Spss window version 21

The data were exported to SPSS Window version 21. Descriptive statistics were used to describe the study population in relation to relevant variables. To identify predictors of each D&A categories, bivariate logistic regressions with each potential covariate were conducted, and variables that have p-value of <0.2 were included in the final multivariable binary logistic regression models. Then, four multivariable logistic regression models (one model for each category of D&A, except for physical abuse, non-consented care, and detention), with a 95% confidence interval were fitted. Physical abuse and non-consented care were reported by too many participants, whereas the number of women who reported detention was too small. As a result, these three categories were excluded as we could not perform further statistical analyses. Adjusted odds ratios and their 95% confidence intervals were computed and statistical significance was declared at p-value of <0.05.

Data were checked for completeness and entered in to Epi data version 3.1 and SPSS window version 21 was used for analysis. Descriptive analysis was done and the results were presented in the form of narrative and table. bivariable and multivariable logistic regression was done to identify factors associated with birth preparedness and complication readiness. All variables with p-value less than 0.25 in bivariable logistic regression model were entered to a multivariable logistic regression model for controlling possible confounding and odds ratios and their 95% CIs were computed.

Data was coded, cleaned, recoded and entered in to epi-data version 3.1 and transported to SPSS window version 21 for analysis. Simple descriptive summary statistics was done. Table and statement was used to present the result of the data. Association between independent and dependent variables was analyzed first using bivariate logistic regression analysis. All variables with p-value less than 0.25 in bivariate logistic regression were entered to multivariable logistic regression for controlling possible confoundering and variable with p-value less than or equals to 0.05 considered as statistically significant.