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 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):  
Spatial Distribution ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Logistic Regression Model ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Factors Associated ◽  
Risk Areas ◽  
Multi Level ◽  
Spatial Scan

Abstract Background Low birth weight (LBW) is a leading cause of neonatal mortality. In Ethiopia, it is a public health problem that contributes to the majority of newborn deaths. To date, the effect of contextual factors on LBW was largely overlooked in Ethiopia. Besides, there is also limited evidence on the geographic variation of low birth weight in Ethiopia. Therefore, this study aimed to explore spatial distribution as well as individual and community-level factors associated with low birth weight in Ethiopia. Method: Secondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 1502 neonates were included in this study. Spatial autocorrelation analysis was conducted to assess the spatial dependency of LBW. Besides, the spatial scan statistics and ordinary kriging interpolation were done to detect the local level clusters and to assess predicted risk areas respectively. Furthermore, a multi-level logistic regression model was fitted to determine individual and community-level factors associated with low birth weight. Finally, most likely clusters with log-likelihood ratio (LLR), relative risk and p-value from spatial scan statistics, and AOR with 95% CI for multi-level logistic regression model were reported. Results Low birth weight was spatially clustered in Ethiopia. Primary (LLR = 11.57; P = 0.002) clusters were detected in the Amhara region. It showed that neonates within the spatial window had 2.66 times higher risk of being LBW baby as compared to those outside the window. Besides, 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. Neonates who were born from severely anemic (AOR = 1.47;95%CI 1.04,2.01), and uneducated (AOR = 1.82;95%CI1.12,2.96) mothers, as well as those who were born before 37 weeks of gestation (AOR = 5.91;95%CI3.21,10.10) and females (AOR = 1.38;95%CI1.04,1.84), had significantly higher odds of being low birth weight babies. Conclusion The high-risk areas of low birth weight were detected in Afar, Amhara, and Oromia regions. Therefore, targeting the policy interventions in those risk areas by focusing on the improvement of maternal education, strengthening anemia control programs and elimination of modifiable causes of prematurity could be vital for reduce the low birth weight disparity in Ethiopia.

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 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.

Relationship between childhood obesity and socioeconomic status among primary school children in Costa Rica

2021 ◽  
pp. 1-24
Author(s):  
Tatiana Gamboa-Gamboa ◽  
Romain Fantin ◽  
Jeancarlo Cordoba ◽  
Ivannia Caravaca ◽  
Ingrid Gómez-Duarte
Keyword(s):  
Socioeconomic Status ◽  
Logistic Regression ◽  
Childhood Obesity ◽  
Costa Rica ◽  
Regression Model ◽  
Primary Schools ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Overweight And Obesity ◽  
Public And Private

Abstract Objective: This article analyzes the relationship between socioeconomic status and the prevalence of overweight and obesity in the primary school population in Costa Rica. Design: A National School Weight/Height Census was disseminated across Costa Rica in 2016. The percentage of children who were overweight or obese was calculated by sex, age, and socioeconomic indicators (type of institution: private, public, mix; type of geographic location: rural, urban; and the level of development of the district of residence: quartiles). A mixed effects multinomial logistic regression model and mixed effects logistic regression model were used to analyze the association between the prevalence of being overweight or obese and district socioeconomic status. Setting: The survey was carried out in public and private primary schools across Costa Rica in 2016. Participants: 347,366 students from 6 to 12 years old, enrolled in public and private primary schools. Results: The prevalence of overweight and obesity among children was 34.0%. Children in private schools were more likely to be overweight or obese than students in public schools (OR=1.10 [1.07, 1.13]). Additionally, children were less likely to be overweight or obese if attending a school in a district of the lowest socioeconomic quartile compared to the highest socioeconomic quartile (OR=0.79 [0.75, 0.83]), and in a rural area compared to the urban area (OR=0.92 [0.87, 0.97]). Conclusions: Childhood obesity in Costa Rica continues to be a public health problem. Prevalence of overweight and obesity in children was associated with indicators of higher socioeconomic status.

scholarly journals Stunting at birth and associated factors among newborns delivered at the University of Gondar Comprehensive Specialized Referral Hospital

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245528
Author(s):  
Almaz Tefera Gonete ◽  
Bogale Kassahun ◽  
Eskedar Getie Mekonnen ◽  
Wubet Worku Takele
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Short Stature ◽  
Odds Ratio ◽  
Associated Factors ◽  
Adjusted Odds Ratio ◽  
Referral Hospital ◽  
Factors Associated ◽  
The University

Background Stunting at birth is a chronic form of undernutrition majorly attributable to poor prenatal nutrition, which could persist in children’s later life and impact their physical and cognitive health. Although multiple studies have been conducted in Ethiopia to show the magnitude of stunting and factors, all are concentrated on children aged between 6 to 59 months. Therefore, this study was done to determine the prevalence and associated factors of stunting at birth among newborns delivered at the University of Gondar Comprehensive Specialized Referral Hospital, Northwest, Ethiopia. Methods An institution-based cross-sectional study was conducted from February 26th to April 25th/2020. A systematic random sampling technique was used, to select a total of 422 newborn-mother pairs. The binary logistic regression was employed to identify factors associated with stunting and all independent variables were entered into the multivariable logistic regression model to adjust for confounders. Variables that had significant association were identified based on p-value < 0.05 and the adjusted odds ratio with its respective 95% confidence interval was applied to determine the strength as well as the direction of the association. Results About 30.5% (95% CI: 26.3%, 35.1%) of newborns were stunted at birth. Being male [Adjusted odds ratio (AOR) = 2.9(1.62, 5.21)], newborns conceived in Kiremt(rainy season) [AOR = 2.7(1.49, 4.97)], being low birth weight [AOR = 3.1(1.64, 6.06)] were factors associated with stunting at birth. Likewise, newborns born to short stature mothers [AOR = 2.8(1.21, 6.62)] and chronically malnourished mothers [AOR = 15.3(8.12, 29.1)] were at greater risk of being stunted. Conclusion Just under a third of newborns are stunted at birth, implying a pressing public health problem. Newborns born to chronically malnourished and short stature mothers were more stunted. Besides, stunting was prevalently observed among male neonates, newborns conceived in Kiremet, and being low birth weight. Thus, policymakers and nutrition programmers should work on preventing maternal undernutrition through nutrition education to reduce the burden of low birth weight and stunting. Further, paying due attention to newborns conceived in Kiremet season to improve nutritional status is recommended.

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 Quantifying the Robustness of the English Sibilant Fricative Contrast in Children

2015 ◽  
Vol 58 (3) ◽  
pp. 622-637 ◽  
Author(s):  
Jeffrey J. Holliday ◽  
Patrick F. Reidy ◽  
Mary E. Beckman ◽  
Jan Edwards
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Vocabulary Size ◽  
Phonological Contrast ◽  
Different Types ◽  
Centroid Frequency ◽  
The Relationship ◽  
Children's Speech

Purpose Four measures of children's developing robustness of phonological contrast were compared to see how they correlated with age, vocabulary size, and adult listeners' correctness ratings. Method Word-initial sibilant fricative productions from eighty-one 2- to 5-year-old children and 20 adults were phonetically transcribed and acoustically analyzed. Four measures of robustness of contrast were calculated for each speaker on the basis of the centroid frequency measured from each fricative token. Productions that were transcribed as correct from different children were then used as stimuli in a perception experiment in which adult listeners rated the goodness of each production. Results Results showed that the degree of category overlap, quantified as the percentage of a child's productions whose category could be correctly predicted from the output of a mixed-effects logistic regression model, was the measure that correlated best with listeners' goodness judgments. Conclusions Even when children's productions have been transcribed as correct, adult listeners are sensitive to within-category variation quantified by the child's degree of category overlap. Further research is needed to explore the relationship between the age of a child and adults' sensitivity to different types of within-category variation in children's speech.

scholarly journals Impact of Transcatheter Aortic Valve Replacement on Hospitalization Rates: Insights From Nationwide Readmission Database

2021 ◽  
Author(s):  
Ahmed Elkaryoni ◽  
Adnan K. Chhatriwalla ◽  
Kevin F. Kennedy ◽  
John T. Saxon ◽  
John J. Lopez ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Regression Model ◽  
Transcatheter Aortic Valve Replacement ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Transcatheter Aortic Valve ◽  
Hospitalization Rates ◽  
Before And After

Background Hospitalization rates after transcatheter aortic valve replacement (TAVR) remain high, given the age and comorbidities of patients undergoing TAVR. To better understand the impact of TAVR on hospitalization, we sought to compare hospitalization rates before and after TAVR and to examine if underlying patient comorbidities are associated with a differential effect of TAVR on hospitalizations. Methods and Results We used the Nationwide Readmissions Database to identify patients who underwent TAVR. As Nationwide Readmissions Database data do not cross over calendar years, we limited our index admission to hospitalizations during April to September of each calendar year to allow 90 days of observation before and after TAVRs. We calculated the daily risk of all‐cause hospitalization and used a mixed‐effects logistic regression model to explore interactions between patient characteristics, TAVR, and hospitalization risk. Among 39 249 patients who underwent TAVR in 2014 to 2017 (median age, 82 years [interquartile range, 76–87 years]; 45.7% women), 32.0% had at least one hospitalization in the 90 days before TAVR compared with 23.2% in the 90 days post‐TAVR (relative reduction, 27.5%; P <0.001). In the mixed‐effects logistic regression model, TAVR was associated with decreased all‐cause hospitalization rate after TAVR in all comorbidity subgroups. However, younger patients and those with heart failure and reduced ejection fraction appeared to have more robust reduction in hospitalizations. Conclusions Although patients who are treated with TAVR have high rates of rehospitalization, TAVR is associated with an overall reduction in all‐cause hospitalizations regardless of underlying patient comorbidities.

Sign in / Sign up

Export Citation Format

Share Document