted, however, “Rural” or “Urban” are preferable for outputs purposes. |
gregion |
An optional integer or character vector representing a geographical region. |
wealthq |
An optional integer or character vector representing wealth quintiles where (1=poorest; 2,3,4,5=richest). All values can either be NA, or 1, 2, 3, 4, 5 or Q1, Q2, Q3, Q4, Q5. |
mothered |
An optional integer or character vector representing the education of the mother. Any number of categories is accepted for the analysis, provided sample sizes are sufficient in all categories. However, the common, standard recommended categories are no education, primary school, and secondary school or higher (“None”, “Primary” and “Secondary”). Note: Mother education refers to the highest level of schooling attained by the mother |
othergr |
An optional integer or character vector that is of interest for stratified analysis. |
In this function, all available (non-missing and non-flagged) z-score values are used for each indicator-specific prevalence estimation (standard analysis).
Note: the function temporarily sets the survey option
survey.lonely.psu to "adjust" and then restores the original values.
The function is a wrapper around the survey package to compute
estimates for the different groups (e.g. by age or sex).
If not all parameter values have equal length, parameter values will be
repeated to match the maximum length of all arguments except
is_age_in_month using rep_len. This happens without warnings.
Returns a data.frame with prevalence estimates for the various groups.
The output data frame includes prevalence estimates with corresponding standard errors and confidence intervals, and z-score summary statistics (mean and standard deviation) with most common cut-offs describing the full index distribution (-3, -2, -1, +1, +2, +3), and at disaggregated levels for all available factors (age, sex, type of residence, geographical regions, wealth quintiles, mother education and one additional factor the user is interested in and for which data are available).
The resulting columns are coded with a prefix, a prevalence indicator and a suffix:
Prefix:
Height-for-age
Weight-for-age
Underweight
Body-mass-index-for-age
Weight-for-height
Height-for-age and weight-for-height combined
Prevalence indicator:
Prevalence corresponding to < -3 SD
Prevalence corresponding to < -2 SD
Prevalence corresponding to < -1 SD
Prevalence corresponding to > +1 SD
Prevalence corresponding to > +2 SD
Prevalence corresponding to > +3 SD
Suffix:
Weighted sample size
Unweighted sample size
Mean/prevalence
lower 95% confidence interval limit
upper 95% confidence interval limit
Standard Deviation
Standard error
For example:
Weight-for-height weighted sample size
Height-for-age z-score mean
Weight-for-age z-score Standard Deviation
Prevalence of weight-for-height >+2 SD (overweight )
Mean weight-for-height z-score
Prevalence of BMI-for-age <-2 SD standard error
Prevalence of BMI-for-age <-3 SD lower 95% confidence interval limit
Prevalence of children Height-for-age and weight-for-height combined (stunted & wasted) lower 95% confidence interval limit
Methods:
The survey package is used to compute all estimates.
Confidence intervals are computed with a significance level of 0.05.
svymean is used the z-score mean and CIs, while for the
prevalence estimates svyciprop is used with the logit
method.
For cases where cluster/strata/sw is NULL a fast approximation
is used that does not use the survey package but computes the same
results within bounds (maximum empirical difference is < 0.0001).
T. Lumley (2024) "survey: analysis of complex survey samples". R package version 4.4.
## Not run: # because it takes too long for CRAN checks library(anthro) # compute the prevalence estimates for 100 random children # with weight around 15kg and height around 100cm res <- anthro_prevalence( sex = c(1, 2), age = 1000, # days weight = rnorm(100, 15, 1), lenhei = rnorm(100, 100, 10) ) # Height-for-age # We extract prevalence estimates for <-3SD, <-2SD (Stunting) # and the z-score mean col_names <- c("Group", "HAZ_unwpop", "HA_3_r", "HA_2_r", "HA_r") res <- res[, col_names] # rename the columns colnames(res) <- c("Group", "Unweighted N", "-3SD", "-2SD", "z-score mean ") # note that we only generated data for one age group res ## End(Not run)## Not run: # because it takes too long for CRAN checks library(anthro) # compute the prevalence estimates for 100 random children # with weight around 15kg and height around 100cm res <- anthro_prevalence( sex = c(1, 2), age = 1000, # days weight = rnorm(100, 15, 1), lenhei = rnorm(100, 100, 10) ) # Height-for-age # We extract prevalence estimates for <-3SD, <-2SD (Stunting) # and the z-score mean col_names <- c("Group", "HAZ_unwpop", "HA_3_r", "HA_2_r", "HA_r") res <- res[, col_names] # rename the columns colnames(res) <- c("Group", "Unweighted N", "-3SD", "-2SD", "z-score mean ") # note that we only generated data for one age group res ## End(Not run)
This function calculates z-scores for the eight anthropometric indicators, weight-for- age, length/height-for-age, weight-for-length/height, body mass index (BMI)-for-age, head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age based on the WHO Child Growth Standards.
anthro_zscores( sex, age = NA_real_, is_age_in_month = FALSE, weight = NA_real_, lenhei = NA_real_, measure = NA_character_, headc = NA_real_, armc = NA_real_, triskin = NA_real_, subskin = NA_real_, oedema = "n" )anthro_zscores( sex, age = NA_real_, is_age_in_month = FALSE, weight = NA_real_, lenhei = NA_real_, measure = NA_character_, headc = NA_real_, armc = NA_real_, triskin = NA_real_, subskin = NA_real_, oedema = "n" )
sex |
A numeric or text variable containing gender information. If it is numeric, its values must be: 1 for males and 2 for females. If it is character, it must be "m" or "M" for males and "f" or "F" for females. No z-scores will be calculated if sex is missing. |
age |
A numeric variable containing age information; age can be in either days or months (if optional argument is_age_in_month is set to TRUE). An exact age in days is expected and should not be rounded if age is in months. Age-related z-scores will NOT be calculated if age is missing (NA). |
is_age_in_month |
A logical flag; if TRUE, variable age unit will be treated as months. The function converts it to days by dividing age by 30.4375 and rounding it to integer so that reference tables can be used. When unspecified, the default value FALSE is used and age unit is treated as days. |
weight |
A numeric variable containing body weight information, which must be in kilograms. Weight-related z-scores are not calculated if body weight is missing. |
lenhei |
A numeric variable containing length (recumbent length) or height (standing height) information, which must be in centimeters. Length/height-related z-scores will not be calculated if lenhei is missing. For children with age below 24 months (i.e. below 731 days) and standing height measured, the function converts it to recumbent length by adding 0.7 cm; and for children with age equal and above 24 months and measured in recumbent length, the function converts it to standing height by subtracting 0.7 cm. This way all the z-scores calculated by this function are length-based for children below 24 months, and height-based otherwise. This converted length/height according to age is assigned to the variable clenhei in the resulting data.frame. |
measure |
A character variable indicating whether recumbent length or standing height was measured for each observation. The values of this variable must be "L" or "l" for recumbent length, and "H" or "h" for standing height. Although it is highly recommended that this variable is provided according to the measurements taken in the survey, it is possible to run the analysis without specifying this variable. If unspecified, the default vector with all missing values is used. The function imputes the missing values according to the following algorithm:
|
headc |
A numeric variable containing head circumference information, which must be in centimeters. Head circumference-for-age z-scores are not calculated if head circumference is missing. |
armc |
A numeric variable containing arm circumference information, which must be in centimeters. Arm circumference-for-age z-scores are not calculated if arm circumference is missing. |
triskin |
A numeric variable containing triceps skinfold information, which must be in millimeters. Triceps skinfold-for-age z-scores are not calculated if triceps skinfold is missing. |
subskin |
A numeric variable containing subscapular skinfold information, which must be in millimeters. Subscapular skinfold-for-age z-scores are not calculated if subscapular skinfold is missing. |
oedema |
The values of this character variable must be "n", "N" or "2"
for non-oedema, and "y", "Y", "1" for oedema. Although it is highly
recommended that this variable is provided by the survey,
it is possible to run
the analysis without specifying this variable. If unspecified,
the default vector of all "n" with values considered as
non-oedema is used. Missing values will be
treated as non-oedema. For oedema, weight related z-scores
(zwei, zwfl and zbmi) are NOT calculated (set to missing),
BUT they are treated as being < -3 SD in the weight-related
indicator prevalence ( |
A data.frame with three types of columns. Columns starting with a "c" are cleaned versions of the input arguments. Columns beginning with a "z" are the respective z-scores and columns prefixed by a "f" indicate if these z-scores are flagged (integers). The number of rows is given by the length of the input arguments.
The following columns are returned:
clenhei converted length/height for deriving z-score
cbmi BMI value based on length/height given by clenhei
zlen Length/Height-for-age z-score
flen 1, if abs(zlen) > 6
zwei Weight-for-age z-score
fwei 1, if zwei < -6 or zwei > 5
zwfl Weight-for-length/height z-score
fwfl 1, if abs(zwfl) > 5
zbmi BMI-for-age z-score
fbmi 1, if abs(zbmi) > 5
zhc Head circumference-for-age z-score
fhc 1, if abs(zhc) > 5
zac Arm circumference-for-age z-score
fac 1, if abs(zac) > 5
zts Triceps skinfold-for-age z-score
fts 1, if abs(zts) > 5
zss Subscapular skinfold-for-age z-score
fss 1, if abs(zss) > 5
If not all parameter values have equal length, parameter values will be
repeated to match the maximum length of all arguments except
is_age_in_month using rep_len. This happens without warnings.
Z-scores are only computed for children younger than 60 months (age in months < 60)
WHO Multicentre Growth Reference Study Group (2006). WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization; pp 312. (web site: http://www.who.int/childgrowth/publications/en/ )
WHO Multicentre Growth Reference Study Group (2007). WHO Child Growth Standards: Head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age: Methods and development. Geneva: World Health Organization; pp 217. (web site: http://www.who.int/childgrowth/publications/en/ )
# you can either use the function to compute zscores for specific values anthro_zscores(sex = "f", age = 10, is_age_in_month = TRUE, weight = 10) # values will be recycled so not all input values need to be of the same length anthro_zscores(sex = "f", age = c(10, 20, 30), weight = 10) # or use it with a compute dataset ## Not run: your_data_set <- read.csv("<your survey>.csv") with( your_data_set, anthro_zscores( sex = sex, age = age_in_days, weight = weight, lenhei = lenhei ) ) ## End(Not run)# you can either use the function to compute zscores for specific values anthro_zscores(sex = "f", age = 10, is_age_in_month = TRUE, weight = 10) # values will be recycled so not all input values need to be of the same length anthro_zscores(sex = "f", age = c(10, 20, 30), weight = 10) # or use it with a compute dataset ## Not run: your_data_set <- read.csv("<your survey>.csv") with( your_data_set, anthro_zscores( sex = sex, age = age_in_days, weight = weight, lenhei = lenhei ) ) ## End(Not run)