Statistical analyses
SECONDARY
Applying a simulation-based power analysis using generalized linear mixed effect model, we estimated that a study with at least 10 000 participants would have 80% power to detect an estimated 10% difference in the primary outcome between groups with a two-sided α of 0.05 and an intra-cluster correlation coefficient of 0.01 (Table S1 in the We descriptively compared baseline characteristics between groups to check that randomization produced broadly similar groups. We conducted a single imputation by worst observation carried forward for missing values of binary outcome measures to maintain a conservative approach to the analysis. We conducted the primary and secondary outcome analyses with generalized linear mixed models assuming logit link and binomial distribution for binary outcomes, and cumulative logit link and multinomial distribution for multinomial and ordinal outcomes. The fixed effects were study group (intervention/control) and municipality location (urban/rural). We assumed clustering at the municipality level within the province in a fully nested framework and set municipalities to the random effect. We expressed effectiveness estimates as odds ratios (ORs) with 95% confidence intervals (CI) and reported using the Consolidated Standards of Reporting Trials (CONSORT) statement extended for cluster randomized controlled trials [