Statistical methods
HIV prevalent infection
REGRESSION
The analyses presented include data from participants in the population cohort enrolled at the start of the trial (PC0). HIV incidence in each community was assessed based on HIV testing at PC12, PC24 and PC36 among participants who were enrolled without HIV. The analyses did not account for missing data due to loss‐to‐follow‐up. Person‐years (PYs) were calculated using the time between visit dates. The midpoint between the last visit without HIV and the first visit with HIV was used as the estimated time of acquisition for seroconversion cases; the time of the first visit with HIV was used as the time of acquisition for cases with acute HIV. Community incidence rates were estimated as the number of new acquisitions divided by the total person time in each community across the entire trial (PC0–PC36).HIV viral load was measured for all participants with HIV at the PC24 visit; at the PC0, PC12 and PC36 visits, viral load was measured for all seroconverters and ∼75 participants with HIV prevalent infection (non‐seroconverters) selected at random from each community. All participants with HIV were tested at PC24 because it was the midway point of the primary analysis period (PC12–PC36) and ∼75 were chosen at random for the other visits due to budgetary constraints. Inverse probability weighting (accounting for these selection probabilities) within each community was used to estimate community PDV at each visit.Community‐level models used a two‐stage analysis approach [Sexual risk covariates were assessed “cross‐sex”; that is male (female) community characteristics were assessed as predictors of female (male) HIV risk in this predominantly heterosexual epidemic.For the analyses of PDV, we used a log‐log linear model, as follows:
Individual‐level models estimated incident rates and RRs using Poisson regression, overall and in males and females separately. We accounted for clustering by using an indicator for community, making the associations specific to the communities under study. These multivariable models used a hierarchical model selection method [