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Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)

Source: R/prev-wasting-mfaz.R
mw_estimate_prevalence_mfaz.Rd

Calculate the prevalence estimates of wasting based on z-scores of MUAC-for-age and/or bilateral oedema. The function allows users to estimate prevalence in accordance with complex sample design properties such as accounting for survey sample weights when needed or applicable. The quality of the data is first evaluated by calculating and rating the standard deviation of MFAZ. Standard approach to prevalence estimation is calculated only when the standard deviation of MFAZ is rated as not problematic. If the standard deviation is problematic, prevalence is estimated using the PROBIT estimator. Outliers are detected based on SMART flagging criteria. Identified outliers are then excluded before prevalence estimation is performed.

Usage

mw_estimate_prevalence_mfaz(df, wt = NULL, oedema = NULL, ...)

Arguments

df

A data.frame object that has been produced by the mw_wrangle_age() and mw_wrangle_muac() functions. The df should have a variable named cluster for the primary sampling unit identifiers.

wt

A vector of class double of the survey sampling weights. Default is NULL which assumes a self-weighted survey as is the case for a survey sample selected proportional to population size (i.e., SMART survey sample). Otherwise, a weighted analysis is implemented.

oedema

A character vector for presence of nutritional oedema coded as "y" for presence of nutritional oedema and "n" for absence of nutritional oedema. Default is NULL.

...

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 for the descriptive statistics about wasting.

Examples

## Without grouping variables ----
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_prevalence_mfaz(
    wt = NULL,
    oedema = oedema,
    analysis_unit
  )
#> ================================================================================
#> # A tibble: 3 × 17
#>   analysis_unit gam_n gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p
#>   <chr>         <dbl> <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>
#> 1 Unit A           68 0.112    0.0776     0.147        Inf     9 0.0149
#> 2 Unit B          286 0.211    0.169      0.253        Inf    59 0.0436
#> 3 Unit C           NA 0.339   NA         NA             NA    NA 0.0786
#> # ℹ 9 more variables: sam_p_low <dbl>, sam_p_upp <dbl>, sam_p_deff <dbl>,
#> #   mam_n <dbl>, mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>,
#> #   mam_p_deff <dbl>, N <dbl>