Data were entered into Epi-Data version 3.5 and exported to STATA version 14 and SPSS version 24 for further analysis. Descriptive analysis such as frequency and percentage was carried out. We used ordinal logistic regression analysis to predict factors associated with the outcome variable. The outcome variables were in 4 scales based on disease severity (well, mildly distressed, moderately distressed, and severely distressed). Each independent variable was fitted in the model separately and check for the assumptions of ordinal logistic regression. We excluded those independent variables with empty cells and those with small in frequency from the cross-tabulation. Then those that passed the assumptions of the model were fitted altogether to predict the outcome variables. The assumptions for ordinal regression which assuming that the effect of the independent variables is the same for each level of the outcome variables was checked via test of parallel lines. Values for the final model assumptions were as follows; model fitting information (Chi-square = 155.691, P-value < .001), goodness of fit (Pearson Chi-square = 1834.933, P-value = .170 and Deviance Chi-square = 1478.908, P-value = 1.000), and test of parallel lines (Chi-square = 147.372, P-value = .203).

Finally, the results were presented using parameter estimates (β) and adjusted proportional odds ratio (AOR). The statistical significance was declared at P-value less than .05 with 95% confidence interval (CI).

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