Using all available EHR AUDIT-C measurements, we created 3 AUDIT-C metrics: 1) a cross-sectional score using the value closest to the date of blood sampling; and two longitudinal measures: 2) the highest AUDIT-C value over the period 2007-2016; and 3) the trajectory of AUDIT-C scores. We differentiated the closest and highest AUDIT-C scores into categories: 0, 1, 2-3, and 4+; the highest category is based on prior work showing AUDIT-C 4+ as maximizing the sensitivity and specificity for identifying unhealthy alcohol use in men (Bradley et al., 2007, Bush et al., 1998b). As previously reported (Marshall et al., 2015), joint trajectory modeling sorts each participant's AUDIT-C values into “clusters” and estimates distinct trajectories. We used age as the time scale to account for decreased alcohol use with age. The procedure calculates each individual's probability of belonging to each trajectory and assigns the individual to the trajectory with the highest probability of membership. We used a zero-inflated Poisson model (Jones et al., 2001) and evaluated 3-, 4- and 5-group models. For maximum precision, trajectories were developed in the full VACS sample.
