Under-five Children In Ethiopia Research Articles

Spatial variations and predictors of overweight/obesity among under-five children in Ethiopia: A geographically weighted regression analysis of the 2019 Ethiopian Mini Demographic and Health Survey.

Overweight/ obesity among under-five children is an emerging public health issue of the twenty-first century. Due to the quick nutritional and epidemiological change, non-communicable diseases, premature death, disability, and reproductive disorders have grown in low-income countries. Besides, little attention has been given. Therefore, we aimed to explore spatial variations and predictors of overweight/obesity among under-five children in Ethiopia using a geospatial technique. A total weighted sample of 3,609 under-five children was included in the study. A cross-sectional study was conducted using a nationally representative sample of the 2019 Ethiopia Mini Demographic and Health Survey data set. ArcGIS version 10.8 was used to explore the spatial variation of obesity. SaTScan version 9.6 software was used to analyze the spatial cluster detection of overweight/obesity. Ordinary least square and geographically weighted regression analysis were employed to assess the association between an outcome variable and explanatory variables. A p-value of less than 0.05 was used to declare it statistically significant. The spatial distribution of overweight/obesity among under-five children in Ethiopia was clustered (Global Moran's I = 0.27, p-value<0.001). The significant hot spot areas or higher rates of childhood obesity, were found in Southern Amhara, Northwest Somalia, Border of Harari, central Addis Ababa, Eastern SNNPR, and Northwestern Oromia region. In spatial SaT Scan analysis, 79 significant clusters of overweight/obesity were detected. The primary clusters were located in SNNPR, Oromia, and Addis Ababa (RR = 1.48, LLR = 31.40, P-value < 0.001). In the geographically weighted regression analysis, urban residence, cesarean section, rich households, and female children were statistically significant predictors. Overweight or obesity among under-five children show spatial variations across Ethiopian regions. GWR analysis identifies cesarean section, wealth index, urban residence, and child sex as significant predictors. The Ministry of Health and Ethiopian Public Health Institute should target regions with these contributing predictors, promoting localized physical education, health education campaigns, and ongoing community monitoring to encourage active lifestyles and reduce sedentary behaviors among children.

Urban-rural disparities in minimum acceptable diet intake among children aged 6-23 months in Ethiopia: A multivariable Decomposition analysis of Ethiopian demographic and health survey 2019.

The achievement of the minimum acceptable diet intake (MAD) stands at 14% among urban and 10% among rural under-five children in Ethiopia. Consequently, identifying the determinants of the urban-rural gap is vital for advancing Sustainable Development Goals (SDGs), fostering healthier communities, and developing evidence-driven approaches to enhance health outcomes and address disparities. The objective of the study was to decompose the urban-rural disparities in minimum acceptable diet intake in Ethiopia using the Ethiopian Mini-Demographic and Health Survey 2019 data. The study was conducted using the Ethiopian Demographic and Health Survey 2019 report. A total of 1,496 weighted children aged 6-23 months were included using stratified sampling techniques. The main outcome variable minimum acceptable diet was calculated as a combined proportion of minimum dietary diversity and minimum meal frequency. A decomposition analysis was used to analyze the factors associated with the urban-rural discrepancy of minimum acceptable diet intake, and the results were presented using tables and figures. The magnitude of minimum acceptable diet among children aged 6-23 months in Ethiopia was 11.0%. There has been a significant disparity in the intake of minimum acceptable diet between urban and rural under-five children with 14 and 10%, respectively. Endowment factors were responsible for 70.2% of the discrepancy, followed by 45.1% with behavioral coefficients. Educational status of college and above was responsible for narrowing the gap between urban and rural residents by 23.9% (β = 0.1313, 95% CI: 0.0332-0.245). The number of children in the household and the age of the child between 18 and 23 months were responsible for widening the gap in minimum acceptable diet intake discrepancy between urban and rural residents by 30.7% and 3.36%, respectively (β = -0.002, 95% CI: -0.003 to -0.0011 and β = -30.7, 95% CI: -0.025 - -0.0085). From the effect coefficients, the effect of institutional delivery was responsible for 1.99% of the widening of the gap between urban and rural residents in minimum acceptable diet intake (β = -0.0862, 95% CI: -0.1711 - -0.0012). There is a significant variation between urban and rural residents in minimum acceptable diet. The larger portion of the discrepancy was explained by the endowment effect. Educational status of mothers with college and above, parity, age of child, and place of delivery were the significant factors contributing to the discrepancy of minimum acceptable diet intake between urban and rural residents.

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey.

Each year, vaccine-preventable diseases cost the lives of 8.8 million under-five children. Although vaccination prevents 1-2 million childhood deaths worldwide, measles vaccination dropouts are not well studied in developing countries, particularly in Ethiopia. Therefore, this study aims to assess the spatial distribution of the measles vaccination dropout and its determinants among under-five children in Ethiopia. Data from Ethiopian Demographic and Health Survey 2019 was used for data analysis. The study used a total of 5,753 children. Spatial autocorrelations was used to determine the spatial dependency of measles vaccination dropout. Ordinary interpolation was employed to forecast measles vaccination dropout. Factors associated with measles vaccination dropout were declared significant at p-values <0.05. The data were interpreted using the confidence interval and adjusted odds ratio. A model with the lowest deviance and highest logliklihood ratio was selected as the best-fit model. In Ethiopia, one in three under-five children had measles vaccination dropouts. Factors such as birth interval (AOR = 1.87, 95% CI: 1.30, 2.70), unmarried marital status women (AOR = 3.98, 95% CI: 1.08, 8.45), ≤1 number of under-five children (AOR = 3.86, 95% CI: 2.56, 5.81), rural place of residence (AOR = 2.43, 95% CI: 2.29, 3.11), low community-level ANC utilization (AOR = 3.20, 95% CI: 2.53, 3.56), and residing in Benishangul Gumuz (AOR = 1.80, 95% CI: 1.061, 3.06) had higher odds of measles vaccination dropout. Measles vaccination dropout rates in Ethiopia among under-five children were high compared to the maximum tolerable vaccination dropout level of 10% by the WHO. Both individual and community-level variables were determinants of measles vaccination dropout. The ministry of health in Ethiopia should give attention to those mothers of under-five children who reported underutilization of ANC services and rural residences while designing policies and strategies in areas of high spatial clustering of vaccine dropout in Ethiopia.

Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS.

Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother's occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.

Burden of Childhood Diarrhea and Its Associated Factors in Ethiopia: A Review of Observational Studies.

Objectives: This systematic review and meta-analysis aimed to: i) determine the pooled prevalence of acute diarrhea; and ii) synthesize and summarize current evidence on factors of acute diarrheal illnesses among under-five children in Ethiopia. Methods: A comprehensive systematic search was conducted in PubMed, SCOPUS, HINARI, Science Direct, Google Scholar, Global Index Medicus, Directory of Open Access Journals (DOAJ), and the Cochrane Library. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The methodological quality of each included article was assessed using the Joanna Briggs Institute (JBI) quality assessment tool for cross-sectional and case-control studies. A random-effect meta-analysis model was used to estimate the pooled prevalence of diarrheal illnesses. Heterogeneity and publication bias were assessed using I2 test statistics and Egger's test, respectively. The statistical analysis was done using STATA™ software version 14. Results: Fifty-three studies covering over 27,458 under-five children who met the inclusion criteria were included. The pooled prevalence of diarrhea among under-five children in Ethiopia was found to be 20.8% (95% CI: 18.69-22.84, n = 44, I2 = 94.9%, p < 0.001). Our analysis revealed a higher prevalence of childhood diarrhea in age groups of 12-23 months 25.42% (95%CI: 21.50-29.35, I2 = 89.4%, p < 0.001). In general, the evidence suggests that diarrheal risk factors could include: i) child level determinants (child's age 0-23 months, not being vaccinated against rotavirus, lack of exclusive breastfeeding, and being an under-nourished child); ii) parental level determinants {mothers poor handwashing practices [pooled odds ratio (OR) = 3.05; 95% CI:2.08-4.54] and a history of maternal recent diarrhea (pooled OR = 3.19, 95%CI: 1.94-5.25)}; and iii) Water, Sanitation and Hygiene (WASH) determinants [lack of toilet facility (pooled OR = 1.56, 95%CI: 1.05-2.33)], lack handwashing facility (pooled OR = 4.16, 95%CI: 2.49-6.95) and not treating drinking water (pooled OR = 2.28, 95% CI: 1.50-3.46). Conclusion: In Ethiopia, the prevalence of diarrhea among children under the age of five remains high and is still a public health problem. The contributing factors to acute diarrheal illnesses were child, parental, and WASH factors. A continued focus on improving access to WASH facilities, along with enhancing maternal hygiene behavior will accelerate reductions in diarrheal disease burden in Ethiopia.

Spatio-temporal distributions and determinants of diarrhea among under-five children in Ethiopia.

The purpose of this study was to evaluate the spatio-temporal pattern of Ethiopia's childhood diarrheal disease and identify its contributing factors. We conducted analyses on secondary data from four Ethiopian Demographic and Health Surveys conducted between 2000 and 2016. Moran's I was used to determine spatial dependence and spatial models were used to evaluate variables associated with diarrhea in under-five children at the zonal level. Childhood diarrhea showed spatial clustering in Ethiopia (Moran's I; p < 0.05). The spatial regression model revealed significant factors at the zonal level: children born at home ( = 1.355, 95% CI: 1.052-1.544, p < 0.001), low birth weight ( = 1.18, 95% CI: 1.017-1.691, p < 0.05), and unimproved source of water ( = 0.8568, 95% CI: 0.671-1.086, p < 0.01). The prevalence of diarrhea among under-five children varied over time by zone, with the Assosa, Hundene, and Dire Diwa zones having the highest rates. Home births and low birth weight contributed to the prevalence of childhood diarrhea. In high-risk zones of Ethiopia, reducing childhood diarrhea requires integrated child health interventions and raising awareness about the potential hazards associated with unimproved water sources.

Barriers to equitable healthcare services for under-five children in Ethiopia: a qualitative exploratory study

BackgroundDisparities in child healthcare service utilization are unacceptably high in Ethiopia. Nevertheless, little is known about underlying barriers to accessing child health services, especially among low socioeconomic subgroups and in remote areas. This study aims to identify barriers to equity in the use of child healthcare services in Ethiopia.MethodsData were obtained from 20 key- informant interviews (KII) and 6 focus group discussions (FGD) with mothers and care givers. This study was conducted in Oromia Region, Arsi Zone, Zuway Dugda District from June 1–30, 2023. The study participants for this research were selected purposively. The information was collected based on the principle of saturation after sixteen consecutives interview were conducted. Both KII and FGD were audio-recorded and complementary notes were taken to record observations about the participants’ comments and their interactions. Each interview and FGD data were transcribed word-for-word in the local Afaan Oromo and Amaharic languages and then translated to English language. Finally, the data were analyzed thematically using NVivo 14 software and narrated in the linked pattern of child health service utilization.ResultsThis study identified six major themes which emerged as barriers to healthcare utilization equity for caregivers and their -under-five children. Barriers related to equity in low level of awareness regarding need, low socioeconomic status, geographical inaccessibility, barriers related to deficient healthcare system, community perception and cultural restrictions, and barriers of equity related to political instability and conflict. The most commonly recognized barriers of equity at the community level were political instability, conflict, and a tremendous distance to a health facility. Transportation challenges, poor functional services, closure of the health facility in working hours, and lack of proper planning to address the marginalized populations were identified barriers of equity at organizational or policy level.ConclusionThis study showed that inequity in child healthcare utilization is an important challenge confronting Ethiopia. To achieve equity, policy makers and planners need to change health policy and structure to be pro-poor. It is also necessary to improve the healthcare system to increase service utilization and access for impoverished women, individuals with lower levels of education, and residents of isolated rural areas. Furthermore, context specific information pertaining to cultural barriers and political ecology are required.

Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia

BackgroundBody Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children’s BMI in Ethiopia.MethodsData on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children’s body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package.ResultsOut of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 – 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children’s. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}= -0.007, 95% CI :(-0.01, -0.004)], the region Afar [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}= 0.19, 95% CI: (0.08, 0.29)], secondary and above [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.25, 95% CI: (-0.41, -0.10)], being female [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children’s BMI.ConclusionsIn conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights

BackgroundA dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI.ObjectiveThis study aims to determine the magnitude and predictors of ARI among under-five children in Ethiopia using used state of the art machine learning algorithms.MethodsData for this study were derived from the 2016 Ethiopian Demographic and Health Survey. To predict the determinants of acute respiratory infections, we performed several experiments on ten machine learning algorithms (random forests, decision trees, support vector machines, Naïve Bayes, and K-nearest neighbors, Lasso regression, GBoost, XGboost), including one classic logistic regression model and an ensemble of the best performing models. The prediction ability of each machine-learning model was assessed using receiver operating characteristic curves, precision-recall curves, and classification metrics.ResultsThe total ARI prevalence rate among 9501 under-five children in Ethiopia was 7.2%, according to the findings of the study. The overall performance of the ensemble model of SVM, GBoost, and XGBoost showed an improved performance in classifying ARI cases with an accuracy of 86%, a sensitivity of 84.6%, and an AUC-ROC of 0.87. The highest performing predictive model (the ensemble model) showed that the child’s age, history of diarrhea, wealth index, type of toilet, mother’s educational level, number of living children, mother’s occupation, and type of fuel they used were an important predicting factor for acute respiratory infection among under-five children.ConclusionThe intricate web of factors contributing to ARI among under-five children was identified using an advanced machine learning algorithm. The child’s age, history of diarrhea, wealth index, and type of toilet were among the top factors identified using the ensemble model that registered a performance of 86% accuracy. This study stands as a testament to the potential of advanced data-driven methodologies in unraveling the complexities of ARI in low-income settings.

Spatial regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia.

There are a number of previous studies that investigated undernutrition and its determinants in Ethiopia. However, the national average in the level of undernutrition conceals large variation across administrative zones of Ethiopia. Hence, this study aimed to determine the geographic distribution of composite index for anthropometric failure (CIAF) and identify the influencing factors it' might be more appropriate. We used the zonal-level undernutrition data for the under-five children in Ethiopia from the Ethiopian Demographic and Health Survey (EDHS) dataset. Different spatial models were applied to explore the spatial distribution of the CIAF and the covariates. The Univariate Moran's I statistics for CIAF showed spatial heterogeneity of undernutrition in Ethiopian administrative zones. The spatial autocorrelation model (SAC) was the best fit based on the AIC criteria. Results from the SAC model suggested that the CIAF was positively associated with mothers' illiteracy rate (0.61, pvalue 0.001), lower body mass index (0.92, pvalue = 0.023), and maximum temperature (0.2, pvalue = 0.0231) respectively. However, the CIAF was negatively associated with children without any comorbidity (-0.82, pvalue = 0.023), from families with accessibility of improved drinking water (-0.26, pvalue = 0.012), and minimum temperature (-0.16). The CIAF across the administrative zones of Ethiopia is spatially clustered. Improving women's education, improving drinking water, and improving child breast feeding can reduce the prevalence of undernutrition (CIAF) across Ethiopian administrative zones. Moreover, targeted intervention in the geographical hotspots of CIAF can reduce the burden of CIAF across the administrative zones.

Time to death and its predictors among Ethiopian under-five children: cluster-level shared frailty survival analysis using the 2019 Ethiopian mini demographic health survey

Introduction: Reducing under-five mortality to less than 25 per 1000 live births by 2030 is one of the Sustainable Development Goals introduced in 2016. However, several countries are still falling short of their targets. In Ethiopia, one out of every 19 children dies before reaching the age of five. Thus, this study aims to identify the time to death and its predictors among under-five children in Ethiopia. Methods: The data were retrieved from the 2019 Ethiopian Mini Demographic and Health Survey data set. Descriptive statistics and survival curves were applied. Shared frailty survival analysis was employed to determine the predictors of under-five mortality. Statistical significance was declared at p-value &lt;0.05. Result: The under-five mortality rate was found to be 59 deaths per 1000 live births with an estimated mean survival time of 57.6 months (95%CI: 57.38, 57.85). A child born into a family of seven or more had an 85% lowered risk of dying (AHR = 0.15, 95% CI: 0.05, 0.44). The risk of mortality among children born in the birth order of five or above was 3 times higher (AHR = 3.00, 95%CI: 1.70, 5.21) compared to those born in the birth order of one to four. The estimated risk of death among under-five children born less than 24 months interval was 2.68 times higher (AHR=2.68, 95%CI: 1.71, 4.21). Never breastfed was also a significant predictor of under-five child death (AHR = 2.76, 95%CI: 1.46, 5.23). Conclusion: Birth order, family size, preceding birth interval, and never breastfed history were significant predictors of under-five child mortality. Health policies should focus on keeping the spirit of a continuum of care to improve the health of the child and the health of the family as a whole. Health care providers should intervene in the community to maximize optimal infant and young child feeding practices.

Trends of socioeconomic and geographic inequalities in severe wasting among under-five children in Ethiopia from 2000 to 2019: using the WHO Health Equity Assessment Toolkit

Severe wasting is the deadliest form of wasting caused by a lack of nutritious food and repeated attacks of illness. The World Health Assembly has agreed to reduce severe wasting to less than 5% and 3% by the end of 2025 and 2030. Significant disparities were observed worldwide in progress towards the goal. However, limited evidence of disparity in severe wasting was available in Ethiopia. Therefore, this study aimed to assess trends in socioeconomic and geographic inequalities in severe wasting among under-five children in Ethiopia between 2000 and 2019. The trend in socioeconomic and geographic inequality was assessed using the World Health Organization Health Equity Assessment Toolkit, employing both absolute and relative measures of inequality. Difference (D), ratio (R), slope index inequality (SII), relative concentration index (RCI), and population attributable ratio (PAR) were utilized to assess disparity across wealth, education, residence, and subnational regions. The 95% uncertainty interval (UI) was used to declare the significant change in inequality through time. The proportion of severe wasting increased from 3.8% to 4.7% between 2000 to 2005 and dropped to 2.9% in 2011 to remain constant until 2016. However, the proportion of severe wasting significantly declined to 1.1% in 2019. As indicated by RCI, significant fluctuation in wealth-related inequality was observed in all five survey years but a significant change in wealth-related inequality was observed in 2005 and 2019. Whereas the education-related inequality in RCI of severe wasting steadily increased from −8.8% in 2005 to −24.3% in 2019. And the change was significantly widened from 2011 to 2019. On the other hand, residence-related inequality of severe wasting was observed in 2000 in ratio, difference and PAR summary measures but disappeared in 2019. Between 2000 and 2016, regional inequalities in severe wasting fluctuated between 8.7 in 2005 to 5.9 in 2016 taking the difference as a measure of inequality. Overall, Wealth-related inequality has significantly widened over time with under five children from the richest households being less affected by severe wasting. Education-related inequality was not changed with under five children whose mothers had not attended formal education highly affected by severe wasting. Regional disparity in severe wasting is also exhibited in Ethiopia in all-round surveys with children from Addis Ababa being least affected whereas children from Somalia were highly affected by severe wasting. However, no significant disparity in the type of residence in severe wasting was revealed in Ethiopia. Therefore, special attention should be paid to under-five children living in the poorest households, whose mothers did not attend formal education and children living in Somalia region.

Levels of stunting associated factors among under-five children in Ethiopia: A multi-level ordinal logistic regression analysis.

Stunting is a major public health problem affecting more than one-third of under five year's old children in Ethiopia. It has short and long (irreversible) consequences, including stunted growth, never reaching physical and cognitive potential, struggles in school, and increased morbidity and mortality due to infections. Though stunting is the leading cause of child mortality in Ethiopia, evidence is scarce on the prevalence and predictors of stunting among under-five years old children in Ethiopia. Therefore, this study aimed to estimate the prevalence and predictors of stunting severity among under-5 children in Ethiopia. This study was based on 2019 Mini-Ethiopian Demographic and Health Survey (EDHS) data. A weighted total sample of 4972 under-five years old children was included in the study. Height measurement was collected for each child. Anthropometric indicator, height-for-age was determined for children using World Health Organization growth standards (Z-scores for Height-for-Age (HAZ)) to asses stunting level. Given the ordinal nature of stunting and the hierarchical nature of EDHS data, a multilevel ordinal logistic regression model was applied. Brant test was used to check the proportional odds assumption, which was satisfied (P-value ≥0.05). Moreover, deviance was used for model comparison. For the multivariable analysis, variables with a p-value ≤0.2 in the bivariable analysis were considered. The Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was reported as associated factor to the severity levels of stunting in the multivariable multilevel proportional odds model. The overall prevalence of stunting among under-5 children in Ethiopia was 35.7% [95% CI: 34.4%, 37.1%]. Of these, 12.1% were severely stunted, and 24.9% were moderately stunted. Being male [AOR = 0.83, 95% CI: 0.74, 0.93], children aged 6-23 months [AOR = 2.38, 95% CI: 1.84, 3.07], ≥ 24 months [AOR = 4.15, 95% CI: 3.26, 5.28], children whose maternal age 15-24 years [AOR = 0.73, 95% CI: 0.58, 0.92], children from the poorest, poorer, middle, and richer household wealth were [AOR = 1.84, 95% CI: 1.32, 2.57], [AOR = 1.66, 95% CI: 1.20, 2.31], [AOR = 1.78, 95% CI: 1.29, 2.44], and [AOR = 1.62, 95% CI: 1.20, 2.17], children whose maternal educational status of no formal education and primary education had [AOR = 1.90, 95% CI: 1.28, 2.82], [AOR = 1.78, 95% CI: 1.22, 2.60], Tigray [AOR = 2.95, 95% CI: 1.78, 4.86], Afar [AOR = 1.85, 95% CI: 1.11, 3.10], Amhara [AOR = 1.90, 95% CI: 1.14, 3.14] and Harari [AOR = 1.97, 95% CI: 1.20, 3.25]regions, low community maternal education [AOR = 0.76, 95% CI: 0.62, 0.92] were significantly associated with stunting severity levelling. Stunting among children under five years of old in Ethiopia remains a major public health issue. Improving access to maternal education is related to appropriate child feeding practices and health, particularly in younger and uneducated mothers. Strengthening the family's wealth status is also recommended to reduce stunting. In addition, it is better to support strategies of preconception care for mothers during pregnancy to reduce stunting in the long term.

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP).

Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques. Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction. The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia. The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

Determinants of severe acute malnutrition among under-five children in Ethiopia: analysis using data from the 2016 Ethiopia Demographic and Health Survey.

Malnutrition is a silent killer that is under-reported, under-addressed, and, as a result, emphasized. This study aimed to identify the determinants of severe acute malnutrition (SAM) among under-five children in Ethiopia. Cross-sectional data from the 2016 Ethiopian Demographic and Health Survey (EDHS) were used. A total of 6,170 under-five children were included in the current analysis. The data were cleaned and analyzed using STATA 14. An adjusted odds ratio (AOR) and their 95% confidence intervals (CIs) were calculated to determine the association between factors and outcomes. A p-value of less than 0.05 was considered significant in multivariable logistic regression. A multivariable logistic regression revealed that under-five children with the age of children in months 6-11 (AOR = 1.52, 95% CI: 1.25, 1.86), 12-23 (AOR = 1.98, 95% CI: 1.65, 2.37), and 24-59 months (AOR = 1.71, 95% CI: 1.40, 2.08), birth order between fourth and fifth (AOR = 1.24, 95% CI: 1.01, 1.54), having fever (AOR = 1.31, 95% CI: 1.09, 1.58), anemic children (AOR = 1.21, 95% CI: 1.07, 1.36), age of mothers in years 25-34 (AOR = 0.60, 95% CI: 0.51, 0.72) and 35-49 (AOR = 0.49, 95% CI: 0.39, 0.63), antenatal care (ANC) visits (AOR = 0.83, 95% CI: 0.71, 0.92), rural residence (AOR = 2.98, 95% CI: 2.54, 3.49), and solid fuels users (AOR = 2.46, 95% CI: 1.86, 3.26) were significant predictors. Older age of children, those with higher birth order, those having fever, anemic children, those living in rural areas, and solid fuel users were more likely to suffer from SAM, while older mothers and those having ANC visits had reduced SAM as significant predictors.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Prevalence and risk factors of childhood diarrhea among wastewater irrigating urban farming households in Addis Ababa.

Childhood diarrhea is one of the major contributors to the morbidity of under-five children in Ethiopia. Although researchers determine the risk factors varyingly, the exposure route to the pathogens is usually complicated. This study aims to investigate the prevalence and risk factors of diarrhea among children under the age of five among wastewater irrigation farming households in Addis Ababa, Ethiopia. Cross-sectional study was conducted among 402 farming households from November 2021 to February 2022. Data was collected using a face-to-face interviewer-administered questionnaire. Stata version 14 software was used to analyze data. Factors associated with the prevalence of diarrhea was identified using binary logistic regression. Multivariable analysis was carried out to determine an adjusted odds ratio at a confidence level of 95% and level of significance at 0.05. The overall prevalence of under-five children diarrheal cases was 22.3%. The odds of diarrhea are associated with a multitude of variables. Major wastewater-related determinants associated with diarrhea are body washing with irrigation water [AOR: 37.7, 95%CI (3.1, 358)], contaminated cloth with irrigation water [AOR:10.8,95%CI(0.6, 205)], use of protective clothing during farm work [28.9,95%CI (3.9, 215)], use of farm work cloths at home [AOR: 31.7, 95%CI (4.4, 226)], and bringing unwashed farm tools to home [94 (5.7, 1575)]. The high prevalence of under-five children diarrheal disease among wastewater irrigation households was strongly associated with factors related to occupational exposure. Thus, to decrease childhood diarrheal among urban agriculture farmers, appropriate precautions need to be taken.

Prevalence and associated factors of stunting among under-five children in Ethiopia: Application of marginal models analysis of 2016 Ethiopian demographic and health survey data.

Stunting, short for age, affects the overall growth and development of the children. It occurs due to chronic under nutrition. Stunting vastly occurs in impoverished regions of the world, including Ethiopia. This study aimed to investigate the prevalence and correlates of stunting among under-five children in Ethiopia using marginal models. Data were taken from the 2016 Ethiopian Demographic Health Survey, which is a nationally representative survey of children in the 0-59 month age group. For marginal models, generalized estimating equations and alternating logistic regression models were used for the analysis. The prevalence of stunting among the under-five children was 34.91% in the area. The proportion was slightly higher among male (36.01%) than female (33.76%) child. The Alternating Logistic Regression model analysis revealed that the child's age, the mother's education level, the mother's body mass index, the place of residence, the wealth index, and the previous birth interval were found to be significant determinants of childhood stunting, and the result shows that children born with a lower previous birth interval (less than 24 months) were more likely to be stunted than those born within a higher birth interval. Children in rural Ethiopia were more likely to be stunted than children in urban Ethiopia. This study found that more than one third of children were stunted in the area. The study also determined that child's age, the mother's education, the mother's body mass index, the place of residence, the wealth index, and birth interval influence stunting. Therefore, it is better enhancing the nutritional intervention programs.

Multilevel analysis of factors associated with untreated diarrhea among under five children in Ethiopia using Ethiopian demographic and health survey

Diarrhea refers to the abrupt onset of three or more loose or liquid stools per day. It is the second leading cause of death in infants worldwide. It is an endemic disease and continues to be a serious threat to children in Ethiopia. Despite being a condition that may be prevented, diarrhea can have a negative impact on a child's health. Also, studies have not been able to explore the role of socio-economic characteristics in hindering the treatment. Therefore, this study aimed to explore socio-economic factors that influence treatment of childhood diarrhea. Secondary data analysis was conducted based on the demographic and health surveys data conducted in Ethiopia. A total weighted sample of 1227 under-five children was included for this study. Mixed-effect binary logistic regression analysis was done to identify associated factors of untreated diarrhea. Adjusted Odds Ratio with 95% CI was used to declare the strength and significance of the association. Prevalence of untreated diarrhea among under five children in Ethiopia was 57.32% (95% CI 54.52–60.06%). In the mixed-effect analysis; Children aged 6–11, 12–23, and 24–35 (AOR 0.384, 95% CI 0.187–0.789), 71% (AOR 0.29, 95% CI 0.149–0.596), and 51% (AOR 0.49, 95% CI 0.238–0.995). Children from family number six and above (AOR 1.635, 95% CI 1.102–2.426). Children from middle wealth of family (AOR 1.886, 95% CI 1.170–3.3040). Children from a community with high level of uneducated (AOR 2.78, 95% CI 1.065–3.442) were significantly associated with untreated diarrhea. The prevalence of untreated diarrhea among under-five children in Ethiopia is high. Age of child, family number, household wealth, and community-level educational status were significantly associated with untreated diarrhea among under-five children in Ethiopia. Hence, increasing community educational status, boosting the economic status of the community, and family planning for the community should get due attention.

Spatial distribution and associated factors of severe malnutrition among under-five children in Ethiopia: further analysis of 2019 mini EDHS

BackgroundMalnutrition is both a significant cause and a result of poverty and deprivation. In developing nations, child malnutrition is still the main public health issue. Severe malnutrition affects every system of the body and leads to medical instability. The assessment of the burden of severe malnutrition is important for ready-to-use therapeutic foods and preparing therapy for these conditions. Therefore, this study aimed to assess the prevalence and spatial distribution of severe malnutrition and the factors associated with it.MethodsData from the 2019 Mini-EDHS (Ethiopian Demographic and Health Surveys) with stratified sampling techniques were used. The data were weighted using sample weight to restore the data's representativeness and provide accurate statistical estimations. A total of 5,006 weighted samples of children under the age of five were used to analyze the study. A multilevel binary logistic regression model was built, and a cutoff P-value of 0.05 was used. The wag staff normalized concentration index and curve as well as spatial analysis were used.ResultsThe prevalence of severe malnutrition practice among under five years children in Ethiopia was 14.89% (95%CI: 13.93%, 15.91%), and ranges from 4.58% in Addis Ababa to 25.81% in the Afar region. Women with secondary and above education status as compared to uneducated [AOR = 0.17; 95%CI;[0.06, 0.48], high community women's education as compared to low [AOR = 0.54; 95%CI; 0.36, 0.78], women from richest household as compared to poorest [AOR = 0.63; 95%CI; 0.26, 0.94] and living in Oromia region as compared to Tigray [AOR = 0.33: 95%CI; 0.15, 0.74] were preventive factors. Whereas children 24–59 months of age as compared to under six months [AOR = 1.62; 95%CI; 1.50, 1.75], and being multiple births as compared to single [AOR = 5.34; 95%CI; 1.36,2 1.01] have significant risk factors for severe malnutrition. There was a pro-poor distribution of severe malnutrition among under-five children in Ethiopia with a concentration index of -0.23 [95%CI: -0.27, -0.19]. Severe malnutrition has significant spatial variation over regions in the country where the entire Afar, Eastern Amhara, Southern, and eastern Tigray regions were severely affected (RR = 1.72, P-value < 0.01).Conclusion and recommendationsThe prevalence of severe malnutrition in Ethiopia is relatively high as compared to other studies and most of them were severe chronic malnutrition. Having an educated mother/caregiver, and living in a cluster with high community women's education were preventive factors for severe malnutrition in children. Whereas having an unmarried mother/caregiver, old age of the child, plurality of birth, and having double children in the family have a positive association with it. Moreover, it was disproportionately concentrated in poor households (pro-poor distribution).The spatial distribution of childhood severe malnutrition was not random. Regions like Tigray, Afar, Eastern parts of Amhara, and Somalia regions should be considered priority areas for nutritional interventions for reducing severe malnutrition. Equity-focused nutritional interventions could be needed to curb the wealth-related inequalities of childhood severe malnutrition.