Bayesian Spatial Analysis of Factors Influencing Neonatal Mortality and its Geographic Variation in Ethiopia

Abstract Background: Ethiopia is a Sub-Saharan country with very high neonatal mortality rates, varying across its regions. The rate of neonatal mortality reduction in Ethiopia is slow, and Ethiopia may not meet the third United Nations sustainable development target by 2030. This study aimed to investigate the spatial variations and contributing factors for neonatal mortality rates in Ethiopia.Methods: We analysed data from the 2016 Ethiopian Demographic and Health Survey, which used a two-stage cluster sampling technique with a census enumeration area as primary and households as secondary sampling units. A hierarchical Bayesian logistic regression model was fitted accounting for socio-economic, health service-related and geographic factors. Results: Higher neonatal mortality rates were observed in eastern, northeastern and southeastern Ethiopia, and the Somali region had higher risks of neonatal mortality. Neonates from dry and drought-affected areas had a higher risk of mortality compared to more humid and less drought-affected areas. Application of traditional substances on the cord increased the risk of neonatal mortality (Adjusted Odds Ratio (AOR) =2.05, 95% Confidence Interval (CI): 1.10 to 4.26) and health provider counselling on newborn dangers signs in the first two days after birth had a lower odds of neonatal mortality (AOR=0.34, 95% CI: 0.13, 0.75).Conclusions: Applying traditional substances on the umbilical cord, lack of counselling services about neonatal danger signs within the first two days of birth and residing in dry and drought-affected areas were associated with a higher risk of neonatal mortality. Policy-makers and resource administrators at different administrative levels could leverage the findings to prioritise and target areas identified with higher neonatal mortality rates.