ABSTRACT
BACKGROUND: Sample surveys are the mainstay of surveillance for acute malnutrition in settings affected by crises but are burdensome and have limited geographical coverage due to insecurity and other access issues. As a possible complement to surveys, we explored a statistical approach to predict the prevalent burden of acute malnutrition for small population strata in two crisis-affected countries, Somalia (2014-2018) and South Sudan (2015-2018). METHODS: For each country, we sourced datasets generated by humanitarian actors or other entities on insecurity, displacement, food insecurity, access to services, epidemic occurrence and other factors on the causal pathway to malnutrition. We merged these with datasets of sample household anthropometric surveys done at administrative level 3 (district, county) as part of nutritional surveillance, and, for each of several outcomes including binary and continuous indices based on either weight-for-height or middle-upper-arm circumference, fitted and evaluated the predictive performance of generalised linear models and, as an alternative, machine learning random forests. RESULTS: We developed models based on 85 ground surveys in Somalia and 175 in South Sudan. Livelihood type, armed conflict intensity, measles incidence, vegetation index and water price were important predictors in Somalia, and livelihood, measles incidence, rainfall and terms of trade (purchasing power) in South Sudan. However, both generalised linear models and random forests had low performance for both binary and continuous anthropometric outcomes. CONCLUSIONS: Predictive models had disappointing performance and are not usable for action. The range of data used and their quality probably limited our analysis. The predictive approach remains theoretically attractive and deserves further evaluation with larger datasets across multiple settings.
