mwana: Utilities for Analysing Children’s Nutritional Status

Child anthropometric assessments, implemented routinely in most countries worldwide, are the cornerstones of child nutrition and food security surveillance around the world. Ensuring the quality of child anthropometric data, the accuracy of child undernutrition prevalence estimates, and the timeliness of reporting is therefore critical in establishing accurate, robust, and up-to-date child undernutrition status globally.

mwana, term for child in Elómwè, a local language spoken in the central-northern regions of Mozambique, and also a word with a similar meaning across other Bantu languages (such as Swahili) spoken in many parts of Africa, is a package that streamlines child anthropometry data quality checks and undernutrition prevalence estimation for children 6-59 months old through comprehensive implementation of the SMART Methodology guidelines in R.

Motivation

mwana was borne out of the author’s own experience of having to work with multiple child anthropometric datasets to conduct data quality appraisal and prevalence estimation as part of the data quality assurance team of the Integrated Phase Classification (IPC). The current standard child anthropometric data appraisal workflow is extremely cumbersome requiring significant time and effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA software) for each step of the process for a single dataset. This process is repeated for every dataset needing to be processed and often needing to be implemented in a relatively short period time. This manual and repetitive process, by its nature, is extremely error-prone.

mwana, which is primarily an R-based implementation of the ENA for SMART software, simplifies this cumbersome workflow into a programmable process particularly when handling large multiple datasets.

[!NOTE]

mwana was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the SMART initiative. Under to hood, mwana bundles the SMART guidance through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in R to build its handy function around plausibility checks. Click here to learn more about the nipnTK package.

What does mwana do?

It automates plausibility checks and prevalence analyses and respective summaries of the outputs.

Plausibility checks.

• mwana performs plausibility checks on weight-for-height z-score (WFHZ)-based data by mimicking the SMART plausibility checkers in ENA for SMART software, their scoring and classification criterion.

• It performs, as well, plausibility checks on MUAC data. For this, mwana integrates recent advances in using MUAC-for-age z-score (MFAZ) for auditing the plausibility of MUAC data. In this way, when the variable age is available: mwana performs plausibility checks similar to those in WFHZ, however with few differences in the scoring. Otherwise, when the variables age is missing, a similar test suit used in the current version of ENA is performed. Read details here.

A useful workflow for plausibility check using mwana

Prevalence analysis

mwana prevalence calculators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. Fundamentally, the calculators loop over the survey areas in the dataset whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result.

mwana computes prevalence for:

• Wasting on the basis of WFHZ and/edema (Read vignettes)
• Wasting on the basis of the absolute values of MUAC and/or edema: here, when variable age is available, mwana applies MFAZ flags, otherwise it applies the flagging criteria around the absolute values of MUAC, to exclude outliers before computing prevalence, but the actual prevalence is done on the absolute values. (Read link to the specific section in the vignettes)
• Wasting on the basis of MFAZ and/edema: outliers excluded using MFAZ flags. (Read link to the specific section in the vignettes)
• Combined prevalence of wasting: here a concept of combined flags is used to streamline the flags removed in WFHZ and those in MUAC. (Read link to the specific section in the vignettes).

mwana provides weighted prevalence analysis, if needed. And this is controlled by the user. This is possible in all calculators, including for MUAC, combined, which is not currently available in ENA for SMART.

In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, mwana provides a handy function for checking if the minimum sample size requirements in a given area were met on the basis of the methodology used to collect the data: survey, screening or sentinel sites. (Check out the vignette).

[!TIP]

If you are undertaking a research and you want to censor your data before including in your statistical models, etc, mwana is a great helper, as it identifies flags out of your anthro data.

[!WARNING]

Please note that mwana is still highly experimental and is undergoing a lot of development. Hence, any functionalities described below have a high likelihood of changing interface or approach as we aim for a stable working version.

Installation

mwana is not yet on CRAN but you can install the development version from nutriverse R universe as follows:

remotes::install_github("tomaszaba/ipccheckr")

Then load to in memory with

library(ipccheckr)

Citation

If you were enticed to use mwana 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},
#>   }

Community guidelines

Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.

This project is releases with Contributor Code of Conduct. By participating in this project you agree to abide by its terms.