Outcomes associated with adolescent marijuana and alcohol use among urban young adults: A prospective study
Introduction
Alcohol and marijuana are two commonly used substances during adolescence, and yet longitudinal patterns of joint use and associated outcomes have rarely been explored. Recently to identify specific subgroups of adolescent alcohol and marijuana users, a number of studies have applied person-centered methods and found four or more distinct patterns of use (e.g., Conway et al., 2013, Dierker et al., 2007, Harrington et al., 2012). For example, Moss, Chen, and Yi (2014) analyzed national data (Wave 4 only of the Add Health Survey) using latent class analysis to identify classes using retrospective reports of onset prior to age 16 of alcohol, marijuana, and cigarettes. They found non-users of alcohol and marijuana (40%), two alcohol only classes (18%), two marijuana only classes (10%), and two classes of alcohol and marijuana users (32%), emphasizing how common dual-use of marijuana and alcohol is during adolescence. Their work also points out the importance of dual-use of marijuana and alcohol as this group had the highest rate of marijuana and other illegal drug use in young adulthood.
Patton et al. (2007) in a rare longitudinal study extended our understanding of adolescent alcohol and marijuana patterning by examining use over time in an Australian cohort (ages 15–24). They classified adolescents in four categories: non-risk use, moderate-risk marijuana only, moderate-risk alcohol only and concurrent moderate-risk of both substances and found more negative outcomes in terms of education/training, relationship status, and illegal drug use associated with moderate marijuana use than with moderate-risk alcohol use compared to non-risk use. Interestingly, concurrent moderate-risk of both substances did not elevate risk over moderate marijuana use only, which is in contrast to work of others that have found worse outcomes associated with dual use of alcohol and marijuana than use of either substance alone among adults (Harrington et al., 2012, Midanik et al., 2007). For example, Shillington and Clapp (2001) interviewed college students and found poorer academic performance, greater substance use problems, and more criminal justice system involvement among dual users compared to alcohol only users. Thus, there remains much to be learned about potential adverse outcomes associated with adolescent marijuana use and drinking patterns, as most studies rely on cross-sectional patterns and have examined outcomes separately for marijuana and alcohol, limiting our understanding of outcomes associated with dual use patterns over time.
The present study utilizes a predominantly Black sample of urban youth as little is known about dual use among this population despite their at-risk status. Evidence suggests that the strength of the association between marijuana use and heavy drinking has increased in recent years among Black, but not White, adolescents (Lanza, Vasilenko, Dziak, & Butera, 2015). Previous work also shows that Blacks are disproportionately impacted by substance use, including higher rates of co-occurring alcohol and marijuana use disorders than Whites (French et al., 2002, Pacek et al., 2012). Blacks also have twice the risk of marijuana arrests than Whites despite similar rates of use (Lurigio & Loose, 2008). Importantly, Blacks experience less economic success in adulthood overall, and substance use disorders and substance use arrests may exacerbate this pattern (Alexander, Entwisle, & Olson, 2014).
This study analyzes prospective data from an urban cohort of predominantly Black Americans followed from childhood to young adulthood. We seek to determine (1) what are typical patterns of alcohol and marijuana use from grades 8–12, (2) what adult outcomes are associated with typical use patterns of adolescent marijuana and alcohol use?, (3) how do outcomes for marijuana use patterns compare to those for alcohol use patterns? We focus on educational, economic, substance use and crime outcomes as previous work examining outcomes separately for adolescent marijuana and for alcohol use have found adverse effects in these domains (Brook et al., 2013, Ellickson et al., 2003, Fergusson and Boden, 2008, Green and Ensminger, 2006, Green et al., 2010, Green et al., 2011, Hill et al., 2000, Lynskey and Hall, 2000, Sloan et al., 2001, Viner and Taylor, 2007, Wells et al., 2004).
Section snippets
Sample
The analytic sample consisted of 608 participants initially recruited in first grade for a randomized universal preventive trial, whose immediate targets were improving academic performance and preventing aggressive behavior (Ialongo et al., 1999). Nearly half were female (47.4%), and 87.2% were Black. The majority of the sample (69.7%) received free or reduced price lunch in first grade. The original sample consisted of 678 children, representative of students entering first grade in nine
Results
Fit statistics for a one through five-class model are provided in Table 1. The BIC value decreased through the addition of five latent classes, but reached an elbow at four classes, suggesting that a four-class model was appropriate for the data. Similarly, Lo–Mendell–Rubin indicated that a four-class model fit better than a five-class model. The Bootstrapped Loglikehood Ratio Test did favor a larger model; however, the addition of a fifth class did not prove meaningful and resulted in a small
Discussion
In this study, we aimed to elucidate and compare young adult outcomes associated with adolescent drinking and marijuana use patterns in a sample of predominantly urban African American participants. It is important to point out up front that findings point to associations not effects due to the observational nature of the study. Despite this limitation, this study has specific advantages over previous work. First, to identify patterns over time, we draw on prospectively gathered substance use
Role of funding sources
Funding for this secondary data analysis was provided by National Institute of Drug Abuse (NIDA) Grant DA032550. The original data collection was funded by DA11796 and MH57005. Dr. Johnson's work was supported by NIDA grant K01-DA031738. Dr. Matson's work was supported by K01-DA035387. NIDA and NIMH had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
Contributors
Drs. Green and Ialongo designed this specific study. Dr. Ialongo oversaw the data collection. Drs. Musci and Ialongo conducted the statistical analyses and wrote the first draft of the methods and results. Dr. Green wrote the introduction and discussion. All authors contributed to data interpretation, and reviewed and approved the final manuscript.
Conflict of interest
None.
Acknowledgments
The authors wish to thank the Society for Prevention Research for allowing us to present this work to our peers and the participants in Baltimore for their willingness and involvement over many years.
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2022, Addictive Behaviors ReportsCitation Excerpt :To answer the second research question, we selected several potentially salient correlates based on findings from past research. Specifically, we examined factors that have been found by past research to be correlated to SAM use among young adults/adolescents: gender (males might have higher risks than females; example OR from prior studies = 1.31–1.57), race/ethnicity (Black/AA and Hispanic Americans might have higher risks than White Americans; example ORBlack/AA = 0.42–0.44, ORHispanic = 0.59–0.66) (Patrick et al., 2019; Terry-McElrath et al., 2013), religiosity (lower religiosity might be associated with higher risks; example ORmedium religiosity = 1.56, ORlow religiosity = 1.58 compared to high religiosity) (Terry-McElrath et al., 2013), delinquency (higher delinquency-higher risk; example OR = 1.4) (Brière et al., 2011), other substance use (higher risk; example OR = 1.9–4.5) (Brière et al., 2011; Patrick et al., 2017), criminal justice involvement and substance use disorders (higher risk) (Green et al., 2016). We also explored other factors that have been commonly found to be associated with substance use among young adults/adolescents as potential correlates of SAM use, such as age, household income, psychological distress, risk propensity, substance accessibility, and risk perception (Cohn et al., 2018; Hai, 2018; Vaughn et al., 2016).