Abstract

Background Globally, low birth weight is one of the neonatal problems, which account approximately 80% of all newborn deaths, ranges from 68 deaths per 1000 live births to 83 deaths per 1000 live births. The problem is especially high in developing countries including Ethiopia. To tackle this critical neonatal problem, identifying the hazard time to death and its predictors is crucial. Therefore, this study aimed to investigate time to death and predictors of mortality among low-birth-weight neonates admitted at Addis Ababa governmental hospitals in Ethiopia, 2021. Methods A retrospective cohort study was employed among 329 neonates with LBW admitted in neonatal intensive care unit of selected four governmental hospitals in Addis Ababa, between January 2018 and January 2021. Epi-data version 7.2.2.6 and SPSS version 25 was used to data entry and analysis respectively. Kaplan Meier survival curve was used to estimate the cumulative survival time. A log rank test was used to compare the probability of hazard among variables. Bi-variable and multivariable cox proportional hazards models were used to identify predictors and variables having p value < 0.05 were considered as statistically significant. Results were presented using text, figure and tables. Result In this study 105 (31.9%) neonates were died with incidence rate of 40.1 (95%CI: (95%CI: 33.24, 48.74) per 1000-person day. Extreme very low birth weight (AHR: 2.14; 95%CI: 1.06, 4.30), apnea (AHR: 3.04; 95%CI: 1.87, 4.94), asphyxia (AHR: 2.34; 95%CI: 1.04, 5.26), unable to get only breast-feeding (AHR: 8.74; 95%CI: (5.26-14.57) and lack of kangaroo mother care (AHR: 8.86; 95%CI: 1.15, 68.52) are significant predictors of time to death. Conclusion and Recommendations The incidence of death was higher compared to other previous studies with median survival time of 18days. Being extremely very low-birth-weight, apnea, asphyxia, kangaroo mother care and exclusive breast feeding were found to be a predictors for time to death. Therefore, intervention on the identified predictors could be so important to improve the survival status of low-birth-weight neonates.

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