Data were entered into EpidataTM version 4.2 and analyzed using STATA™ version 14.0 statistical software. At the end of the data collection period, the outcome of each study subject was dichotomized into censoring or event (Jaundice). In univariate analysis, we used to mean with standard deviations to describe normally distributed continuous data and median with interquartile range for skewed continuous data. The categorical data of the neonates were described using frequencies or percentages. Moreover, to identify the predictor variables, a cox-proportional hazard regression model was fitted. The Kaplan-Meier survival plot was used to estimate the survival time of their NICU admission to discharge and/or developing jaundice. A Generalized Log rank test was used to compare the survival curves between the categorical variables. The necessary assumption of the Cox-proportional hazard model was assessed using the Schoenfeld residual test and log-log plot and all variables fulfilled the assumption (the graphs did not cross or overlap each other). Regarding bivariable analysis, the outcome variable (Jaundice) and explanatory variables were entered into the Cox-proportional hazard regression model to select important variables for the multivariable Cox-proportional hazard regression model. As a result, variables having a “p-value” ≤ of 0.25 in the bi-variable analysis were fitted into the multivariable cox-proportion regression model.40 In the multivariable Cox-proportional hazard regression model, variables with “p-values” <0.05 were considered as statistically significant predictors. Adjusted relative risk with their 95% confidence intervals and p-values was used to measure the strength of association and identify statistically significant predictors of neonatal jaundice.

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