An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R • mwana

Child anthropometric assessments are central to child nutrition and food-security surveillance worldwide. Ensuring the quality of these data is essential for accurate estimates of child undernutrition prevalence. Timely reporting is also critical for situation analysis and for mounting effective responses.

The mwana package streamlines data-quality checks and acute-undernutrition prevalence estimation from anthropometric data for children aged 6–59 months. It builds on methods and guidance from the SMART initiative and provides convenient wrappers around functions in the nipnTK package.

The term mwana means child in Elómwè, a local language spoken in the central-northern regions of Mozambique where the author hails from. It also has a similar meaning across other Bantu languages, such as Swahili, spoken in many parts of Africa.

Motivation

The standard child-anthropometry appraisal workflow is complex and time-consuming, relying on multiple tools—such as SPSS, Microsoft Excel, and SMART ENA software—for various steps of the process. Each dataset requires the repetition of these steps—often under tight deadlines, which makes the manual and repetitive workflow highly error-prone.

mwana provides functions that simplify this cumbersome workflow, enabling it to be programmatically designed, particularly when handling multi-area datasets.

Installation

mwana is not yet on CRAN but can be installed from GitHub:

# First install remotes package with: install.package("remotes")
# Then install mwana package from GitHub with: 
remotes::install_github(repo = "mphimo/mwana", dependencies = TRUE)

What does `mwana` do?

[!NOTE]

mwana is experimental and currently in late-stage alpha testing, approaching a stable release. Future changes are expected to be backward-compatible (patch or minor releases), but some functionality may still change.

Currently, mwana has the following functionalities that support the creation of a programmatic workflow illustrated in the figure to the left.

1. Data plausibility checks of acute undernutrition anthropometric data of children 6-59 months old

mwana provides functions to perform data plausibility checks on weight-for-height z-score (WFHZ) data. These are based on the SMART plausibility checkers, data quality scoring, and classification criteria implemented in the ENA for SMART software. Moreover, it provides functions to perform data plausibility checks on Mid-Upper Arm Circumference (MUAC) data. These are based on recent research and recommendations concerning the MUAC-for-age z-score (MFAZ) and its utility for assessing the plausibility of MUAC data. To learn more, see Plausibility check guide.

2. Prevalence estimation of acute undernutrition

mwana provides prevalence estimators that follow SMART guidelines on the estimation approach to apply, based on an assessment of data quality. These functions accept datasets containing multiple survey domains and produce summary tables with prevalence estimates for each domain.

3. IPC sample size checker

mwana provides a function to check whether each domain in an anthropometric dataset meets IPC minimum sample-size requirements. The check accounts for the data-collection mode (survey, screening exercise, or sentinel-site surveillance). Read IPC check guide.

4. Reporting of data plausibility checks and prevalence estimation summary outputs

mwana includes helper functions that process summary outputs into presentation- or report-ready tables.

[!TIP]

If you are researching anthropometric data for children aged 6–59 months (focusing on acute undernutrition), mwana includes functions to wrangle weight, height, age, WFHZ, MUAC, and MFAZ prior to analysis.

Shiny App

This package has a lightweight, field-ready and convenient web-based application (mwanaApp) that enables users to upload their data and benefit from the mwana utilities needless to be well versed in R. Learn more about mwanaApp and how to install and use here.

Citation

If you use mwana package in your work, please cite using the suggested citation provided by a call to citation() function as follows:

citation("mwana")

To cite mwana in publications use:

  Tomás Zaba, Ernest Guevarra, Mark Myatt (2025). _mwana: An Efficient
  Workflow for Plausibility Checks and Prevalence Analysis of Wasting
  in R_. R package version 0.2.3, <https://mphimo.github.io/mwana/dev>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R},
    author = {{Tomás Zaba} and {Ernest Guevarra} and {Mark Myatt}},
    year = {2025},
    note = {R package version 0.2.3},
    url = {https://mphimo.github.io/mwana/dev},
  }

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 released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

mwana: An efficient workflow for plausibility checks and prevalence analysis of wasting in R