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_,
headc = NA_real_,
armc = NA_real_,
triskin = NA_real_,
subskin = NA_real_,
oedema = "n",
sw = 1,
cluster = 1L,
strata = 1L,
typeres = NA_character_,
gregion = NA_character_,
wealthq = NA_character_,
mothered = NA_character_,
othergr = NA_character_
)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.
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).
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.
A numeric variable containing body weight information, which must be in kilograms. Weight-related z-scores are not calculated if body weight is missing.
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.
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:
If age is not missing, then it is recumbent length if age below 24 months (731 days), and standing height if age equal and above 24 months.
If age is missing, then it is recumbent length if measurement < 87 cm and standing height if measurement >= 87 cm.
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.
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.
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.
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.
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 (anthro_prevalence)
estimation.
An optional numeric vector containing the sampling weights. If unspecified, the all 1 vector is used, i.e. where all records have equal sampling weights, and un-weighted analysis is performed. Negative values are not allowed.
An optional integer vector representing clusters
An optional integer vector representing strata
An optional integer or character vector representing a type of residence. Any values are accepted, however, <U+201C>Rural<U+201D> or <U+201C>Urban<U+201D> are preferable for outputs purposes.
An optional integer or character vector representing a geographical region.
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.
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 (<U+201C>None<U+201D>, <U+201C>Primary<U+201D> and <U+201C>Secondary<U+201D>). Note: Mother education refers to the highest level of schooling attained by the mother
An optional integer or character vector that is of interest for stratified analysis.
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
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.
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.
# 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
# }
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