Estimate the prevalence of wasting based on MUAC for survey data

Estimate the prevalence of wasting based on MUAC and/or nutritional edema. 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 and the p-value of the age ratio test. Prevalence is calculated only when the standard deviation of MFAZ is not problematic. If both standard deviation of MFAZ and p-value of age ratio test is not problematic, straightforward prevalence estimation is performed. If standard deviation of MFAZ is not problematic but p-value of age ratio test is problematic, age-weighting is applied to prevalence estimation to account for the over-representation of younger children in the sample. If standard deviation of MFAZ is problematic, no estimation is done and an NA value is returned. Outliers are detected based on SMART flagging criteria for MFAZ. Identified outliers are then excluded before prevalence estimation is performed.

Usage

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

mw_estimate_smart_age_wt(df, edema = NULL, .by = NULL)

Arguments

df

A tibble object produced by mw_wrangle_muac() and mw_wrangle_age() functions. Note that MUAC values in df must be in millimeters unit after using mw_wrangle_muac(). Also, df must have a variable called cluster which contains 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.

edema

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

.by

A character or numeric vector of the geographical areas or identifiers for where the data was collected and for which the analysis should be summarised for.

Value

A summary tibble for the descriptive statistics about combined wasting.

References

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

See also

mw_estimate_smart_age_wt() mw_estimate_prevalence_mfaz() mw_estimate_prevalence_screening()

Examples

## When .by = NULL ----
mw_estimate_prevalence_muac(
  df = anthro.04,
  wt = NULL,
  edema = edema,
  .by = NULL
)
#> # A tibble: 1 × 3
#>    sam_p  mam_p gam_p
#>    <dbl>  <dbl> <dbl>
#> 1 0.0212 0.0889 0.110

## When .by is not set to NULL ----
mw_estimate_prevalence_muac(
  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   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682
#> 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA      
#> 3 Province 3    NA NA       NA         NA             NA    NA NA       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>

## An application of `mw_estimate_smart_age_wt()` ----
.data <- anthro.04 |> subset(province == "Province 2")

mw_estimate_smart_age_wt(
  df = .data,
  edema = edema,
  .by = NULL
)
#> # A tibble: 1 × 3
#>    sam_p  mam_p gam_p
#>    <dbl>  <dbl> <dbl>
#> 1 0.0201 0.0922 0.112