Evidence on the prevalence of acute malnutrition used in the IPC Acute Malnutrition (IPC AMN) can come from different sources: representative surveys, screenings, or community-based surveillance system (known as sentinel sites). The IPC sets minimum sample size requirements for each of these sources (IPC Global Partners, 2021).

In the IPC AMN analysis workflow, the first step a data analyst has to take is the checking of sample size requirements as set by IPC for each survey area to be included in the IPC AMN analysis. mwana provides the mw_check_ipcamn_ssreq() function for this purpose.

To demonstrate its usage, we will use the built-in sample dataset anthro.01.

head(anthro.01)
#> # A tibble: 6 × 11
#>   area      dos        cluster  team sex   dob      age weight height oedema
#>   <chr>     <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 
#> 1 District… 2023-12-04       1     3 m     NA        59   15.6  109.  n     
#> 2 District… 2023-12-04       1     3 m     NA         8    7.5   68.6 n     
#> 3 District… 2023-12-04       1     3 m     NA        19    9.7   79.5 n     
#> 4 District… 2023-12-04       1     3 f     NA        49   14.3  100.  n     
#> 5 District… 2023-12-04       1     3 f     NA        32   12.4   92.1 n     
#> 6 District… 2023-12-04       1     3 f     NA        17    9.3   77.8 n     
#> # ℹ 1 more variable: muac <int>

anthro.01 contains anthropometry data from SMART surveys from anonymized locations. To learn more about this dataset, call help("anthro.01") in your R console.

Now that we got acquainted with the dataset, we can proceed to executing the task. The function takes three arguments: df, the dataset you want to assess sample sizes for (anthro.01 in this case); cluster, the unquoted variable name in df that contains information for the unique cluster or screening or sentinel site identifiers (anthro.01 has a variable called cluster which we supply here unquoted); and .source, the type of source for the data in df (since anthro.01 data is from a survey, we specify this argument as “survey”). To achieve this, we simply do:

mw_check_ipcamn_ssreq(
  df = anthro.01,
  cluster = cluster,
  .source = "survey"
)

We can also chain anthro.01 to the function using the native pipe operator |>:

anthro.01 |>
  mw_check_ipcamn_ssreq(
    cluster = cluster,
    .source = "survey"
  )

Either way, the returned output will be:

#> # A tibble: 1 × 3
#>   n_clusters n_obs meet_ipc
#>        <int> <int> <chr>   
#> 1         30  1191 yes

A tibble object is returned with three columns:

• n_clusters counts the number of unique cluster or villages or community identifiers in the dataset where the data collection took place.

• n_obs counts the number of children from which data were collected.

• meet_ipc indicates whether the IPC AMN sample size requirements (for surveys in this case) were met or not.

The above output is not quite useful yet as we often deal with multiple-area datasets. We can get a summarized output by area as follows, by supplying area—a vector containing the categories for which the analysis should be summarised (more than one vector can be specified, separated by ,):

anthro.01 |>
  mw_check_ipcamn_ssreq(
    cluster = cluster,
    .source = "survey",
    area
  )

This will return:

#> # A tibble: 2 × 4
#>   area       n_clusters n_obs meet_ipc
#>   <chr>           <int> <int> <chr>   
#> 1 District E         28   505 yes     
#> 2 District G         30   686 yes

For screening or sentinel site-based data, we approach the task the same way; we only have to change the .source parameter to “screening” or to “ssite” as appropriate, as well as to supply cluster with the right column name of the sub-areas inside the main area (villages, localities, comunas, communities, etc).

References