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Abstract Background Under-five mortality remains a global public health concern, particularly in East African countries. Short birth interval is highly associated with under-five mortality, and birth spacing has a significant effect on a child’s likelihood of survival. The association between short birth intervals and under-five mortality was demonstrated by numerous observational studies. However, the effect of short birth intervals on under-five mortality has not been investigated yet. Therefore, this study aimed to investigate the impact of short birth intervals on under-five mortality in East Africa using Propensity Matched Analysis. Methods A secondary data analysis was conducted based on the most recent Demographic and Health Survey (DHS) data of 12 East African countries. A total weighted sample of 105,662 live births was considered for this study. A PSM analysis was carried out to evaluate the effect of short birth intervals on under-five mortality. Under-five mortality was the outcome variable, while the short birth interval was considered a treatment variable. To determine the Average Treatment Effect on the population (ATE), Average Treatment Effect on the treated (ATT), and Average Treatment Effect on the untreated (ATU), we performed PSM analysis with a logit-based model using the psmatch2 ate STATA function. The quality of matching was assessed statistically and graphically. The common support assumption was checked and fulfilled. We have employed Mantel-Haenszel bounds to examine whether the result would be free from hidden bias or not. Results The prevalence of short birth intervals in East Africa was 44%. The under-five mortality rate among mothers who had optimal birth intervals was 39.9 (95% CI: 38.3, 41.5) per 1000 live births while it was 60.6 (95% CI: 58.5, 62.8) per 1000 live births among mothers who had a short birth intervals. Propensity score matching split births from mothers into treatment and control groups based on the preceding birth interval. In the PSM analysis, the ATT values in the treated and control groups were 6.09% and 3.97%, respectively, showed under-five mortality among births to mothers with short birth intervals was 2.17% higher than births to mothers who had an optimal birth interval. The ATU values in the intervention and control groups were 3.90% and 6.06%, respectively, indicating that for births from women who had an optimal birth interval, the chance of dying within five years would increase by 2.17% if they were born to mother with short birth interval. The final ATE estimate was 2.14% among the population. After matching, there was no significant difference in baseline characteristics between the treated and control groups (p-value > 0.05), which indicates the quality of matching was good. Conclusions We conclude that enhancing mothers to have optimal birth spacing is likely to be an effective approach to reducing the incidence of under-five mortality. Our findings suggest that births to mothers with short birth intervals have an increased risk of death in the first five years of life than births to mothers who had an optimal birth interval. Therefore, public health programs should enhance interventions targeting improving birth spacing to reduce the incidence of under-five mortality in low-and middle-income countries like East African countries. Moreover, to achieve a significant reduction in the under-five mortality rate, interventions that encourage birth spacing should be considered. This will improve child survival and help in attaining Sustainable Development Goal targets in East African countries.

Abstract Background Despite the proportion of receiving a minimum acceptable diet (minimum meal frequency and minimum dietary diversity) is lower in east Africa, there is limited evidence on minimum acceptable diet. Therefore, this study aimed to investigate the minimum acceptable diet and associated factors among children aged 6–23 months in east Africa. Methods A secondary data analysis of the most recent Demographic and Health Survey (DHS) data of 12 east African countries was done. A total weighted sample of 34, 097 children aged 6–23 months were included. A multilevel binary logistic regression model was applied. The Intra-class Correlation Coefficient (ICC) and Median Odds Ratio (MOR) were calculated to assess the clustering effect. Besides, deviance was used for model comparison as the models are nested models. Both crude and adjusted Odds Ratio (OR) with a 95% Confidence Interval (CI) were reported as potential predictors of minimum acceptable diet feeding practice. Results The prevalence of minimum acceptable diet feeding practice among children in east Africa was 11.56%; [95%CI; 11.22%, 11.90%]. In the multilevel analysis; child age of 12–17 month (AOR = 1.33: 95%CI; 1.20, 1.48), maternal primary (AOR = 1.21: 95%CI; 1.08, 1.35), secondary (AOR = 1.63: 95%CI; 1.44, 1.86) higher (AOR = 2.97: 95%CI; 2.30, 3.38) education level, media exposure (AOR = 1.38, 95%CI; 1.26, 1.51), household wealth statues (AOR = 1.28, 95%CI; 1.15, 1.42 for middle and AOR = 1.50: 95%CI; 1.42, 1.71 foe rich), employed mother (AOR = 1.27: 95%CI; 1.17, 1.37), maternal age 25–34 (AOR = 1.20: 95%CI; 1.09, 1.32) and 35–49 (AOR = 1.22: 95%; 1.06, 1.40) years, delivery in health facility (AOR = 1.43: 95%CI; 1.29, 1.59) and high community education level (AOR = 1.05: 95%CI; 1.01, 1.17) were positively associated with minimum acceptable diet child feeding practice. Meanwhile, the use of wood (AOR = 0.72: 95%CI; 0.61, 0.86) and animal dug (AOR = 0.34: 95%CI; 0.12, 0.95) as a source of cooking fuel and being from female-headed households (AOR = 0.88: 95%CI; 0.81, 0.96) were negatively associated with minimum acceptable diet feeding practice. Conclusion Child age, mother’s educational level, source of cooking fuel, exposure to media, sex of household head, household wealth status, mother working status, age of the mother, place of delivery and community-level education were the significant determinants of minimum acceptable diet feeding practices. Therefore, designing public health interventions targeting higher-risk children such as those from the poorest household and strengthening mothers’ education on acceptable child feed practices are recommended.

Abstract Background Maternal anemia continues as a global public health concern particularly in developing countries including Ethiopia. It is associated with an increased risk of maternal death, obstetric complications, preterm birth, and low birth weight. Even though maternal anemia is the commonest problem in Ethiopia, there is limited evidence on the spatial distribution and determinants of iron supplementation. Therefore, this study aimed to investigate the spatial distribution and determinants of iron supplementation among pregnant women in Ethiopia. Method A secondary data analysis was conducted based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 7589 women was included for analysis. For the spatial analysis; ArcGIS version 10.6, and SaT Scan version 9.6 statistical software were employed to explore the spatial distribution, and to identify significant hotspot areas of iron supplementation in Ethiopia. For the determinant factors, multilevel logistic regression analysis was fitted to identify significant individual and community level determinants of iron supplementation. Deviance, Median Odds Ratio (MOR), and Intra-class Correlation Coefficient (ICC) were used for model comparison and for assessing model fitness. Variables with a p-value of less than 0.2 in the bivariable analysis were considered in the multivariable multilevel analysis. In the multivariable multilevel analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was used to declare significant determinants of iron supplementation. Results The spatial distribution of iron supplementation was significantly varied across the country with Global Moran’s index value of 0.3 (p

Abstract Background Health insurance is one of the instruments to achieve universal health coverage. However, in Ethiopia, the coverage of health insurance is very low and varies from place to place as well. Therefore, exploring the spatial distribution of health insurance is important to prioritize and design targeted intervention programs in the country. Methods A total of 16,583 reproductive age group women (15–49 years) were included in this study. The Bernoulli model was used by applying Kulldorff methods using the SaTScan software to analyse the purely spatial clusters of health insurance coverage. ArcGIS version 10.3 was used to visualize the distribution of health insurance coverage across the country. Mixed-effect logistic regression analysis was also used to identify predictors of health insurance coverage. Results Health insurance coverage among women aged 15–49 years had spatial variations across the country (Moran’s I: 0.115, p