Determinant of Infant and Child Mortality in Ethiopia

Shiferaw, Yegnanew A.; Zinabu, Meseret; Abera, Tesfaye

doi:10.2139/ssrn.2188355

Cited by 9 publications

(14 citation statements)

References 12 publications

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“…Protected sources of drinking water supply are less likely to be infected and are less likely to prevent water-related diseases such as infections and cholera. This finding is in line with [16,17]. Compared to households that have improved toilet facilities, the mortality risk of neonates among household that has not improved toilet facilities was higher.…”

Section: Discussionsupporting

confidence: 89%

“…Previous studies conducted in Ethiopia to investigate the risk factors of neonatal mortality were institutionalbased [14,15] and considering only individual-level factors [11][12][13]. However, neonatal mortality can be affected by community-level factors, such as the source of drinking water [16,17], types of toilet facilities [18,19], and cluster (enumeration area) [20,21]. Moreover, the uses of a single-level logistic regression analysis approach to analyze data with a hierarchical structure (i.e., neonates nested within communities) violate the regression's independence assumptions [22].…”

Section: Introductionmentioning

confidence: 99%

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Risk factor of neonatal mortality in Ethiopia: multilevel analysis of 2016 Demographic and Health Survey

2021

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Background In sub-Saharan African countries, neonatal mortality rates remain unacceptably high. Ethiopia is one of the countries in Sub-Saharan Africa with the highest death rates of newborn children. Therefore, this study aimed to identify the risk factors associated with neonatal mortality in Ethiopia at the individual and community level. Methods The 2016 Ethiopian Demographic and Health Survey data was accessed and used for the analysis. A total of 2449 newborn children were included in the analysis. The multilevel logistic regression model was used to identify the significant factor of neonatal mortality. Adjusted odds ratio with a 95% confidence interval and p-value < 0.05 in the multilevel model was reported. Results A total of 2449 newborn children were included in this study. Multiple birth type (AOR = 3.18; 95% CI 2.78, 3.63), birth order of ≥ 5 (AOR = 2.15; 95% CI 1.75, 2.64), pre-term birth (AOR = 5.97; 95% CI 4.96, 7.20), no antenatal care (ANC) visit during pregnancy (AOR = 2.33; 95% CI 2.09, 2.61), not received TT injection during pregnancy (AOR = 2.28; 95% CI 1.92, 2.71), delivered at home (AOR = 1.99; 95% CI 1.48, 2.69), less than 24 months of preceding birth interval (AOR = 1.51; 95% CI 1.35,1.68), smaller birth size (AOR = 1.58; 95% CI 1.46, 1.71), never breastfeeding (AOR = 2.43; 95% CI 2.17, 2.72), poor wealth index (AOR = 1.29; 95% CI 1.17,1.41), non-educated mothers (AOR = 1.58; 95% CI 1.46, 1.71), non-educated fathers (AOR = 1.32; 95% CI 1.12, 1.54), rural residence (AOR = 2.71; 95% CI 2.23, 3.29), unprotected water source (AOR = 1.35; 95% CI 1.16, 1.58), and have no latrine facility (AOR = 1.78; 95% CI 1.50, 2.12) were associated with a higher risk of neonatal mortality. Neonates living in Amhara, Oromia, Somali, Harari, and Dire Dawa had a higher risk of neonatal mortality compared to Tigray. Moreover, the random effects result showed that about 85.57% of the variation in neonatal mortality was explained by individual- and community-level factors. Conclusions The findings suggest that attention be paid to education-based programs for mothers that would highlight the benefits of delivery care services, such as ANC visits, TT injections, and facility births. Meanwhile, public health initiatives should focus on expanding access to quality sanitation facilities, especially for latrines and drinking water that could improve neonatal health at the community-level as a whole.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Risk factor of neonatal mortality in Ethiopia: multilevel analysis of 2016 Demographic and Health Survey

2021

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Section: Backgroundsmentioning

confidence: 99%

“…Usually one half of all deaths take places in the age group below five years in which the major health problems of the countries are concentrated. More than 97% of all deaths takes place below age five year in less developed countries (Shiferaw, 2018). Among children and young adolescents, the risk of dying was highest in the first month of life at an average rate of 18 deaths per 1,000 live births (including children under age 5, children aged globally in 2017.…”

Section: Backgroundsmentioning

confidence: 99%

Count Models for Identifying Factors Associated with Child Mortality in Ethiopia

Tilahun

2021

Preprint

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Introduction: The under-five mortality rate, often known by its acronym U5MR or simply as the child mortality rate, indicates the probability of dying between births exactly five years of age, expressed per 1,000 live births. In comparison, the probability of dying after the first month and before reaching age 1 was 12 per 1,000, the probability of dying after age 1 and before age 5 was 10 per 1,000, and the probability of dying after age 5 and before age 15 was 7 per 1,000. Objectives: The study was aimed to determine the major factors of child mortality in Ethiopia using different counting models. In detail the study has the objective of identifying the risk factors of child mortality in Ethiopia and also to prioritize the best counting models that fit the data well. Methods: The Ethiopian demographic and health survey of 2016 was used for this study. About 10641 women aged between 15-49 were included in the survey. To analyze the data, counting models like the Poisson regression model, negative binomial model, zero-inflated regression models, and zero-inflated negative binomial regression model were applicable. Results: The results of the study indicated that of the total 10641 women respondents, 7576 (71.2%) have not faced the problem of child mortality. Thus, this result has the clue that the count models, especially the models that can handle the dispersion may be applicable. The average rate of child mortality is less than the variance of child mortality and this indicated that there is an over-dispersion of the data. Of all the candidate models, a zero-inflated negative binomial regression model was found to be the best model since it has a minimum AIC(15517). The coefficient table of the best model indicated that of child mortality for the women from rural residence is 1.2532 greater than those from urban with a 95% confidence interval (0.0905, 0.3610). Conclusion: The model comparison technique is indicated that the zero-inflated negative binomial regression models were the best mode that fit the data well. Under this model, the residency of women, birth order, Preceding Birth Interval, Size of a child at birth (smaller than average), and number of household members are significant variables in determining the status of child mortality in Ethiopia

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“…The findings of the study suggest that the household's environmental and socio-economic characteristics have a significant influence on the child mortality; therefore, policies to improve both the household's environmental and socio-economic status were advocated in order to ensure a reduction in the child mortality in Kenya. Using the 2011 demographic and health survey, Meseret et al (2012) conclude that different factors affect the child and infant mortality which include the mother's educational status, birth order, number of children, birth intervals, household size, breast feeding, mother's marital status and source of drinking water.…”

Section: Determinants Of Child Mortality In Africamentioning

confidence: 99%

Impact of food availability on child mortality: a cross country comparative analysis

Oluwatoyin¹

2017

AGR ECON-CZECH

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Th e study examined the impact of food security on child mortality (infant mortality and under-fi ve mortality), using a dynamic panel data analysis for 114 countries for the period 1995-2009 by considering a wide range of controlled variables such as income, social indicators and policy variables. Th e result suggests that food security has a negative impact on child mortality for all countries and even more impact on child mortality within the food insecure African countries. Th erefore, based on the fi ndings of the study; it is recommended that an increase in food security is indeed a positive policy option, particularly within the food insecure African countries, since it ensures a decrease in child mortality within these countries.

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Determinant of Infant and Child Mortality in Ethiopia

Cited by 9 publications

References 12 publications

Risk factor of neonatal mortality in Ethiopia: multilevel analysis of 2016 Demographic and Health Survey

Risk factor of neonatal mortality in Ethiopia: multilevel analysis of 2016 Demographic and Health Survey

Count Models for Identifying Factors Associated with Child Mortality in Ethiopia

Impact of food availability on child mortality: a cross country comparative analysis

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