Journal of Biometrics & Biostatistics

Multilevel Analysis of Acute Respiratory Infection Symptoms amo ng under Five Children in Ethiopia

Abstract

Shibiru Jabessa

The main objectives of this study is modelling acute respiratory infection symptoms among under five children and to investigate how different explanatory variables measured at different level of hierarchical structures affects symptoms of ARI. This study used Ethiopian Demographic and Health Survey (EDHS) 2011 data, collected for 9625 children under five years old in Ethiopia and children are nested within eleven geographical regions. Binary logistic regression analysis and multilevel models were employed to predict the outcome. The study revealed that mothers educational level, age of children, number of children, mothers occupational status, supplementation of vitamin A, source of drinking water, type of toilet facility and wealth index of family were found to be the most important factors. And, the final model, random coefficient multilevel logistic regression suggests that there exists considerable differences in the ARI symptoms among under five children across the regions. It indicates that the variance of random component related to the random term were found to be statistically significant, implying that their is differences in the ARI symptoms for children across regions. The study suggests that improve mothers educational level in all of areas in order to address the problem through improving their income earning capacity, improve access of safe drinking water and the researcher who want to conduct ARI symptoms among children under five using EDHS data set should use multilevel model than classical regression models.