Assessing Individual- and Community-Level Variability in Predictors of Neonatal, Infant, and Under-Five Child Mortality in Ethiopia Using a Multilevel Modeling Approach

Abstract

Background: In low-and middle-income countries, child mortality rates are basic indicators of a country’s socio-economic situation and quality of life. The Ethiopian government is currently working to reduce child mortality to accomplish its long-term development goals. Using data from the Ethiopian Mini Demographic and Health Survey, 2019, this study analyzed the determinants of child mortality in Ethiopia. Methods: A total of 4806 children were considered in the final analyses. Multivariate analysis was used to estimate the effects of the predictors simultaneously on each child mortality outcome. Results: The findings revealed that 31.6% of children died during the neonatal stage, 39.1% during the infant stage, and 48.5% during the under-five stage. Variation in child mortality was discovered between Ethiopian community clusters, with the result of heterogeneity between clusters on newborn mortality (χ2 = 202.4, p-value < 0.0001), (χ2 = 777.35, p-value < 0.0001), and (χ2 = 112.92, p-value < 0.0001). Children’s neonatal, infant, and under-five mortality intracluster correlation coefficient (ICC) were 0.35, 0.33, and 0.36, respectively, across communities. Conclusion: In Ethiopia, under-five mortality remains a serious public health issue, with wide variations and high rates among community clusters. Intervention measures focusing on lowering rates of household poverty, increasing education opportunities, and improving access to health care could assist in reducing child mortality in Ethiopia.

The influence of socioeconomic and demographic factors on rural-urban disparities in infant mortality in Kakamega Central Sub-County, Kakamega County, Kenya

“…The findings on the association of wait period prior to the index birth and infant mortality are in agreement with findings of studies by Wardani et al (2022) and Kuse et al (2022). A study by Wardani et al (2022) found the risk of infant mortality with a preceding birth interval of ≥ 24 months to be lower than that with a preceding birth interval of < 24 months (aOR = 0.48; CI = 0.26 -0.90).…”

“…These results are consistent with those of other studies (Ikamari, Imbo, Mbuthia and Ngotho (2021) found mothers with no education to have had higher odds of experiencing neonatal mortality (aOR = 2.201; ρ <0.05; CI = 1.43 -4.15) compared to mothers with higher education. Neonatal mortalities for mothers with secondary and higher educational levels were about 22 percent and 46 percent less likely compared to those of mothers with no education, respectively (Kuse et al, 2022). The odds of infant mortality decreased by 64 percent, 88 percent and 82 percent respectively, for mothers with primary, secondary, and higher educational levels with reference to their colleagues with no any education qualifications.…”

“…A study by Wardani et al (2022) found the risk of infant mortality with a preceding birth interval of ≥ 24 months to be lower than that with a preceding birth interval of < 24 months (aOR = 0.48; CI = 0.26 -0.90). Children born after a wait period greater than or equal to 24 months were 46 percent less likely to die at infancy compared to children born after a less than 24 months wait period (Kuse et al, 2022). Akinyemi et al (2016) explained that short preceding birth intervals and high parity largely increased early childhood mortality risk after accounting for unobserved heterogeneity between them.…”

Zero-inflated Poisson regression analysis of factors associated with under-five mortality in Ethiopia using 2019 Ethiopian mini demographic and health survey data

“…The main limitation of this study that the EMDHS is mostly based on respondents' self-report and might have the possibility of recall bias. In addition, variables such as weight at birth [48,55,56], anemia status of mother [56][57][58], breastfeeding status of child [7,48,59], diarrhea [49,60,61], desire of pregnancy [62][63][64][65], employment status of mother [49,66,67], educational level of father [68][69][70] and maternal HIV status [71][72][73] were not included in the study due to large number of missing values/unavailability in the dataset.…”