Data analysis
REGRESSION
All the data were collected using computer-assisted personal interview (CAPI) and extracted into Stata version 13 for analysis. The characteristics of the respondents for each arm were described using summary statistics, frequency, and percentage as applicable. The difference between sites was examined using a t-test or the non-parametric alternative for continuous variables, and the chi-square test of independence for categorical variables. The outcome variable was a count variable. Thus, the count of reporting the ten indicators was estimated using Poisson regression. The vce (robust) command in Stata was used to adjust for mild violation of the Poisson regression assumptions. Two models (unadjusted and adjusted) were estimated to compare the outcome between sites at baseline and intervention periods and within sites between the two periods. Individual-level factors that were significant at the bivariate level were added as control variables in the adjusted models. Hospital-level variable such as the provider who attended to the woman during labour and childbirth was not significantly different between the sites and period. Further analysis using logistic regression models was conducted for each of the ten QIs as an outcome and site as the independent variable in unadjusted and adjusted models for baseline and intervention periods. The overall average effect of the intervention was estimated with propensity score matching. All the regression analyses were two-tailed, and statistical significance was set at p < = 0.05.