Modelling The Risk Factors Of Neonatal Mortality Using Survival Analysis

Abstract

Several strategies have been put in place in an attempt to reduce childhood mortality in Ghana, however the proportions of death among neonates are still quite high. The study therefore seeks to model neonatal mortality using survival analysis approach. The data used for the study was obtained from the neonate’s folders at St. Jude Hospital in Obuasi in the Ashanti Region between January 1, 2012 and December 31, 2015. Data on maternal characteristics was also obtained. Neonates who were born before the 28th day and those who have experienced the event (death) were considered for the study. The study employed the Kaplan Meier (K-M) and Log rank test for the descriptive analysis. The Cox PH and Parametric models (Exponential, Weibull, Gompertz, Log-logistic and Lognormal) were fitted to the neonatal data and their results were compared using the AIC to determine the best model to explain survival of neonates. A semi parametric shared frailty model was also fitted to the data to examine whether there are unobserved heterogeneity among neonates at the community level. The Proportional Hazards assumption was checked using both graphical methods and the PH assumption test based on the Schoenfeld residual and was observed that the PH assumption was not violated. Results from the study showed that hazard ratios for the PH models (Cox, Exponential, Weibull and Gompertz) were similar, however comparison of the PH models using the AIC showed that the Gompertz PH model best fit the data. A comparison of AFT models (Weibull, Exponential, Lognormal, Gompertz, and Log logistic) also showed that the Lognormal AFT fit the data best. A comparison of the best PH (Gompertz PH) and AFT (Lognormal AFT) model using the AIC showed that the Gompertz PH is the best model in predicting neonatal survival. Parity, Apgar score 1, birth weight and place of residence were significantly related with neonatal mortality. A comparison of the shared frailty models (Cox, Exponential, Weibull, Gompertz, Lognormal and Log-logistics) using AIC revealed that exponential distribution with Gamma frailty is the best model for checking the unobserved heterogeneity in the data. Unobserved heterogeneity in categories of neonates based on place of residence was found.