DOI: 10.5061/dryad.3j9kd51sg
title: Rank likelihood-based estimation of low birth weight in Ethiopia

description: 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.
description: # Rank likelihood-based estimation of low birth weight in Ethiopia Low
 birth weight data was obtained from the Ethiopian Demographic and Health
 Survey (EDHS). Raw data: **Lowbirthweight.sav** ## Description of the data
 and file structure #### Lowbirthweightdata\_data *childweight*:
 categorical weight of the child at birth *motherage*: age of the mothers
 *ancvisti*: number of antenatal care visits that the mothers attended
 *birthorder*: order of birth for the child *birthinterval*: time between
 successive births (months) *bmi*: body mass index of the mothers
 *Regions*: the region where the child born *CLID*: cluster-level ID that
 indicates from which cluster the information is obtained ## Sharing or
 accessing information Our data is taken from the DHS website
 ([http://dhsprogram.com](http://dhsprogram.com). Low birth weight data was
 extracted from the 2016 EDHS. EDHS 2016 was conducted using standardized
 survey design and data collection procedures.