Estimate age-weighted prevalence of wasting by MUAC — mw_estimate_age_weighted_prev

Estimates age‑weighted prevalence of wasting using MUAC. Accepts age in months or in categories ('6–23', '24–59'). The default is age in months.

The prevalence is weighted as: $$( prevalence_{6-23} + 2 \times prevalence_{24-59} ) / 3$$

Whilst the function is exported to users as a standalone, it is embedded into the following major MUAC prevalence functions of this package: mw_estimate_prevalence_muac(), mw_estimate_prevalence_screening(), and mw_estimate_prevalence_screening2().

Usage

mw_estimate_age_weighted_prev_muac(
  df,
  muac,
  has_age = TRUE,
  age = NULL,
  age_cat = NULL,
  oedema = NULL,
  raw_muac = FALSE,
  ...
)

Arguments

df: A tibble object produced by mwana data wranglers.
muac: A numeric or integer vector of raw MUAC values. The measurement unit should be millimetres.
has_age: Logical. Specifies whether the input dataset provides age in months or in categories ('6–23', '24–59'). Defaults to TRUE when age is given in months.
age: A vector of class double of child's age in months. Defaults to NULL. Only use if has_age = TRUE, otherwise set it to NULL.
age_cat: A character vector of child's age in categories. Code values should be "6-23" and "24-59". Defaults to NULL. Only use it if has_age = FALSE.
oedema: A character vector for presence of nutritional oedema. Code values should be "y" for presence and "n" for absence. Defaults to NULL.
raw_muac: Logical. Whether outliers should be excluded based on the raw MUAC values or MFAZ. For the former, set it to TRUE, otherwise FALSE. Defaults to MFAZ.
...: A vector of class character, specifying the categories for which the analysis should be summarised for. Usually geographical areas. More than one vector can be specified.

Value

A summary tibble with wasting prevalence estimates, as given by the SMART updated MUAC tool (see references below).

Details

As a standalone function, the user must check data quality before calling the function.

References

SMART Initiative (no date). Updated MUAC data collection tool. Available at: https://smartmethodology.org/survey-planning-tools/updated-muac-tool/

Examples

## Example application when age is given in months ----
anthro.04 |>
  mw_wrangle_age(age = age) |>
  mw_wrangle_muac(
    muac = muac,
    .recode_muac = TRUE,
    .to = "cm",
    age = age,
    sex = sex,
    .recode_sex = FALSE
  ) |>
  transform(muac = recode_muac(muac, "mm")) |>
  mw_estimate_age_weighted_prev_muac(
    muac = muac,
    has_age = TRUE,
    age = age,
    age_cat = FALSE,
    oedema = oedema,
    raw_muac = FALSE,
    analysis_unit
  )
#> ================================================================================
#> # A tibble: 3 × 13
#>   analysis_unit u2oedema  u2sam u2mam u2gam o2oedema   o2sam  o2mam  o2gam
#>   <chr>            <dbl>  <dbl> <dbl> <dbl>    <dbl>   <dbl>  <dbl>  <dbl>
#> 1 Unit A               0 0.0368 0.121 0.158        0 0       0.0216 0.0216
#> 2 Unit B               0 0.0748 0.218 0.292        0 0.00665 0.0293 0.0359
#> 3 Unit C               0 0.0851 0.128 0.213        0 0.00617 0.0494 0.0556
#> # ℹ 4 more variables: sam_p <dbl>, mam_p <dbl>, gam_p <dbl>, N <int>

## Example application when age is given in categories ----
anthro.04 |>
  transform(age_cat = ifelse(age < 24, "6-23", "24-59")) |>
  mw_wrangle_muac(
    muac = muac,
    .recode_muac = FALSE,
    .to = "none",
    sex = sex,
    .recode_sex = FALSE
  ) |>
  mw_estimate_age_weighted_prev_muac(
    has_age = FALSE,
    age = NULL,
    age_cat = age_cat,
    oedema = oedema,
    raw_muac = TRUE
  )
#> Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
#> ℹ Please use `cross_join()` instead.
#> ℹ The deprecated feature was likely used in the mwana package.
#>   Please report the issue at <https://github.com/mphimo/mwana/issues>.
#> # A tibble: 1 × 12
#>   u2oedema  u2sam u2mam u2gam o2oedema   o2sam  o2mam  o2gam  sam_p  mam_p gam_p
#>      <dbl>  <dbl> <dbl> <dbl>    <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1        0 0.0718 0.188 0.260        0 0.00747 0.0291 0.0366 0.0289 0.0823 0.111
#> # ℹ 1 more variable: N <int>