Epidata version 3

After the data were checked for its consistency and completeness, it was entered into EpiData version 3.1 exported to STATA version 14 for cleaning and analysis. Descriptive statistics like mean, median, proportions were carried out to summarize baseline socio-demographic and clinical characteristics. A p-value of less than 0.2 was used to select candidate variables for multivariable analysis. A binary logistic regression model was fitted to identify factors associated with wasting. Adjusted odds ratio with 95% confidence interval (CI) was calculated and variables with a p-value less than 0.05 in the multi-variable analysis were considered to declare factors associated with wasting. We used the Kaplan-Meier survival estimation curve and log-rank tests to compare the difference in LOS as well as mortality between normal and wasted patients and among normal, moderately wasted, and severely wasted patients.

The Data was entered using Epi-data version 3.1 and exported to Stata 14 and R 3.5.3 software for analysis. Descriptive statistics including proportions, median, tables, and charts was done to describe the characteristics of the study participants. Nonparametric estimation of cumulative incidence function (CIF) was done both graphically and using Gray’s test. After fitting the model, the proportional sub distribution hazard assumption was also checked by using the plot of log (- log (1-CIF)) versus the log of time to failure for each covariate, by interacting each covariate with time and using Schoenfeld residual test. Bivariable competing regression analysis was fitted to identify factors associated with LTFU. Those variables with a p-value of <0.2 in the bivariable analysis were again fitted to the multivariable competing risk regression analysis. Both crude and adjusted sub distribution hazard ratio with the corresponding 95% Confidence Interval (CI) was calculated to show the strength of association. In multivariable analysis, variables with a P-value of <0.05 were considered statistically significant.

Data were entered into Epi Data version 3.1 and exported to the Stata version 14 software for further analysis. Missing values, outliers, and other inconsistencies were avoided after organizing and exporting data into Stata version 14, and cleaning was done to avoid missing values, outliers, and other discrepancies. Frequency, sorting, and a list were used to clean up the data. Descriptive statistics, including frequencies, means, and proportions were computed and visualized using tables, graphs, and diagrams to describe the data.
To identify factors associated with the attitude of mental healthcare providers toward Tele-psychiatry services, ordinal logistic regression was a reasonable approach to use our data given that all the dependent variables are ordered categorically [33 (link)]. However, it’s founded that the proportional odds assumption was violated for all three categories of dependent variables in the preliminary analysis. Therefore, multinomial logistic regression analysis was utilized. Variables were declared statistically and significantly associated with dependent variables at p < 0.05. Moreover, the strength of association between factors and the dependent variables was determined using an Adjusted Odds Ratio (AOR) with a 95% confidence level.

The survey data were entered into Epidata version 3.1 and analyzed by STATA 14 software. Descriptive statistics were used to describe the study population in relation to different variables, and it is presented using texts, graphs, and tables. The chi-square assumption was checked for all categorical independent variables. A binary logistic regression model was used to identify factors associated with elevated blood sugar. Both bi-variable and multivariable logistic regression models were carried out. Variables with a p-value of less than 0.2 in the bi-variable analysis were entered into the multivariable analysis. Both Crude Odds Ratio (COR) and AOR with a 95% confidence interval were estimated to show the strength of associations. Finally, p-value < 0.05 in the multivariable logistic regression analysis was used to declare a statistically significant association. Hosmer and Lemeshow goodness of fit test was used to check the goodness of fit of the model. All methods were performed in accordance with the relevant guidelines and regulations.