Abstract

The World Health Organization (WHO) prescribed 2.5 kilograms to 4.2 kilograms as the standard for normal birth weight (NBW) and every child whose birth weight is below the lowest bound is regarded as low birth weight (LBW) while above the highest bound is regarded as macrosomia. The odds for a LBW child to die as reported is about 40 times high when compared to a NBW child and these overwhelming death records are higher in developing countries. Therefore, urgent research about the causes of LBW especially in developing countries is very necessary and this motivated this research in Nigeria. In this paper, the Multinomial Logistic Regression (MLR) model was applied to secondary data from the 2018 Nigerian Demographic and Health Survey (NDHS) report to predict the probability of giving birth to NBW and macrosomia babies referenced to LBW babies. The maternal education level and age were considered as the predictor variables. The data was naturally stratified by maternal education level, that is (1 = Higher Education, 2 = Secondary Education, 3 = Primary Education, and 4 = No Education). The equal sample allocation technique which assigns equal stratum sample sizes (ni = 200 for the ith maternal education level) was adopted. The 800 sample size was reduced to 735 after screening for outliers in the maternal age variable. Considering the 54 LBW babies, 57% (0.5741) were from mothers with no education. The results showed that maternal education level and age have causal effects on child birth weight in Nigeria. Younger mothers (less than 28years) are 96.5% likely to have LBW babies while mothers who attained a minimum of primary, secondary, and higher education are 88.00%, 82.00%, and 57.90% likely to have NBW babies respectively when compared to those with no formal education. The research recommends that mothers should acquire at least primary school education and early child marriage (less than 28years) of the girl child should be discouraged.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call