Community level characteristics
A total weighted 7,590 live births within five years preceding the EDHS 2016 were included in the analysis. About 6,621 (87.23%) of the study participants were from rural and 3,130 (41.23%) of the neonates were from Oromia (Table 1).
Individual level characteristics
Socioeconomic and Demographic characteristics
About 2,165 (28.53%) neonates were born from mothers aged 25-29 years. The mean age of the mothers was 29.25 (±6.8 SD) years. Of the mothers, 4,791(63.12%) of them have no formal education and 7,165 (94.4%) were married (Table 2).
Neonatal related characteristics
Of the neonates, 3,941(51.92%) were males and 3,081(40.59%) had average birth size according to their mother’s perception. About 6,378(84.09) neonates were initiated breast milk within one hour of birth.
Maternal health care services and obstetric characteristics
Among mothers 4,757(62.67%) had antenatal care visits during their last pregnancy, and 2,524 (33.25%) of the mothers were delivered at health facilities. Only 636 (8.36%) of the neonates had postnatal care checkup (Table 3).
Regional neonatal mortality rate
The NMR varies across the regions of the country. The highest and the lowest NMR were observed in Oromia (25 per 1000 life birth) and Adis Abeba (7.5 per 1000 life birth) respectively (Figure 1).
Spatial Analysis of Geographic Information System
The spatial autocorrelation analysis indicated that the spatial distribution of neonatal mortality was non-random. The Global Moran’s Index was 0.19 (P-value <0.001) indicates that there was significant clustering of neonatal mortality (Figure 2).
Hot spot (high risk) regions for neonatal mortality were observed in Amhara, west Tigray, northeast, southwest and central part of Oromia. However, Afar, Benshangul-Gumz, Gambela, DireDawa, Harari and, northeast part of Southern Nations, Nationalities and Peoples (SNNP) were identified as cold spot (low risk) regions for neonatal mortality in Ethiopia (Figure 3).
The analysis of cluster and outliers indicated that high outlier clusters and low outlier clusters had nearly equal occurrence. These high outliers were observed on Oromia, border of SNNP and Oromia, border of Benshagul-Gumiz and Oromia, border of Benshagul-Gumiz and Amhara regions. However, low outliers were found in Tigray, Amhara, Oromia, South Afar and SNNP (Figure 4).
The spatial kriging interpolation analysis was used to predicted high risk regions for neonatal mortality. Predication of high risk areas were indicated by red predictions. Southeast and northwest part of Amhara, central and northeast Oromia, Adis Abeba, Tigray and north SNNP were predicted as more risky areas compared to other regions. Whereas, Afar, Benshagul-Gumiz, west Oromia, Somali, SNNP and Gambela were predicated as having less risk for neonatal mortality (Figure 5).
Spatial sat scan analysis
Nine most likely (primary) clusters were identified and all of them were significant. The primary clusters spatial window was located in central Oromia. It was centered at 7.634301 N, 39.484474 E with 80.38 km radius, with RR of 4.61 and LLR of 21.05, at P-value < 0.001. The RR of 4.61 for clusters of spatial window means neonates within the spatial window had 4.61 times higher risk for death than neonates outside the window. In addition to this, 52 significant secondary clusters were located in Benshagul-Gumiz, west Amhara and western Oromia. It was centered at 11.340042 N, 35.12673 E with 212.45 km radius, with RR of 3.69 and LLR of 11.65, at P-value = 0.003. The RR of 3.69 for clusters spatial window means neonates within the spatial window had 3.69 times higher risk for death than neonates outside the window (Figure 6).
Multilevel logistic regression analysis
The value ICC for Null model was 0.38 which indicates 38% of the total variance in neonatal mortality in Ethiopia can be attributed to the communities in which the mothers were residing Four multi-level models (null model, model fitted with community level variables, model fitted with individual level variables and model fitted with community and individual level variables) were fitted in this study. Then, models were compared by their log likelihood and AIC. Model fitted with community and individual level variables had highest log likeli hood and lowest AIC. This model was selected for analysis.
The fixed effects (measure of association) and the random intercepts for the use of neonatal mortality are presented in Table 4. In the multi-variable multilevel logistic regression mother’s age, family size, smaller than average birth size, birth interval, sex of the neonate, breastfeeding initiation time, ANC visit, PNC visit and mode of delivery were statically significant variables for neonatal mortality.