Determinants Of Under-five Mortality Research Articles

Determinants of under-five mortality in informal settlements in Nairobi, Kenya from 2002 to 2018

BackgroundChildhood mortality persists as a significant public health challenge in low and middle-income countries and is uneven within countries, with poor communities such as urban informal settlements bearing the highest burden. There is limited literature from urban informal settlements on the risk factors of mortality. We assessed under-five mortality and associated risk factors from the period 2002 to 2018 in Nairobi urban informal settlements.MethodsWe used secondary data from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), a longitudinal surveillance platform that routinely collects individual and household-level data in two informal settlements (Viwandani and Korogocho) in Nairobi, Kenya. We used Kaplan-Meier curves to estimate overall survival and the Cox proportional hazard model with a frailty term to evaluate the impact of risk factors on survival time.ResultsOverall under-five survival rate was 96.8% and this improved from 82.6% (2002-2006) to 95% (2007-2012) and 98.4% (2012-2018). There was a reduced risk of mortality among children who had BCG vaccination, those born to a married mother or a mother not engaging in any income-generating activity (both from 2007 to 2011), children from singleton pregnancy, children born in Viwandani slum and ethnicity of the child.ConclusionUnder-five mortality is still high in urban informal settlements. Targeted public health interventions such as vaccinations and interventions empowering women such as single mothers, those with multiple pregnancies, and more impoverished slums are needed to further reduce under-five mortality in urban informal settlements.

Exploring the determinants of under-five mortality and morbidity from infectious diseases in Cambodia—a traditional and machine learning approach

Cambodia has made progress in reducing the under-five mortality rate and burden of infectious diseases among children over the last decades. However the determinants of child mortality and morbidity in Cambodia is not well understood, and no recent analysis has been conducted to investigate possible determinants. We applied a multivariable logistical regression model and a conditional random forest to explore possible determinants of under-five mortality and under-five child morbidity from infectious diseases using the most recent Demographic Health Survey in 2021–2022. Our findings show that the majority (58%) of under-five deaths occurred during the neonatal period. Contraceptive use of the mother led to lower odds of under-five mortality (0.51 [95% CI 0.32–0.80], p-value 0.003), while being born fourth or later was associated with increased odds (3.25 [95% CI 1.09–9.66], p-value 0.034). Improved household water source and higher household wealth quintile was associated with lower odds of infectious disease while living in the Great Lake or Coastal region led to increased odds respectively. The odds ratios were consistent with the results from the conditional random forest. The study showcases how closely related child mortality and morbidity due to infectious disease are to broader social development in Cambodia and the importance of accelerating progress in many sectors to end preventable child mortality and morbidity.

Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset

BackgroundUnder-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors.MethodsThe study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew’s correlation coefficient, and the Area Under the Receiver Operating Characteristics.ResultsThe study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria.ConclusionsThe findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study’s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.

Determinants of under-five mortality in Zimbabwe: Evidence from the 2015–2016 Zimbabwe demographic Health Survey data

BackgroundChild mortality is an important measure of a population’s health status. It is included in the third sustainable development goal that aims to improve global health by reducing under-five mortality to at least as low as 25 per 1000 live births by 2030. The study determines the factors associated with under-five child mortality in Zimbabwe. MethodsCross-sectional secondary data from the 2015 Zimbabwe Demographic Health Survey (ZDHS) were analyzed. The sample included 5,806 women aged 15–49 years of reproductive age. The Chi-square test was used to analyze the association between child death and independent variables. We identified the individual and contextual factors associated with child deaths in Zimbabwe using the Cox proportional hazard model. ResultsThe risks of under-five mortality were highest among children of first birth order (adjusted hazard ratio (aHR) ​= ​2.37, P ​= ​0.04), multiple births (aHR ​= ​2.37, P= 0.04), mothers with primary or less maternal education (aHR ​= ​1, Ref), mothers below 18 years old (aHR ​= ​1, Ref), apostolic mothers (aHR ​= ​2.90, P= 0.002), mothers who do not use contraceptives (aHR ​= ​2.20, P ​< ​0.001), formerly married women (aHR ​= ​6.42, P= 0.005), women with 5 or more children (aHR ​= ​15.84, P ​< ​0.001), women who read newspapers less than once a week (aHR ​= ​1.75, P= 0.13), and households that use high-polluting fuels (aHR ​= ​1.92, P= 0.023). ConclusionThis study establishes that child health, maternal, socioeconomic, household, and ecological factors are important determinants of under-five mortality in Zimbabwe. Understanding these determinants is crucial for designing effective interventions and policies to reduce child mortality rates. This requires comprehensive approaches such as improving access to healthcare, education, and basic sanitation facilities; prioritizing nutrition; providing clean water; enhancing poverty reduction and immunization; and promoting breastfeeding and social empowerment, with a particular focus on vulnerable populations and marginalized communities.

Determinants of Under-five Mortality Due to Pneumonia: A Negative Binomial Regression Analysis

Pneumonia is one of the main causes of under-five mortality in Indonesia. In under-fives, pneumoniais the number one killer in the world. Meanwhile, in Indonesia, it ranks second after diarrhea. Onaverage, the disease affects half a million children a year. This study aims to identify and analyze therisk of variables that affect the number of under-five mortality due to pneumonia in Indonesia in 2021.The novelty of this research focuses on the macro variables used, making it easier for policy makers tomake decisions. The research method used is negative binomial regression. The results showed that thehighest number of under-five mortality due to pneumonia was in Central Java Province. Meanwhile, thelowest was in Jambi Province, South Sumatra, Riau Islands, DKI Jakarta, North Kalimantan, SoutheastSulawesi, and Papua. The per capita income significantly reduces the number of under-five mortalitydue to pneumonia, while the number of under-fives with severe pneumonia significantly reduces thenumber of under-five mortality due to pneumonia in Indonesia. The government needs attention toreduce the death rate of children under five due to pneumonia by providing social protection in thefields of health and education for underprivileged communities.

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

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.

Trend Analysis and Determinants of under-5 Mortality in Nigeria: A Machine Learning Approach

The study aimed to examine the trend of the under-five mortality rate in Nigeria from 2003 to 2018 and the determinants of under-five mortality using the Nigeria Demographic and Health Survey (NDHS) data. The data for the study was the Nigeria Demographic and Health Survey data conducted in 2003, 2008, 2013, and 2018. These four surveys were used to study under-five mortality trends within the study period, while machine learning was applied only to the 2018 dataset being the latest in Nigeria. The data were partitioned into training and testing sets. 30% of the dataset was randomly selected for testing, while 70% was used in training the model. Before applying logistic regression and neural networks, the essential under-five mortality variables were first selected using a random forest classifier.&#x0D; The trend showed that the mortality rates were 200.72, 156.86, 128.05, and 132.02 in 2003, 2008, 2013, and 2018 respectively, per 1,000 live births. This result means that one in every five children died before their fifth birthday in 2003, one in six in 2008, one in eight in 2013, and one in seven in 2018. The forecast result indicated that the under-five mortality rate would likely be 102.17 in 2023. The variable importance result of the random forest showed that breastfeeding (when the child was put to the breast after birth) had the highest contribution to under-five mortality. The breakdown of breastfeeding from the logistic regression result showed that delaying the breastfeeding of a child to 6-23 hours in comparison with 0-5 hours after birth increases by 1.4 fold the likelihood of child death. The accuracy of logistic regression (LR) on the test set was 60%, and that of deep neural network (DNN) was 74%, recall (sensitivity) for LR was 63%, and DNN was 75%), Precision (LR=97%, DNN=95), F1 score (LR=76%, DNN=84%) and area under the curve (AUC) (LR=79%, DNN=77%).&#x0D; Both logistic regression and deep neural network models performed very well in discriminative ability and accuracy. The deep neural network had a better performance than the logistic regression.

Determinants of Under-Five Mortality in an Armed Conflict Setting: Empirical Findings from the Demographic and Health Surveys.

Insurgencies like Boko Haram may impact the physical health and well-being of adults and children living in geographic areas under their political control. However, it is difficult to obtain reliable health data in conflict-affected areas. This study explored the potential to use data from the Demographic and Health Surveys (DHS) to examine the determinants of under-five mortality in Northern Nigeria. Data were derived from DHS conducted before and after the start of the Boko Haram insurgency in 2009. A multi-level mixed effects logistic regression model was used to identify predictors of under-five mortality in an armed conflict setting. Results were reported as adjusted odds ratios (aOR) and 95% confidence intervals (CI). Residence in an armed conflict setting was not significantly associated with under-five mortality (aOR = 1.06; 95% CI: 1.00, 1.10). However, twin gestation (aOR = 3.18; 95% CI:2.96, 3.42), wealth index of family (richest versus poorest: aOR = 0.42; 95% CI: 0.37, 0.47), religion of mother (Islam versus Christianity: aOR = 1.50; 95% CI: 1.43, 1.57); highest educational level of mother (higher versus none: aOR = 0.33; 95% CI: 0.29, 0.37), and parity of mother, significantly predicted death before the fifth birthday. Repeated studies are needed to assess the impact of Boko Haram insurgency on physical health outcomes, particularly in areas where primary data collection is difficult or impossible.

Machine Learning Algorithms for understanding the determinants of under-five Mortality

BackgroundUnder-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality.MethodThe data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software.ResultBy applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality.ConclusionThe neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

Risk factors of under-five mortality in Ethiopia using count data regression models, 2021.

BackgroundDespite the global reduction in under-five mortality, still many children die before their fifth birthday in Ethiopia. The main aim of this study was to identify determinants of under-five mortality using count data regression models based on 2019 Ethiopia mini demography and health survey data.MethodsThe data source for this study was the 2019 Ethiopia mini demography and health survey data. Various count data regression models were applied to identify the determinants of under-five mortality.ResultsA total of 5,535 mothers with children aged 0–59 months were included in the study. Of the total, 1,277 (23.07%) women had lost at least one child by death before celebrating fifth birthday. Zero-Inflated Poisson model was found to be the best model, and it revealed that mother's age, marital status, mother's age at 1st birth, place of delivery, current contraceptive type used, type of cooking fuel, residence, region, religion, time to get drinking water, number of children at home, birth order, and birth type were significant factors that determine U5 mortality in Ethiopia.Mothers aged 15–24 years (IRR = 1.24, p = 0.007) and above 24 years (IRR = 1.66, p = 0.000) at their 1st births, mothers from rural area (IRR = 1.27, p = 0.000), mothers traveled for 1–30 min (IRR = 1.62, p = 0.000) and >30 min (IRR = 1.82, p = 0.000) to get drinking water, mothers used charcoal (IRR = 1.86, p = 0.009) and wood (IRR = 1.64, p = 0.033), children with birth order of 2nd-3rd (IRR = 3.91, p = 0.000), 4th -5th (IRR = 13.14, p = 0.000), 5th and above (IRR = 38.17, p = 0.000), and multiple born children (IRR = 1.5, p = 0.000) had higher risk of under-five mortality while mothers aged 25–34 years (IRR = 0.73, p = 0.000), unmarried mothers (IRR = 0.68, p = 0.004), mothers delivered in public health sectors (IRR = 0.59, p = 0.000), mothers used Pill/IUD (IRR = 0.64, p = 0.018), mothers who had 3 to 5 (IRR = 0.51, p = 0.000) and more than 5 (IRR = 0.27, p = 0.000) children at home had lower risk of under-five mortality.ConclusionsMothers should be encouraged to deliver at health institutions. Mothers also should be given awareness to use Pill/IUD contraceptive type. Moreover, facilitating rural areas to get electricity and drinking water near to homes helps to reduce the burden of U5M and to be in line with sustainable development goal.

Social autopsy analysis of the determinants of neonatal and under-five mortalities in Nigeria, 2013-2018

Background Nigeria suffers from one of the world’s highest child mortality rates, with about 900,000 deaths in a single year, despite being classified as a middle-income country. Over the past few years, substantial efforts have been made to reduce child mortality, with under-five mortality declining by 31.6% between 1990 and 2018. However, this decline is slower than needed to reduce child mortality significantly. This study presents the social autopsy component of the 2019 verbal and social autopsy (VASA) survey to provide an in-depth understanding of the social determinants of under-five mortality in Nigeria. Methods The study was a cross-sectional inquiry into the social determinants of neonatal and 1-59 months child deaths from the 2018 Nigeria Demographic and Health Survey (NDHS) weighted to represent the Nigerian population. The social autopsy survey asked about maternal care for neonates and 1-59 months children during the final illness. Results Child mortality in Nigeria in children aged 1-59 months is strongly associated with levels of wealth, place of residence, and maternal education. The association of these same socio-economic factors with neonatal mortality is weaker. While there were significant associations with wealth quintiles and geopolitical zones, higher maternal education was not significantly associated with lower neonatal death rates. Maternal complications in pregnancy and/or labour and delivery were common and strongly associated with stillbirths and deaths in the first two days. Severity scores at the inception of the illnesses did not show differences between children who only received informal care versus those who went to formal care providers. The main barriers to care were distance, cost, transport, and the need to travel at night, and these barriers were interlinked. More distant facilities usually required vehicle transport, which was expensive for low-income families. Travelling for an emergency at night was even more difficult in terms of finding and paying for transport and involving problems with insecurity and bad roads. Conclusions The family, community, and health system factors related to neonatal and 1-59 months child deaths in Nigeria were highlighted in this study. Deaths were commonly associated with numerous factors, each of which could contribute to the sequence of events resulting in a preventable death.

Determinants of Under-Five Mortality in Tach- Armachiho District, North Gondar, Ethiopia

Background: Under-five mortality rate, often known by its acronym U5MR, indicates the probability of dying between birth and five years of age, expressed per 1,000 live births. Globally, 16,000 children under-five still die every day. Especially in Sub-Saharan Africa every 1 child in 12, dying before his or her fifth birthday. This study aims to identify the determinants of under-five mortality among women in child bearing age group of Tach-Armachiho district using count regression models. Methods: For achieving the objective, a two-stage random sampling technique (simple random sampling and systematic random sampling techniques in the first and second stages respectively) was used to select women respondents. The sample survey conducted in Tach-Armachiho district considered a total of 3815 households of women aged 15 to 49 years out of which the information was collected from 446 selected women through interviewer administrated questionnaire. Results: The descriptive statistics result showed that in the district 16.6% of mothers have faced the problem of at least one under-five death. In this study, Poisson regression, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models were applied for data analysis. Each of these count models were compared by different statistical tests. So that, zero-inflated poisson regression model was found to be the best fit for the collected data. Results of the zero-inflated Poisson regression model showed that education of husband, source of water, mother occupation, kebele of mother, prenatal care, place of delivery, place of residence, wealth of house hold, average birth interval and average breast feeding were found to be statistically significant determinants of under-five mortality. Conclusions: In this study, it was found that the factors like average birth interval and average breast feeding were found to be statistically significant factors in both groups (not always zero category and always zero category) with under-five child deathwhereas education of husband, source of water, place of delivery, mother occupation and wealth index of the household have significant effect on under-five mortality under not always zero group. Place of residence, kebele of mother and prenatal care have a significant effect on under-five mortality in TachArmachiho district on inflated group

Socio-economic, demographic, and environmental determinants of under-five mortality in Indonesia: insights from a national survey

Despite great improvements in childhood mortality in the past several decades, many Indonesian children did not surpass their fifth birthday. This paper investigates the social and environmental determinants of under-five (U-5) mortality in Indonesia. By utilising the most recent Indonesia Demographic and Health Survey (IDHS) in 2017, we fitted survival regression models to the data. We observed that maternal smoking (aHR = 2.15, 95% CI = 1.15–4.03), maternal age (aHR = 2.48, 95% CI = 1.43–4.30), and maternal employment status (aHR = 1.68, 95% CI = 1.34–2.11) correspond to shorter lives of U-5 children. Conversely, we found that higher maternal education (aHR = 0.50, 95% CI = 0.29–0.87) corresponds to better child survival. Improved household sanitation also negatively associated with child mortality, albeit statistically insignificant at 5% (aHR = 0.78, 95% CI = 0.60–1.01). These associations hold after we take into account proximate and biological correlates. These findings corroborate extant literature that living environment (not only socio-economic and demographic factors) plays a prominent role in determining child survival. Hence, increasing access to and reducing the rural-urban gap related to improved sanitation facilities and curtailing smoking prevalence are paramount in further curbing child mortality.

Determinants of under-five mortality in Ethiopia using the recent 2019 Ethiopian demographic and health survey data: nested shared frailty survival analysis

BackgroundWorldwide, there is remarkable progress in child survival in the past three decades. Ethiopia is off-track on sustainable development targets in under-five mortality since 2020. Therefore, this study aimed to investigate time to death and its associated factors among under-five children in Ethiopia.MethodsNationally representative demographic and health survey data were used for this study. A total of 5772 under-five children were included. Data were analyzed using R software. Semi-parametric nested shared frailty survival analysis was employed to identify factors affecting under-five mortality. Adjusted hazard ratio (AHR) with 95% Confidence interval (CI) was reported and log-likelihood was used for model comparison. Statistical significance was declared at P-value < 0.05.ResultsThe weighted incidence of under-five death before celebrating the first fifth year was 5.76% (95% CI: 5.17 – 6.40). Female sex and under-five children living in urban areas were high probability of survival than their counterparts. After controlling cluster and region level frailty, multiple births (AHR = 7.03, 95% CI: 4.40—11.24), breastfed within one hour after birth (AHR = 0.41, 95% CI: 0.28—0.61), preceding birth interval 18–23 months (AHR = 1.62, 95% CI: 1.12 -2.36), and under-five children younger than 18 months (AHR = 2.73, 95% CI: 1.93 -3.86), and teenage pregnancy (AHR = 1.70, 95% CI: 1.01—2.87) were statistically significant factors for time to under-five death.ConclusionEven though Ethiopia has a significant decline under-five death, still a significant number of under-five children were dying. Early initiation of breastfeeding, preceding birth interval and teenage pregnancy were the preventable factors of under-five mortality. To curve and achieve the SDG targets regarding under-five mortality in Ethiopia, policymakers and health planners should give prior attention to preventable factors for under-five mortality.

Socio-economic and proximate determinants of under-five mortality in Guinea.

BackgroundThe death of children under-five years is one of the critical issues in public health and improving child survival continues to be a matter of urgent concern. In this paper, we assessed the proximate and socio-economics determinants of child mortality in Guinea.MethodsUsing the 2018 Guinea Demographic and Health Survey (GDHS), we extracted demographic and mortality data of 4,400 children under-five years. Both descriptive and multivariable logistic regression analyses were conducted.ResultsUnder-five mortality was 111 deaths per 1,000 live births in Guinea. The likelihood of death was higher among children born to mothers who belong to other religions compared to Christians (aOR = 2.86, 95% CI: 1.10–7.41), smaller than average children compared to larger than average children (aOR = 1.97, 95% CI: 1.28–3.04) and those whose mothers had no postnatal check-up visits after delivery (aOR = 1.72, 95% CI: 1.13–2.63). Conversely, the odds of death in children with 2–3 birth rank & >2 years of birth interval compared to ≥4 birth rank and ≤2 years of birth interval were low (aOR = 0.53, 95% CI: 0.34–0.83).ConclusionWe found that household/individual-level socioeconomic and proximate factors predict under-five mortality in Guinea. With just about a decade left to the 2030 deadline of the Sustainable Development Goals (SDGs), concerted efforts across all key stakeholders, including government and development partners, need to be geared towards implementing interventions that target these predictors.

Potential determinant factors of under-five mortality in the Amhara region of Ethiopia

BackgroundEven though child mortality decreased greatly (44%, since 1990), children in developing countries are eight times more likely to die before they attain their five years birthday. When comparing under-five mortality around the world, the African including Ethiopia and Southeast Asian regions showed an uneven child death rate. Therefore, this study was aimed to identify the potential determinant factors of under-five mortality in the Amhara regional state of Ethiopia.MethodsStatistics from a national representative cross-sectional survey of the Ethiopian Demographic and Health Survey (EDHS) of the year 2016 were used. Data was collected from the population of all under-five children in randomly selected enumeration areas of the Amhara region of Ethiopia. To investigate the relationship between the dependent variable (under-five mortality) and various independent factors, inferential statistics such as binary logistic regression and multiple logistic regressions were used. In multivariable analysis, statistically significant variables in binary logistic regression analysis, i.e. (p-value 0.250), were entered, and P-value 0.050 was considered significant at 95% CI.ResultsThe survey was included 977 children under the age of five and more than half of children in the family (68%) were ≤ 4. The findings showed that children whose mothers had no formal education were 2.59 times more likely to die than children whose mothers had formal education [AOR: 2.59(1.12–5.99)]. Similarly, children who did not receive breastfeeding from their mothers were 3.61 times more likely to die than children who received breastfeeding from their mothers [AOR: 3.61(1.83–6.19)].Conclusion and RecommendationThe number of children in the family, as well as the mother’s educational status and current breastfeeding status, were all found to be important factors in under-five mortality in the study area. As a result, the potential determinants of under-five mortality should be addressed as part of a program targeted at lowering childhood mortality.

Determinants of under-five mortality in Indonesia: A nationwide study

BackgroundThe mortality rate for infants and children under five in Indonesia is an ongoing challenge for the government, with figures exceeding the targets set for the country by the Sustainable Development Goals (SDGs). ObjectivesThis study aims to discover the factors causing under-five mortality in Indonesia. MethodsThis study will employ a cross-sectional study design with data sourced from the 2017 Indonesian Demographic and Health Survey (IDHS) between July and September 2017. The sample included 10,014 women who had given birth in the five years prior to the survey. The data was analyzed using Binary logistic regression with a significance level of p < 0.05. ResultsThe significant factors relating to under-five mortality in Indonesia are: mother's age at birth (AOR = 2.04; 95% CI 95% = 1.11–3.77); birth weight (AOR = 7.60; 95% CI 95% = 5.17–11.19); the sex of the child (AOR = 1.80; 95% CI 95% = 1.28–2.52); frequency of using the internet (AOR = 1.13; 95% CI 95% = 0.02–0.95); residence (AOR = 0.64; 95% CI 95% = 0.33–0.94); and birth interval (AOR = 0.52; 95% CI 95% = 0.29–0.92). Birth weight is the more likely cause for under-five mortality in Indonesia. ConclusionsThis study revealed that the characteristics of mothers, children, the area of residence, and the behavior of the mother affect the under-five mortality. Practice implicationsPediatric nurses can have a role to play in developing knowledge about health for both mothers and families. Additionally, accessible health education on issues from planning a pregnancy to childcare should be promoted in both rural and urban areas as well as a campaign on proper hygiene practices.

Survival time discrepancy among under-five-year children of rural parts of Ethiopia.

Background: Ethiopia is ranked as the fifth of heavy under-five death burdened countries with the highest burden in its rural areas. This study aimed to identify the determinants of under-five deaths in rural parts of Ethiopia. Study Design: A population-based cross-sectional study. Methods: The data for this study was extracted from the 2016 Ethiopian Demographic and Health Survey. Descriptive analysis, non-parametric estimation, and Cox proportional hazards regression model were used to examine the determinants of under-five mortality.Results: A total of 7301 under-five eligible children from rural areas were involved in this survey, and 6.5% of the cases were passed away before reaching their fifth birthday. Male children’s death accounted for 59.7% of the death rate in the participants. An estimated median survival time was 31 months [95% CI: 30-32]. About 83% of children’s death occurred among children delivered at home. Cox proportional hazard regression model revealed that gender, delivery-place, family-size, mother’s education, number of children, contraceptive use, and source of drinking water had significant effects on survival time of underfive children. Under-five mortality was significantly fewer in female children (HR = 0.728; 95% CI: 0.606- 0.875, P = 0.001), children delivered at health facilities (HR = 0.738; 95% CI: 0.572-0.951, P = 0.019), and those from secondary and above educated mothers (HR = 0.464; 95% CI: 0.301-0.714, P = 0.001), compared to the reference category.Conclusion: Significant risk factors were associated with under-five mortality in rural areas. Delivering in health facilities, uses of contraceptives, mother’s education, and improvement of infrastructures should be areas of concern to decrease under-five children’s deaths.

Uncertainties in the path to 2030: Increasing trends of under-five mortality in the aftermath of Millennium Development Goal in Eastern Ethiopia.

BackgroundAlthough Ethiopia was applauded for achieving the Millennium Development Goal (MDG) target of reducing child mortality, whether the gains sustained beyond the MDG era was rarely studied. In this study, we reported the trends and determinants of under-five mortality (U5M) from 2015 to 2020 in a population based cohort under the Kersa Health and Demographic Surveillance System (HDSS), eastern Ethiopia.MethodsWe followed pregnant women and their pregnancy outcomes from 2015 to 2020. Each year, data related to death and live births among the follow up population was retrieved. Automated verbal autopsy (InterVA-4) was used to assign the cause of death and Stata 14 was used for analysis. U5M rate was calculated as death among under five children divided by all live births during the study period and described per 1000 live births along with 95% Confidence Interval (CI). A multivariable Cox proportional regression model was used to identify determinant of U5M using adjusted hazard ratio (AHR). Finally, P value <0.05 was considered for declaring statistically significant association.ResultsFrom January 2015 to December 2020, a total of 28 870 live births were registered under the Kersa HDSS, of whom 1335 died before their fifth birthday. The overall U5M rate was 46.3 per 1000 live births (95% confidence interval (CI) = 43.79-48.79), with significant increase from 27.9 in 2015 to 54.7 in 2020 (P < 0.041). Diarrheal diseases, acute respiratory tract infection including pneumonia, meningitis and encephalitis, and HIV related deaths were the leading causes of U5M. The hazard of death was higher among children born to poor household (AHR = 1.52; 95% CI = 1.27-1.81), rural residents (AHR = 6.0; 95% CI = 3.65-9.91), born to adolescent mothers (AHR = 1.41; 95% CI = 1.02-1.95), whose mother didn’t receive antenatal care (AHR = 1.43; 95% CI = 1.21-1.69), were born preterm (AHR = 14.1; 95% CI = 9.96-19.89) and had low birth-weight (AHR = 1.74; 95% CI = 1.39-2.18).ConclusionWe found high level of U5M rate with an increasing trend in the aftermath of the praised MDG4 achievement. Achieving the ambitious U5M of 25 per 1000 live births by 2030 requires addressing diarrheal disease, and respiratory tract infections, and HIV/AIDS. Reasons behind the persistent increase over the study period require further inquiry.

Socio-ecological determinants of under-five mortality in Nigeria: exploring the roles of neighbourhood poverty and use of solid cooking fuel.

Globally, despite the decline in under-five mortality rate from 213 per 1000 live births in 1990 to 132 per 1000 live births in 2018, the pace of decline has been slow, and this can be attributed to poor progress in child survival interventions, including those aimed at reducing children's exposure to household pollution. This study examined the influence of neighbourhood poverty and the use of solid cooking fuels on under-five mortality in Nigeria. Data for the study comprised a weighted sample of 124,442 birth histories of childbearing women who reported using cooking fuels in the kitchens located within their house drawn from the 2018 Nigeria Demographic and Health Survey. Descriptive and analytical analyses were carried out, including frequency tables, Pearson's chi-squared test and multivariate analysis using a Cox proportional regression model. The results showed that the risk of under-five mortality was significantly associated with mothers residing in areas of high neighbourhood poverty (HR: 1.44, CI: 1.34-1.54) and the use of solid cooking fuels within the house (HR: 2.26, CI: 2.06-2.49). Government and non-governmental organizations in Nigeria should initiate strategic support and campaigns aimed at empowering and enlightening mothers on the need to reduce their use of solid cooking fuels within the house to reduce harmful emissions and their child health consequences.