Background: The weight of a newborn is measured for the first time shortly after birth. The World Health Organization divides newborns’ birth weight into three categories: low birth weight (2.5 kg), normal birth weight (2.5 kg-4 kg), and high birth weight (> 4 kg). Both the mother and the infant are at risk of mortality and morbidity as a result of their birth weight. Using hierarchical data, there is scant evidence in Ethiopia of factors linked with birth weight. The goal of this study was to use a multilevel ordinal logistic regression model to investigate geographical variance and factors related to baby birth weight. Methods: Using missing factors in datasets, data for this study was collected from the Ethiopia Demographic Health Survey 2016. To address missing data and increase the inference’s reliability, hot deck multiple imputations were utilized. A multilevel ordinal logistic regression model was used to examine factors associated with birth weight. R software was used for analysis. Results: The study took into account a total of 8,328 newborns. According to a descriptive study, 1292 (15.5%) of the 8,328 babies were born with low birth weight, 6143 (73.8%) were born with normal birth weight, and 893 (10.7%) were born with high birth weight. Mother’s age, residence, mother’s age at first birth, wealth index, BMI, anemia level, gestational age, total children, mother delivery, multiple pregnancies, and baby’s sex were all found to be significant factors associated with a birth weight of Ethiopian babies in a multilevel ordinal logistic regression analysis. Conclusions: The multilevel ordinal logistic regression analysis revealed that there was significant variance in baby birth weight between and within Ethiopian regions. Among the three multilevel models, the random coefficient model fits the data the best.

Background: Globally, there is an increase in the prevalence and incidence of fetal macrosomia. In Sub-Sahara African countries including Ethiopia, all infants were not weighed at birth, and there is a limit to knowledge regarding fetal macrosomia in Ethiopia. The main objective of this study is to assess the regional variation and determinants of fetal macrosomia using the multilevel logistic regression model.Methods: The study was based on the recent Ethiopian Demographic and Health Survey of 2016. A total of 2110 weighted infants at birth were extracted. Multilevel logistic regression analysis is performed to identify the factors associated with fetal macrosomia after various candidate models for their efficiency have been compared based on Akaike’s Information Criteria. Chi-square test of association and the inter-class correlation (ICC) are used to test and compute the variation of fetal macrosomia among the regions, respectively.Results: The overall prevalence of fetal macrosomia among the weighted infants at birth is 219 (10.4%). Based on the estimated chi-square test, there is a significant difference in fetal macrosomia across the regions of Ethiopia. The ICC reveals that 14% of the variation in fetal macrosomia can be explained by grouping the infants into the regions. Random intercept with fixed slope model fits the study data well as compared to the other competitors. Based on this model, the age of the mother, residence, educational level of mother, body mass index of mother, gestational age, wealth index, multiple pregnancies, and the infant sex are the significant factors associated with fetal macrosomia in all regions of Ethiopia.Conclusion: Concerned bodies, including the ministry of health and its hierarchical body, need to give special support and attention to women aged between 35 and 49, post-term pregnant women, and overweight or obese women to minimize the prevalence of fetal macrosomia.