Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models

Abstract

BackgroundSevere acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.MethodsA secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.ResultsIn Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.ConclusionsEradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.