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
