Statistical analyses
REGRESSION
In order to describe the study population and examine the differences between the control and intervention groups, the frequency and percentage of hazardous alcohol use among employees were calculated using Pearson’s chi-square test. The mean value and standard deviation for alcohol use and policy awareness were calculated using independent t-test. The differences between employees in the control and intervention groups regarding the sociodemographic variables were only observed at baseline.Because a large proportion of data was missing due to loss to follow-up, and the outcome measure was based on two-time points, imputation of missing values was considered problematic. Thus, all analyses followed a complete case analysis approach. Logistic regression analyses were performed to assess hazardous alcohol use among employees, with abstainers and individuals with low-risk alcohol use serving as the reference group. Since employees were clustered within the organizations, multilevel modeling was applied to account for both fixed and random effects. Moreover, a multiplicative interaction term of First, changes in hazardous alcohol use were estimated using group (control vs. intervention), time (baseline vs. follow-up), and the multiplicative interaction term (group × time) as the main predictors (Model 1). Next, this model was adjusted for sex, age, and education level (Model 2). Finally, this model was adjusted for awareness of alcohol policy (Model 3) to explore any potential mediating effect. In all the adjusted models, the inclusion of covariates accounted for changes in those covariates between baseline to follow-up. All results were presented as odds ratios (OR) with 95% confidence intervals (CI). An alpha level of p < 0.05 was considered statistically significant.