Analysis Of Risk Factors For Under-five Child Malnutrition In Ethiopia Multi-level Approach

Although the problem of malnutrition affects the entire population, children are more vulnerablernbecause it reduces their physical growth, proper organ formulation and cognitive developmentrnand weakens immune system. Under-nutrition contributes half of all deaths in children underfivernin Africa and Asia. The solution of this big public health problem is essential. From thisrnperspective, this study aimed to explore risk factors for under-five child malnutrition in Ethiopia.rnThe data for the study were taken from Ethiopian Mini Demographic and Health Surveyrn(EMDHS) of year 2014. To achieve objectives of the study, two indicators (stunting and wasting)rnwere studied. The two indicators were treated separately due to their biological differences.rnStatistical models that handle the complexities of correlated data were employed. GeneralizedrnEstimating Equations (GEEs), Alternating Logistic Regression (ALR), Proportional Odds Modelrn(POM), Partial Proportional Odds Model (PPOM) and baseline category logit models werernused for analysis. Results showed that mothers with less educational level, preceding birthrninterval less than 24 months, from Tigray, Affar and SNNP regions were associated with higherrnprobability of stunting. Child age in months had positive effect on child stunting. Children fromrnnon-educated mothers, from Affar and Somali regions had higher chance of wasting. Two-levelrnALR analysis indicated strong association ( ) between two children from the samernhouseholds. In conclusion, results suggest that region, mother’s education level, wealth index,rnpreceding birth interval and child age were associated with stunting, while region, mother’srneducation level, mother’s current marital status, sex and age of child in months were associatedrnwith wasting among children under-five in Ethiopia. To reduce under-five child malnutrition,rnsome crucial steps regarding educating mothers and improving economic situation of populationrnas well as supplementary feeding programs should be considered.rnKey Words: Alternating Logistic Regression (ALR), Generalized Estimating Equations (GEEs),rnProportional Odds Model (POM), Partial Proportional Odds Model (PPOM) Baseline CategoryrnLogit, Stunting, Wasting

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