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.
Background: South Sudan has experienced ongoing civil and environmental problems since gaining independence in 2011 that may influence childhood nutritional status. Objective: To estimate the prevalence of undernutrition among children in South Sudan in 2018 and 2019 compared to the prevalence in 2010. Methods: Data on height and weight were collected using a 2-stage stratified sample framework in which households were randomly selected at the county level and nutritional status was calculated for all children under 5 years of age to determine height-for-age, weight-for-height, and weight-for-age Z-scores (HAZ, WHZ, and WAZ) and the prevalence of stunting, wasting, and underweight. Linear and logistic regression analyses were used to determine factors associated with nutritional status and the odds ratio for nutritional outcomes. Results: In 2010, the mean HAZ, WHZ, and WAZ was −0.78, −0.82, and −1.15, respectively, and the prevalence of stunting, wasting, and underweight was 30%, 23%, and 32%, respectively. In 2018 and 2019, the mean HAZ, WHZ, and WAZ was −0.50, −0.70, −0.77 and −0.53, −0.77, −0.76, respectively. The prevalence of stunting, wasting, and underweight in 2018 and 2019 was 17%, 14%, 15% and 16%, 16%, 17%, respectively. Age was negatively associated with all nutritional indices and girls had higher HAZ, WHZ, and WAZ and a lower mid upper arm circumference ( P < .01) compared to boys. The risk of poor nutritional outcomes was associated with vaccine status and varied by state of residence. Conclusions: Following independence in 2010, the prevalence of undernutrition in South Sudan decreased, but the risk for undernutrition varied by state and efforts to address food security and health need to ensure equitable access for all children in South Sudan.
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