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: $$ \frac{ \mathrm{prevalence}_{6\text{--}23} + 2 \times \mathrm{prevalence}_{24\text{--}59} }{3} $$

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 ----
mw_estimate_age_weighted_prev_muac(
  df = anthro.04,
  muac = muac,
  has_age = TRUE,
  age = age,
  age_cat = FALSE,
  oedema = oedema,
  raw_muac = FALSE,
  province
)
#> # A tibble: 3 × 12
#>   province   oedema_u2  u2sam u2mam u2gam oedema_o2   o2sam  o2mam  o2gam    sam
#>   <chr>          <dbl>  <dbl> <dbl> <dbl>     <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#> 1 Province 1         0 0.0328 0.176 0.209         0 0.00127 0.0380 0.0393 0.0118
#> 2 Province 2         0 0.0369 0.165 0.202         0 0.00368 0.0239 0.0276 0.0148
#> 3 Province 3         0 0.0678 0.142 0.209         0 0.00763 0.0534 0.0611 0.0277
#> # ℹ 2 more variables: mam <dbl>, gam <dbl>

## 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 = FALSE
  )
#> # A tibble: 1 × 11
#>   oedema_u2  u2sam u2mam u2gam oedema_o2   o2sam  o2mam  o2gam    sam    mam
#>       <dbl>  <dbl> <dbl> <dbl>     <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1         0 0.0430 0.163 0.206         0 0.00313 0.0357 0.0389 0.0164 0.0783
#> # ℹ 1 more variable: gam <dbl>