Trends of infant mortality and its determinants in Ethiopia: mixed-effect binary logistic regression and multivariate decomposition analysis

Correlates of infant mortality in Benshangul-Gumuz regional State, Ethiopia

BACKGROUND

Infant mortality is one of the commonest health-related indicators that are used to assess the health status of the society of a nation. Despite the decline in global infant mortality rate, the rate of decline in low-and middle-income countries including Ethiopia is below the rate expected to attain Sustainable Development Goal targets. Benshangul-Gumuz regional state is among the regions in Ethiopia with high infant mortality rate. Therefore, this study aimed at determining the infant mortality rate and identifying risk factors associated with the infant mortality in Benshangul-Gumuz regional state, Ethiopia.

METHODS

This study used data drawn from 2019 Ethiopian Mini Demographic and Health Survey (2019 EMDHS). Multivariable logistic regression model was used to identify the risk factors associated with infant mortality.

RESULTS

A total of 530 live births were included in this study. The infant mortality rate in this study was 81 (95% CI: 59, 108) per 1000 live births. Females (AOR: 0.690, 95% CI: 0.342, 0.899), multiple births (AOR: 3.067, 95% CI: 2.313, 10.139), mothers with secondary and higher education (AOR: 0.460, 95% CI: 0.287, 0.885), mothers aged 34 years and older (AOR: 1.539, 95% CI: 1.183, 9.802), mothers having ANC visits (AOR: 0.597, 95% CI: 0.326, 0.709) and health institution delivery (AOR: 0.611, 95% CI: 0.0.294, 0.0.899) were significant risk factors associated with infant mortality.

CONCLUSION

Factors sex of child, type of birth, maternal educational status, age of mother, ANC visits and place of delivery were found to be correlates of infant mortality in Benshangul-Gumuz regional state, Ethiopia. Thus, enhancing mothers to have the ANC follow up during pregnancy and deliver at health institution is recommended in the region.

Multilevel and subnational analysis of the predictors of maternity continuum of care completion in Nigeria: a cross-sectional survey

Understanding population discrepancy in maternity continuum of care (CoC) completion, particularly in sub-Saharan Africa is significant for interventional plan to achieve optimal pregnancy outcome and child survival. This study thus investigated the magnitudes, distribution, and drivers of maternity CoC completion in Nigeria. A secondary analysis of 19,474 reproductive age (15-49 years) women with at least a birth (level 1) in 1400 communities (level 2) across 37 states covered in the 2018 cross-sectional survey. Stepwise regression initially identified important variables at 10% cutoff point. Multilevel analysis was performed to determine the likelihood and significance of individual and community factors. Intra-cluster correlation assessed the degree of clustering and deviance statistics identified the optimal model. Only 6.5% of the women completed the CoC. Completion rate is significantly different between communities "4.3% in urban and 2.2% in rural" (χ2 = 392.42, p < 0.001) and was higher in southern subnational than the north. Education (AOR = 1.61, 95% CI 1.20-2.16), wealth (AOR = 1.73, 95% CI 1.35-2.46), media exposure (AOR = 1.22, 95% CI 1.06-1.40), women deciding own health (AOR = 1.37, 95% CI 1.13-1.66), taking iron drug (AOR = 1.84, 95% CI 1.43-2.35) and at least 2 dose of tetanus-toxoid vaccine during pregnancy (AOR = 1.35, 95% CI 1.02-1.78) are associated individual factors. Rural residency (AOR = 1.84, 95% CI = 1.43-2.35), region (AOR = 1.84, 95% CI 1.43-2.35) and rural population proportion (AOR = 1.84, 95% CI 1.43-2.35) are community predictors of the CoC completion. About 63.2% of the total variation in CoC completion was explained by the community predictors. Magnitude of maternity CoC completion is generally low and below the recommended level in Nigeria. Completion rate in urban is twice rural and more likely in the southern than northern subnational. Women residence and region are harmful and beneficial community drivers respectively. Strengthening women health autonomy, sensitization, and education programs particularly in the rural north are essential to curtail the community disparity and optimize maternity CoC practice.

Mother's education and infant survival in Ethiopia

OBJECTIVE

Few studies in Ethiopia have explored the impact of the mothers' education on infant survival. Therefore, this study aimed to identify and analyze the proximate factors in the relationship between maternal education and infant survival in Ethiopia.

METHODS

This study used the nationally representative 2016 Ethiopian Demographic and Health Survey dataset. It analyzed a sample of 3831 newborn children using Cox regression models.

RESULTS

The findings revealed that infants born to educated mothers had lower odds of infant mortality than those born to mothers without formal education. Specifically, infants whose mothers had completed secondary school and had a better antenatal care attendance rate had 49.9% lower odds of infant mortality than those born to mothers with no formal education and a poor antenatal care attendance rate. Furthermore, infants whose mothers had at least some postsecondary education and who used delivery by health-care professionals, clean drinking water, and improved toilet facilities had 65.3%, 56.3%, and 68.6% lower odds of infant mortality, respectively, than those born to mothers with no formal education and who did not use those facilities.

CONCLUSIONS

This study concluded that the mothers' educational disparity is intimately tied to infant mortality, and that access to formal education, mainly for women, increases infant survival in Ethiopia. Future research should focus on mothers without formal education who do not have access to antenatal care visits for safe pregnancy, delivery by health-care professionals, clean drinking water, and improved toilet facilities in Ethiopia and elsewhere in Sub-Saharan Africa.

Geographical disparities and determinants of infant mortality in Ethiopia: mapping and spatial analysis using EDHS data

BACKGROUND

Infant mortality remains a public health challenge in Ethiopia. Exploring infant mortality will aid in tracking the progress toward achieving sustainable development goals.

OBJECTIVE

The study aimed to explore the geographical variations and associated factors of infant mortality in Ethiopia.

METHOD

A total of 11,023 infants from the 2016 Ethiopian Demographic and Health Survey (EDHS) data were extracted and included in the analysis. EDHS used a two-stage cluster sampling design with a census enumeration area as the primary sampling unit and households as the secondary sampling unit. Arc GIS software was used for spatial analysis using clusters for exploring geographical variations in infant mortality. A binary logistic regression was employed using R software to identify the significant determinants of infant mortality.

RESULTS

The study revealed that the spatial distribution of infant mortality was non-random in the country. Infants whose mothers not receiving ANC (AOR = 1.45; 95%CI: 1.17, 1.79), not breastfed status (AOR = 3.94; 95%CI: 3.19, 4.81), poor wealth index (AOR = 1.36; 95%CI: 1.04, 1.77), male infants (AOR = 1.59; 95%CI: 1.29, 1.95), birth order of six or above (AOR = 3.11; 95%CI: 2.08, 4.62), small birth size (AOR = 1.27; 95%CI: 1.26, 1.60), birth spacing [(≤ 24 months (AOR = 2.29; 95%CI: 1.79, 2.92), 25-36 months (AOR = 1.16; 95%CI: 1.12, 1.49)], multiple births (AOR = 6.82; 95%CI: 4.76, 10.81), rural residence (AOR = 1.63; 95%CI: 1.05, 2.77) and regions [Afar (AOR = 1.54; 95%CI: 1.01, 2.36), Harari (AOR = 1.56; 95%CI: 1.04, 2.56), and Somali (AOR = 1.52; 95%CI: 1.03, 2.39)] were the determinants of infant death in Ethiopia.

CONCLUSIONS

There is a great geographical disparity in infant mortality rates across regions. Afar, Harari, and Somali regions were verified as hot spot areas. ANC usage, breastfed status, wealth index, sex of the infant, birth order, birth size, birth spacing, birth type, residence, and region were the determinants of infant death in Ethiopia. Therefore, appropriate interventions need to be implemented in the hot spots to alleviate the risk factors for infant mortality.

Prevalence and determinants of neonatal near miss in Ethiopia: A systematic review and meta-analysis

INTRODUCTION

Neonatal near miss is a condition of newborn infant characterized by severe morbidity (near miss), but survived these conditions within the first 27 days of life. It is considered as the first step to design management strategies that can contribute in reducing long term complication and mortality. The aim of this study was to assess prevalence and determinants of neonatal near miss in Ethiopia.

METHODS

The protocol of this systematic review and meta-analysis was registered at the Prospero with a registration number of (PROSPERO 2020: CRD42020206235). International online databases such as PubMed, CINAHL, Google scholar, Global Health, Directory of open Access journal and African Index Medicus were used to search articles. Data extraction was undertaken with Microsoft Excel and STATA11 was used to conduct the Meta-Analysis. Random effect model analysis was considered when there was evidence of heterogeneity between the studies.

RESULTS

The overall pooled prevalence of neonatal near miss was 35.51% (95%CI: 20.32-50.70, I2 = 97.0%, p = 0.000). Primiparity (OR = 2.52, 95%CI: 1.62, 3.42), referral linkage (OR = 3.92, 95%CI: 2.73, 5.12), premature rupture of membrane (OR = 5.05, 95%CI: 2.03, 8.08), Obstructed labor (OR = 4.27, 95%CI: 1.62, 6.91) and maternal medical complications during pregnancy (OR = 7.10, 95%CI: 1.23, 12.98) had shown significant statistical association with neonatal near miss.

CONCLUSION

The prevalence of neonatal near miss in Ethiopia is evidenced to be high. Primiparity, referral linkage, premature rupture of membrane, obstructed labor and maternal medical complications during pregnancy were found to be determinant factors of neonatal near miss.

The Causes and Factors Associated with Infant Mortality Rate in Ethiopia: The Application of Structural Equation Modelling

Infant mortality rate is a proxy measure of population health. Previous studies on the infant mortality rate in Ethiopia did not consider measurement errors in the measured variables and had a one-directional effect; little emphasis was placed on testing multiple causal paths at the same time. We used structural equation modelling for a better understanding of the direct, indirect, and total effects among causal variables in a single model. A path analysis was part of an algorithm providing equations that were relating the variances and covariances of the indicators. From the results, the maternal mortality ratio (MMR) was significantly mediating the influence of out-of-pocket expenditure (OOP) on infant mortality rate (IMR), and the fertility rate (FR) was significantly mediating the influence of GDP to IMR (β = 1.168, p < 0.001). The GDP affects the IMR directly and indirectly while the OOP affects IMR indirectly. This study showed that there was a causal linkage between the World Bank Health and Population Variables for causing IMR in Ethiopia. The MMR and FR were found to be the intermediate indicators in this study. Through the indicators, FR had the highest standardised coefficients for increasing the IMR. We recommended that the existing interventions to reduce IMR be strengthened.

Prevalence, spatial distribution and determinants of infant mortality in Ethiopia: Findings from the 2019 Ethiopian Demographic and Health Survey

INTRODUCTION

Infant mortality declined globally in the last three decades. However, it is still a major public health concern in Ethiopia. The burden of infant mortality varies geographically with the highest rate in Sub-Saharan Africa. Although different kinds of literature are available regarding infant mortality in Ethiopia, an up to date information is needed to design strategies against the problem. Thus, this study aimed to determine the prevalence, show the spatial variations and identify determinants of infant mortality in Ethiopia.

METHODS

The prevalence, spatial distribution, and predictors of infant mortality among 5,687 weighted live births were investigated using secondary data from the Ethiopian Demographic and Health Survey 2019. Spatial autocorrelation analysis was used to determine the spatial dependency of infant mortality. The spatial clustering of infant mortality was studied using hotspot analyses. In an unsampled area, ordinary interpolation was employed to forecast infant mortality. A mixed multilevel logistic regression model was used to find determinants of infant mortality. Variables with a p-value less than 0.05 were judged statistically significant and adjusted odds ratios with 95 percent confidence intervals were calculated.

RESULT

The prevalence of infant mortality in Ethiopia was 44.5 infant deaths per 1000 live births with significant spatial variations across the country. The highest rate of infant mortality was observed in Eastern, Northwestern, and Southwestern parts of Ethiopia. Maternal age between 15&19 (adjusted odds ratio (AOR) = 2.51, 95% Confidence Interval (CI): 1.37, 4.61) and 45&49(AOR = 5.72, 95% CI: 2.81, 11.67), having no antenatal care follow-up (AOR = 1.71, 95% CI: 1.05, 2.79) and Somali region (AOR = 2.78, 95% CI: 1.05, 7.36) were significantly associated with infant mortality in Ethiopia.

CONCLUSION

In Ethiopia, infant mortality was higher than the worldwide objective with significant spatial variations. As a result, policy measures and strategies aimed at lowering infant mortality should be devised and strengthened in clustered areas of the country. Special attention should be also given to infants born to mothers in the age groups of 15-19 and 45-49, infants of mothers with no antenatal care checkups, and infants born to mothers living in the Somali region.

Negative effects of short birth interval on child mortality in low- and middle-income countries: A systematic review and meta-analysis

Methods

Eight databases, PubMed, CINAHL, Web of Science, Embase, PsycINFO, Cochrane Library, Popline, and Maternity and Infant Care, were searched, covering the period of January 2000 to January 2022. Studies that had examined the association between SBI and any form of child mortality were included. The findings of the included studies were summarized through fixed-effects or random-effects meta-analysis and the model was selected based on the heterogeneity index.

Results

A total of 51 studies were included. Of them, 19 were conducted in Ethiopia, 10 in Nigeria and 7 in Bangladesh. Significant higher likelihoods of stillbirth (odds ratio (OR) = 2.11; 95% confidence interval (CI) = 1.32-3.38), early neonatal mortality (OR = 1.58; 95% CI = 1.04-2.41), perinatal mortality (OR = 1.71; 95% CI = 1.32-2.21), neonatal mortality (OR = 1.85; 95% CI = 1.68-2.04), post-neonatal mortality (OR = 3.01; 95% CI = 1.43-6.33), infant mortality (OR = 1.92; 95% CI = 1.77-2.07), child mortality (OR = 1.67; 95% CI = 1.27-2.19) and under-five mortality (OR = 1.95; 95% CI = 1.56-2.44) were found among babies born in short birth intervals than those who born in normal intervals.

Conclusions

SBI significantly increases the risk of child mortality in LMICs. Programmes to reduce pregnancies in short intervals need to be expanded and strengthened. Reproductive health interventions aimed at reducing child mortality should include proper counselling on family planning, distribution of appropriate contraceptives and increased awareness of the adverse effects of SBI on maternal and child health.

Factors Associated with Infant Deaths in Indonesia: An Analysis of the 2012 and 2017 Indonesia Demographic and Health Surveys

BACKGROUND

This exploratory study aimed to investigate factors related to infant deaths using a conceptual framework that explains the risk factors of infant deaths in developing countries.

METHODS

The study adopted a cross-sectional study design and used data from the 2012 and 2017 Indonesia Demographic and Health Surveys, with a sample of 3694 singleton live births in 2012 and 3413 in 2017.

RESULTS

Female infants had a lower chance of mortality compared to male infants [adjusted odds ratio (aOR) = 0.51; 95% confidence interval (CI) = 0.34-0.77]. Infants with a smaller birth size had a higher risk of infant death compared to those with an average size (aOR = 5.66; 95% CI = 3.66-8.77). The risk of infant death with a preceding birth interval of ≥24 months was lower than that with a preceding birth interval of <24 months (aOR = 0.48; 95% CI = 0.26-0.90). An older maternal age was a risk factor for infant death compared to younger mothers (aOR = 3.61; 95% CI = 1.42-9.23). Infants who were born in Sumatra (aOR = 0.38; 95% CI = 0.16-0.89) and Java and Bali (aOR = 0.33; 95% CI = 0.14-0.78) were less likely to die than infants who were born in Papua and Maluku.

CONCLUSIONS

A higher infant death risk was associated with male babies and a shorter birth interval (<24 months). Mothers who perceived their babies to be small and mothers who were older (35-49 years old) were high-risk factors for infant mortality. Mothers who lived in Java and Bali as well as Sumatera were less likely to experience infant mortality compared to those who lived in Papua and Maluku.

Method for Data Quality Assessment of Synthetic Industrial Data

Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.