Determinants of stunting among under-five years children in Ethiopia from the 2016 Ethiopia demographic and Health Survey: Application of ordinal logistic regression model using complex sampling designs

• Published in: Clinical epidemiology and global health 2020-06, Vol.8 (2), p.404-413
• Main Authors: Fenta, Haile Mekonnen, Workie, Demeke Lakew, Zike, Dereje Tesfaye, Taye, Belaynew Wassie, Swain, Prafulla Kumar
• Format: Article
• Language: English
• Subjects: Child stunting; Complex sampling design; Determinant; Ethiopia; Ordinal logistic regression
• ISSN: 2213-3984; 2213-3984
• DOI: 10.1016/j.cegh.2019.09.011

Stunting is a result of chronic under nutrition and a major public health issue in Ethiopia. This study aimed to calculate the prevalence of stunting, and associated factors among children younger than five years. A total of 9588 children in Ethiopia were included. Proportional Odds Model was used to identify determinants of stunting. The score test and plots were used to see the proportional odds model assumptions. The prevalence of stunting was 38% (21% moderately, 17% severely). Children with illiterate mothers were 2 times more likely to be moderately and severely stunted compared with their counterparts with secondary education. The odds of being stunted for children whose age group 24–35 months respectively as compared to children 0–5 months of age were 4.71 times higher. Being female children were 9.66 times more likely to be in normal nutrition status as compared to male. Children of families in the highest wealth quintile were 7.92 times more likely to have normal stature compared with children from poorest ones. Child age, child sex, birth interval, mother's educational status, wealth index, were the important determinants of stunting. Addressing these factors will help to prevent future injury of physical and mental development in children and will assist in alleviating malnutrition and refining their quality of life. Moreover, in a DHS data set, complex sampling design should be incorporated in order to make a valid statistical inference.