Open Access Research Article Article ID: GJFR-7-121

    Statistical analysis of regional variation and factors associated with birth weight of babies in Ethiopia: Multilevel ordinal logistic regression

    Gurmessa Nugussu*, Jaleta Abdisa and Dechasa Bedada

    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. 


    Published on: Jun 8, 2022 Pages: 1-15

    Full Text PDF Full Text HTML DOI: 10.17352/gjfr.000021
    CrossMark Publons Harvard Library HOLLIS Search IT Semantic Scholar Get Citation Base Search Scilit OAI-PMH ResearchGate Academic Microsoft GrowKudos Universite de Paris UW Libraries SJSU King Library SJSU King Library NUS Library McGill DET KGL BIBLiOTEK JCU Discovery Universidad De Lima WorldCat VU on WorldCat


    Case Reports

    Pinterest on GJFR

    Help ? Google Reviews 11