Quantitative data analyses
For the analyses concerned with attrition, Random Forest (RF) algorithms were used to explore missing data patterns in the first cluster randomized to the intervention. RFs use recursive binary splitting to grow a tree on training data by segmenting the feature space into regions that minimize the classification error. RFs build de-correlated trees and average them, yielding a single consensus prediction [For the analyses concerned with secular trends, the frequency of endorsement for COVID-related items was examined in relation to the number of sexual acts during the 30 days prior to the interview, and the number of protected acts of vaginal and anal sex in the 30 days prior to the interview. We conducted a test of overdispersion of the dependent variable by evaluating the 