Bayesian Spatial Quantile Interval Model with Application to Childhood Malnutrition in Ethiopia

Abstract Background: The national prevalence of stunting and wasting in Ethiopia is still very high and it is the most common causes of morbidity and mortality among less than five years old children. The aim of the current study was to investigate the determinant of stunting and wasting in Ethiopia. Methods: Malnutrition data-sets collected through EDHS 2016 were analyzed by Bayesian spatial quantile interval regression models using R- INLA package. Results: The present study found that child sex, child age, mother's education, mother's age, source of drinking water, mother's BMI , wealth index, region, residence, cooking fuel and toilet facility were significantly associated with childhood malnutrition (stunting and wasting). Furthermore, these findings imply that a multisectorial and multidimensional approach is important to address malnutrition in Ethiopia. Conclusion: The education sector should promote reduction of gender barriers that contribute to childhood malnutrition and also the health sector should encourage positive behaviors toward childcare and other feeding practices. Moreover, both governmental and non-governmental potential stakeholders should pay attention on the significant factors identified through the current study so that stunting and wasting in Ethiopia minimized optimally.