 Regression analyses were employed to address the effect of potential explanatory factors for outcomes. When conducting the regression analysis, method of Last Observation Carried Forward LOCF was employed to deal with missing outcomes at follow-up reviews. Linear regression was adopted when the dependent variables were continuous and residuals of the regression followed a normal distribution. Negative binomial regression was adopted when the continuous dependent variables had over-dispersed count data. Logistic regression was adopted when the dependent variables were binary. Since the effect of intervention on the outcomes was of our core interest, the model fitting adopted a forward-wald method with the factor of intervention fixed in the model and one of the other explanatory factors added into the model each time. Each time the coefficient of the independent variables with the smallest significant p value significance level p0.05 was entered into the regression model. The model fitting process was repeated again until no more independent variable had significant p values. 