Assessing Food Insecurity Risk in Protracted Crisis: An Application of Generalized Linear Mixed Model

This article explores a statistically robust technique for assessing the likelihood of food insecurity risk in distressful livelihood situations amidst intense humanitarian crisis. Using South Sudan as a case study, a Generalized Linear Mixed Model was fitted to data from food insecurity surveillance. The technique identified a set of possible determinants of food insecurity risk in populations experiencing extensive livelihood distress, which include displacement, soaring commodity prices, and inability to engage in agriculture, animal husbandry, and employed work. The method is found to exert reasonable statistical efficiency for fulfilling the study's end; providing reasonably sufficient evidence for food security analysts and development policymakers. The article concludes by recommending possible actions for improving the quality of data and consequently the efficiency of the technique as a measure of population resilience to food insecurity risk in strained livelihoods.