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Anthro

The anthro package allows you to perform comprehensive analysis of anthropometric survey data based on the method developed by the Department of Nutrition for Health and Development at the World Health Organization.

The package is modeled after the original R macros provided by WHO. In addition to z-scores, the package adds more accurate calculations of confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs, whenever is the case by using the survey package.

Installation

install.packages("anthro")
remotes::install_github("worldhealthorganization/anthro")

Examples

library(anthro)

Z-Score

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 = c(1, 2, 1, 1),
  age = c(1001, 1000, 1010, 1000),
  weight = c(18, 15, 10, 15),
  lenhei = c(120, 80, 100, 100)
)
#>   clenhei    cbmi cmeasure csex  zlen flen  zwei fwei  zwfl fwfl  zbmi fbmi zhc
#> 1     120 12.5000     <NA>    1  7.31    1  2.20    0 -2.39    0 -3.01    0  NA
#> 2      80 23.4375     <NA>    2 -3.50    0  0.95    0  4.13    0  4.66    0  NA
#> 3     100 10.0000     <NA>    1  1.62    0 -2.76    0 -5.19    1 -5.61    1  NA
#> 4     100 15.0000     <NA>    1  1.70    0  0.69    0 -0.29    0 -0.58    0  NA
#>   fhc zac fac zts fts zss fss
#> 1  NA  NA  NA  NA  NA  NA  NA
#> 2  NA  NA  NA  NA  NA  NA  NA
#> 3  NA  NA  NA  NA  NA  NA  NA
#> 4  NA  NA  NA  NA  NA  NA  NA

The returned value is a data.frame that can further be processed or saved as a .csv file as in the original function.

You can also use the function with a given dataset with with

your_data_set <- read.csv("my_survey.csv")
with(
  your_data_set,
  anthro_zscores(
    sex = sex, age = age_in_days,
    weight = weight, lenhei = lenhei
  )
)

To look at all parameters, type ?anthro_zscores.

Prevalence estimates

The prevalence estimates are similar to anthro_zscores: again they take vectors instead of a data frame and column names for the aforementioned reasons.

anthro_prevalence(
  sex = c(1, 2, 2, 1),
  age = c(1001, 1000, 1010, 1000),
  weight = c(18, 15, 10, 15),
  lenhei = c(100, 80, 100, 100)
)[, 1:5]
#>                         Group HAZ_pop HAZ_unwpop HA_3_r  HA_3_se
#> 1                         All       4          4     25 25.00000
#> 2         Age group: 00-05 mo       0          0     NA       NA
#> 3         Age group: 06-11 mo       0          0     NA       NA
#> 4         Age group: 12-23 mo       0          0     NA       NA
#> 5         Age group: 24-35 mo       4          4     25 25.00000
#> 6         Age group: 36-47 mo       0          0     NA       NA
#> 7         Age group: 48-59 mo       0          0     NA       NA
#> 8                 Sex: Female       2          2     50 40.82483
#> 9                   Sex: Male       2          2      0  0.00000
#> 10 Age + sex: 00-05 mo.Female       0          0     NA       NA
#> 11 Age + sex: 06-11 mo.Female       0          0     NA       NA
#> 12 Age + sex: 12-23 mo.Female       0          0     NA       NA
#> 13 Age + sex: 24-35 mo.Female       2          2     50 40.82483
#> 14 Age + sex: 36-47 mo.Female       0          0     NA       NA
#> 15 Age + sex: 48-59 mo.Female       0          0     NA       NA
#> 16   Age + sex: 00-05 mo.Male       0          0     NA       NA
#> 17   Age + sex: 06-11 mo.Male       0          0     NA       NA
#> 18   Age + sex: 12-23 mo.Male       0          0     NA       NA
#> 19   Age + sex: 24-35 mo.Male       2          2      0  0.00000
#> 20   Age + sex: 36-47 mo.Male       0          0     NA       NA
#> 21   Age + sex: 48-59 mo.Male       0          0     NA       NA

Using the function with it is easy to apply anthro_prevalence to a full dataset.

To look at all parameters, type ?anthro_prevalence.

Contribution

Contributions in the form of issues are very welcome. In particular if you find any bugs.

Using the package in your own analyses

The package has been tested thoroughly, but we cannot guarantee that there aren’t any bugs nor comes this with any warranty. If you find a bug or cannot reproduce results obtained with other implementations, please post an issue.

anthro's People

Contributors

dirkschumacher avatar

Watchers

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anthro's Issues

na. errors when calculation z score with WHO Anthro

I get the error below when calculating z scores using WHO anthro for all time points except at baseline. It works just fine when I use a different age variable (for the first time point/baseline). My understanding was that even if you have missing data in the raw variables, it should generate missing data for the zscore calculation.

df2<-data.frame(dm_sex = c(2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
1L, 2L), age_days_wk3 = c(531, 584, 667, NA, NA, NA, NA, 206, 
267, 310, 228, 222, 544, 558, 405, NA, NA, NA, NA, NA, 363, 393, 
328, 308, 434, 342, 325, 264, NA, 264, NA, NA, NA, NA, NA, NA, 
250, -240, NA, NA, NA, NA, NA), wkly_weightwk3 = c(7.23, 8.55, 
7.44, NA, NA, NA, NA, 4.2, 6.52, 5.87, 4.22, 5.79, 7.95, 6.25, 
7.06, NA, NA, NA, NA, NA, 6.68, 6.42, 7.86, 6.16, 7.1, 5.44, 
7.4, 5.76, NA, 5.22, NA, NA, NA, NA, NA, NA, 6.83, 7.67, NA, 
NA, NA, NA, NA), ht_fwk3 = c(70.95, 79.2, 74.35, NA, NA, NA, 
NA, 59.45, 66.65, 65.25, 57.3, 61.75, 73.35, 71.9, 67.65, NA, 
NA, NA, NA, NA, 67.5, 68.45, 75.15, 68, 72, 63.2, 70.2, 62.55, 
NA, 59.45, NA, NA, NA, NA, NA, NA, 66.55, 73.05, NA, NA, NA, 
NA, NA))

anth3<- with(
  df,
  anthro_zscores(
    sex = adm_sex, age =age_days_wk3,
    weight =wkly_weightwk3, lenhei = ht_fwk3
  ))

Error in round_up(res) : all(x >= 0, na.rm = TRUE) is not TRUE

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