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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 edema. The function allows users to get the prevalence estimates calculated in accordance with the complex sample design properties; this includes applying survey weights when needed or applicable.

Before estimating, the function evaluates the quality of data by calculating and rating the standard deviation of z-scores of MFAZ. If rated as problematic, the prevalence is estimated based on the PROBIT method.

Outliers are detected based on SMART flags and get excluded prior prevalence analysis.

Usage

mw_estimate_prevalence_mfaz(df, wt = NULL, edema = NULL, .by = NULL)

Arguments

df

A data set object of class data.frame to use. This must have been wrangled using this package's wrangling function for MUAC data. The function uses a variable name called cluster where the primary sampling unit IDs are stored. Make sure to rename your cluster ID variable to cluster, otherwise the function will error and terminate the execution.

wt

A vector of class double of the final survey weights. Default is NULL assuming a self weighted survey, as in the ENA for SMART software; otherwise, when a vector of weights if supplied, weighted analysis is done.

edema

A vector of class character of edema. Code should be "y" for presence and "n" for absence of bilateral edema. Default is NULL.

.by

A vector of class character or numeric of the geographical areas or respective IDs for where the data was collected and for which the analysis should be summarized at.

Value

A summarized table of class data.frame of the descriptive statistics about wasting.

Examples

## When .by = NULL ----
mw_estimate_prevalence_mfaz(
  df = anthro.04,
  wt = NULL,
  edema = edema,
  .by = NULL
)
#> # A tibble: 1 × 16
#>   gam_n gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low sam_p_upp
#>   <dbl> <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl>     <dbl>
#> 1   330 0.107    0.0873     0.127        Inf    53 0.0144   0.00894    0.0198
#> # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>,
#> #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl>

## When .by is not set to NULL ----
mw_estimate_prevalence_mfaz(
  df = anthro.04,
  wt = NULL,
  edema = edema,
  .by = province
)
#> # A tibble: 3 × 17
#>   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low
#>   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl>
#> 1 Province 1   154 0.119     0.0851     0.153        Inf    15 0.0102   0.00132
#> 2 Province 2    98 0.0854    0.0565     0.114        Inf    16 0.0117   0.00510
#> 3 Province 3    NA 0.257    NA         NA             NA    NA 0.0491  NA      
#> # ℹ 8 more variables: 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>,
#> #   wt_pop <dbl>