Epidata version 4

The quantitative data were checked for completeness, edited, coded, and entered into Epi-data version 4.6 and exported to STATA version 14 software for analysis. Descriptive statistics were computed and results were presented using tables and narrations. A binary logistic regression model was fitted to identify factors associated with the acceptability of services, and in the final model, a p-value of <0.05 and adjusted odds ratio (AOR) with 95% confidence intervals (CI) were used. Qualitative data were gathered, audio records of key informant interviews were transcribed into the text format of the local language, translated into English, and then coded and categorized by using a thematic analysis approach under the dimensions that supplemented the quantitative findings. Weight for the dimensions and the respective indicators were given depending on their level of relevance to the program. Finally, the dimensions were judged based on the pre-determined judgmental criteria.

Data were entered using Epi-Data version 4.6 and then exported to Stata version 14 for further data cleaning and analysis. Descriptive statistics like mean (standard deviation) or median (inter-quartile range) for continuous variables and frequencies (%) for categorical variables were used to describe the population. Cumulative incidence was computed by the number of new cases (CVD) per total initial population and incidence rate was calculated as the number of new cases per by PY at risk. Bivariable analysis with a p-value less than 0.2 were included in the multivariable regression. The Kaplan-Meier procedure was used to estimate the survival curves and Log rank test to compare survival time between groups of categorical variables. Schoenfeld residuals method was used to assess proportional hazard assumptions (PHA). The parsimonious survival model was selected based on Akaike Information Criterion (AIC) and goodness of fitness of the model was checked using Cox Snell residual plot. To deal with missing data, we compared the model for multiple imputed data with complete case analysis and the model with the lowest AIC was used for analysis. Finally, Weibull regression analysis was used to calculate the hazard ratios and variables with a significance level of <0.05 were used to identify the predictors of CVD.

Data was checked for completeness, entered into Epi-data version 4.6, and
exported to STATA version.14 for analysis. Descriptive statistics, frequencies,
and percentages were computed and presented using graphs and tables. A
bi-variable and multi-variable logistic regression model was used to assess
factors associated with adverse reaction of COVID-19 vaccination. Variables with
a p-value less than or equal to 0.2 was included to
multi-variable logistic regression. Multicollinearity was checked among
independent variables using a variance inflation factor and model fitness was
checked using Hosmer-Lemeshow goodness of fit test. Finally, the results were
reported as adjusted odds ratio (AOR) with their 95% CIs.