Estimate the prevalence of combined wasting — mw_estimate_prevalence

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 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 WFHZ and MFAZ and the p-value of the age ratio test. Prevalence is calculated only when all tests are rated as not problematic. If any of the tests rate as problematic, no estimation is done and an NA value is returned. Outliers are detected in both WFHZ and MFAZ datasets based on SMART flagging criteria. Identified outliers are then excluded before prevalence estimation is performed.

Usage

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

Arguments

df: A tibble object produced by sequential application of the mw_wrangle_wfhz() and mw_wrangle_muac(). 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.

Details

A concept of combined flags is introduced in this function. Any observation that is flagged for either flag_wfhz or flag_mfaz is flagged under a new variable named cflags added to df. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis.

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>