Does abstaining from alcohol in high school moderate intervention effects for college students? Implications for tiered intervention strategies

Abstract

Brief motivational intervention (BMI) and personalized feedback intervention (PFI) are individual-focused brief alcohol intervention approaches that have been proven efficacious for reducing alcohol use among college students and young adults. Although the efficacy of these two intervention approaches has been well established, little is known about the factors that may modify their effects on alcohol outcomes. In particular, high school drinking may be a risk factor for continued and heightened use of alcohol in college, and thus may influence the outcomes of BMI and PFI. The purpose of this study was to investigate whether high school drinking was associated with different intervention outcomes among students who received PFI compared to those who received BMI. We conducted moderation analyses examining 348 mandated students (60.1% male; 73.3% White; and 61.5% first-year student) who were randomly assigned to either a BMI or a PFI and whose alcohol consumption was assessed at 4-month and 15-month follow-ups. Results from marginalized zero-inflated Poisson models showed that high school drinking moderated the effects of PFI and BMI at the 4-month follow-up but not at the 15-month follow-up. Specifically, students who reported no drinking in their senior year of high school consumed a 49% higher mean number of drinks after receiving BMI than PFI at the 4-month follow-up. The results suggest that alcohol consumption in high school may be informative when screening and allocating students to appropriate alcohol interventions to meet their different needs.

The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations

“…There may be other packages for R implementation that can fit count regression models, which can be searched on the Comprehensive R Archive Network (CRAN, http://cran.r-project.org). Note, there are simulated or real data and annotated R codes that are publicly accessible to run the hurdle NB model (Huh et al., 2019b), ZIP model (Zhou et al., 2023b), and MZIP model (Mun, Zhou, et al., 2022; Tan et al., 2022b).…”

“…If zero inflation is identified, it needs to be accounted for in the data analysis with the aforementioned zero‐inflated (ZI), hurdle, or marginalized ZI models. When the effects on the overall mean of the entire population are of interest, such as the treatment effect on all participants, the marginalized ZI models (e.g., MZIP) can be considered, as they provide effect estimates on the overall mean, while accounting for zero inflation (Mun et al., 2023; see also Tan et al., 2022a for an application to a single sample). Therefore, MZIP would be preferred over hurdle or ZINB under typical BAI effect size estimation settings, especially when the overall intervention effect on the entire population is of interest.…”

“…ZIP model (Zhou et al, 2023b), and MZIP model (Mun, Zhou, et al, 2022;Tan et al, 2022b). programmer, PROC NLMIXED can be used to implement the marginalized ZIP .…”