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 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 data quality is first assessed by calculating and rating the standard deviation (SD) of WFHZ. Then it calculates the observed proportion of children aged 24–59 months out of all children in the dataset. Next, it estimates the p-value for the difference between this observed proportion and the expected (0.66), and rates the result.

Prevalence is estimated only when the WFHZ SD is not problematic and the age ratio test is not problematic, or — if the age ratio test is problematic — the proportion of children aged 24–59 months is ≥ 0.66.

Usage

mw_estimate_prevalence_combined(df, wt = NULL, oedema = 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 millimetres unit after using mw_wrangle_muac(). In addition, 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.
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 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 wt are set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = NULL,
  oedema = oedema
)
#> # A tibble: 1 × 4
#>   std_wfhz  age_ratio_prop age_ratio_pval path   
#>   <chr>              <dbl> <chr>          <chr>  
#> 1 Excellent          0.662 Excellent      analyse
#> # 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    143 0.0685     0.0566     0.0804         Inf     27 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>,
#> #   N <dbl>

## When `wt` is not set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = wtfactor,
  oedema = oedema
)
#> # A tibble: 1 × 4
#>   std_wfhz  age_ratio_prop age_ratio_pval path   
#>   <chr>              <dbl> <chr>          <chr>  
#> 1 Excellent          0.662 Excellent      analyse
#> # 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    143 0.0708     0.0563     0.0853        1.72     27 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>,
#> #   N <dbl>