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Wrangle MUAC data

Source: R/wrangle_muac.R
mw_wrangle_muac.Rd

Calculate z-scores for MUAC-for-age (MFAZ) and identify outliers based on the SMART methodology. When age is not supplied, only outliers are detected from the raw MUAC values. The function only works after age has gone through mw_wrangle_age().

Usage

mw_wrangle_muac(
  df,
  sex,
  muac,
  age = NULL,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = c("cm", "mm", "none"),
  .decimals = 3
)

Arguments

df

A data.frame object to wrangle data from.

sex

A numeric or character vector of child's sex. Code values should only be 1 or "m" for males and 2 or "f" for females.

muac

A numeric vector of child's age in months.

age

A numeric vector of child's age in months. Default is NULL.

.recode_sex

Logical. Set to TRUE if the values for sex are not coded as 1 (for males) or 2 (for females). Otherwise, set to FALSE (default).

.recode_muac

Logical. Set to TRUE if the values for raw MUAC should be converted to either centimeters or millimeters. Otherwise, set to FALSE (default)

.to

A choice of the measuring unit to convert MUAC values into. Can be "cm" for centimeters, "mm" for millimeters, or "none" to leave as it is.

.decimals

The number of decimal places to use for z-score outputs. Default is 3.

Value

A tibble based on df. If age = NULL, flag_muac variable for detected MUAC outliers based on raw MUAC is added to df. Otherwise, variables named mfaz for child's MFAZ and flag_mfaz for detected outliers based on SMART guidelines are added to df.

References

Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement quality of mid‐upper arm circumference data in anthropometric surveys and mass nutritional screenings conducted in humanitarian and refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

See also

flag_outliers() remove_flags() mw_wrangle_age()

Examples

## When age is available, wrangle it first before calling the function ----
w <- mw_wrangle_age(
  df = anthro.02,
  dos = NULL,
  dob = NULL,
  age = age,
  .decimals = 2
)

### Then apply the function to wrangle MUAC data ----
mw_wrangle_muac(
  df = w,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm",
  .decimals = 3
)
#> ================================================================================
#> # A tibble: 2,267 × 15
#>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz
#>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl>
#>  1 Zambezia Rural      391     2  6.01    8.2   68   n      15.2     825.  0.349
#>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n      13.9     287. -0.006
#>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n      15.5     130.  0.9  
#>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n      14.8    1277.  0.876
#>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n      13.2     792.  1.38 
#>  6 Zambezia Urban      517     2  6.34    6     61.8 n      12.9     480. -0.583
#>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n      12.3     977. -0.732
#>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n      12.6     165. -0.349
#>  9 Zambezia Urban      502     2  6.41    7.5   66   n      14.2    1083. -0.006
#> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n      13.5     972. -0.441
#> # ℹ 2,257 more rows
#> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>,
#> #   age_days <dbl>

## When age is not available ----
mw_wrangle_muac(
  df = anthro.02,
  sex = sex,
  age = NULL,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm",
  .decimals = 3
)
#> # A tibble: 2,267 × 15
#>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz
#>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl>
#>  1 Zambezia Rural      391     2  6.01    8.2   68   n       152     825.  0.349
#>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006
#>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n       155     130.  0.9  
#>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876
#>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38 
#>  6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583
#>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n       123     977. -0.732
#>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n       126     165. -0.349
#>  9 Zambezia Urban      502     2  6.41    7.5   66   n       142    1083. -0.006
#> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n       135     972. -0.441
#> # ℹ 2,257 more rows
#> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>,
#> #   flag_muac <dbl>