Checking if IPC Acute Malnutrition sample size requirements were met

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 data set anthro.01.

head(anthro.01)

## # A tibble: 6 × 11
##   area       dos        cluster  team sex   dob      age weight height edema  muac
##   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> <int>
## 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n       146
## 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n       127
## 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n       142
## 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n       149
## 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n       143
## 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n       132

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 data set, we can proceed to executing the task. To achieve this, we simply do:

mw_check_ipcamn_ssreq(
  df = anthro.01,         # <1>
  cluster = cluster,      # <2>
  .source = "survey"      # <3>
)

The argument df should be specified with the dataset you want to assess sample sizes for. In this case, anthro.01.
The argument cluster should be specified with the unquoted variable name in df that contains information for the unique cluster or screening or sentinel site identifiers. In this case, anthro.01 has a variable called cluster which we supply to this argument unquoted.
The argument .source should be specified with the type of the source for the data in df. Since anthro.01 data is from a survey, we specify this argument as “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 data set 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:

## Load the dplyr package ----
library(dplyr)

## Use the group_by() function ----
anthro.01 |>
  group_by(area) |>
  mw_check_ipcamn_ssreq(
    cluster = cluster,
    .source = "survey"
  )

This will return:

## # A tibble: 2 × 4
##   area       n_clusters n_obs meet_ipc
##   <chr>           <int> <int> <chr>   
## 1 District E         30   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

IPC Global Partners (2021) Integrated food security phase classification technical manual version 3.1: Evidence and standards for better food security and nutrition decisions. Available at: https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/.

Tomás Zaba

References