Statistical analysis

All data were analysed using SPSS 22.0 for windows (SPSS Inc., Australia). Quantitative variables were intimated as mean ± SD, while qualitative data were denoted as number of observations with percentages. Comparisons of categorical variables were performed by using χ² test or Fisher’s exact test, where appropriate. To evaluate the strength of association between 2/2 and greater than 2/2 variables, Phi coefficient and Cramer’s V were used. The association was considered strong, moderate or weak, if Phi coefficient and Cramer’s V values were (0.5–1), (0.3–0.49) and (0.1–0.29), respectively (Mallhi et al., 2015). For the comparison of continuous variable, student-t test was performed. A logistic regression model was used to determine risk factors independently associated with MDR- and XDR-TB. Variables with p value < 0.05 in univariate analysis were subjected to multivariate logistic regression. Adjusted OR and 95% CI were calculated. Receiver operating curve (ROC) analyses was performed to observe how the predicted model can distinguish between true positives and negatives. To assess the calibration of final multivariable logistic regression model, hosmer-lameshow test was probed. A double sided p-value < 0.05 was used to consider variables as statistically significant.