Neonatal near Miss and Its Predictors among Neonates Delivered at Debretabor General Hospital, Northern Ethiopia; A Retrospective Analysis

Background. In many low-resource countries, the progress of neonatal mortality reduction is very slow. The scenario is notably true in sub-Saharan Africa including Ethiopia. For every neonatal death, there are lots of near missed neonates. Generating evidences on the extent and predictors of neonatal near miss is a key step in neonatal mortality reduction efforts. However, there is limited evidence in this aspect in Ethiopia. Objective. This study is aimed at assessing the proportion of neonatal near miss and associated factors among neonates delivered at Debretabor General Hospital, Northern Ethiopia, 2019. Methods. An institution-based cross-sectional study was conducted on 422 neonates delivered at Debretabor General Hospital from July 1st, 2018, to June 30th, 2019. Both pragmatic and management criteria of definition of neonatal near miss were utilized. A systematic random sampling technique was used to select the cards of the study participants. Data were extracted with structured and pretested checklist, entered in the EpiData, and then exported to SPSS version 20. Both descriptive and analytical procedures have been done. Descriptive statistics such as frequencies and cross tabulations were carried out. The binary logistic regression model was fitted and variables with p value < 0.20 were entered in the multivariable logistic regression model. Both crude and adjusted odds ratios with the corresponding 95% CI were computed. The level of significance has been claimed based on the adjusted odds ratio with 95% CI and its p value of ≤0.05. Results. The proportion of neonates experiencing near miss was obtained to be 32.2% with 95% CI (28, 36). Rural residence (AOR=4.41; 95% CI: 2.57,7.55), incomplete ANC visit (AOR=3.16; 95% CI: 1.90,5.25), primiparous (AOR=2.55; 95% CI: 1.59,4.12), pregnancy-induced hypertension (AOR=3.23; 95% CI: 1.19,8.78), premature rupture of membrane (AOR=4.65; 95% CI: 1.70,12,73), cephalic-pelvic disproportion (AOR=3.05; 95% CI: 1.32,7.01), and antepartum hemorrhage (AOR=4.95; 95% CI: 1.89,12.96) were the independent predictors of neonatal near-miss. Conclusion and Recommendations. The proportion of neonatal near miss was found to be high in the study setting. Most of the determinants of near miss are modifiable obstetric-related factors. Hence, stakeholders need to consider the aforesaid factors while they design interventions.

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Determinants of full childhood immunization among children aged 12–23 months in sub-Saharan Africa: a multilevel analysis using Demographic and Health Survey Data

Tropical Medicine and Health ◽

10.1186/s41182-021-00319-x ◽

2021 ◽

Vol 49 (1) ◽

Author(s):

Setegn Muche Fenta ◽

Hailegebrael Birhan Biresaw ◽

Kenaw Derebe Fentaw ◽

Shewayiref Geremew Gebremichael

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Health Facility ◽

Health Survey ◽

Logistic Regression Model ◽

Immunization Coverage ◽

Demographic And Health Survey ◽

Sub Saharan Africa ◽

Childhood Immunization ◽

Sub Saharan

Abstract Background Sub-Saharan Africa is one of the highest under-five mortality and low childhood immunization region in the world. Children in Sub-Saharan Africa are 15 times more likely to die than children from high-income countries. In sub-Saharan Africa, more than half of under-five deaths are preventable through immunization. Therefore, this study aimed to identify the determinant factors of full childhood immunization among children aged 12–23 months in sub-Saharan Africa. Methods Data for the study was drawn from the Demographic and Health Survey of nine sub-Saharan African countries. A total of 21,448 children were included. The two-level mixed-effects logistic regression model was used to identify the individual and community-level factors associated with full childhood immunization Result The prevalence of full childhood immunization coverage in sub-Saharan Africa countries was 59.40% (95% CI: 58.70, 60.02). The multilevel logistic regression model revealed that secondary and above maternal education (AOR = 1.38; 95% CI: 1.25, 1.53), health facility delivery (AOR = 1.51; 95% CI: 1.41, 1.63), fathers secondary education and above (AOR = 1.28, 95% CI: 1.11, 1.48), four and above ANC visits (AOR = 2.01; 95% CI: 1.17, 2.30), PNC visit(AOR = 1.55; 95% CI: 1.46, 1.65), rich wealth index (AOR = 1.26; 95% CI: 1.18, 1.40), media exposure (AOR = 1.11; 95% CI: 1.04, 1.18), and distance to health facility is not a big problem (AOR = 1.42; 95% CI: 1.28, 1.47) were significantly associated with full childhood immunization. Conclusion The full childhood immunization coverage in sub-Saharan Africa was poor with high inequalities. There is a significant variation between SSA countries in full childhood immunization. Therefore, public health programs targeting uneducated mothers and fathers, rural mothers, poor households, and those who have not used maternal health care services to promote full childhood immunization to improve child health. By enhancing institutional delivery, antenatal care visits and maternal tetanus immunization, the government and other stakeholders should work properly to increase child immunization coverage. Furthermore, policies and programs aimed at addressing cluster variations in childhood immunization need to be formulated and their implementation must be strongly pursued.

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Predicting the Use Labor Induction Intervention: A Machine Learning Approach for the Birth-Cohort Registry at A Tertiary Hospital in North Tanzania.

10.21203/rs.3.rs-147720/v1 ◽

2021 ◽

Author(s):

Clifford Tarimo ◽

Soumitra Bhuyan ◽

Quanman Li ◽

Michael Mahande ◽

Jian Wu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Characteristic Curve ◽

Labor Induction ◽

Sub Saharan Africa ◽

Learning Models ◽

Computational Performance ◽

Machine Learning Models

Abstract Introduction: Following an increased use of labor induction procedure to prevent adverse maternal and fetal outcomes in Sub-Saharan Africa, hitting the best algorithm that accurately classify subjects in need of the intervention is of paramount importance. This study aimed at comparing the potential benefits of applying machine learning (ML) algorithms over the conventional logistic regression model in predicting the use of labor induction intervention in pregnant women attending one of the tertiary hospitals in north Tanzania for delivery. Methods: We conducted a secondary data analysis of the Kilimanjaro Christian Medical Centre (KCMC) birth registry database for women with uncomplicated pregnancies from the year 2000 to 2015. We excluded observations with non-vertex presentation and those with missing information on labor induction status. Model accuracy and Area under the receiver operating characteristic curve (AUC - ROC) were used to assess the discriminative ability of the selected models. We plotted the decision curve analysis (DCA) to assess the clinical utility of the models under observation. Results: A total of 21,578 deliveries were analyzed. Among these, 8814 (41%) were induced during the study period. Among the selected machine learning models, Random forest algorithm exhibited the best performance in terms of accuracy [0.75; 95%CI (0.73 – 0.76)] and AUC-ROC [AUC-ROC: 0.75; 95% CI (0.74 – 0.76)] compared to other models including logistic regression. Among assessed maternal attributes, parity, maternal age, body mass index, gestational age and birthweight were deemed most important predictors for labor induction intervention. Conclusion: The selected machine learning methods offered better computational performance compared to the conventional logistic regression model in predicting the use of labor induction intervention. The current study lends substantial support to the use of machine learning models in predicting the use of labor induction intervention.

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Timely initiation of antenatal care and its associated factors among pregnant women in sub-Saharan Africa: A multicountry analysis of Demographic and Health Surveys

PLoS ONE ◽

10.1371/journal.pone.0262411 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262411

Author(s):

Adugnaw Zeleke Alem ◽

Yigizie Yeshaw ◽

Alemneh Mekuriaw Liyew ◽

Getayeneh Antehunegn Tesema ◽

Tesfa Sewunet Alamneh ◽

...

Keyword(s):

Logistic Regression ◽

Antenatal Care ◽

Regression Model ◽

Health Facility ◽

Associated Factors ◽

Logistic Regression Model ◽

Sub Saharan Africa ◽

Timely Initiation ◽

Sub Saharan ◽

Ssa Countries

Background Timely initiation of antenatal care (ANC) is an important component of ANC services that improve the health of the mother and the newborn. Mothers who begin attending ANC in a timely manner, can fully benefit from preventive and curative services. However, evidence in sub-Saharan Africa (sSA) indicated that the majority of pregnant mothers did not start their first visit timely. As our search concerned, there is no study that incorporates a large number of sub-Saharan Africa countries. Thus, the objective of this study was to assess the prevalence of timely initiation of ANC and its associated factors in 36 sSA countries. Methods The Demographic and Health Survey (DHS) of 36 sSA countries were used for the analysis. The total weighted sample of 233,349 women aged 15–49 years who gave birth in the five years preceding the survey and who had ANC visit for their last child were included. A multi-level logistic regression model was used to examine the individual and community-level factors that influence the timely initiation of ANC. Results were presented using adjusted odds ratio (AOR) with 95% confidence interval (CI). Results In this study, overall timely initiation of ANC visit was 38.0% (95% CI: 37.8–38.2), ranging from 14.5% in Mozambique to 68.6% in Liberia. In the final multilevel logistic regression model:- women with secondary education (AOR = 1.08; 95% CI: 1.06, 1.11), higher education (AOR = 1.43; 95% CI: 1.36, 1.51), women aged 25–34 years (AOR = 1.20; 95% CI: 1.17, 1.23), ≥35 years (AOR = 1.30; 95% CI: 1.26, 1.35), women from richest household (AOR = 1.19; 95% CI: 1.14, 1.22), women perceiving distance from the health facility as not a big problem (AOR = 1.05; 95%CI: 1.03, 1.07), women exposed to media (AOR = 1.29; 95%CI: 1.26, 1.32), women living in communities with medium percentage of literacy (AOR = 1.51; 95%CI: 1.40, 1.63), and women living in communities with high percentage of literacy (AOR = 1.56; 95%CI: 1.38, 1.76) were more likely to initiate ANC timely. However, women who wanted their pregnancy later (AOR = 0.84; 95%CI: 0.82, 0.86), wanted no more pregnancy (AOR = 0.80; 95%CI: 0.77, 0.83), and women residing in the rural area (AOR = 0.90; 95%CI: 0.87, 0.92) were less likely to initiate ANC timely. Conclusion Even though the WHO recommends all women initiate ANC within 12 weeks of gestation, sSA recorded a low overall prevalence of timely initiation of ANC. Maternal education, pregnancy intention, residence, age, wealth status, media exposure, distance from health facility, and community-level literacy were significantly associated with timely initiation of ANC. Therefore, intervention efforts should focus on the identified factors in order to improve timely initiation of ANC in sSA. This can be done through the providing information and education to the community on the timing and importance of attending antenatal care and family planning to prevent unwanted pregnancy, especially in rural settings.

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

10.1101/2020.06.04.134007 ◽

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.

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