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.
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.
mwana is not yet on CRAN but can be
installed from the nutriverse R
Universe as follows:
install.packages(
"mwana",
repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org')
)then loaded into the current environment via
library(mwana)Warning
Please note that mwana is still experimental but is already in late
stage alpha version testing nearing a stable release with development
focusing on backwards compatible patch or minor changes. Current
functionalities described below may still change in the future but are
likely to be compatible with the current interface or approach.
Currently,
mwana has the following functionalities that support the creation of a
programmatic workflow illustrated in the figure to the left.
mwana has functions for performing data plausibility checks on
weight-for-height z-score (WFHZ) data based on the SMART plausibility
checkers, data quality scoring, and data quality classification
implemented by the ENA for SMART software, their scoring and
classification criterion. To learn more about these WFHZ plausibility
checks, the functions that implement them, and how to use these
function, read this
guide.
mwana also has functions for performing data plausibility checks on
mid-upper arm circumference (MUAC) data based on recent research and
recommendations on MUAC-for-age z-score (MFAZ) and its utility for data
plausibility checks of MUAC data. To learn more about these MUAC
plausibility checks, the functions that implement them, and how to use
these functions, read this
guide.
mwana has prevalence estimators developed to take into account SMART
guidelines on estimation approach to use based on an assessment of data
quality. These functions accept input datasets that include multiple
survey domains and return summary output tables with prevalence
estimates for each survey domain.
-
To read about the functions and the process for estimating acute undernutrition prevalence from WFHZ and/or edema data, read this guide.
-
To read about the functions and the process for estimating acute undernutrition prevalence from MUAC data, read this guide on using raw MUAC and/or edema data and this guide on using MFAZ and/or edema data.
-
To read about functions and the process for estimating combined acute undernutrition prevalence, read this guide.
mwana provides a handy function for checking whether a specific
anthropometric dataset has met the minimum sample size requirements for
each of the dataset domains based on IPC requirements. The function
assesses this sample size requirement based on whether the dataset was
collected through a survey, a screening exercise, or a sentinel site
surveillance. To learn more about this function, read this
guide.
mwana has helper functions that process summary output tables and turn
them into presentation and/or report ready tables.
Tip
If you are undertaking research using anthropometric data of children
6-59 months old with a focus on acute undernutrition, mwana has
functions to wrangle weight, height, age,
WFHZ, MUAC, and MFAZ data before using it in your
models.
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 (2024). _mwana: An Efficient Workflow for
#> Plausibility Checks and Prevalence Analysis of Wasting in R_.
#> doi:10.5281/zenodo.14176624
#> <https://doi.org/10.5281/zenodo.14176624>, R package version 0.2.1,
#> <https://nutriverse.io/mwana/>.
#>
#> 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}},
#> year = {2024},
#> note = {R package version 0.2.1},
#> url = {https://nutriverse.io/mwana/},
#> doi = {10.5281/zenodo.14176624},
#> }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.