Determinants Of Low Birth Weight Infants Among Various Regions Of Ethiopia: A Multilevel Logistic Regression Model

Abstract

Background: Low birth weight is defined as weight of child at birth less than 2500 g measured within 24 hours of birth. It is a public health problem affecting 15%-20% of births worldwide. Low birth weight is the cause of 28%-30% of neonatal deaths. Therefore, this study is conducted to determine the prevalence of Low birth weight and associated factors among newborns delivered in Ethiopia. Objective: The objective of the study is determination of Low birth weight infants variations among various regions of Ethiopia using multilevel logistic regression models. Methods: Data is taken from the 2011 Ethiopian demographic and health survey, which is a nationally representative survey of children in the 0-59 month age groups. Three model families, Empty model, Random intercept model and random slope model will be used for the analysis. MQL-1 and PQL-2 estimation methods are likely to be adequate for producing unbiased estimates compared to other methods. Results: The result showed that 53.2% of children were born with low birth weight. Based on the model adequacy test the random slope binary logistic regression model is found to be the best fitting to the data. The variance of the random component model related to the intercept and sex of child variable are statistically significant. Conclusion This study suggests that sex of child, maternal wealth index and maternal no antenatal visit have been found simultaneously statistically significant. But univariate analysis shows that sex of child, maternal wealth index, maternal place of residence, maternal education level, maternal anemia level, multiple birth, maternal age and maternal weigh have been found statistically significant and are varies across region. Key words: low birth weight; Null model, random intercept model and Random slope model.