ipccheckr:Toolkit for Performing IPC Acute Malnutrition-related Data Checks
Background
IPC AMN is a global tool that uses a set o protocols to classify geographical areas into a 5-phases severity based on the prevalence of acute malnutrition among children aged 6:59 months. Its main objective is to provide actionable information for decision-makers. It is not a data collection method per se, therefore, it relies on evidence generated through other methods (representative surveys, screening, sentinel sites, etc). Before any evidence is used an IPC AMN analysis, checks must performed to ascertain the quality of data, following the minimum quality standards set in the protocol, hence its reliability. As such, only evidence that pass the checks are allowed to be used in an IPC analysis exercise.
Why ipccheckr?
The typical analysis workflow is overly complex, tiring and tedious,
time-consuming and not not reproducible. Oftentimes, due to demand, the
workflow needs to be implemented on a pile of data in absolute short
time, making the process prone to human errors due to fatigue. In the
workflow, data “checker” needs to travel from software to software as
follows: SPSS software (mostly) to process data and export to Excel or
CSV to then import it into Emergency Nutrition Assessments (ENA) for
SMART software for to run the quality. From this stage downstream, the
workflow needs to be done one-by-one for the number of survey area a
data set may hold: run the quality check, then transfer the summary
results into a summary table (Excel spreadsheet or other). Then, run the
prevalence analysis (also one-by-one), and then transfer the results
into a spreadsheet and so on and so forth. Sometimes, a fourth tool is
used to correct for a likely overestimation of acute malnutrition
prevalence based on MUAC when there are excess of younger children (6:23
months) over older children (24:59 months) in the sample. Therefore, the
ipccheckr was developed simply to make the workflow simpler, joyful
and reproducible thanks to its array of handy functions.
What does ipccheckr do?
In a nutshell, the array of exported functions span from sample size checkers, data processors, quality checkers, prevalence calculators as well as some handy functions to render formatted and presentable output tables on the two latter groups of functions.
Plausibility checks with ipccheckr
You can run plausibility checks on:
- Weight-for-height z-score (WFHZ) data
- MUAC-for-age z-score (MFAZ) data when age variable is available
- Absolute values of MUAC data when age variable is not available
Useful workflow with ipccheckr for data quality check
On the prevalence analysis
After the data quality checks workflow, you will now be in position to
decide whether or not you can proceed to the prevalence analysis. The
workflow is quite simple: you use the data returned by the data
processors in the above workflow. ipccheckr can compute acute
malnutrition prevalence based on:
- WFHZ and/or edema - guide
- MUAC and/or edema - guide
- Combined prevalence (of WFHZ and MUAC and edema) - guide
- MFAZ and/or edema - guide
Installation
ipccheckr is not yet on CRAN but you can install the development
version from GitHub with:
# install.packages("devtools")
devtools::install_github("tomaszaba/ipccheckr")Then load to in memory with
library(ipccheckr)Citation
If you were enticed to using ipccheckr package and found it useful,
please cite using the suggested citation provided by a call to
citation function as follows:
citation("ipccheckr")
#> To cite ipccheckr: in publications use:
#>
#> Tomás Zaba, Ernest Guevarra (2024). _ipccheckr: Toolkit for
#> Performing IPC Acute Malnutrition-related Data Checks_. R package
#> version 0.0.0.9000, <https://github.com/tomaszaba/ipccheckr>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ipccheckr: Toolkit for Performing IPC Acute Malnutrition-related Data Checks},
#> author = {{Tomás Zaba} and {Ernest Guevarra}},
#> year = {2024},
#> note = {R package version 0.0.0.9000},
#> url = {https://github.com/tomaszaba/ipccheckr},
#> }