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

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: experimental DOI

Child anthropometric assessments are the cornerstones of child nutrition and food security surveillance around the world. Ensuring the quality of data from these assessments is paramount to obtaining accurate child under nutrition prevalence estimates. The timeliness of reporting is, as well, critical to allowing timely situation analyses and responses to tackle the needs of the affected population.

The {mwana} package streamlines data quality checks of and acute undernutrition prevalence estimation from anthropometric data of children aged 6 to 59 months old. This is made possible through the many years of leadership and development work in nutrition surveys of the Standardized Monitoring and Assessment of Relief and Transitions (SMART) initiative through its nutrition survey guidance which {mwana} builds upon as a development framework. The main functionalities of the {mwana} package on acute undernutrition data quality checks are mainly convenience wrappers to 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

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

{mwana} provides functions that can simplify this cumbersome workflow into a process that can be programmatically designed particularly when handling multiple-area datasets. Whilst developed with the analytic and reporting needs of the IPC Global Support Unit in mind, {mwana} can be used generally for anthropometric datasets of children for the purpose of assessing data quality and for estimating prevalence of acute undernutrition in children 6-59 months old.

For more information on the {mwana} package, you can visit the project website. To learn about the underlying code for this software development project, see the GitHub code repository.

If you are interested in contributing to this project, please be aware of its Contributor Code of Conduct. By participating in this project you agree to abide by its terms. To see a list of features that we are either actively working on, are considering, or need help on, visit our GitHub issues page. If you find something that you can help with, follow these steps on how to contribute.