Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

Bitew, Fikrewold; Nyarko, Samuel H.; Potter, Lloyd B.; Sparks, Corey S.

doi:10.21203/rs.2.13113/v2

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The results show that children who were not breastfed were at a significantly higher risk of U5M compared to those who were. These results are in agreement with studies done in India, Bangladesh, and Ethiopia [18,33,34]. However, the very high odds ratio should be interpreted with caution because the confidence interval is very wide.…”

Section: Factors Associated With U5m In Sri Lankasupporting

confidence: 91%

“…A study in Nepal [17] found that children of mothers who did not use contraceptives, did not receive the Tetanus Toxoid (TT) vaccine during pregnancy, and had a previous child death were at higher odds of dying before five years old. A study in Ethiopia [18] found that the income of mothers, source of drinking water, breastfeeding status, type of birth, family size, and birth interval were significant determinants of U5M. Another study in Bangladesh [19] concluded that factors such as contraceptive use by mothers, having other children under 5 years old, previous death of a sibling, birth interval, and birth rank were associated with U5M.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of under-five mortality in Sri Lanka: A multilevel analysis of 2016 Sri Lankan DHS data

Thabrew,

Sooriyarachchi,

Jayakody

2023

PLoS ONE

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Under-five mortality (U5M) is considered a major public health issue directly impacts a country’s development. This study analyzed the prognostic factors of U5M in Sri Lanka using data from the 2016 Demographic and Health Survey (DHS) of 8123 children. The study employed both a binary logistic regression model (BLRM) and a binary logistic random intercept multilevel model (BLRIMM) and compared the accuracy of each model’s prediction percentage. The results showed that the BLRIMM had a higher correct prediction percentage (98.67%) compared to the BLRM (98.31%). The study found that children who were not breastfed (Odds Ratio (OR) = 116.74, 95% Confidence Interval (CI) = 62.97–216.41), were part of multiple births (OR = 3.73, 95% CI = 1.21–11.51), did not have a normal delivery (OR = 1.86, 95% CI = 1.11–3.12), were born to mothers who had experienced previous miscarriages or child loss (OR = 2.27, 95% CI = 1.26–4.11), and were born to mothers with a higher Body Mass Index (BMI) (OR = 1.05, 95% CI = 1.003–1.10) had higher odds of U5M. The odds of U5M were found to be lower among Buddhists (OR = 0.06, 95% CI = 0.01–0.50), Hindus (OR = 0.05, 95% CI = 0.01–0.46), and Roman Catholics (OR = 0.032, 95% CI = 0.003–0.307) compared to the "Other Religions" category in the dataset. The estimated covariance parameter of the random intercept (0.8231, p-value = 0.0405) indicated significant unobserved cluster-level variation in U5M. The study’s results emphasize the importance of addressing religion related differences of U5M and improving maternal education regarding healthy lifestyle, proper food intake, the significance of breastfeeding, safe delivery methods, safety measures during pregnancy and childbirth in cases of multiple births, and proper child care after birth.

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Section: Factors Associated With U5m In Sri Lankasupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Determinants of under-five mortality in Sri Lanka: A multilevel analysis of 2016 Sri Lankan DHS data

Thabrew,

Sooriyarachchi,

Jayakody

2023

PLoS ONE

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“…Wealth index was classified as poorer, poorest, middle, richer and richest in EDHS 2016. 23 Maternal educational status was categorised as no formal education, primary, secondary and higher education in our study and by the DHS programme. 24 Data management and model selection Following extraction from the source, data were entered, coded, cleaned, recoded and analysed using Stata V.14.0.…”

Section: Open Accessmentioning

confidence: 99%

Prevalence and determinants of early neonatal mortality in Ethiopia: findings from the Ethiopian Demographic and Health Survey 2016

Tamir

Asmamaw

Negash

et al. 2023

bmjpo

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BackgroundEarly neonatal death accounts for a significant number of under-5 mortality worldwide. However, the problem is under-researched and under-reported in low-income and middle-income countries, particularly in Ethiopia. The magnitude of mortality during the early neonatal period and associated factors should be studied for designing appropriate policies, and strategies that could help tackle the problem. Hence, this study aimed to determine the prevalence and identify factors associated with early neonatal mortality in Ethiopia.MethodsThis study was conducted by using data from Ethiopian Demographic and Health Survey 2016. A total of 10 525 live births were enrolled in the study. A multilevel logistic regression model was used to identify determinants of early neonatal mortality. Adjusted OR (AOR) at a 95% CI was computed to assess the strength and significance of the association between outcome and explanatory variables. Factors with a p<0.05 were declared statistically significant.ResultsThe national prevalence of early neonatal mortality in Ethiopia was 41.8 (95% CI 38.1 to 45.8) early neonatal deaths per 1000 live births. The extreme ages of pregnancy (under 20 years (AOR 2.7, 95% CI 1.3 to 5.5) and above 35 years (AOR 2.4, 95% CI 1.5 to 4)), home delivery (AOR 2.4, 95% CI 1.3 to 4.3), low birth weight (AOR 3.3, 95% CI 1.4 to 8.2) and multiple pregnancies (AOR 5.3, 95% CI 4.1 to 9.9) were significantly associated early neonatal mortality.ConclusionsThis study revealed a higher prevalence of early neonatal mortality as compared with prevalence in other low-income and middle-income countries. Thus, it is determined to be essential to design maternal and child health policies and initiatives with a priority on the prevention of early neonatal deaths. Emphasis should be given to babies born to mothers at extreme ages of pregnancy, to those born of multiple pregnancies delivered at home and to low birthweight babies.

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Predicting Child Mortality With Diverse Regression Algorithms Using a Machine Learning Approach

Ashwini,

Bose,

Padmavathy

et al. 2024

Advances in Computer and Electrical Engineering

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This chapter uses machine learning methodologies to investigate the prediction of child mortality rates for ages 1-4 across diverse countries. Drawing upon a comprehensive review of global health data from organizations such as the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF), which highlight the urgency and significance of accurate child mortality prediction, the authors analyze a dataset spanning from 1967 to 2019, containing 30,940 entries from countries worldwide. Regression algorithms, including XGBoost, CatBoost, Random Forest, AdaBoost, and DecisionTree Regressor, are employed to predict child mortality rates. Evaluation metrics such as R^2, adjusted R^2, mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) are utilized to assess model performance. Additionally, Matplotlib and Seaborn use visualization techniques to illustrate the findings through pie charts and graphs. The analysis aims to identify the most effective algorithm for accurately forecasting child mortality rates, thereby contributing to advancing healthcare planning and intervention strategies to reduce child mortality globally.

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Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

Cited by 3 publications

References 21 publications

Determinants of under-five mortality in Sri Lanka: A multilevel analysis of 2016 Sri Lankan DHS data

Determinants of under-five mortality in Sri Lanka: A multilevel analysis of 2016 Sri Lankan DHS data

Prevalence and determinants of early neonatal mortality in Ethiopia: findings from the Ethiopian Demographic and Health Survey 2016

Predicting Child Mortality With Diverse Regression Algorithms Using a Machine Learning Approach

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