Examination of marijuana use in this context will improve our understanding of whether marijuana use lessens the efficacy of alcohol interventions, even when delivered sequentially in stepped care. Furthermore, it will inform future intervention efforts aimed at reducing both alcohol and marijuana use.Alcohol use was assessed using the Alcohol and Drug Use Measure at baseline and each follow-up. To determine if participants who completed Step 1 of the intervention would also complete Step 2, participants reported the number of times they engaged in heavy episodic drinking , defined as consumption of 5+ drinks for males , in the past month. The maximum number of drinks consumed during their highest drinking event in the past month and the amount of time spent drinking during this episode were used to calculate the students’ estimated peak blood alcohol concentration using the Matthews and Miller equation and an average metabolism rate of 0.017 g/dL per hour.Alcohol-related consequences were assessed using the Brief Young Adult Alcohol Consequences Questionnaire , a 24-item subset of the 48-item Young Adult Alcohol Consequences Questionnaire . Dichotomous items are summed for a total number of consequences experienced in the past month. The B-YAACQ is reliable and sensitive to changes in alcohol use over time and has demonstrated high internal consistency in research with college students . In this study, the B-YYACQ demonstrated good internal consistency at baseline, 6-week and follow-up assessments .First, distributions of outcome variables were examined, and outliers falling three standard deviations above the mean were recoded to the highest non-outlying value plus one , resolving initial non-normality in outcomes. Demographic information and descriptive statistics for the outcome variables were calculated . To examine marijuana users’ drinking behavior following BA for alcohol misuse ,greenhouse benches multiple regression models were run to predict each alcohol outcome variable at the 6- week assessment from baseline marijuana user status , controlling for gender and the corresponding alcohol outcome assessed at baseline.

To test hypotheses 2 and 3, hierarchical linear models were run in the HLM 7.01 program , using full maximum likelihood estimation. HLM is ideal for data nested within participants across time, for testing between-person effects and within-person effects on outcomes. An additional advantage of HLM is its flexibility in handling missing data at the within-person level, allowing us to retain for analysis any participant that contributed at least one follow-up assessment. We interpreted models that relied on robust standard errors in the determination of effect significance. All intercepts and slopes were specified as random in order to account for individual variation in both mean levels of the outcomes and time-varying associations. Fully unconditional HLM models were run first in order to determine intraclass correlations for each outcome. ICCs provided information on the percentage of variation in each outcome at both the between- and within-person level. Next, three dummy coded time components were created for inclusion at Level 1. The first was coded and therefore allowed examination of the impact of effects on change in the outcome variable from baseline to the first followup, the second was coded to model the impact of effects on change in the outcome variable from baseline to the second follow-up , and the third was coded in order to estimate the impact of effects on change in the outcome variable from the first to the third follow-up . In the context of these three dummy codes, effects on the intercept represent effects when all time effects are equal to 0 . Of note, as all participants received a BA session in the interim between the true baseline and 6-week assessment, marijuana user status at the 6-week assessment was used as the baseline for these analyses .To address hypothesis 2 , Level 2 effects for marijuana user status, treatment condition, and the interaction between marijuana user status and treatment condition were regressed on the three time components. Following recommendations of Aiken and West , prior to forming interactions, marijuana user status and treatment condition were recoded using effects coding , to remove collinearity with interaction terms so that all main effects of time could be evaluated in the context of models including interactions. To control for potential baseline group differences, we also regressed marijuana user status and treatment condition on the intercept.

To address hypothesis 3 [i.e., whether treatment group impacts marijuana use frequency at any of the three follow-up time points, among those who reported marijuana use at 6-week pre-BMI assessment], at Level 2, treatment condition was regressed on the Level 1 intercept and all three time effects of marijuana use frequency. In models for both hypotheses 2 and 3, at Level 2, gender also was included as a covariate.The purpose of the current study was to examine whether heavy drinking marijuana users demonstrate poorer response to two different alcohol-focused interventions compared to non-users and to examine the efficacy of an alcohol-focused BMI on marijuana use frequency among marijuana users receiving stepped care for alcohol use. Our findings indicated that marijuana users and nonusers evidenced equivalent treatment responses to the alcohol-focused BA session and reported similar alcohol-related outcomes following the BMI. Consistent with prior research , the alcohol-focused BMI did not significantly reduce marijuana use frequency in comparison to the assessment-only group. In our sample, marijuana users did report higher alcohol consumption and problems at baseline/pre-BMI regardless of condition, and these differences between users and nonusers persisted over time. The findings of the current study are somewhat consistent with studies indicating that marijuana use does not decrease the efficacy of alcohol interventions . Although marijuana use did not necessarily lessen the efficacy of the BA and BMI sessions on alcohol use and consequences, regardless of condition, marijuana users reported higher levels of alcohol consumption and consequences at baseline and the pre-BMI assessment. These patterns suggest that heavy drinking marijuana users may still benefit from alcohol use interventions. This is especially noteworthy because dual users typically report increased consequences related to their alcohol use and may have a higher likelihood of being referred to alcohol-focused treatment or mandated to receive intervention for alcohol-related sanctions. Although heavy drinking marijuana users may demonstrate reductions in alcohol consequences following an alcohol-focused intervention , their frequency of marijuana use did not change as a result of receiving a BMI.

We can posit several reasons for the participants’ continued use of marijuana, despite a decrease in alcohol-related consequences. First, the parent study found a reduction in alcohol consequences following the alcohol-focused BMI, but not a decrease in alcohol consumption. Prior research examining secondary effects of alcohol BMIs have noted a decrease in marijuana use when there was also a decrease in alcohol consumption . It could be that factors that result in students’ experiencing fewer alcohol-related consequences without changing their drinking differ from ones that would lead to reductions in alcohol or marijuana use. Although our study did not include a measure of marijuana-related consequences, future research should examine changes in marijuana consequences to investigate whether changes in alcohol-related consequences correspond with changes in marijuana consequences following alcohol-focused BMIs. Second, a lack of effects may be due to the fact that our BMI was focused solely on changing alcohol-related behaviors and did not discuss the participant’s marijuana use. Future research should examine process coding in BMIs that do discuss marijuana use to explore possible in-session processes that may be related to changes in marijuana use and can be targeted in future interventions3 . Similarly, although alcohol and marijuana use share similar predictors , they may differ in their mechanisms of change. For example, the underlying motives that drive these two behaviors may vary so changing one will not ultimately lead to changes in the other and existing BMIs may not be targeting or altering both. Third, the referral incident in this study may not have been severe enough to warrant an overall re-evaluation of substance use, as may have been the case for those who required a visit to the ED as a result of their alcohol use . Marijuana users may require a more focused intervention or a supplemental session that targets alternative substance free activities to facilitate changes in marijuana use . Finally,growers equipment with growing trends in decriminalization and legalization of marijuana in the US, the perceived risk of marijuana has decreased among college students . Marijuana use may be more entrenched in the college social environment and more difficult to change without a targeted marijuana specific intervention. The results of this study should be interpreted within the context of its limitations. First, our study is restricted by our measure of marijuana use, which was limited to frequency and did not assess for marijuana-related consequences. Future studies may include assessments of quantity, days smoked, and consequences to get a better of understanding of the severity of participants’ marijuana use. Although daily marijuana use is on the rise, with almost 6% of college students reporting daily use , marijuana users in our study were using about 13.7 times in the past month. This is fairly low compared to those seeking treatment for marijuana use or being seen in an emergency department. For example, Metrik et al. found that compared to lighter users, those who reported weekly marijuana use demonstrated a significant decrease in use following treatment. Furthermore, our measure of pBAC was derived from participants’ reported heaviest drinking event and may not be the best way to capture peak BAC levels. Additionally, the study sample was predominantly white which may limit our ability to generalize findings to other populations of interest. Finally, we relied on self-reported data collection that did not include corroborating measures. Research using collateral informants indicated that mandated students may under-report alcohol use . Despite these limitations, this study adds to the existing literature on the secondary effects of alcohol-focused BMIs. To our knowledge it is the first study to examine the influence of two different alcohol interventions on marijuana use in the context of stepped care. Furthermore, findings indicate that heavy drinking college students who also use marijuana may still benefit from alcohol treatment especially in reducing their alcohol related consequences.

From a theoretical perspective, our results suggest that changing one behavior does not necessarily mean changes in another will occur, at least with respect to marijuana. However, future work should examine other health behaviors that might change as a result of reducing alcohol consequences. For example, it may be that increases in substance free activities like exercising, volunteering, or academic related behaviors occur alongside changes in alcohol-related behaviors . Future research examining marijuana focused interventions of different intensity implemented in a stepped care approach may enhance our understanding of which interventions are most effective for college students with varying levels of involvement with marijuana.Humans support the growth and maintenance of diverse sets of microbes in niches in contact with the environment including skin, lungs, mouth and gut. Studies of these microbes in the gut and oral cavity have uncovered key interactions between bacteria and human hosts in a wide variety of normal and pathological states. Many of these interactions are inferred from correlations between the composition of the microbial populations and changes in health status. For example, in gingivitis, an increase in Gram negative and anaerobic bacteria causes inflammation in the mouth. Our understanding of the basis for changes in microbial composition, and of how these changes influence human phenotypes, is still a work in progress. Clearly environmental factors and host genetic factors have important influences, perhaps best demonstrated to date by studies in the gut. Candidate gene studies have been most effective at identifying human genetic influences on the microbiome. By this approach, informed hypotheses about human genes that may conceivably influence a particular microbiological phenotype are tested with family or population-based studies to identify human variants that are statistically consistent with the hypothesis. Examples include MHC genes, SLC11A1, the MEFV gene, FUT2 gene, and loci linked to susceptibility to infectious disease. While often successful, the candidate gene approach is limited by the ability to formulate hypotheses given current knowledge. They are neither comprehensive nor sufficient to identify the entire range of human genes involved in population changes associated with complex phenotypes or with maintenance of the composition of the “normal” microbiome. In addition the significant inter-individual variation in microbiome composition often masks specific effects of human genes if insufficient numbers of individuals are studied. Moreover, the microbiome of a niche includes complex mixtures of organisms and is in part defined by interactions among its members making the identification of a “microbial phenotype” complicated.