Modeling Time To Neonatal Mortality at Wallaga University Comprehensive Specialized Hospital: Application of Bayesian Survival Model with INLA

Abstract

Background: The neonatal period is the most vulnerable time for survival in which children face the highest risk of dying in their lives. Although, better progress has been made in reducing Neonatal Mortality before 2016, Ethiopia is currently one of the top ten countries affected by NM. Therefore, this study aims to utilize Bayesian Survival Models to analyze and model the time to neonatal mortality at WUCSH. Methods: A retrospective study of was conducted among 343 neonates admitted to WUCSHfromJanuary 1, 2022 to December 30, 2023. A Bayesian survival model with INLA was used to identify the risk factors associated with time to neonatal mortality. Results: Among the 343 neonates admitted to WUCSH, 187 (54.52%) were male, and more male were died, that means 61 (17.78%). The variable residence (p = 0.0220), gestational age (p = 0.0355), neonate age (p = 0.0048), and the global test (p = 0.0042) in multivariate Cox-PH were shows a statistically significant violation of the proportional hazards assumption. In the Bayesian Log-logistic AFT model, rural residence with AFT factor ˆγ= 0.573 (-0.975,-0.137), had significantly shorter survival time. Conversely, being married was associated with longer neonatal survival with AFTfactor ˆγ = 1.817(0.078, 1.117). Conclusion: In conclusion, the findings of this study shows that residence, neonate sex, gestational age, marital status, age of neonate and birth weight are the most determinant and statistically associated with time to neonatal mortality. It is therefore awareness should be raised about the burden of these risk factors contributing to neonatal mortality