Determinants of wasting among under-five children in Ethiopia: Multilevel ordinal logistic regression analysis of EDHS 2016

BACKGROUND: In Ethiopia, wasting is still a serious public health problem in under-5 children regardless of the efforts done within the country. Although studies have been conducted in Ethiopia, multilevel analysis has rarely been used to identify the factors associated with wasting among children. Therefore, this study aimed to identify the factors of wasting among under-5 children in Ethiopia using multilevel ordinal logistic regression analysis. METHODS: The data were obtained from the 2016 Ethiopia Demographic and Health Survey, conducted from January to June 2016. A sample of 8,919 children under-5 years was included. Data were analyzed using STATA version 14. A multilevel logistic regression model was fitted and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) was obtained. RESULTS: The prevalence of wasting among children under-5 years was 10.1% (901), of which 8.1% (632) had moderate wasting and 3.0% (269) had severe wasting. Children aged 36–47 months (AOR = 0.5; 95% CI: 0.4, 0.63), 48–59 (AOR = 0.5; 95% CI: 0.4–0.63), girls (AOR = 0.75; 95% CI: 0.65, 0.87), smaller-than-average birth weight (AOR = 1.94; 95% CI: 1.44, 2.61), very small birth weight (AOR = 1.75; 95% CI: 1.34, 2.30) were the individual-level factors associated with wasting, whereas husband’s educational status (AOR = 0.37; 95% CI: 0.29, 0.69) was the household-level factor. Somalia (AOR = 1.72; 95% CI: 1.08, 2.74), Sothern Nations Nationalities and People (SNNP) (AOR = 0.39; 95% CI: 0.24, 0.64), and Addis Ababa (AOR = 0.43; 95% CI: 0.21, 0.88) regions were the community-level factors associated with child wasting. CONCLUSION: This study shows that wasting among children under-5 years is affected by individual, household, and community-level factors. Interventions should be strengthened to give attention to the child’s early age, male sex, low birth weight, and husband’s educational status. In addition, interventions should target the regions identified to have a high risk of wasting.