The ‘SEASNUT’ project final report shows that improved estimates of child wasting – that take account of seasonal differences in survey times and wasting prevalence – can be generated and modelled from available data.
The results indicate that there are seasonal patterns and that statistical models can establish these patterns and estimate monthly wasting values using a range of covariates.
To overcome key data gaps, the authors recommend that predictive models and analyses include more diverse data as well as more specific geospatial metrics. Adding further data on wasting to build the time series/seasonal patterns along with further work on the geospatial covariate design should lead to a more accurate set of estimations for specific countries.
The challenge
Undernutrition in children is assessed from measurements of growth, primarily weight and height. In 2020, wasting, or low weight for height, was estimated to affect 45 million children under 5 years of age. Thirteen percent of under-five child deaths are attributed to wasting each year.
While the UNICEF-WHO-World Bank Joint Malnutrition Estimation group (JME) hold a large amount of data on child wasting, it is irregular, incomplete and not representative across seasons. According to the UNICEF JME site: “Since the prevalence data [for wasting] are collected infrequently (every 3 to 5 years) in most countries and measure wasting at one point in time, it is not possible to capture the rapid fluctuations in wasting over the course of a given year or to adequately account for variations in seasons across survey years.”
The project
Facing this timing issue, a project led by the Data for Children Collaborative at the University of Edinburgh and funded by the Scottish Funding Council and the Jameel Observatory for Food Security Early Action explored the seasonal effects of wasting scores.
Its goal of the project was to establish if it is possible to answer the following question: “what would wasting scores have been had they been measured in different months of a year?”
Results from four countries – Bangladesh, Burkina Faso, Ethiopia and Nigeria – indicate that:
- Wasting does vary seasonally in each country and there appears to be a ‘wasting season’;
- Controlling for wealth and education, wasting scores varied monthly;
- A multi-level logistic regression model provided a list of variables that are correlated with wasting, many of which vary seasonally;
- The model was able to accurately estimate monthly wasting values using a year and month of survey.
Watch the webinar recording of March 2024:
Results
While the study only used Demographic and Health Survey (DHS) data, the results indicate that prediction is more accurate when data are available from multiple months and years. It is recommended that this study be repeated by combining SMART surveys with the DHS data to establish a longer and deeper time series for each country.
Geospatial data had a limited but significant impact on the models. It is recommended that future work should establish proxy metrics for specific issues affected child wasting from geospatial sources. For example, the remotely sensed normalised difference vegetation index (NDVI) is the most commonly used geospatial variable in these types of models across the literature, however, in our project, it was often not significantly related with wasting.
NDVI is an artificially created index, so it cannot be mechanistically linked to wasting or food production. It could be used to generate additional metrics that are more directly related to wasting or food production and availability. For example, converting an NDVI time series into the number of growing days in the year and then linked this to a cropping calendar to see if these days were above or below a threshold for particular crops.
Furthermore, the geospatial variables used in the project only focused on food production (temperature, rainfall, drought, and soil moisture) and did not consider food access which is important for child nutrition.
The project was undertaken as part of the ‘impact collaboration’ approach pioneered by the Data for Children Collaborative.
“The Data for Children Collaborative has been delighted to come together with the Jameel Observatory to further our understanding of this perennial challenge to improve child wasting estimates. Sharing our organisational philosophies – that impact starts by being challenge-led and that complex challenges need diverse skills and expertise to come together in a structured, purpose-driven way – paved the way for this team to unite in tackling this challenge. We look forward to partnering further with the Jameel Observatory to co-create such collaborations for social impact.”
More
Watch the webinar recording of March 2024
Download a summary presentation from the 2024 Observatory community of practice meeting