In the scenario of no effect of marijuana use on drinking behavior the percentage of nondrinkers rises from 50% to 59% and the percentage of heavy drinkers falls from 13% to 7%. The analogous values in Jefferson High were 35% to 42% and 16% to 10%. It is notable that setting the influence effect of friends’ drinking on one’s own drinking behavior to zero reduces drinking somewhat. In Jefferson High, the number of heavy drinkers rises from 16% to 20%. For marijuana usage, very pronounced strong effects are observed for friends’ influence. Setting this influence effect to zero results in a sharp decrease in non-marijuana users from 62% to 47%, and a parallel large increase in heavy users from 19% to 32%. In Jefferson High, the analogous values were 61% to 43% and 18% to 33%. In sum, when the effect from marijuana use to cigarette use is turned off, more non-smokers and fewer heavy-smokers are expected in both schools. When the peer influence effect with regard to each substance use is turned off, fewer non-users and more heavy-users of each substance are expected in both schools. In the scenarios in which we set other parameters to zero, the simulation results indicated that the substance use distribution was not altered in either school.Overall, our findings indicate some evidence of sequential substance use, as adolescent marijuana use increased subsequent smoking and drinking behavior in our two school samples. Whereas some existing research has found evidence that marijuana use leads to use of these substances,greenhouse tables an important contribution of our study was simultaneously taking into account the substance use behavior of adolescents’ peer networks and other social processes occurring in networks.
Our findings are partially consistent with Pearson et al., who found that that marijuana users smoked cigarettes more over time. Our findings are suggestive that marijuana use increases both alcohol and cigarette use. In addition, we made a distinction between whether interdependent substance use going from marijuana to cigarettes and alcohol results in initiation, cessation, or both. We found that marijuana use resulted in drinking initiation in both samples, and smoking initiation in Sunshine High. In contrast, marijuana use decreased the likelihood of smoking cessation in Jefferson High. Previous literature suggests that alcohol use is not a prerequisite for the initiation of marijuana use and the effect of alcohol use on the onset of marijuana use has declined while that of marijuana use on the onset of alcohol use has increased since 1965, and our findings are consistent with this prior literature. Moreover, we tested cross-substance influence effects, which assessed whether the substance use behavior of one’s friends on a particular substance affected an individual’s own use of the other two substances. We found no evidence that such effects exist in our samples. We did, however, find peer influence effects for each specific substance, which is consistent with multiple past studies. Note, however, that whereas one implication is that having more friends who use marijuana, for example, results in greater marijuana use behavior on the part of the individual, another implication is that having more friends who do not use marijuana results in less marijuana use behavior. This relative symmetry of influence effects is sometimes overlooked when interpreting influence results, and our simulation results confirmed that this influence effect is in fact more likely to have a negative effect on substance use behavior. These results are similar to an earlier simulation study that found that increasing the amount of peer influence in two high schools diminished school level smoking and drinking behavior.
These results are consistent with theoretical insights from the Dynamic Social Impact Theory, which would predict that youth in friendship networks would adopt the same substance use behaviors through peer influence pathways, likely through social proximity and consolidation of youths’ attitudes and behaviors in adolescent networks. This highlights that the presumption that influence effects will always increase behavior is not necessarily accurate. In fact, we might expect that the dominant norms in a context will drive the direction of influence effects: in a school with little substance use, the greater number of non-users will push adolescents towards non-use, whereas in a school with high levels of substance adolescents are more likely pushed towards greater use. Given the complexity of our agent-based network models, we demonstrated the relative magnitude of the effects by combining a small-scale simulation with a strategy in which we constructed hypothetical models that set certain key effects to zero and simulated the networks and behaviors forward. A key finding was that in a simulated world in which one’s own marijuana use did not affect smoking or drinking behavior, there would be a notable decrease in overall levels of smoking and alcohol usage in these schools, even controlling for the complexity of these models. We also saw that marijuana use operates as a mechanism between friends’ marijuana use and one’s own smoking and drinking behavior, as adolescents’ use of marijuana is impacted by their friends’ marijuana use, and this then affects the adolescent’s level of cigarette and alcohol use. Furthermore, one of the strongest effects detected was the influence effect of friends’ marijuana usage, as this has a particularly strong relationship to adolescents’ own marijuana use. Our findings highlight the importance of understanding interdependence in the use of multiple substances in adolescence, particularly those which operate through peer influence effects within friendship networks. Another notable finding was that depressive symptoms increased smoking behavior in Jefferson High. This high school has a relatively high average level of substance use compared to Sunshine High.
Perhaps in a social milieu with a high average level of drug use, adolescents reporting higher levels of depressive symptoms may be more likely to display higher levels of cigarette smoking as compared to those who report lower level of depressive symptoms, given that past studies link depression and adolescent smoking. There are some limitations to note in this study. First, the time lags between the two sets of waves are not equal. Although it is preferable to have equal time periods, we performed a post hoc time heterogeneity test to ensure that the co-evolution of substance use behaviors and friendship networks was not significantly different across the three waves,vertical farming or two time periods. Second, our SAB model specification is data intensive and can only be estimated for the two large schools among the 16 saturated schools in Add Health which are feasible for this type of analysis. This limits generalizability and does not allow assessing why the interdependent effect from marijuana use to smoking is different across the two schools. Third, we had indirect information about marijuana use at time one, for a large percentage of the sample. Using this indirect information allowed us to avoid discarding a large amount of information at t1, however with a relatively small amount of potentially misclassified cases. Fourth, while the data are relatively old, we are aware of no evidence that the mechanisms of in person friendship formation, as captured in these Add Health network data,have changed significantly since the mid-nineties. In the current study, friendship networks were constructed through name generator items instead of real-time communication technology such as cell phone use. While future studies are needed to leverage existing technology such as cell phone usage for collecting adolescent social network data, these in person network data are likely still meaningful. Moreover, research suggests that cell phones help reinforce and reproduce existing social roles and structures rather than alter them. That said, future studies are needed to collect nationally representative contemporary data from US adolescents and investigate how the findings herein would be different if such technology was considered.Our findings have important implications for future studies. First, our findings suggest both feasibility and merit in exploring concurrent or sequential substance use behaviors across multiple time periods. Interdependence in substance use should be studied within one single model framework with multiple simultaneous on-going processes to reduce the risk of over-estimation of each process due to the auto correlation among them. Second, further explication of the interdependent effects from marijuana use to smoking and drinking is a useful direction for future research. Third, given smoking rates among adolescent youth have decreased significantly since the mid-1990s, more recent data are required to test whether our findings from these two Add Health large schools can be replicated in future research. Our findings also have practical implications for health behavior change interventions targeting adolescent substance use. Studies have found evidence of a protective effect of social network ties for adolescent substance use. Moreover, other research indicates that social networks can be leveraged for health behavior change interventions and may even be superior to non-network based interventions. Peer network based interventions targeting adolescent substance use might address the possibility that marijuana use increases alcohol and cigarette use.
One approach to do this would be to disseminate tailored messages through adolescent peer networks to modify norms favoring the concurrent use of these substances, and therefore alter peer influences condoning the use of one of these substances or the concurrent use of two these substances. Such messages could act as cues to action to halt peer influences facilitating the progression from use of one substance to using both, concurrently. Lastly, a policy-relevant implication of our finding that marijuana use appears to lead to more cigarette and alcohol use is that there may be unintended consequences for adolescent substance use from the legalization of marijuana in states. If such legalization leads to greater marijuana use among adolescents, our results suggest that more cigarette smoking and alcohol drinking behavior among adolescents might occur concurrently. This is a possibility that has received some research attention and should be given more consideration in future work.The past 50 years of research supported by the National Institute on Alcohol Abuse and Alcoholism have resulted in an accumulation of invaluable data to address the multifaceted problems surrounding underage drinking. Youth use of alcohol remains a pervasive social and public health concern in the United States and a leading cause of disability and mortality during adolescence.1,2 Alcohol use in adolescence has a distinct pattern from adult drinking, whereby adolescents may have fewer drinking occasions but consume relatively high levels per occasion, referred to as binge or heavy episodic drinking and defined as consuming four or more standard ethanol consumption units on an occasion for females and five or more for males.3-5 Highly prevalent among youth in Western countries is an intermittent pattern of heavy alcohol consumption that typically is associated with social leisure occasions on weekend nights.6 Moreover, adolescent alcohol use, along with smoking and illicit drug use, has undergone changes in prevalence and patterns in recent decades. For example, alcohol use peaked in the mid-1990s, with approximately 50% of 12th graders reporting past-month alcohol use, followed by a steady long term decline to 30% in 2018. In 2020, the downward trend reversed course, with 34% of 12th graders reporting past month alcohol use.Recent reports indicate that prevalence estimates for 2021 will need to account for impacts of the COVID-19 global pandemic on underage substance use behavior and availability.High-risk alcohol consumption patterns and associated problems alone increase risk for adverse outcomes—such as motor vehicle accidents, high-risk sexual behaviors, other illicit substance use, and mental health challenges—for adolescents who drink. These risks are further compounded by the fact that adolescence is a period of crucial brain development and maturation.Neuroimaging studies have provided clear evidence that the brain continues to develop throughout adolescence and into adulthood, and undergoes important structural and functional changes in synaptic plasticity and neural connectivity during adolescence.These changes and the enormous plasticity of the teen brain make adolescence a time of both great risk and great opportunity.This article begins with an overview of typical adolescent brain development, followed by a summary of four key themes in the current understanding of alcohol and the adolescent brain: predictors of underage drinking; consequences of alcohol on adolescent brain structure and function; moderating and confounding factors, including age of onset, sex disparities, family history, co-use of other substances, and mental health comorbidities; and reversibility of and recovery from alcohol misuse. The article concludes with a discussion of where the data lead us to reach the next milestones in NIAAA-supported research.