The third recommendation was to improve surveillance capacities to ensure that sufficient population-based data are available to query the health effects of cannabis. Finally, the fourth recommendation addressed the barriers that impede research on cannabis and cannabinoids. This recommendation highlighted significant obstacles for both preclinical and clinical researchers who are interested in studying cannabis and cannabinoids, but are hindered due to regulatory barriers associated with the Schedule I classification of these drugs and the lack of funding opportunities to support research, which needs to be done to address both the therapeutic and adverse effects of cannabis and cannabinoids. Many important issues were included under the prioritized research areas, including addressing understudied health endpoints like epilepsy, PTSD symptoms, child and adult cancers, and the effects of cannabis and cannabinoids in under-researched and at-risk populations. An area that I would like to see added to the research agenda is long-term randomized, double-blind, placebo-controlled studies of cannabis and cannabinoids for their clinical utility. These studies should evaluate safety and tolerability, address potential issues of tolerance to their therapeutic effects, and systematically evaluate adverse effects, including abuse liability and psychomotor and cognitive function. Additionally, the clinical efficacy of cannabis and cannabinoids should be compared with other established pharmacotherapies for a therapeutic endpoint of interest for example opioids for pain. Another understudied area of research that needs to be addressed is the effect of lesser-studied phytocannabinoids,rolling benches canada including tetrahydrocannabinolicacid, cannabigerol, and tetrahydrocannabivarin, with hypothesized therapeutic utility.
I would add that longer term clinical trials on cannabis, variously administered, both botanical and pure compounds, to understand the long-term benefits or toxicities of medicinal cannabis. More studies are needed on potential salutary or problematic combinations of cannabis and available drugs for various conditions. For example, is there a true ‘‘opioid-sparing effect’’ in pain management? That would be a major public health benefit. Is to possible that cannabinoids like cannabidiol are good antianxiety, antipsychotic, and antiepileptic drugs that reduce or even eliminate need for more toxic agents? We need enhanced preclinical and translational research on physiologic and pathophysiologic alterations in the endocannabinoid system which may point to the development of novel therapeutic agents. This is especially relevant for neuropsychiatry which has been basically frozen in paradigms that seek to modify monoamine physiology. Lastly, we need better outcome research that moves beyond cross-sectional association to longitudinal analysis of possible true causal–consequent links between cannabis and outcomes, such as motor vehicle accidents. At present, the suggestion that cannabis policies have led to more accidents rests on an association that cannabis is more frequently detected now in motor vehicle accident actors, but without demonstrating a significant and sustained rise in accidents in jurisdictions that have legalized medical or recreational use. The association might simply mean that more drivers are using cannabis or more are being tested, which would mean more drivers are indeed cannabis positive without necessarily establishing a causal link.Marijuana is the most widely used illicit substance worldwide . In 2010, more US high school students used marijuana in the prior 30 days than tobacco . Co-use with tobacco is of increasing interest . Smoking marijuana with tobacco, either in a tobacco leaf or mixed with tobacco, is an increasingly common practice among adolescents thought by some users to prolong the effects and/or increase the high from marijuana .
A recent national online, anonymous survey of young smokers reported that roughly half also smoked marijuana in the past 30 days . Co-use of marijuana and tobacco may contribute to the development of nicotine dependence and thus, is an important area of research for the investigation. Adult co-users of tobacco and marijuana have an increased risk of developing nicotine dependence and have worse tobacco cessation outcomes . While overall rates of tobacco use and co-use with marijuana are lower in adolescents compared with adults , most addicted adults develop nicotine dependence during adolescence. Therefore, adolescence is a critical period to study the effects of marijuana on tobacco.Although the transition from experimentation with tobacco to addiction is likely multi-factorial, marijuana use may play a role for some adolescents and has been identified as a risk for nicotine addiction in a study of young adults . Possible mechanisms of action include common routes of administration ; hence, one behavior may reinforce the other. Furthermore, both nicotine and cannabis affect similar pathways within the mesolimbic addiction pathways, suggesting similar and overlapping mechanisms for addiction . Finally, smoking cues are also similar between the two substances, which may contribute to the poorer tobacco cessation outcomes observed in adult co-users of marijuana . Despite the increasing prevalence of marijuana use in adolescents, particularly among smokers, and evidence of harm from marijuana-tobacco co-use in adults, little is known about the interaction between marijuana and tobacco in adolescents. The goal of this study was to examine the severity of nicotine addiction among teen smokers as a function of co-occurring marijuana use. Given the literature on adult smokers, we hypothesized marijuana would contribute to symptoms of nicotine dependence among adolescents. Marijuana smoking was prevalent in this adolescent sample of tobacco smokers: 80% reported past month marijuana use and more than a third smoked marijuana daily. Notably, among adolescent tobacco smokers who also smoked marijuana, the frequency of marijuana use was associated with greater levels of nicotine addiction on all three major scales used in studies with adolescents plus the ICD-10.
Moreover, models incorporating age, frequency and years of tobacco smoking with marijuana accounted for 25-44% of variance in adolescent nicotine dependence. Interestingly, CPD was only minimally associated with the frequency of marijuana use and made minimal contribution to the model since associations with the mFTQ were similar after removing the question about CPD.The finding that with the exception of drive and priority, the other sub-scales of the NDSS were not significantly associated with marijuana frequency was not surprising since most of these adolescent smokers were light and intermittent tobacco users and dimensions of dependence such as stereotypy and tolerance become more prominent as teens develop more regular and established patterns of smoking . However, despite relatively light tobacco use, the drive sub-scale, which measures the compulsion to smoke, and the priority sub-scale, which measures the preference of smoking over other reinforcers, were associated with marijuana use. It is possible that since both marijuana and tobacco share common pathways of use, smoking cues for one substance may trigger craving for the other, and thus reinforce patterns of use. As such, tobacco and marijuana may serve as reciprocal reinforcers. Some limitations of this brief include the relatively small sample size and the lack of detailed information on the timing of the initiation of marijuana use with regard to cigarette smoking. Future studies will need to examine how the proximity of marijuana use to cigarette smoking affects the degree of nicotine addiction. For example, examining whether concomitant use impacts the level of nicotine addiction more than smoking marijuana separately from tobacco. The sample largely consisted of light smokers, which reflects adolescent smoking in the US. That we found such a strong association between marijuana use and nicotine addiction in this group of relatively light tobacco smokers is notable, and reinforces the relevance of the association. Lifestyle behaviors are leading contributors to preventable morbidity and mortality worldwide. Among the top 20 risk factors are smoking, alcohol and drug use, poor diet, and physical inactivity. In the USA, health risk behaviors are common among all age groups with increased mortality and substantial costs for the health care system. A majority of U.S. adults meet criteria for multiple HRBs, and among smokers, 97% carry at least one additional risk behavior. Even in young adulthood, individuals who engage in multiple HRBs are at increased risk for cardiovascular disease, underscoring the importance of identifying high-risk groups and potentially high-yield behavioral targets. The co-occurrence of multiple HRBs within individuals is well documented, developing during adolescence and highly prevalent by young adulthood. For example, 87.5% of German college students reported two or more of the following risks: current smoking, heavy episodic drinking, poor diet,flood table and insufficient exercise. Young adulthood is an ideal time to intervene, as nutrition, physical fitness, and not smoking in young adulthood are associated with better health later in adulthood. Most investigations of HRBs in young adults have used college student samples focusing on alcohol and tobacco co-use or multiple HRBs. Only a few studies have examined clustering of young adults’ HRBs outside of the college context. A nationally representative survey of American young adults identified three profiles of HRBs, and a systematic review of studies on the co-occurrence of young adults’ multiple HRBs conducted in the UK concluded that smoking, sexual risk behavior, and substance use often cluster. Although these studies measured smoking, they did not focus specifically on smokers. Young adult smokers are more likely to develop negative health consequences such as cardiovascular disease and cancer later in life, and such risks may be compounded by co-occurring HRBs. A better understanding of HRB patterns in young adults is needed. HRBs often co-occur and can covary. That is, change in one risk behavior is associated with change in another behavior. Interventions targeting multiple HRBs can result in multiple HRB change among targeted and complementary behaviors. Given that risk behaviors often covary and multiple HRBs may be successfully targeted at once, it is important to understand how HRBs may group together in young adult smokers. In addition to clustering of HRBs, a better understanding of young adult smokers’ readiness to change HRBs is needed to inform intervention design.
Research suggests that interventions targeting multiple HRBs show promise and may be more beneficial with regard to public health impact than interventions focusing on single risk behaviors. However, when readiness to change multiple HRBs is generally low, excessive behavioral targets demanding action may increase participant resistance and decrease intervention effectiveness. The Transtheoretical Model conceptualizes the process of change as proceeding through five stages: precontemplation , contemplation , preparation , action , and maintenance . The TTM can guide behavior change interventions and may be especially helpful in addressing multiple HRBs. Notably, Keller et al. found that less than a third of college students with multiple HRBs was preparing to change at least one behavior domain and fewer were ready to change multiple risks. Identifying patterns of HRBs and examining stage of change for each behavior may help determine which HRBs should be targeted for direct action and which may require motivational intervention. Latent variable modeling approaches can be used to examine patterns of HRBs and characterize changes in those patterns over time. Such approaches seek to discover the underlying latent structure of discrete data, thereby identifying naturally occurring mutually exclusive and exhaustive categories. To understand patterns of smoking, LCA has been used to classify symptoms of nicotine dependence in adult smokers and patterns of smoking among college students based on multiple indicators of smoking behavior. Examining patterns over time, LTA has been used to categorize transitions into and out of stages of smoking as a function of home smoking bans throughout young adulthood. As applied to multiple HRBs among adult smokers with serious mental illness, LCA identified three subgroups of risk behaviors: a low-risk group, a global risk group, and a mood and metabolic risk group, characterized by inactivity, unhealthy diet, sleep problems, and poor stress and depression management. LCA has not yet been applied to understand health risk profiles among young adult smokers or to understand patterns of HRBs in young smokers over time. Using both LCA and LTA, this exploratory study identified latent classes of HRBs at each time point before exploring transitions between latent classes. Understanding patterns of HRBs and stage of change for specific behaviors among young adult smokers is imperative in prioritizing content to be included in future health behavior change interventions for this population. The present study sought to examine risk status, stage of change, and latent profiles of HRBs over 12 months’ time in young adult smokers participating in a smoking cessation treatment trial. We hypothesized that health risks other than smoking would be common among young adult smokers and that there would be multiple distinct patterns of health risk among young adult smokers at baseline. We also explored changes in these patterns over time.