scholarly journals Trends of proximate low birth weight and associations among children under-five years of age: Evidence from the 2016 Ethiopian demographic and health survey data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246587
Author(s):  
Mesfin Wudu Kassaw ◽  
Ayele Mamo Abebe ◽  
Ayelign Mengesha Kassie ◽  
Biruk Beletew Abate ◽  
Seteamlak Adane Masresha
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Regression Model ◽  
Maternal Age ◽  
Logistic Regression Model ◽  
Maternal Education ◽  
Multiple Birth ◽  
Health Survey Data ◽  
Birth Type

Background Low birth weight puts a newborn at increased risk of death and illness, and limits their productivity in the adulthood period later. The incidence of low birth weight has been selected as an important indicator for monitoring major health goals by the World Summit for Children. The 2014 World Health Organization estimation of child death indicated that 4.53% of total deaths in Ethiopia were due to low birth weight. The aim of this study was to assess trends of proximate low birth weight and associations of low birth weight with potential determinants from 2011 to 2016. Methods This study used the 2016 Ethiopian Demographic and Health Survey data (EDHS) as data sources. According to the 2016 EDHS data, all the regions were stratified into urban and rural areas. The variable “size of child” measured according to the report of mothers before two weeks of the EDHS takes placed. The study sample refined from EDHS data and used for this further analysis were 7919 children. A logistic regression model was used to assess the association of proximate low birth weight and potential determinates of proximate low birth weight. But, the data were tested to model fitness and were fitted to Hosmer-Lemeshow-goodness of fit. Results The prevalence of proximate low birth weight in Ethiopia was 26.9% (2132), (95%CI = 25.4, 27.9). Of the prevalence of child size in year from 2011 to 2016, 17.1% was very small, and 9.8% was small. In the final multivariate logistic regression model, region (AOR = xx), (955%CI = xx), Afar (AOR = 2.44), (95%CI = 1.82, 3.27), Somalia (AOR = 0.73), (95%CI = 0.55, 0.97), Benishangul-Gumz (AOR = 0.48), (95%CI = 0.35, 0.67), SNNPR (AOR = 0.67), (95%CI = 0.48, 0.93), religion, Protestant (AOR = 0.76), (95%CI = 0.60, 0.95), residence, rural (AOR = 1.39), (95%CI = 1.07, 1.81), child sex, female (AOR = 1.43), (95%CI = 1.29, 1.59), birth type, multiple birth during first parity (AOR = 2.18), (95%CI = 1.41, 3.37), multiple birth during second parity (AOR = 2.92), (95%CI = 1.86, 4.58), preparedness for birth, wanted latter child (AOR = 1.26), (95%CI = 1.09, 1.47), fast and rapid breathing (AOR = 1.22), (95%CI = 1.02, 1.45), maternal education, unable to read and write (AOR = 1.46), (95%CI = 1.56, 2.17), and maternal age, 15–19 years old (AOR = 1.86), (95%CI = 1.19, 2.92) associated with proximate low birth weight. Conclusions The proximate LBW prevalence as indicated by small child size is high. Region, religion, residence, birth type, preparedness for birth, fast and rapid breathing, maternal education, and maternal age were associated with proximate low birth weight. Health institutions should mitigating measures on low birth weight with a special emphasis on factors identified in this study.

scholarly journals Comparison between the maximum likelihood and the bayesian estimation methods for logistic regression model (case study: risk of low birth weight in Indonesia)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012001
Author(s):  
P R Sihombing ◽  
S R Rohimah ◽  
A Kurnia
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Low Birth Weight ◽  
Regression Model ◽  
Bayesian Estimation ◽  
Logistic Regression Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Model Case

Abstract This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.

scholarly journals Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian demographic and health survey 2016: spatial and multilevel analysis

2020 ◽  
Author(s):  
Alemneh Mekuriaw Liyew ◽  
Malede Mequanent Sisay ◽  
Achenef Asmamaw Muche
Keyword(s):  
Logistic Regression ◽  
Spatial Distribution ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Sub Saharan Africa ◽  
Factors Associated ◽  
Risk Areas

AbstractBackgroundLow birth weight (LBW) was a leading cause of neonatal mortality. It showed an increasing trend in Sub-Saharan Africa for the last one and half decade. Moreover, it was a public health problem in Ethiopia. Even though different studies were conducted to identify its predictors, contextual factors were insufficiently addressed in Ethiopia. There was also limited evidence on the spatial distribution of low birth weight. Therefore, this study aimed to explore spatial distribution and factors associated with low birth weight in Ethiopia.MethodSecondary data analysis was conducted using the 2016 EDHS data. A total of 1502 (weighted sample) mothers whose neonates were weighed at birth five years preceding the survey were included. GIS 10.1, SaTscan, stata, and Excel were used for data cleaning and analysis. A multi-level mixed-effects logistic regression model was fitted to identify factors associated with low birth weight. Finally, hotspot areas from GIS results, log-likelihood ratio (LLR) and relative risk with p-value of spatial scan statistics, AOR with 95% CI and random effects for mixed-effects logistic regression model were reported.ResultsLow birth weight was spatially clustered in Ethiopia. Primary (LLR=11.57; P=0.002) clusters were detected in the Amhara region. Whereas secondary (LLR=11.4; P=0.003;LLR=10.14,P=0.0075) clusters were identified at Southwest Oromia, north Oromia, south Afar, and Southeast Amhara regions. Being severely anemic (AOR=1.47;95%CI1.04,2.01), having no education (AOR=1.82;95%CI1.12,2.96), Prematurity (AOR=5.91;95%CI3.21,10.10) female neonate (AOR=1.38;95%CI1.04,1.84)were significantly associated with LBWConclusionLBW was spatially clustered in Ethiopia with high-risk areas in Amhara,Oromia, and Afar regions and it was affected by socio demographic factors. Therefore, focusing the policy intervention in those geogrsphically low birth weight risk areas and improving maternal education and nutrtion could be vital to reduce the low birth weight disparity in Ethiopia.

scholarly journals Maternal Age, Multiple Birth, and Extremely Low Birth Weight Infants

2009 ◽  
Vol 154 (4) ◽  
pp. 498-503.e2 ◽  
Author(s):  
Betty R. Vohr ◽  
Jon E. Tyson ◽  
Linda L. Wright ◽  
Rebecca L. Perritt ◽  
Lei Li ◽  
...  
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Maternal Age ◽  
Extremely Low Birth Weight ◽  
Multiple Birth ◽  
Low Birth Weight Infants

Factors determining child mortality in Uganda

2017 ◽  
Vol 44 (5) ◽  
pp. 633-642 ◽  
Author(s):  
Will Kaberuka ◽  
Alex Mugarura ◽  
Javan Tindyebwa ◽  
Debra S. Bishop
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Birth Weight ◽  
Regression Model ◽  
Marital Status ◽  
Birth Order ◽  
Child Mortality ◽  
Logistic Regression Model ◽  
Household Wealth ◽  
Content Type

Purpose The purpose of this paper is to establish socio-economic factors and maternal practices that determine child mortality in Uganda. Design/methodology/approach The paper examines the role of sex, birth weight, birth order and duration of breastfeeding of a child; age, marital status and education of the mother; and household wealth in determining child mortality. The study employs a logistic regression model to establish which of the factors significantly impacts child mortality in Uganda. Findings The study established that education level, age and marital status of the mother as well as household wealth significantly impact child mortality. Also important are the sex, birth weight, birth order and breastfeeding duration. Research limitations/implications Policies aimed at promoting breastfeeding and education of female children can make a significant contribution to the reduction of child mortality in Uganda. Practical implications Health care intervention programs should focus on single, poor and uneducated mothers as their children are at great risk due to poor and inadequate health care utilization. Originality/value This paper could be the first effort in examining child mortality status in Uganda using a logistic regression model.

scholarly journals Maternal age as a risk factor for cerebral palsy

2021 ◽  
Vol 84 (2) ◽  
pp. 117-131
Author(s):  
Marta Sternal ◽  
Barbara Kwiatkowska ◽  
Krzysztof Borysławski ◽  
Agnieszka Tomaszewska
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cerebral Palsy ◽  
Regression Model ◽  
Maternal Age ◽  
Logistic Regression Model ◽  
Significant Risk ◽  
Control Group ◽  
Logistic Regression Models ◽  
Pregnancy And Childbirth

Abstract The relationship between maternal age and the occurrence of cerebral palsy is still highly controversial. The aim of the study was to examine the effect of maternal age on the risk of CP development, taking into account all significant risk factors and the division into single, twin, full-term, and pre-term pregnancies. The survey covered 278 children with CP attending selected educational institutions in Poland. The control group consisted of data collected from the medical records of 435 children born at Limanowa county hospital, Poland. The analyses included socio-economic factors, factors related to pregnancy and childbirth, and factors related to the presence of comorbidities and diseases in the child. Constructed logistic regression models were used for statistical analyses. For all age categories included in the estimated models (assessing the effect of demographic factors on the development of CP), only the category of ≤24 years of age (in the group of all children) was significant. It was estimated that in this mother’s age category, the risk of CP is lower (OR 0.6, 95% CI: 0.3–1.0) in comparison to mothers aged 25-29 (p = 0.03). However, estimation with the use of a complex logistic regression model did not show any significant effect of maternal age on the incidence of CP in groups from different pregnancies types. It became apparent that maternal age is a weak predictor of CP, insignificant in the final logistic regression model. It seems correct to assume that the studies conducted so far, showing a significant effect of maternal age in this respect, may be associated with bias in the estimators used to assess the risk of CP due to the fact that other important risk factors for CP development were not included in the research.

scholarly journals Risk factors for neonatal mortality at Moewardi Hospital, Surakarta

2014 ◽  
Vol 54 (4) ◽  
pp. 219 ◽  
Author(s):  
Dwi Hidayah ◽  
Yulidar Hafidh
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Birth Weight ◽  
Mortality Rate ◽  
Low Birth Weight ◽  
Neonatal Mortality ◽  
Potential Risk ◽  
Maternal Age ◽  
Delivery Mode ◽  
Potential Risk Factors

Background Neonatal mortality remains a major concern indeveloping countries. Identifying potential risk factors is importantin order to decrease the neonatal mortality rate. In MoewardiHospital, Surakarta, the risk factors for neonatal mortality havenot been assessed.Objective To evaluate potential risk factors of n eonatalmortality.Methods We reviewed medical records of all neonates hospitalizedin the neonatal intensive care unit (NICU) at Dr. MoewardiHospital from January to December 2011. Analyzed variables weresex, birth weight, gestational age, maternal age, place of delivery,mode of delivery, and sepsis. Data were analyzed by Chi square andbinary logistic regression with 95% confidence intervals (CI).Results Out of841 neonates, the mortality rate was 212 (25.2%).Univariate logistic regression revealed that the significant riskfactors for neonatal mortality were preterm (OR 4.41 ; 95%CI4.24 to 4.57; P=0.0001) , low bir th weight (OR 4.30; 95%CI4.13 to 4.47; P=0.0001), sepsis (OR 2.99; 95%CI 2.81 to 3.17;P=0.0001), maternal age 2:35 years (OR 1.53; 95%CI 1.37 to1.70), and non-spontaneous delivery (OR 1.67; 95%CI 1.50 to1.84). Further multivariate regression analysis revealed that thesignificant risk factors were preterm (OR 2.2 7; 95%CI 2.05 to 2.48;P=0.0001), low birth weight (OR 2.49; 95%CI 2.27 to 2.71; P=0.0001), and sepsis (OR 2.50; 95%CI 2.30 to 2.69; P= 0.0001).Conclusion The risk factors for neonatal mortality in the NICUare preterm, low birth weight, and sepsis.

Prevalence of Low Birth Weight and its Determinants in an Urban Resettlement Area of Delhi

2004 ◽  
Vol 16 (2) ◽  
pp. 95-98 ◽  
Author(s):  
P. Chhabra ◽  
A.K. Sharma ◽  
V.L. Grover ◽  
O.P. Aggarwal
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Maternal Age ◽  
Multiple Logistic Regression Analysis ◽  
Baseline Survey ◽  
Age Group ◽  
Multiple Logistic Regression ◽  
Infant And Child Mortality

Birth weight remains an important factor affecting infant and child mortality. Many factors influence the occurrence of low birth weight (LBW). The present study was conducted to study the prevalence and determinants of LBW in an urban resettlement area of Delhi. A baseline survey was done to enroll all pregnant women in the area. These women were followed up every month till outcome. The prevalence of LBW was 39.1%. Occurrence of LBW was related to age, parity, weight and height of the mother. In multiple logistic regression analysis, mother's weight and parity alone emerged as significant variables. On further analysis, on adjusting for age, parity and birth weight were associated for the 20-35-year age group only. Thus, prevalence of LBW remains high in the urban under privileged of Delhi; maternal age, parity and weight and height are important determinants. Increasing age of first birth to more than 20 years can minimize effect of primi parity. Asia Pac JPublic Health 2004' 16(2): 95-98.

Sign in / Sign up

Export Citation Format

Share Document