Statistical
REGRESSION
All variables were described by frequency (%), with missing data, for categorical variables, and mean (SD) or median (Q1-Q3), with missing data, for quantitative variables. The effectiveness of the intervention was measured comparing the delta of before-after participation rate in the intervention arm and the control arm. The primary outcome (the difference between before/after participation rates) was analyzed by logistic regression analysis methods taking into account the interactions between the period and area. Completion time of the screening test between intervention areas and control areas before and after the intervention was also compared. In order to address cluster design issues, the influence of other collected variables was tested using univariate tests (Chi2 or Fisher test for categorical variables and Student or Wilcoxon test for quantitative variables). A multivariate model was implemented in order to take possible confounding factors into account. Analysis of delay to participation was performed with an ANOVA model (on the sub-group of participating patients). A priori strata analyses were performed for the same outcomes, with the same statistical methods, inside each stata.All analyses were carried out using SAS version 9.3 and R software (version 3.2.2).