Estimate the prevalence of combined wasting

Estimate the prevalence of wasting based on the combined case-definition of weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows users to get the prevalence estimates 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 WFHZ and MFAZ, as well as the p-value of the age ratio test. Prevalence will be calculated only when the rating of all test is as not problematic concurrently. If either of them is problematic, it cancels out the analysis and NAs get thrown.

Outliers are detected in both WFHZ and in MUAC data set (through z-scores) based on SMART flags get excluded prior being piped into the actual prevalence analysis workflow.

Usage

mw_estimate_prevalence_combined(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 functions for both WFHZ and MUAC data sequentially. The order does not matter. Note that MUAC values should be converted to millimeters after using the MUAC wrangler. If this is not done, the function will stop execution and return an error message. Moreover, the function uses a variable 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 a weighted analysis is computed.

edema

A vector of class character of edema. Code will 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 summarised at.

Value

A summarised table of class data.frame for the descriptive statistics about combined wasting.

Details

A concept of "combined flags" is introduced in this function. It consists of defining as flag any observation that is flagged in either flag_wfhz or flag_mfaz vectors. A new column cflags for combined flags is created and added to df. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis.

A glimpse on how cflags are defined:

flag_wfhz	flag_mfaz	cflags
1	0	1
0	1	1
0	0	0

Examples

## When .by and wt are set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = NULL,
  edema = edema,
  .by = NULL
)
#> # A tibble: 1 × 16
#>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low
#>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl>
#> 1    199 0.0685     0.0566     0.0804         Inf     68 0.0129    0.00770
#> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>,
#> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>,
#> #   wt_pop <dbl>

## When wt is not set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = wtfactor,
  edema = edema,
  .by = NULL
)
#> # A tibble: 1 × 16
#>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low
#>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl>
#> 1    199 0.0708     0.0563     0.0853        1.72     68 0.0151    0.00750
#> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>,
#> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>,
#> #   wt_pop <dbl>