Rank likelihood-based estimation of low birth weight in Ethiopia

Low birth weight is a significant risk factor associated with high rates
of neonatal and infant mortality, particularly in developing
countries. However, most studies conducted on this topic in Ethiopia have
small sample sizes, often focusing on specific areas and using
standard models employing maximum likelihood estimation, leading
to potential bias and inaccurate coverage probability. This study
used a novel approach, the Bayesian rank likelihood method, within a
latent traits model, to estimate parameters and provide a
nationwide estimate of low birth weight and its risk factors in
Ethiopia. Data from the Ethiopian Demographic and Health Survey
(EDHS) of 2016 were used as a data source for the study. Data
stratified all regions into urban and rural areas. Among 15, 680
representative selected households, the analysis included complete cases
from 10, 641 children. The evaluation of model performance
considered metrics such as the root mean square error, the mean absolute
error, and the probability coverage of the corresponding 95%
confidence intervals of the estimates. Based on the values of
root mean square error, mean absolute error, and probability coverage, the
estimates obtained from the proposed model outperform the
classical estimates. According to the result, 40.92% of
the children were born with low birth weight. The study also
found that low birth weight is unevenly distributed across
different regions of the country. Furthermore, there were
significant associations between birth weight and several
factors, including the age of the mother, number of antenatal
care visits, order of birth and the body mass index as indicated
by the average posterior beta values of (β1= -0.269, CI = -0.320, -0.220),
(β2= -0.235, CI = -0.268, -0.202), (β3= -0.120, CI = -0.162,
-0.074) and (β5= -0.257, CI = -0.291, -0.225). The study showed
that the low birth weight estimates obtained from the latent trait model
outperform the classical estimates. The study also revealed that
the prevalence of low birth weight varies between
different regions of the country, indicating the need for
targeted interventions in areas with a higher prevalence.
To effectively reduce the prevalence of low birth weight and
improve maternal and child health outcomes, it is important to
concentrate efforts on regions with a higher burden of low birth weight.
This will help implement interventions that are tailored to the
unique challenges and needs of each area. Health
institutions should take measures to reduce low birth weight,
with a special focus on the factors identified in this study.