Epi info

STROBE checklist was used to analyze and report data [17 (link)]. Data were checked, coded, and entered into EpiInfo and then, exported to SPSS version 21 for analysis. Both descriptive and analytical statistical procedures were utilized. Descriptive statistics like percentage, mean, median, and standard deviation were used for the presentation of socio-demographic data and the prevalence of early discontinuation of Implanon. Binary logistic regressions were used to identify factors associated with early discontinuation of Implanon. Variables with a p-value < 0.25 on the bi-variable analysis were entered into multiple logistic regression models to identify their independent effects on the outcome variable. Model fitness was checked using the Hosmer and Lemeshow goodness of fit test. All the assumptions like the normality of continuous variables and multicollinearity of independent variables were checked to be satisfied using Variance Inflation Factors (VIF). Multiple logistic regression models were used to control the possible effect of confounders. Variables that have an independent association with early discontinuation of Implanon were identified based on OR with its 95% CI and p-value less than 0.05 were used to declare the level of significance.

Data of the two groups were collected and tabulated for statistical analysis by PC using the Epi Info and SPSS version 26 for windows software packages [11 ]. Both the homogeneity test and the Leven test were used. Descriptive analysis as the arithmetic mean and standard deviation of the age and scores of examinations of the participated HCWs were calculated. Student’s t test was used for comparing the continuous parametric data of the two groups. Z-test and chi-square were used for in-between proportions. Correlations and multiple linear regressions were used for estimating the relation between post-COVID-19 and various cognitive scores (Rosner, 2015). All statistical tests were interpreted in a two-tailed fashion at a cut-off value equal to 0.05.

The data were checked for completeness, coded, and entered into the Epi info software, and exported to SPSS version 20 for analysis. The descriptive analysis was done, and the results were presented using texts, frequency tables, figures, and mean with standard deviation.
A binary logistic regression analysis was done to assess the association between the outcome variable with each explanatory variable. Independent variables with a p value less than 0.25 in the bivariable logistic regression analysis were considered for the final model. Correlation between independent variables was assessed but we did not find any correlation. Finally, multivariable logistic regression analysis was done to control potential confounders and to identify the factors associated with the outcome variable. A statistical significance level was declared at a p value of less than 0.05.

The collected data were entered to Epi Info then exported to window based Statistical Package for Social Sciences (SPSS) version 21.0. Data were cleaned for inconsistencies and missing values and amendment was considered as needed. Simple frequencies were run to see the overall distribution of the study subject with the variables under study. Bivariate analysis was used to determine the association between different factors and the outcome variable.
Multivariable logistic regression was used to identify the relative importance of each independent variable to the outcome variable by controlling for the effects of other variables. Variables with p-value of 0.2 or less on bivariate analysis were included in the multivariable analysis. Multi-collinearity was checked using the Variance Inflation Factor (VIF), and those with VIF greater than 10 were excluded from the model. Participants’ age, their index child’s age, family size, and total number of births were treated as continuous variable. Family size was correlated with total number of births, and excluded from the multivariable model. The association between modern contraceptive use and independent variables was determined using Odds Ratio (OR) with 95% Confidence Interval (CI). The level of significance was taken at α = 0.05.