Data analysis

The SPSS version 26 was used to analyse the data. Simple frequency and means (standard deviation) were used to describe the variables. For binary analysis, Pearson’s chi-square or Fisher’s texts, where appropriate, were used. Spearman’s correlation was used to test the association between the dependent variables because of skewness. Binary logistic regression was used at multivariate analysis to control for confounders. Before then, multicollinearity between the independent variable was tested with the variance inflation factor (VIF). No evidence of collinearity was found as all the VIF were < 2.0. Variables with a p-value of ≤ 0.1 at bivariate analysis were considered for multivariate analysis, while regression analysis was computed at a 95% CI. However, some potential confounders were forced into a model, if necessary, for statistical relevance as suggested by Bursac et al.²⁷ Hosmer-Lemeshow test was used to assess the fitness of the models. Nagelkerke R² was deployed to measure the level of variance explained by the independent variables. Later, AUC was used to assess the predictability power of the dependent variables.