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Briefly, issues in the moral domains are defined as obligatory, non-alterable, and generalizable. This stands in contrast to evaluations in the conventional domain; the criterion characteristic of acts in the conventional domain include judgments of wrongfulness that are 1) contingent on rules, authority, and existing social practice, and 2) tied to aspects of the social context . In contrast to moral and conventional domains, the personal domain encompasses non-moral issues that are “not part of the conventionally regulated system” , but are instead considered to primarily apply to the individual and therefore to be within the realm of an individual’s personal prerogative. Although some social issues clearly fall under one domain or another, other more intricate social issues are not always as clear-cut and therefore not consistently judged to be within one domain. In fact, there seem to be developmental trends in the ways in which children and adolescents reason about complex social issues. Nucci and Turiel explain the complexity of the reasoning process during moral development: Development moves from early childhood set of judgments about unprovoked harm to notions of fairness as regulated by just reciprocity. Along with this understanding of fairness, however, comes an expanded capacity for incorporating facets of moral situations that render the application of morality more ambiguous and divergent. Thus, rather than presenting a straightforward picture of moral development as linear moral ‘progress’ toward shared answers to moral situations, moral development includes periods of transition in which the expanded capacity to consider aspects of moral situations leads to variations in the application of moral criteria . Because adolescence is a time in which individuals are beginning to broaden their repertoire of social knowledge and gain exposure to the various elements involved in social issues,curing cannabis the ability to effectively reason about and understand social matters is still expanding and transforming.

With development, adolescents’ capacity for recognizing and incorporating multiple aspects of an issue increases the potential for complexity and variation in judgments . The capacity to incorporate the various features of a single issue is a part of reasoning about multi-faceted or more ambiguous social issues, or issues involving numerous components. Ambiguous issues can be differentiated from “prototypical” moral and personal issues through the application of the criteria commonly used to define and study social domain issues . A large body of research has provided ample evidence that certain criterion judgments are applied to issues that are unambiguously moral, conventional, or personal . With regard to marijuana use, specifically, each of the social domains can be seen to be pertinent to the issue. For example, issues within the moral domain are those that include evaluations that an act is wrong regardless of the convention/context and therefore not based on the existence of rules or command of an authority figure . Marijuana use may have facets related to the moral domain, as individuals may reason that using marijuana is physically harmful or harms others because it hurts society at large when people engage in illegal acts. On the other hand, other judgments about an individual’s personal rights and freedoms to do as he/she wishes with his/her own body may become salient but stand in contrast to the ‘other-focused’ moral considerations just described. In the sections that follow, I consider why marijuana use is an “ambiguous” social issue involving several, at times contradictory, considerations that make judgments less consistent among individuals. Several factors have contributed to the complexity of the marijuana use issue, and relatedly, the evolution and continued ambivalence of public thinking about marijuana. A brief overview of the trajectory of public information, opinions, and behaviors regarding marijuana over the past few decades may assist in further elucidating the basis for the increasingly controversial and ambiguous nature of marijuana use.

The public’s understanding, perspectives, and attitudes toward marijuana use have undergone substantial changes in the past few decades. These changes are in large part due to the advances in scientific research on marijuana, as well as shifts in the commonality and illegality of marijuana use. Earlier in the 20th century, the effects of marijuana use were in many ways unknown and merely speculated about. Public opinions and fears about marijuana are partly illustrated by the 1930’s movie, Reefer Madness, which through a dramatization of the devastating effects of marijuana use , fostered public fear and alarm about its use. However, by the 1960’s and 70’s, previous anxieties and frightful speculations about the detrimental consequences of marijuana use soon transformed into more lax attitudes about what had become a commonplace drug. Furthermore, as extensive research made marijuana and its effects far less elusive than it had been in previous generations, public opinion about the drug seemed to move in the direction of greater acceptance and less restrictiveness. Interestingly, however, as further research has provided clearer data on the positive and negative effects of marijuana use, and as use of the drug has become more unremarkable, the issue has become more contentious. This is partly due to mixed findings about the benefits and harm associated with marijuana use. For example, besides clarifying that the impact of the drug on the brain and body is less consequential than previously assumed, research has suggested that marijuana may be beneficial for use by patients with certain diagnoses, such as cancer, glaucoma, and various chronic pain conditions. Accordingly, debates about the true harmfulness of the drug have led to questions and concerns about the legitimacy of its illegality and the reaches of individuals’ personal freedom to choose to engage in use or not. On the other hand, because the use of marijuana remains illegal in most states, trafficking of marijuana continues to be a lucrative business, one related to gangs and cartel crime as well as many drug-dealing related deaths each year.

So, whereas much of the American public has come to understand the immediate harm of marijuana use to be more marginal, concerns over the indirect harm caused by the purchase and sales of illegal substances, in addition to considerations of the general harm caused by any form of drug use, are some of the factors making marijuana a moral issue for many individuals. Given that various considerations make marijuana use a complex issue even for many adults in American society, it is not surprising that research on adolescents’ evaluations of marijuana use have likewise suggested the ambiguity of the matter through inconsistent and/or multilayered findings . Besides the ambivalence over the morality of marijuana use, conventional considerations are also indeterminate. Marijuana has become so easily accessed and commonly used in the general populace, public perception and reaction to the use of marijuana has become more relaxed and tolerated in many cities across the United States. In fact, in the recent 2012 and 2016 elections, the states of Colorado, Washington, Oregon, California, Massachusetts, Maine, Nevada, and Alaska voted to make the recreational use of marijuana legal. Moreover, a total of 26 states have legalized the use of marijuana for medical purposes since the 1970’s. Indeed, debates regarding the effectiveness and purpose of the illegality of marijuana use have been taking place for several years, making marijuana illegality a controversial issue. Practical considerations such as the benefits of legalizing, controlling, and taxing the sales of marijuana have also become compelling arguments for legalization. The politically-charged controversy,how to dry cannabis in conjunction with the dramatic legislative changes in the acceptability of marijuana use, elucidate some of the ambiguity around the legitimacy of the illegality of marijuana use. This in turn lends support to the proposition that marijuana use may be an ambiguous social issue for many individuals in society. This ambiguity is in turn reflected in research indicating that adolescents perceive each of the social domains to be relevant to the marijuana issue . Furthermore, with regard to adolescents specifically, risk-taking behaviors have come to be considered a quintessential part of the adolescent period and, arguably, an important part of the process of adolescent identity formation and social development . However, certain adolescent risk-taking behaviors, such as drug and alcohol use, have become particularly common and have generated a great deal of concern in the past few decades. In fact, adolescent engagement in marijuana use has gained greater public attention for the past several years. This is likely due to the fact that, with over 21% of youth reporting use, marijuana is the most highly used drug among adolescents, even surpassing the proportion of youth who use cigarettes . More specifically, findings from the 2016 Monitoring the Future Survey, suggest that 6% of high school seniors report daily marijuana use and about 35% report using marijuana in the past year .

Increases in the availability and commonality of marijuana, in conjunction with the controversy around the issue, have made marijuana a public ‘hot topic’ that continues to warrant debate and dubiousness among many individuals. In addition to the timeliness and relevance of this issues, the commonality of marijuana use among adolescents and the negative potential consequences early engagement in use can have on adolescents’ life trajectory make research about decisions to engage in marijuana use a valuable and relevant area of study. Moreover, common public belief and anecdotal cases have come to suggest that marijuana may be ‘gateway drug’ leading to experimentation with and use of even more dangerous and addictive drugs and lifestyle choices . Such concerns not only highlight the pragmatic relevance of the issue, but also further suggest the value of studying the cognitive processes that precede and predict adolescents’ decisions to engage in use. Ongoing research indicating both positive and negative consequences of marijuana use has contributed to the continuous confusion and controversy about it. For example, research has suggested that not only does marijuana use have temporary negative impact on cognitive functions such as memory, attention, learning, and decision-making, but it has also been linked to negative long-term consequences such as decreased academic performance and increased risk of poverty, unemployment, and anxious mood . On the other hand, research has also demonstrated several uniquely effective benefits of marijuana use, including relief from pain, nausea, insomnia, anxiety, or addiction to other substances . Advancing research and the resultant shifts in the public’s understandings and perceptions of risks involved in marijuana use have led to changes in behaviors. According to research by the Substance Abuse and Mental Health Services Administration of the U.S. Department of Health and Human Services, the percentage of adolescents who report perceiving ‘great risk’ in smoking marijuana once a month decreased from 34.4% to 24.2% from 2007 to 2013. Likewise, the rate of adolescents who perceived ‘great risk’ in smoking marijuana once or twice a week decreased from 54.6% to 39.5% within this same time frame. Along with this decrease in adolescents’ perceived risk of marijuana use came a respective increase in the prevalence of adolescents reporting engagement of marijuana use during this 2007 to 2013 time frame. Shifts in teens’ perceptions of the safety of marijuana use, and the associated behavioral changes that seem to have accompanied these shifts, further highlight the evolving nature of public understanding of this issue. These changes coupled with the dearth of conclusive scientific information about the short- and long- term consequences of engaging in use make research on reasoning about marijuana use particularly worthwhile. The examples discussed above regarding the ambiguity of marijuana use indicate that many factors and considerations could become salient and hold more or less weight when an individual is reasoning about the legitimacy of marijuana use. It is the multiplicity of facets involved in an issue that in fact make it ‘ambiguous,’ or otherwise known as ‘non-prototypical.’ Non-prototypical issues differ from those that are clearly within a single social domain because they involve considerations that cross different domains of reasoning, and thereby, require one to coordinate these the various consideration during the reasoning process. In contrast, prototypical issues do not typically summon multiple domain considerations. For example, judging the morality of murder does not conjure concerns about personal freedom or the right of the murder to kill his victim. Instead, issues of welfare, justice, and rights become salient, making the issue of murder clearly understood to be within the moral realm. Non-prototypical issues are thus by definition ‘not prototypes,’ or not typical of domain because they involve variable considerations that may fall within more than one domain .

We sent out the survey on September 27, 2017, and responses were completed by October 9, 2017. Participants are reminded to complete the survey 3 days after the initial survey is sent. As modest incentives to encourage survey completion, participants are entered into raffles or sweepstakes with both cash rewards and other prizes. Participants are provided with no more than six surveys a month and are expected to complete an average of four surveys a month. The median time for survey completion was 8 min. Sampling was stratified by legalization status of marijuana in the state of residence . California residents and young adults aged 18 to 26 years old were over sampled to facilitate a future investigation into the role of recreational legalization on use patterns among young adults in California. Sampling weights were provided by GfK. The University of California, San Francisco Committee on Human Research considered this study to be exempt.Details of survey development have been previously published.The survey development team comprised multidisciplinary research staff and investigators. We asked about a wide range of topics, including perception of risks and benefits associated with marijuana use, comparisons of marijuana to other substances , and pertinent public health questions relevant to implementing marijuana legalization. We also asked detailed questions about marijuana use and conducted reliability testing among 300 current marijuana users. Reliability testing was only conducted on questions related to marijuana use. Questions were either derived from previously published national surveys or created internally after several iterations and pilot tests with volunteers . Volunteers were comprised of a panel of patients from the investigator’s clinics and were offered no incentives to volunteer.We asked about the most influential source of information about marijuana as follows: Which information source about the benefits and risks of marijuana is the most influential for you?

Response options were friends, relatives, health professionals , politicians, law enforcement professionals, traditional media platforms , Google or other Internet searches, social media platform , advertisement ,rolling greenhouse tables marijuana dispensary or other marijuana industry sources , and other. Only one most influential source was allowed per respondent. For the purposes of analysis, some response categories were grouped as follows: friends or relatives; social media platform or the Internet; politician or law enforcement professional; and advertisement, marijuana dispensary, or other marijuana industry sources.We asked several questions aimed at assessing the extent to which individuals endorsed commonly circulated misinformation about marijuana. A Likert scale was used to respond to each question. The questions were as follows: smoking marijuana has preventative health benefits, how safe is it to expose adults to secondhand marijuana smoke?, how safe is it for pregnant women to use marijuana?, and how addictive is marijuana? A 4-point Likert scale was used to answer questions 1 through 3 and a 3-point Likert scale was used to answer question 4. We chose these statements given that the evidence to support these claims is lacking. The notion that marijuana has preventative health benefits remains unproven.While less is known about the harms of secondhand exposure to marijuana compared with secondhand exposure to tobacco,there is an emerging body of literature using animal studies and studies in humans suggesting that marijuana smoke may be toxic. In addition, exposure to particulate matter is associated with cardiovascular and respiratory risks.There is an emerging evidence base suggesting marijuana use during pregnancy may adversely affect fetal development.The American College of Obstetricians and Gynecologists recommends avoidance of marijuana use during preconception, pregnancy, and lactation, citing concerns for impaired neurodevelopment and maternal and fetal exposure to the adverse effects of smoking.

Finally, while the threshold for addiction to marijuana is higher compared with other addictive substances among adults, it is a recognized clinical problem which is encapsulated within the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition diagnosis of Bcannabis use disorder.Characteristics of the survey respondents and most influential sources of information were weighted using weights provided by GfK to approximate the US population based on age, sex, race/ethnicity, education, household income, home ownership, and metropolitan area. All analyses used weighting commands using the weight variable provided by GfK to generate national estimates. To determine how well our sample compared with a national federally sponsored survey on substance use and marijuana use, we first compared the socio-demographic characteristics of our survey respondents with those of the National Survey on Drug Use and Health .The NSDUH is an annual federal survey implemented by the Substance Abuse and Mental Health Services Administration , which is an agency of the Department of Health and Human Services . The NSDUH provides data on substance use epidemiology in the US.We conducted multivariate logistic regression analyses to examine the association between each source of information about marijuana , the belief of any or all statements defined as misinformation about marijuana adjusted for socio-demographic characters , and legalization status in the state of residence. There was a separate model for each source of information, and the referent in each model was all other sources of information. Analyses were performed using R statistical software .The most influential sources of information about marijuana were health professionals , traditional media platforms , friends or relatives , and social media or the Internet . Individuals reporting social media or the Internet , the marijuana industry , and friends or relatives as the most influential source of information about marijuana were more likely to believe any statement consistent with misinformation about marijuana in comparison with those who reported other sources as most influential .

In contrast, those who reported health professionals , traditional media platforms , and politicians or law enforcement professionals as the most influential information source were less likely to believe any statement. Individuals reporting the marijuana industry as the most influential information source were more likely to believe all statements in comparison with those who reported other sources as most influential. Individuals reporting traditional media platforms as the most influential source were less likely to believe all statements. Findings on associations between sources of information and beliefs were also generally consistent when examined according to individual beliefs .In a nationally representative survey of US adults, the most influential sources of information about marijuana were health professionals, traditional media platforms, friends, relatives, social media, and the Internet. Individuals reporting social media or the Internet, the marijuana industry , and friends or relatives as the most influential source of information about marijuana were more likely to believe unsubstantiated claims about marijuana. We found that individuals who identified the marijuana industry as a source of information were more likely to believe misinformation. The marijuana industry is a growing multi-billion-dollar business, and it is supported by influential trade and lobbying groups and venture capital investment.Although there is little evidence to support these claims, the marijuana industry promotes marijuana as a potential treatment for nausea during pregnancy,ebb and flow rolling benches in addition to a potential treatment for conditions such as autism, cancer, and diabetes.Both Health Canada and the US Food and Drug Administration have issued warning letters to marijuana producers and distributors as a result of their advertising claims.In November 2017, the FDA issued warning letters to several online marijuana purveyors for making unsubstantiated claims that their products could prevent, diagnose, treat, or cure cancer. Thus, the marketing of marijuana, which inadequately regulated, may have a role in shaping misinformed public views on marijuana. Without more effective marketing regulations, the marijuana industry may continue to disseminate unfounded claims about marijuana with potentially harmful public health consequences.Unlike the growth of the tobacco industry, which came of age prior to the advent of the Internet, the marijuana industry has the opportunity to promote its expansion with marketing on the Internet and social media, where regulation is minimal and relatively undefined.Despite policies restricting marijuana advertising on Facebook and Google,prior work has demonstrated the predominance of positive messaging about marijuana and normalization of its use on Twitter and other Internet sources .

Furthermore, there is an abundance of articles listing unproven health benefits of marijuana on the Internet, many targeting consumers in different countries. Given the extent of misinformation about marijuana on the worldwide web, it is not surprising that adults who believed misinformation were more likely to obtain information from social media and the Internet. Public health campaigns that use social media are necessary to combat misinformation about marijuana. Unregulated promotion on the Internet and social media has public health ramifications for consumers worldwide and poses a challenge to public health leaders and policymakers. Our findings point to the need for investment in public health campaigns to better communicate risks to the public. Moreover, these results suggest the need for a targeted and cohesive strategy on the part of health providers to address misinformation with patients. Due to a lack of evidence and possible therapeutic benefit of some forms of cannabis for specific indications, physicians have not been able to provide a clear or unified message to the public. In contrast, individuals who reported traditional media platforms as the most influential information source were less likely to believe misinformation. There is roughly equal representation of pro- and anti-legalization viewpoints by traditional news outlets,and it is possible that balanced reporting could have counteracted the development of misperceptions. The lower risk of believing misinformation could also reflect restrictions on marijuana advertising on traditional media outlets.However, it is important to note that several unrelated factors could contribute to this association, including unaccounted for demographic information or the intended audience of a traditional media outlet .The response rate of our survey was 55%. However, the response rate was similar to that of other Internet surveys.Use of an Internet survey might limit generalizability because individuals who choose to join an ongoing Internet panel may be different from individuals who choose not to participate. However, studies that have examined non-response to panel recruitment in GfK’s Knowledge Panel have found no evidence of non-response bias in the panel on core demographic and socioeconomic variables. In addition, while there were some differences in income distribution in the sample compared with the NSDUH, the respondents of both panels were very similar in terms of age, gender, race/ethnicity, education, household size, and employment status. Additionally, it is important to note that the survey questions and response items analyzed in this paper could have been interpreted differently by respective respondents. We did not conduct reliability testing of the opinion questions, and it is possible that the wording of the questions introduced bias that may have impacted interpretation by the respondents. Specifically, describing information sources as Bmost influential may be perceived differently between respondents. Additionally, we did not offer an BOther or BUnknown category for respondents when choosing an answer. Though a deliberate decision to force participants to choose an answer to obtain an understanding of prevailing views, this may have biased responses. Future research should include more psychometric testing of the items to minimize bias introduced by the content and order of the questions. Finally, we were unable to examine causal relationships between sample characteristics and endorsement of misinformation. Our results are only able to demonstrate association.Marijuana use is legal for medical and/or recreational purposes in thirty-three states, including the District of Columbia.Use of marijuana has steadily increased along with growing availability through legalization. The National Survey on Drug Use and Health found past-year marijuana use among US adults doubled over the last decade, rising to 13.3% in 2014.Of those who used marijuana in the last year, 90.2% reported recreational use only, 6.2% reported medical use only, and 3.6% reported use for both purposes.However, commercialization of marijuana along with direct to consumer advertising in recreational dispensaries, on the internet, and through social media allows self-medication without the involvement of healthcare professionals. In addition, the increasing rate of marijuana use has been paralleled by a decreasing perception of harm. Therefore, many Americans may be using marijuana for reasons not supported by evidence.Chronic pain has been more widely studied than other conditions, with most trials focusing on neuropathic pain.While many of these trials demonstrated benefits in reducing pain, most used pharmaceutical cannabis extracts not available in the USA or federally available research-grade marijuana that differ considerably from products used by consumers. Therefore, a recent review by the National Academy of Sciences concluded that more evidence is needed to explore the proper forms, routes, and dosages of marijuana for use in chronic pain.

The NEO Five-Factor Inventory ascertained characteristics according to the five factor model of personality: degree of neuroticism, extraversion, openness, agreeableness, and conscientiousness. Parents were given the Child Behavior Checklist to assess level of psychopathological syndromes. The SWM task consisted of 18 21-second blocks that alternated between resting fixation, baseline vigilance, and working memory conditions. Each block began with a one-second word cue that indicated the type of upcoming block. In the SWM condition, the word “WHERE” cued subjects to remember the locations of abstract line drawings that were individually presented in one of eight spatial locations on a screen. Subjects were instructed to press a button every time a figure appeared in the same location as a previous design within that block, regardless of the shape. Unbeknownst to subjects, repeat location stimuli were 2-back, and 3 of 10 trials in each block were targets. The baseline vigilance condition began with the word “DOTS”, followed by presentation of the same abstract stimuli shown in the same possible spatial locations as in the SWM condition; subjects were to press a button every time a figure appeared with a dot above it . Resting blocks displayed the word “LOOK” followed by presentation of a fixation cross in the center of the screen. For both the vigilance and working memory conditions, stimuli were presented for 1000 ms with an interstimulus interval of 1000 ms . All teens were trained with a 4-minute version of the task and monitored to ensure comprehension of task instructions prior to scanning. Responses were collected with a fiber optic button box. The toxicology procedure was designed to minimize the possibility that participants used substances in the 28 days prior to fMRI assessment. Cannabinoid metabolites remain detectable in urine for at least four days and 27 days on average in heavy users .

Urine samples were collected 2 – 3 times per week during the 28 days prior to the fMRI session to detect metabolites indicating recent use of cannabis grow systems,amphetamines, methamphetamines, benzodiazepines, cocaine, barbiturates, codeine, morphine, phencyclidine, and ethanol. Samples were analyzed in the VA Medical Center laboratory using cloned enzyme donor immunoassay assay kits . Observed sample collection reduced the possibility of participant tampering. Quantitative indices from samples were tracked to determine if cannabinoid metabolite levels decreased over the 28 days. Youths with initial samples positive for cannabis remained eligible if the values continued to decrease. If levels increased, the participant was given one chance to restart the 28-day toxicology screening process. Three quarters of marijuana users successfully completed this toxicology screening indicating abstinence for 28 days before scanning, and only these 15 subjects were included in analyses. Participants who were unable to complete 28 days of abstinence were not scanned, and were more likely to be male and slightly heavier users than those who remained abstinent. Imaging data from each teen were processed and analyzed using Analysis of Functional NeuroImages . Prior to statistical analyses, the time series data were corrected for motion by registering each acquisition to a selected repetition with an iterated least squares algorithm , creating an output file specifying adjustments made for three rotational and three displacement parameters for each participant. Using deconvolution processing , the time series data were correlated with a reference function coding the hypothesized BOLD signal across the task and modeling anticipated delays in hemodynamic response . This multiple linear regression approach yielded a fit coefficient for each subject in each voxel, representing the relationship between the observed and hypothesized signal change while controlling for linear trends and degree of motion correction applied. Fit coefficients were obtained for contrasts between SWM and vigilance conditions, SWM and fixation, and vigilance and fixation. Anatomical and functional datasets were warped into standard space , and functional data were resampled into 3.0 mm 3 voxels and smoothed with a 5.0 mm full-width half-maximum Gaussian filter. Group differences in BOLD response contrast to SWM relative to vigilance were evaluated using independent samples t-tests in each brain voxel.

Single sample t-tests identified regions of task-related brain response in each group separately. Tocontrol for Type I error, significant group difference clusters consisted of contiguous significant voxels that exceeded 1328 µl in volume, yielding an overall clusterwise α = 0.05. To understand the nature of group differences between SWM and vigilance brain response, we performed follow-up analyses examining SWM relative to fixation, and vigilance relative to fixation, in each significant cluster. Exploratory follow-up regressions among MJ teens determined the influence of substance use and behavioral characteristics on BOLD response in brain regions demonstrating significant group differences. This study examined fMRI brain activation during a spatial working memory task among marijuana using teens and controls after 28 days of monitored abstinence, verified by biweekly urine toxicology screens. Despite similar overall patterns of brain response to SWM, group differences were observed in right dorsolateral prefrontal cortex, right posterior parietal cortex, medial superior occipital cortex, and medial inferior occipital cortex. MJ teens displayed reduced SWM BOLD response relative to control teens in right dorsolateral prefrontal cortex, which is consistently implicated in spatial working memory . In contrast, our previous work revealed increased SWM response in this region among heavy alcohol and marijuana usingteens who had been abstinent an average of just 8 days . Kanayama and colleagues also observed greater dorsolateral prefrontal response among adult heavy marijuana users on a similar SWM task 6 – 36 hours after marijuana use. However, after 25 days of abstinence, adult marijuana users showed decreased left dorsolateral prefrontal blood flow during a modified Stroop task . Moreover, during visual attention, active marijuana users with positive urine toxicology screens evidenced greater reductions in right prefrontal fMRI response than abstinent users . Considered together with the results of the current study, these findings suggest a change in neural recruitment throughout the course of abstinence. This could relate to residual drug effects or withdrawal symptoms during early abstinence, less need for neural compensation, or a change in neurocognitive strategy as the brain adapts to different stages of sobriety.

We did not observe a correlation between brain response and recency of marijuana use in this sample, but most neuropsychological recovery appears to occur during the first week of abstinence . Thus, there may be little change in neurocognitive functioning after 28 days of abstinence, or such an effect may be too subtle to detect with a relatively small sample. Careful examination of neural response within the first month of abstinence may better clarify this relationship. Compared to controls, MJ teens demonstrated increased SWM activation in right posterior parietal cortex,ebb and flow cannabis a region involved in SWM and attentional processes . Research on parietal functioning during working memory among individuals with substance use disorders has been somewhat inconsistent. Using the same SWM task, our previous work failed to observe parietal abnormalities among adolescent users of alcohol and marijuana , though teens with alcohol use disorders alone showed increased posterior parietal brain response compared to controls despite similar task performance between groups. Greater fMRI activation in posterior parietal cortex has also been observed during SWM among adult marijuana users as well as during verbal working memory among adolescent marijuana users experiencing nicotine withdrawal . Heightened activation among individuals with substance use disorders may be associated with compensatory neural responding to perform well on a task . MJ teens in the current study displayed increased response in parietal cortex, yet diminished activation in prefrontal cortex, both of which play important roles in SWM . Frontal cortex may be primarily involved in general executive functioning components of working memory tasks, while superior parietal cortex may more specifically sub-serve attentional allocation and visuospatial rehearsal demands of SWM . Thus, abstinent MJ teens may rely more on spatial rehearsal and attention rather than general executive abilities to perform the task, resulting in increased recruitment of posterior parietal cortex, but decreased right dorsolateral prefrontal activity. This altered pattern is consistent with previous evidence of reorganized attention networks in MJ users . Further, estimated typical blood alcohol concentration achieved was negatively associated with parietal response among MJ teens, which could indicate that heavier drinking MJ teens may demonstrate less neural compensation or be less likely to utilize spatial strategies as those with lighter alcohol use histories. This is consistent with previous findings of diminished parietal activation during SWM among alcohol use disordered young adults , and suggests a potential interaction between heavy alcohol and marijuana use in youth . We previously characterized the relationship between age and fMRI response among 49 typically developing teens ages 12 – 17 using the same SWM task . Younger teens evidenced increased response in superior portions of posterior parietal cortex, while older teens utilized more inferior aspects of posterior parietal cortex. This shift in localization of parietal response across adolescence indicates a change in strategy, with younger teens relying on rote spatial rehearsal and older teens implementing more spatial storage. MJ teens in the current study demonstrated increased SWM activity relative to controls in superior portions of right parietal cortex, paralleling response patterns of younger adolescents. Thus, MJ teens may employ spatial rehearsal strategies more consistent with those used by younger youths. This may suggest the possibility of altered neuromaturation among adolescent marijuana users, which could implicate an adverse influence of marijuana on the developing brain or preexisting neural differences that may have contributed to the initiation of substance use.

MJ teens demonstrated increased vigilance response compared to controls in two regions of medial occipital cortex: superior portions of the cuneus, and lingual gyrus/inferior cuneus. Occipital cortex has been associated with visual attention, and may become more active as attentional capacity is reached yet less active during practiced tasks . Greater occipital response among MJ teens may indicate less efficient processing and greater attentional demand during vigilance blocks. Such occipital hyper activation among MJ teens was not observed during SWM blocks, during which attentional resources were not focused solely on visual selective attention, but allocated to accommodate working memory processing. Previous studies have implicated diminished attention capacity in heavy marijuana using adults and adolescents . During SWM, MJ teens may allocate limited attentional resources to spatial processing, depriving attentional input to executive systems, resulting in increased parietal and decreased frontal activation. A SWM task with greater executive demand may require more attention input to frontal systems, diminishing response capability in parietal cortices. Among adolescent marijuana users, those who began regular use earlier in adolescence demonstrated greater abnormalities in occipital brain response.Similarly, adults who began using during early adolescence showed greater neural dysfunction during spatial attention and poorer functioning on tests of attention , and verbal abilities and short term memory . Animal models also indicate that cannabinoid exposure during adolescence is associated with greater impairments in working memory and spatial learning than adult exposure . Thus, evidence suggests that marijuana use during adolescence may influence the course of brain development, and those who begin using at a younger age may be more susceptible to dysfunction with continued use. Brain response differences between groups may also relate to aberrant cerebral blood flow among MJ teens. Adult marijuana users have demonstrated reduced resting frontal and cerebellar blood flow during short-term abstinence . Further, elevated cerebrovascular resistance and systolic blood flow remained high after a month of abstinence, suggesting lasting blood flow abnormalities . These blood flow abnormalities could affect the magnitude of the observed BOLD response . Specifically, reductions in frontal blood flow may contribute to diminished frontal SWM activation among MJ teens. Future investigations could more closely account for resting perfusion when examining BOLD response among marijuana users.This study raises several questions. First, most MJ users in this study were moderate to heavy drinkers. Though this is representative of adolescent marijuana users, the functional impact of marijuana use alone is difficult to determine. Studies with larger samples and variability in drinking patterns will help elucidate the substance-specific neurocognitive effects. MJ and control teens were comparable on demographics, behavior, personality and intellectual functioning, and differed slightly on mood, but none of these measures accounted for group differences in brain response. However, abnormal activation patterns among MJ teens may relate to preexisting traits that were not measured.

Computer IP addresses were tracked in the study. If the respondent’s computer indicated that the survey had already been completed, the Qualtrics system would not allow the participant to regain access to the survey. Participants’ contact information was stored in a separate file unlinked to survey responses and destroyed immediately following the study. Identifiable research information was protected by a Certificate of Confidentiality from the National Institutes of Health. All materials and procedures were approved by the Institutional Review Board at the University of Cincinnati.The Nicotine and Marijuana Interaction Expectancy questionnaire is a 14-item measure designed to examine client’s perceptions of the interaction between cigarettes and marijuana. Participants rated each of the items on a Likert scale ranging from 1 to 5 . The NAMIE includes three scales: the effects of marijuana use on cigarette smoking ; the effects of cigarette use on marijuana smoking ; and smoking cigarettes to cope with urges to use marijuana . Participants’ tobacco and marijuana history and use were assessed using items from the National Survey on Drug Use and Health . Tobacco use measures included age of first tobacco use, days of past month tobacco use, type of cigarettes smoked and use of other tobacco products . Marijuana use measures included age of first use, days of past month marijuana use and days of past month blunt use. Participants who reported past month blunt use and cigarette use were categorized as “blunt co-users”, while participants who reported cigarette use but no past month blunt use were categorized as “non-blunt co-users.” The survey also assessed past month use of other drugs,commercial plant racks including cocaine, opioids, and alcohol.Descriptive statistics and Cronbach’s alpha coefficients were estimated to characterize the sample and examine the reliability of the NAMIE sub-scales among African American young adults.

Six multiple regression models examined the relationship between each of three NAMIE sub-scales and marijuana use and initiation and tobacco use and initiation.To control for multiple comparisons but allow for meaningful patterns to emerge from the data, significance level was set at .01. The sample was mostly male and had an average age of 23.7 . Most participants were college graduates and had full-time jobs . Approximately 49% of the sample reported legal trouble. Cigars, cigarillos, and little cigars were the second most popular tobacco product used in the past month following cigarettes . Participants also reported smoking menthol cigarettes and drinking alcohol in the past month. The majority of the sample also reported smoking blunts in the past year , and in the past month . In the month preceding the study, participants reported smoking marijuana for an average of 12.6 days and tobacco for an average of 20.4 days. As shown in Table 1, relative to non-blunt co-users, blunt co-users were more likely to have some college experience or be a college graduate and reported drinking alcohol in the past month. The current study examined the interaction expectancies of African American young adults who reported marijuana and cigarette use in the past month. Expectations regarding the interaction of marijuana and tobacco as measured by the NAMIE showed strong internal consistency, supporting use of this measure in the African-American population. Reliability compares to that among a predominately White sample of marijuana and tobacco co-users participating in a national online survey ; however mean scores were relatively higher in the current study compared to the Ramo et al study on all three NAMIE scales. It is unclear whether the scores are statistically different across groups. This could not have been accounted for solely by the extent of blunt use in this African-American sample, as scores were higher for non-blunt co-users in the current study as well. More work is needed to clarify ethnic differences in interactions expectancies more fully.

African American young adults reported the highest rating on scale 1 , which might partially explain why tobacco initiation often follows marijuana use among African American young adults . Longitudinal research is needed to examine the relationship between co-use expectations and marijuana and tobacco initiation among African American young adults. Relative to blunt co-users, non-blunt co-users were more likely to report that smoking cigarettes increased their marijuana use and urges and that smoking cigarettes helps them to cope with their marijuana use and urges. Blunt smokers are exposed to nicotine when preparing blunts with the outer wraps of cigars and cigarillos , while non-blunt marijuana users are not exposed to nicotine in the same way. Non-blunt co-users might lean more heavily on cigarettes for exposure to nicotine and therefore may be more likely to believe that cigarettes have a more powerful role in the interaction between marijuana and tobacco. It might follow that nonblunt co-users would smoke more cigarettes than blunt co-users, however, findings here do not support that notion. It is important to note that all of the participants in this sample reported 20 or more days of past month tobacco use, and 87.2% reported smoking menthol cigarettes in the past month, consistent with other African-American smoking samples . Additional research with a wide range of light and heavy menthol and non-menthol cigarette smokers is needed. Notably, we found that days of past-month marijuana use were associated with co-use expectations among blunt co-users but not among other co-users. This could indicate that the NAMIE is a better assessment tool for blunt co-users than non-blunt co-users, or that substituting tobacco for marijuana and vice-versa is equally common among non-blunt cousers of all levels. Research with larger samples is needed to clarify this further. In addition to a small sample size, there are a few other limitations that should be noted. First, the cross-sectional design of the study prevents causal interpretation of the findings. Second, the sample is mostly male and may not be generalizable to a more diverse sample of African American young adults.

The study relied on self-reported, anonymous data; however, the targeted recruitment method increases the validity of the data. Despite these limitations, the current findings highlight preliminary evidence for the significant relationship between drug use outcomes and co-use expectancies among African American young adults that has important research and clinical implications. Our findings highlight the extent to which tobacco and marijuana use can perpetuate use of the other substance, and the importance of treatments targeting thoughts and expectations about the interaction of these two substances. Understanding the relationship between marijuana and tobacco will increase the effectiveness of existing interventions and facilitate the development of new interventions that target cognitions related to co-use among African American young adults, especially among individuals who co-use cigarettes and blunts.Marijuana is commonly used in adolescence, yet the impact on the developing brain is unclear. Working memory impairments have been observed in adult marijuana users after recent use, but may remit after a month of abstinence. The differential effects related to recent use and abstinence have not been delineated in adolescents. To address this question, three studies examined functional magnetic resonance imaging brain response during spatial working memory among adolescents.Adolescent brain development may be influenced by heavy marijuana use, yet the neural underpinnings of SWM have not been well described in adolescents. Study 1 investigated fMRI response to SWM across normal adolescent development. Participants were 49 youths ages 12 – 17 without histories of neurological or psychiatric disorders. Results demonstrate the emergence of left prefrontal activity and superior-to-inferior shift in localization of parietal response with increasing age, suggesting that younger teens utilize more rote spatial rehearsal,ebb and flow tray while older teens rely more on spatial storage and verbally-mediated strategies. Study 2 evaluated fMRI response during SWM among 15 heavy marijuana using adolescents after 28 days of verified abstinence relative to 17 non-abusing controls, ages 16 – 18 years old. Marijuana users demonstrated decreased right prefrontal and increased right superior parietal response relative to controls, which could suggest greater reliance on spatial strategies and less general executive control among marijuana users. These results were observed after 28 days of abstinence, suggesting persisting differences in brain functioning among heavy marijuana users. Study 3 characterized the differential residual and persisting changes in neural activation patterns associated with adolescent marijuana use by examining fMRI during SWM among adolescent marijuana users after recent use or after one month of abstinence.

Participants were 15- to 18-year-olds: 13 marijuana users who used in the week before scanning, 13 marijuana users who were abstinent for 27 – 60 days before scanning, and 18 demographically similar controls. Recent users demonstrated increased medial/left superior frontal and right parietal response relative to abstinent users, which could suggest greater neural effort for inhibitory control and spatial rehearsal. Although cross-sectional, results may indicate a shift in neural processing strategies through early abstinence.Marijuana is the most commonly used illicit drug among teenagers, and most users first try marijuana in adolescence . While 17% of 8th graders have tried marijuana, almost half of 12th graders have used cannabinoids . Frequency of use escalates so that by 12th grade, 20% report past-month use and 5% reveal daily use . During this period of increasing marijuana use, the brain remains in an active state of development, characterized by synaptic refinement , myelination , and improved cognitive and functional efficiency . In particular, working memory abilities improve throughout childhood and adolescence as the brain regions sub-serving these functions mature. Yet working memory may be adversely impacted by marijuana use, as marijuana most likely affects frontal and parietal brain regions involved. Given continued development of these systems in adolescence, heavy marijuana use during youth could negatively impact working memory functioning. The possible influence of marijuana use in adolescence and the potential for recovery with abstinence have not been well delineated, but could have important implications for academic, occupational, and social achievement among both current and former users. This dissertation aims to characterize the influence of chronic marijuana use on functional magnetic resonance imaging brain response during spatial working memory in adolescents through the completion of three studies. In order to understand the effects of chronic marijuana use in youth, a better depiction of neural response patterns in normal adolescents is needed. To this end, Study 1 investigated fMRI response to SWM across normal adolescent development. Study 2 then evaluated fMRI response during SWM among heavy marijuana using adolescents after 28 days of verified abstinence relative to non-abusing controls. Finally, Study 3 characterized the differential neurocognitive impact of recent adolescent marijuana use relative to the potentially persisting effects by examining fMRI during SWM among adolescent marijuana users within one week of use or after one month of abstinence. The results of these three studies provide a better understanding of the neural impact of heavy marijuana use during adolescence.Modern neuroimaging techniques have a provided a wealth of information about human brain development. Whereas it was once believed that the human brain was largely developed by the onset of puberty, it has now been established that the brain continues to develop throughout adolescence and well into adulthood . A recent longitudinal investigation demonstrated that higher order association cortices, such as superior temporal, posterior parietal, and prefrontal cortex, develop later than primary sensorimotor cortices, with the dorsolateral prefrontal cortex developing last . This late occurring development is predominantly a function of the progressive and regressive processes of myelination and synaptic pruning that result in increasing white matter volumes and cortical thinning and a more efficient central nervous system. During adolescence and this time of active neural maturation, many cognitive processes are also developing. One such process is working memory. Working memory refers to the ability to actively store and manipulate information online over brief periods of time . This ability is fundamental to intact performance in a variety of other cognitive domains, including language comprehension, abstract reasoning, and learning and memory . Verbal and spatial working memory abilities improve throughout childhood and adolescence , with accuracy and reaction times increasing and decreasing respectively during spatial n-back and spatial delayed response tasks . It is likely that these behavioral improvements in working memory are the result of the described neuromaturational processes that are occurring during the child and adolescent years. With the advent of functional magnetic resonance imaging , the neural substrates of working memory functioning have begun to be identified.

Despite broad implications, there is limited data on clinical outcomes for patients who use marijuana before and after LT and no consensus within the transplant community surrounding marijuana use. Approximately 15,000 patients are currently listed for LT in the US according to the Organ Procurement and Transplantation Network . Therefore, given the rising prevalence of marijuana use, LT listing policies around marijuana use may affect several thousand patients in the US alone. Using psychosocial assessment and urine toxicology, our study is the first report on the prevalence and frequency of marijuana use and its effect on LT wait list outcomes among a cohort of LT candidates in the Unites States. In the only prior study evaluating LT-related outcomes among marijuana users, Ranney et al 17 found that marijuana users were less likely to receive LT but had similar overall survival rates as nonusers. Their study, however, was limited by the exclusion of a large portion of LT wait list candidates. They also did not assess wait list outcomes like rate of delisting in this study and their use of urine toxicology alone to define marijuana led to a low prevalence estimate and may have led to misclassification of marijuana users. In contrast, we used a more robust definition of marijuana use based on psychosocial interviews combined with urine toxicology to describe the frequency and patterns of marijuana and other substance use among LT candidates. We also used a competing risk model to assess for rates of death or delisting in addition to receiving LT among marijuana users and nonusers. Our study, and that of Ranney et al.,rolling tables did not find clear evidence of harm associated with historical marijuana use, and raises the question whether ongoing marijuana use could be considered safe on the LT wait list.

This question is especially relevant given recent passage of laws that protect medical marijuana users from transplant restrictions across several states in the United States. Further, could medical marijuana have a potential therapeutic role for LT candidates? A recent report documents successful use of prescription marijuana to decrease opiate use following liver transplantation. Perhaps marijuana could be effectively used for appetite stimulation, treating nausea, reducing opiate addiction, or postoperative pain relief. This is especially relevant considering that almost a quarter of LT candidates at our institution had recent opiate/BDZ prescriptions. It is important to note that our understanding of the metabolism and effects of marijuana is still developing – marijuana use affects the endocannabinoid system, including the hepatic cannabinoid receptors, which are also modulated by chronic liver disease. Upregulation of the CB1 receptor in chronic liver disease has been implicated in progression of liver fibrosis. However, CB2 is also upregulated in liver disease and prevents fibrosis progression. It has been postulated that the balance between CB1 and CB2 receptor activation may modulate liver fibrosis – if both receptors are targeted equally then they may not be any net effect on liver fibrosis. However, there have been isolated cases of invasive aspergillosis related to marijuana use among post transplant patients. Our study has several important limitations and should be interpreted with caution. We could not assess impact of ongoing marijuana use on wait list outcomes because our institutional policy did not allow LT listing for active marijuana users. Those with active marijuana use, including heavy users, had to demonstrate abstinence prior to listing for LT. Therefore, based on our data we cannot comment on active marijuana use and our results should only be applied to historical marijuana use prior to LT listing.

Those subjects who were able to satisfy the selection committee concerns and demonstrate abstinence from marijuana use were classified as ‘recent’ users in our study. All outcomes are presented in strata of ‘recent’ and ‘prior’ marijuana use to capture any differences between these 2 groups. Accordingly, we also cannot provide relevant data on the effects of ongoing marijuana use on post-LT outcomes. Though we attempt to adjust for confounding variables, given the limited prior work in this field there is potential for unmeasured confounding in our analysis. Further, our definition of marijuana use does not incorporate duration or method of marijuana use, as these data were not collected systematically at our institution. Finally, we defined marijuana use via combination of self-report in a psychosocial assessment and urine toxicology, which likely yields an underestimate of the true prevalence since patients had a conflict of interest in self-reporting marijuana use and urine toxicology to detect marijuana is an imperfect test. In conclusion, we found a high prevalence of historical marijuana use that did not have clear adverse effects on LT wait list outcomes. Recent use of illicit substances was, however, associated with higher risk of death or delisting from the LT wait list. This suggests historical marijuana use alone may not be equivalent to use of other illicit drugs. Yet, this data should be interpreted with restraint as further research is needed to assess the impact of ongoing marijuana use among candidates on the LT wait list. Further, post transplant outcomes must also be followed in these patients to determine safety of continued marijuana use after LT. Recent passage of laws protecting medical marijuana users has created an urgent need to further study LT-related outcomes among this population. MARIJUANA, THE MOST used illicit drug in the United States and the world, is frequently used in association with alcohol. Marijuana use is prospectively associated with both heavy drinking and with the development and maintenance of alcohol use disorders as well as with the deleterious AUD treatment outcomes . Couse of marijuana and alcohol is associated with heavy episodic drinking and AUDs .

Among marijuana users with cannabis use disorder , there is increased likelihood for development of a comorbid AUD , with nationally representative data indicating that 68% of individuals with current CUD and over 86% of those with a history of CUD meeting criteria for an AUD . Marijuana dependence doubles the risk for long-term persistent alcohol problems , and marijuana-dependent alcohol users are 3 times more likely to develop alcohol dependence than non-marijuana-involved drinkers . Co-use of marijuana and heavy alcohol use is linked to a number of behavioral problems with exceptionally heightened risk for impaired driving , psychiatric comorbidity , and poor clinical treatment outcomes . Importantly, the risk associated with the use of marijuana in combination with alcohol is greater than that from either drug alone . Thus, increased attention has been called to the importance of examining inter-relations among alcohol and marijuana use patterns and the impact of the use of one substance on risk of excessive use of the other . The majority of the epidemiological studies using individual-level outcomes indicate that marijuana use increases or complements alcohol consumption . Similarly, studies of economic policies that reduce access to alcohol or increase the price of alcohol demonstrate complementary reductions in both alcohol and marijuana use . However,growers solutions longitudinal general population studies that mostly used state-level data on marijuana policy suggest marijuana and alcohol can be substitutes . Research with individuals using marijuana for medicinal purposes also indicates that alcohol use is lower or less likely with concurrent marijuana use . These findings suggest that individuals who use marijuana for medicinal purposes may use it as a harm-reduction strategy to substitute for alcohol . Preliminary evidence of alcohol substitution was also noted in a clinical study where controlled abstinence from marijuana was linked with increased alcohol craving and consumption among individuals with AUD and also in an experimental study that demonstrated decreased alcohol consumption over time when smoked marijuana was available during an operant task . Collectively, this research indicates that marijuana use is strongly linked with alcohol use, although whether marijuana serves as a complement to or substitute for alcohol use remains unclear. These mixed findings on co-occurrence between alcohol and marijuana use behaviors may reflect methodological limitations of correlational research which precludes causal inference. Similarly, epidemiological and laboratory studies are not designed to determine whether marijuana and alcohol use are linked at the event-level within individuals in a natural setting. The few experimental studies have primarily focused on pharmacokinetic interactions or on performance impairments from combined use of marijuana and alcohol , and thus offer limited information on marijuana’s influence on alcohol consumption.

Although several studies have asked respondents to recall their most recent marijuana-alcohol use event , they cannot distinguish different use events within the same person. Therefore, it is critical to use nuanced methods that examine co-use of marijuana and alcohol, such as event or daily level. To our knowledge, there have been only a few event-level studies on the co-occurrence of marijuana and alcohol use. One recent study used ecological momentary assessment methods to characterize the context of adolescent simultaneous marijuana and alcohol use, but did not examine event-level associations between the 2 behaviors . Another study examining daily marijuana and alcohol use found that marijuana intoxication was greater on days when participants used any alcohol or had 5 or more alcoholic drinks on 1 occasion . However, whether marijuana use predicted heavy drinking was not examined. Furthermore, neither study examined whether meeting criteria for AUD or CUD moderated the concurrent marijuana and alcohol use. A recent online daily diary study showed evidence for complementary alcohol and marijuana use at both the within- and between-person levels . However, individuals with coping-oriented patterns of substance use showed evidence of substitution by increasing levels of drinking while decreasing marijuana use. Heterogeneous samples may have contributed to the mixed findings in research examining marijuana–alcohol associations. For example, marijuana use may be associated with worse drinking outcomes among heavy drinkers, especially those with AUD. For these individuals, learned associations of conjoint use may be particularly salient. Marijuana also impairs executive control functioning , which may already be reduced among chronic heavy drinkers . Thus, in individuals with AUD, marijuana use may increase alcohol craving and may result in heavy drinking. Likewise, given that individuals with CUD are known to be at greater risk for problematic drinking , and CUD and AUD are highly comorbid , alcohol involvement may be even greater in individuals with the dual diagnoses of CUD and AUD. This study extends the growing literature on the association of marijuana and alcohol use and use disorders using event-level data to examine daily associations between marijuana and alcohol use in a clinical population with high base rates of use of these substances. The sample was recruited from the Veterans Health Administration facility to capitalize on the disproportionately high rates of substance use disorders in veterans relative to the general population . Veterans are at increased risk for substance use disorders because of the significantly elevated rates of mental health disorders such as post traumatic stress disorder and major depressive disorder, which are strongly associated with using alcohol and marijuana specifically to cope with aversive psychological and mood states as well as with sleep disturbance . Returning veterans experience high rates of suicide and impaired psychosocial functioning post deployment, which further exacerbate the severity of substance use disorders in this vulnerable population . Participants were selected based on co-use of marijuana and alcohol with a full range of marijuana and alcohol involvement . As there may be different associations for any use versus level of alcohol use, we examined any alcohol use as well as heavy and moderate levels of drinking. There are 2 main hypotheses of this study. First, we hypothesized that marijuana use on a given day will be associated with greater alcohol consumption /4 drinks, versus moderate drinking ; and moderate drinking vs. None on that day. Second, we examined the potential moderating effects of AUD and CUD diagnosis, as ascertained by the Structured Clinical Interview for DSM , on the marijuana–alcohol relationship. Specifically, we expected that marijuana use on a given day will be associated with heavy alcohol use that day specifically among individuals with a diagnosis of AUD or CUD but not among individuals without these diagnoses. Furthermore, we expected that a dual diagnosis of CUD and AUD would amplify the association between marijuana and alcohol use relative to a single diagnosis of AUD or CUD.In the 1990s, states across the United States began to legalize marijuana for medical use, which helped usher in the transition to the legalization of non-medical marijuana use.In 2012, Colorado and Washington were the first states to legalize recreational marijuana for adult use and sales through voter-initiated ballots, with legal sales beginning in 2014.

In addition to high rates of marijuana use, young adults have higher rates of cigarette smoking, binge drinking and heavy alcohol use than any other age group.As marijuana becomes more widely available, it will be imperative to monitor its use along with health risk behaviors. Substance use interventions may also benefit from addressing multiple substances at once. Study limitations include the cross-sectional design; we cannot draw definitive conclusions about causal relationships between variables in our model and marijuana use. Second, the study was conducted with young adults in California and may not generalize to all young adults or to the population as a whole. In addition, urban young adults are notoriously difficult to reach in population surveys and our relatively low response rate reflects this challenge; though robust, the sample may suffer from unidentified non-response bias. Finally, marijuana use was measured using self-report, which could not be validated biologically. Nevertheless, these findings offer new insight into the correlates of marijuana use among young adults. Neural and mental health vulnerabilities and use-related problems play a role in maintenance of problematic marijuana use patterns after initiation of use, barriers and failed attempts to quit or cut back on use,vertical growing racks and consequently, increasing prevalence rates of marijuana use disorders . Focusing efforts on better understanding cannabis-related processes and barriers that may promote use and influence behavioral interventions for adolescent marijuana users is a critical public health concern . Contingency Management is an evidence-based treatment for reducing marijuana use .

Biochemical verification is typically an important aspect of abstinence-based CM. Vouchers are given as positive reinforcement for negative drug screening . Limited work pointedly explores how a monitored abstinence protocol with adolescents simultaneously influences trajectories of sub-syndromal mental health symptoms , sleep disturbance, marijuana use expectancies and consequences, and reward sensitivity in non-treatment seeking marijuana users compared to matched controls . Barriers to treatment success may be associated with cannabis-related problems and processes that can influence emotional processing , cognitive attributions and self efficacy , and risk taking behaviors . We recently examined neural health changes and neural recovery in adolescent marijuana users pre- and post monitored abstinence and found alterations in cortical thickness that continue to persist after 28-days of monitored abstinence, and associations between cortical thickness and lifetime marijuana use and age of marijuana use onset. Findings also suggest resolution of cerebral blood flow differences . Secondary aims of the larger neuroimaging study included characterization of stress and reward-related addiction cycle symptoms in the sample. Gaining a better understanding of how physiological symptoms , mental health symptoms, and cannabis-related factors and barriers may be affected by common behavioral interventions targeting marijuana use may help uncover potential treatment interfering factors for adolescent marijuana users that have clinical implications in preventing or treating problematic use .Therefore, this study aimed to evaluate 1) the influence of 28-days of monitored abstinence on changes in subsyndromal emotional functioning, sleep difficulties, marijuana withdrawal, marijuana craving, marijuana expectancies, and marijuana-related problems, and 2) characterize reward sensitivity and attention impulsivity measured after cessation of marijuana use in a sample of adolescent marijuana users.

Associations between age of marijuana use onset and lifetime marijuana use was also explored. The sample included n=26 marijuana users and n=30 demographically matched controls on age, gender, ethnicity, and family history of substance use disorder, who completed bi-weekly urine toxicology for 4 weeks and repeated administration of self-report instruments assessing emotional functioning and marijuana use symptoms over the 28-day protocol. We hypothesized that following completion of monitored abstinence, marijuana users would report less depression and anxiety symptoms, sleep-related problems, and marijuana-related problems and symptoms by day 28 of the protocol compared to baseline; and minimal group differences would be observed at follow-up. Notably, the marijuana users recruited for the study were not treatment-seekers or experiencing severe levels of mental health distress, despite regular use of marijuana. Adolescents were recruited from local San Diego schools and included 26 marijuana users ≥ 200, past month marijuana use episodes range 1–28, past three-month average marijuana use days range 7–30 and 30 control teens with minimal substance use histories . A district-approved research flyer that described a paid research opportunity at the University of California, San Diego was distributed throughout San Diego high schools. Teens and demographically matched controls were screened for substance use and exclusionary criteria. Ninety-six percent of participants in the MJ group met current Diagnostic and Statistical Manual for Mental Disorder-Fourth Edition cannabis abuse or dependence criteria, while 15% met current alcohol abuse or dependence criteria. Only one individual in the CON group met current abuse criteria for alcohol use, and none of the individuals in the CON group met cannabis abuse/dependence criteria. Comprehensive screening interviews were administered to adolescents and parents/guardians; adolescents provided assent for their own participation and guardians were required to provide consent in accordance with the University of California, San Diego Human Research Protections Program.

Exclusionary criteria included history of a DSM-IV Axis I disorder other than alcohol or cannabis use disorder, use of psychoactive medications, learning disability or mental retardation, neurological condition , or traumatic brain injury with loss of consciousness >2 min; prenatal alcohol or drug exposure; premature birth; left handedness; and non-fluency in English. Participants completed all appointments at the University of California, Department of Psychiatry and asked to refrain from all intoxicants during participation . Self-report measures were administered during the toxicology appointments . Participants were compensated $10 for each successful urine toxicology screen . CON did not test positive for urine marijuana metabolites at baseline or over the course of the study. Participants were not required to be abstinent at the Day 0 appointment, and days since last use of marijuana ranged from 1–18 at Day 0; 80% of MJ reported use within 1–5 days of the Day 0 appointment and 73% tested positive for marijuana metabolites in urine /mL cut-off concentration. Starting at the first toxicology appointment, THCCOOH to creatinine concentration ratios were examined in relation to published data on these ratios determined in marijuana users during sustained monitored abstinence for confirmation of abstinence over the course of 4 weeks. New cannabis use was determined by dividing each THCCOOH normalized to creatinine concentration by the previously collected THCCOOH normalized to creatinine concentration and comparing this ratio to the 95% CI ratio for the time interval between the collections. For example, the 95% limit for the U2/U1 ratio was 1.when the collection interval was ≤ 24 h and 0.91, 0.51, 0.24, and 0.14 for collections ranging from 1–4 days, respectively. A successful urine toxicology screen was determined by determining the time difference between the urine specimens, selecting the correct metabolite ratio for this time frame, and comparing the obtained U2/U1 ratio for the participant to the 95% limit for the specific time difference . Breath alcohol with the Alco-Sensor IV Breathalyzer was also evaluated for all participants at each urine toxicology screen appointment and sobriety from alcohol was confirmed for all participants . Fifty-six individuals finished the 28-day protocol ; 8 of n=26 users reported ≤4 days of cannabis use during the monitored abstinence period; however,vertical grow room design biweekly toxicology screening showed a trend of decreasing THCCOOH/creatinine ratios among all users that completed. Loss to follow-up was relatively small and within the acceptable range for clinical trials ; the four individuals that did not complete the protocol were marijuana users that continued to use during monitored abstinence and failed to complete the final appointments. Those four individuals were not included in the final sample or any statistical analysis presented in this manuscrip.The Customary Drinking and Drug Use Record assessed quantity and frequency of lifetime marijuana, alcohol, cigarette, and other drug use and age of marijuana use onset . The Timeline Follow back quantified self-reported substance use at each visit during the 28-day monitored abstinence protocol . Marijuana symptoms, expectancies, and consequences questionnaires were administered throughout the protocol .

The Marijuana Craving Questionnaire is a 10- item self-report questionnaire -70 that evaluates intention and desire to smoke marijuana, anticipated pleasure, and anticipated relief from negative affect and withdrawal . The Marijuana Withdrawal Discomfort Scale is a 30-item self-report form on which participants rate the severity of withdrawal symptoms to severe over the past 24-hours ; these symptoms change with marijuana use but include experiences related to mood and sleep that CON may also experience. Total MWDS scores range from 0–90. The Marijuana Problem Scale assesses 19 functional problems to serious problem associated with marijuana use and total scores range from 0–38. The Marijuana Effect Expectancy Questionnaire provides a measure of appraisal on six sub-scales , relaxation/tension , social/sexual facilitation , perceptual/cognitive enhancement , global negative effects , and craving/physical effects ; this 48-item instrument asks participants to identify a value between 1 and 5 for each item to identify if a participant expects marijuana-related effects to occur in one or more of these domains . High scores reflect a high level of expectancy on the corresponding sub-scale. The Beck Depression Inventory Second Edition and Spielberger State Trait Anxiety Inventory assessed depressive symptoms and state anxiety . State Trait Anxiety scores were converted to gender-normed T-scores for high-school age boys and girls . The Family History Assessment Module evaluated family history of psychiatric and substance use disorders. The Pittsburgh Sleep Quality Index is a brief self-report measure administered to capture sleep quality via a global summary score. The PSQI contains 18 items and yields seven sub-scales – worse that measure sleep onset latency, efficiency, duration, disturbance, days of dysfunction, overall quality -21; poor sleep quality threshold >5, and sleep medication usage. The Behavioral Inhibition System and Behavioral Approach System scales consist of 24 items that measure avoidance and approach sensitivities reflective of reward sensitivity personality traits. Four response options range from very true to very false for me ; BAS sub-scales include reward responsiveness, fun seeking, and drive. The Barratt Impulsiveness Scale is a 30- item self-report measure administered to assess impulsivity; items are on a 4-point scale and range from rarely to almost/always . Barratt sub-scales examined include cognitive impulsivity , motor impulsivity , and non-planning impulsivity . The Wechsler Abbreviated Scale of Intelligence Vocabulary sub-test was included as an estimate of premorbid intellectual functioning . Parental income and grade point average were collected during a comprehensive clinical interview at baseline. We focused on four secondary a priori analyses for measures in which we observed a change over time. These correlations focused on two key variables 1) cumulative marijuana use , and 2) age of marijuana use onset. These variables show robust associations with neurodevelopmental and mental health functioning outcomes in the research literature and with neural health in this sample in particular . Therefore, the study addressed three key questions: is age of MJ use onset or cumulative MJ use associated with 1) self-reported changes in depression, anxiety, or sleep quality over monitored abstinence, 2) changes in MJ use expectancies, withdrawal, and craving over monitored abstinence, or 3) reward sensitivity and attentional impulsivity. We also examined if change in MJ use expectancies was related to change in emotional distress over monitored abstinence, given the increasing attention to how beliefs about marijuana use may distinctly influence treatment outcomes and use patterns . The current findings expand the literature in several ways including: 1) MJ demonstrated decreased self-reported subsyndromal depression symptoms by week three of monitored abstinence, and greater changes in depression and anxiety symptoms were observed in those reporting more lifetime marijuana use at baseline; 2) group differences in perceptions of sleep quality and sleep disturbance resolved by Day 28, although MJ continued to report less sleep than controls; 3) MJ reported increased expectation of global negative effects and less expectation that marijuana helps reduce tension and anxiety after completing 28-days of abstinence; and 4) MJ reported less incentive sensitivity and more attentional impulsivity compared to controls, measured after self-reported subsyndromal emotional symptoms substantially decreased . Findings also support the extant literature identifying withdrawal and craving symptoms following cessation of use .

In each experiment, therefore, we included a 5-min sampling period prior to the source period to account for the variation in PM2.5 background concentration. For each indoor experiment, we added a 60-min sampling period following the source period for determining the PM2.5 decay inside the building. The decay rates were determined by the log linear regressions between 1-min PM2.5 concentrations averaged over the 5 monitors versus time during the well-mixed decay periods. For the same source type , the PM2.5 decay rate could reflect the relative strength of air mixing indoors. A higher air change rate will lead to stronger indoor air mixing , enhancing the particle surface deposition . This suggests both the cause and consequence of stronger air mixing could contribute to a higher decay rate. Therefore, given a comparable evaporation loss rate , a larger decay rate could indicate stronger air mixing indoors, which could cause more uniform concentration and a smaller proximity effect. Air mixing is one governing factor that affects the spatial distribution of concentration and exposure close to a source . By examining the decay rates for experiments with the same source type, we can ensure comparisons are based on comparable air exchange and air mixing conditions.To determine the source and environmental characteristics in each indoor experiment, we calculated the average exhalation peak velocity and duration and the decay rate . Table 1 summarizes the statistics of average exhalation peak velocities, average exhalation durations,vertical grow system and decay rates for indoor smoking versus indoor vaping from 16-17 experiments with all the windows and doors closed without fan operating . These base-case experiments had background air velocities below the anemometer’s detection limit – this enabled more accurate determination of exhalation velocities for the two different sources.

The mean of average exhalation peak velocities for indoor smoking was ~2 times as high as that for indoor vaping . The mean of average exhalation durations for indoor smoking was ~70% of that for indoor vaping . The mean decay rate for indoor vaping was higher than the mean decay rate for indoor smoking . Particle losses due to air exchange and particle settling are expected to be comparable for indoor smoking and vaping experiments; the sizable difference was likely due to the higher aerosol volatility for vaping. This finding was consistent with previous studies testing the decay rates of 4 different marijuana sources inside a car chamber and in a residential bedroom . Li et al found PM2.5 particle loss rates for vaping aerosols were >4 times as high as that for – Di-EthylHexyl-Sebacat aerosols with little evaporation. In addition to exhalation pattern, aerosol evaporation could have a significant effect on exposure versus distance from the source. The average air velocities for outdoor experiments ranged from 0.21 to 0.33 m/s. The highest average velocity was recorded when the overhead outdoor umbrella was folded . This could be due in part to less blockage of the air movement. Klepeis et al and Acevedo-Bolton et al measured ground-level air velocities in the backyard of a California home. Their reported average air velocities were comparable to our measured values. These backyard measurements are expected to be affected by eddy currents near buildings. Figures 2 and 2 show examples of the 1-s concentration time series of PM2.5 measured indoors and outdoors at 1 m, 2 m, and 3 m horizontal distances from the participant performing marijuana vaping in the residential property . Unlike the standard indoor experiments that were performed separately with 1-h decay periods , continuous indoor measurements were taken across multiple source periods with only 5 minutes apart. This was to align with the emission sequence of the outdoor time series to allow comparisons between Figures 2 and 2. Here, all concentrations greater than the monitor’s upper limit were replaced with 20 mg/m3 , giving maximum concentrations ~10 mg/m3 . For both the indoor and outdoor experiments, the magnitudes and occurrences of transient concentration spikes – “micro-plumes” – increased with decreasing distances, showing the proximity effect during active emissions .

Striking differences were observed between indoor and outdoor situations. Micro-plumes were much more likely indoors than outdoors. In the indoor environment , aerosols could follow the exhaled airflow, moving toward the monitors that were in front of the vaper. In contrast, aerosol movement outdoors was primarily governed by the wind patterns. The rapidly changing directionality of outdoor air flows near the building made micro-plumes less likely to emerge. The durations of micro-plumes were longer indoors than outdoors. The slower air movement indoors could make emitted plumes linger at a monitoring location. This effect can also be seen from the persistent PM2.5 concentration time series after each source emission period ended indoors. As expected, the more frequent occurrences and longer durations of micro-plumes indoors greatly increased the average concentration and exposure at close proximity to the active emission source. Figure 3 summarizes the time-averaged PM2.5 concentrations over the 5- min source periods at 1, 2, and 3 m distances from the source in all the 35 indoor and outdoor experiments with marijuana smoking and vaping. Figures 3-3 correspond to the condition with all windows and doors closed and without HVAC fan running whereas Figure 3 involves opening a door and two windows and with HVAC fan running . Figures 3-3 correspond to the condition with the umbrella open and above the smoker whereas Figure 3 involves fully closing the overhead umbrella . Each boxplot contains measurements from the 5 SidePak monitors at different angles in front of the smoker with the dashed line representing the mean value and the solid line representing the median. Background concentrations ranged from 1.2 to 6.8 mg/m3 ; they were subtracted from these 5-min PM2.5 averages. Statistics of each boxplot are available in the Supplementary Material . The 5-min PM2.5 concentrations at 1 m were higher and more variable for indoor vaping than for indoor smoking versus 3). However, the levels of indoor vaping decreased more noticeably with distance than for indoor smoking .

This finding could be associated with the difference in exhalation pattern – the exhalation peak velocity for indoor vaping was only ~50% that of indoor smoking. Therefore,indoor vertical garden systems vaping aerosols are expected to have longer time for decay before reaching a given distance. Another consideration involves the aerosol evaporation process – the higher decay rate of the vaping aerosols due to their higher volatility could also result in a greater concentration decrease over distance. The PM2.5 exposures for indoor marijuana smoking were much higher than for indoor tobacco smoking . This could be caused by the higher emission rate for marijuana smoking accompanied with the smaller indoor volume . Another factor was the different monitoring setups – our study used 5 monitors to cover 60o angle facing the smoker, making it more likely to capture the emitted plumes than a single monitor. Similarly, PM2.5 exposures for indoor marijuana vaping were much higher than indoor e-cigarette vaping . This again was likely due to more monitors at each distance and the smaller indoor volume . Both vaping sources had a significant concentration decrease over distance, but the marijuana decrease was smaller . This could be due in part to the lower aerosol volatility of marijuana vaping compared to ecigarette vaping . Figure 3 shows the measurements from the only 3 indoor vaping experiments with the HVAC fan operating in the house . In addition to lowering the 5-min PM2.5 levels , mechanical ventilation greatly reduced the variation of the 5-min PM2.5 averages measured at the 5 different angles at each distance versus 3). In addition, it diminished the pronounced concentration gradient over distance observed without mechanical ventilation operating. As expected, stronger air mixing due to mechanical ventilation made the PM2.5 concentration more uniform in space. The outdoor 5-min PM2.5 levels at each distance were less than 5% of the indoor levels for either smoking or vaping. Therefore, a different vertical scale was needed for Figures 3-3. Again, the varied airflow direction and more rapid plume movement outdoors made the PM2.5 exposures in front of the smoker much lower than indoors. The PM2.5 exposure for outdoor marijuana smoking was higher than for outdoor tobacco smoking: 13 g/m3 at 1 m and 29 g/m3 at 0.8-1.5 m .

In addition to the higher emission rate for marijuana smoking , use of 5 1-m monitors under an outdoor umbrella with the smoker made plume encounters more likely . Most of the outdoor experiments involved the participant smoking or vaping under an outdoor umbrella except for the 3 alternative-case experiments in Figure 3 . In these 3 experiments without an umbrella above the smoker, the lower exposures were likely caused by the less-enclosed setting. This, in combination with the highest recorded average air velocity , could cause greater dispersion of emitted particles near the smoker. For each box plot in the 4 base-case graphs -3 and Figures 3-3, we separated the 5-min averages into two groups based on 1 and 1.5 m breathing heights and calculated the mean for each group. For indoor vaping, the means of the 5-min averages for all the 3 distances were higher at 1 m than at 1.5 m height. This is not surprising as the source was closer to 1 m height. In contrast, the means for all the 3 distances were higher at 1.5 m than at 1 m height for indoor smoking. The difference was greatest at the shortest distance ; the mean at 1.5 m height was ~1.7 times as high as the mean at 1 m height. This might be due to the stronger plume buoyancy created by acombustion source – the burning joint – thus increasing the means at 1.5 m height. The means of the 5-min averages outdoors -4) did not necessarily follow the same pattern observed indoors; for outdoor smoking, the mean at the 1.5 m height was greater at 1 m distance, but the outdoor means at 1 m height became greater at the 2 and 3 m distances. In the presence of outdoor wind, the effect of plume buoyancy could become less noticeable, especially for greater distances from the source. Figures 4-4 show the cumulative frequency distributions of 1-s PM2.5 concentrations collected during 5-min source periods on log-probability graphs for 18 indoor and outdoor experiments with smoking and vaping. Again, the left four graphs corresponded to the base-case experiments indoors -4; with all windows and doors closed; without HVAC fan running and outdoors -4); outdoor umbrella open above the smoker. The right two graphs and 4) corresponded to the alternative-case experiments indoors and outdoors , respectively. Each frequency distribution contains aggregated measurements from the 5 SidePak monitors at different angles . Each graph compared the cumulative frequency distributions at 1, 2, and 3 m distances from the 3 experiments with similar environmental conditions. Indoor experiments that had comparable decay rates were grouped together for each graph: 0.34-0.37 h-1 for smoking, 0.97-1.06 h-1 for vaping, and 6.9-7.8 h-1 for vaping with a door and two windows opened and HVAC fan running. Experiments in each outdoor graph -4) were conducted consecutively with 5 min intervals to minimize the outdoor weather variation. To avoid negative values for the log scale concentrations, the background concentrations were included in these 1-s PM2.5 concentration frequency distributions. Plotting a cumulative frequency distribution on the log-probability graph, one can visualize the frequency of exceeding any given concentration limit. Taking figure 4 as an example, 10% of the concentrations exceeded 1000 g/m3 at 2 m from the source. The frequency increased to ~40% at 1 m and decreased to 0% at 3 m. For the same frequency of exceedance , the concentration limit increased to ~4000 g/m3 at 1 m and decreased to ~150 g/m3 at 3 m. Compared to indoor smoking, the frequency distributions for indoor vaping showed much greater separation at the 3 distances. For example, from 1 to 3 m distance, the frequency of exceeding 1000 mg/m3 dropped ~40% for indoor vaping but only ~10% for indoor smoking. The more noticeable decrease in the frequencies for vaping again could be associated with the longer travel time and the higher decay rate compared to smoking. Turning on the mechanical ventilation system flattened the cumulative frequency distribution at each distance for the middle range of concentrations .

After regressing all other arrest rates, drunken arrests remains the only significant category affected by MMICs. So with the given data, there is no evidence that marijuana is a substitute for dangerous drugs, other drugs, felony drugs, nor narcotics. It is particularly surprising that we see no effect on narcotics, considering most medical marijuana patients specifically use cannabis as a substitute for narcotics. An explanation for this can be that some medical marijuana users do not use for medical reasons many of the MMIC holders in this particular data base may only use for recreational purposes. To observe any further substitution effects, I used Equation 5.7 to regress alcoholinduced crude rates, drug-induced crude rates, and all other crude rates on MMICs and unemployment still controlling for county and year fixed effects. Unlike the arrest rate data, no substitution effects were found. Referring to the regression output in Table 5.9 for alcohol-induced deaths, MMICs actually had a statistically significant positive effect on alcohol related deaths. The interpretation is that for every new medical marijuana user, the alcohol crude rate increases by 0.0068 deaths per 100,000. However, observing that zero is in the confidence interval and that the t-statistic is borderline significant, it is likely that there is no effect at all. While this is still a positive number, its suggested effect is so small, it becomes negligible. This can be determined by looking at the average crude rate for alcohol related deaths,grow slide racks which is 15.8. There would have to be an additional 147 MMICs per 100,000 to increase this crude rate by 1 death per 100,000. This is a highly unlikely scenario, and could therefore be dismissed. By applying this same model to drug-related deaths, we again get a statistically significant positive effect on the crude rate, shown in Table 5.10.

While this would typically suggest that marijuana is a complement drug to other drugs, the effect is again, miniscule. With the average drug-induced crude rate of 13.4 deaths per 100,000, the number of medical marijuana cardholders would have to increase by 142 to cause 1 drug-related death. Similar to the effect on alcohol-induced mortality rates, this is a very unlikely event, and can be disregarded. While the drug and alcohol related deaths were affected slightly by medical marijuana, all other crude rates did not. There was no statistically significant effect when applying Equation 5.7 to all other crude rates.Papers range from casual discussion, passionate and involved such as those by Annas1 and Okie2 , to serious logical argument exemplified beautifully in Cohen’s3 work. Annas1 and Okie2 focused on California’s 1996 medical marijuana law and the 2005 Supreme Court trial Gonzales v. Raich respectively. Cohen3 had a larger scope, reviewing marijuana’s history in the United States from the colonial era to present-day. While the former sources made mention of some valuable scientific evidence, they did so amidst a great deal of personal appeal and anecdotes about those affected. Quotes from doctors, talking about their personal recommendations for patients to use marijuana, and, admittedly, evocative statements from politicians or newspapers frame the discussions. For instance, Annas quotes a Boston Globe writer’s question asking that if legalizing medical marijuana would send the terrible message to children that “If you hurry up and get cancer, you, too, can get high?”1 . Cohen’s argument did not lack pathos, but he presented his opinion in a strong logical argument, clearly referencing medical findings. All three papers argued, presuming that sufficient medical evidence exists to prescribe marijuana. They, instead, focused on the issue marijuana’s legality, rather than on analyzing the validity of the cited data. Drug abuse and dependence are important considerations for both FDA and Congressional policymakers. While marijuana is relatively non-addictive, especially when compared to FDA-approved opium, cocaine, and methamphetamine, it remains the most abused drug in America.The authors of “Medical marijuana laws in 50 states: Investigating the relationship between state legalization of medical marijuana and marijuana use, abuse and dependence” analyzed use, abuse, and dependence statistics across the U.S. to measure variance caused by marijuana’s legal status.

They concluded that rates of addiction, abuse, and dependence did not vary with overall use, but did not develop the idea much further. To expand upon the study the authors could have spent more time discussing why use rates varied with legality. The authors also could have discussed the consequences of the observed use, abuse, and dependence rates and how they should concern or placate readers. While ample research has been done on the cannabinoids thought to give marijuana its medical value, not all results have been conclusive or widely accepted. “Endocannabinoids in nervous system health and disease: the big picture in a nutshell” provides a broad yet detailed overview of the endocannabinoid system, which is the biochemical pathway that delta-9-tetrahydrocannabinol and other cannabinoids act upon.Some of the sections within this article require more than a casual knowledge of biochemical pathways, or at least their terminology, to follow. Though it occasionally delves into deeper discussion of biochemical pathways, the paper is not too difficult to follow and certainly delivers a “big picture in a nutshell.Borgelt, Franson, Nussbaum, and Wang6 and the Harvard Mental Health Letter article “Medical marijuana and the mind”put an emphasis on the pharmacology of marijuana and discuss both the current drug delivery methods and the side effects. These two articles differ drastically in their tone, however. Borgelt, Franson, Nussbaum, and Wang discuss, in detail, the mechanisms by which marijuana elicits its effects. “Medical marijuana and the mind”lists the effects of marijuana and discusses the drugs that contain THC, but doesn’t delve into the pharmacokinetics. Unlike most papers, emphasis was placed upon findings that indicate marijuana may increase psychotic episodes in those with schizophrenia and bipolar disorder. The debate on these findings continues to this day without a clear consensus. The author refrains from discussing precise biochemical pathways in favor of discussing the consequences of each mode of delivery or side effect. By keeping avoiding technical terms when possible, the author achieves a casual tone capable of reaching out to a broad audience. Both “Medical marijuana and the mind” and “The pharmacological and clinical effects of medical cannabis” agree that smoking constitutes the largest barrier to marijuana’s acceptance within the medical community.Should a viable alternative be developed, marijuana could become legal once again. With the exception of Cohen3 , these two articles have the most balanced discussion of both the pros and cons of medical marijuana in its current state. Increasing amounts of research have been performed on the effects of marijuana smoke and ways to replicate its efficient drug delivery without its harmful side effects. Owen, Sutter, and Albertson look exclusively at the harm of marijuana smoke on the lungs as it compares to tobacco smoke. They found that, like tobacco smoke, marijuana smoke increases the risk of “pulmonary symptoms such as wheeze, cough, and sputum production.”However, it may not lead to chronic obstructive pulmonary disease.

Somewhat confusingly, the paper also discusses marijuana’s effect on the immune system and cancer cells, which doesn’t seem to be directly related to the title of the paper. Though they explain how marijuana smoke can harm the lungs quite thoroughly, there are often departures into less closely related subjects such as the immune system. As a result, it sometimes feels as though the paper is about the endocannabinoid system as a whole, rather than how marijuana smoke affects the lungs. Hazekamp, Ruhaak, Zuurman, Van gerven, and Verpoorte decided to analyze the dosage delivery of the “Volcano” vaporizer. Vaporizers attempt to circumvent the harm of smoke during marijuana inhalation by boiling the THC and cannabinoids into a vapor without actual combustion,fl0od tables for greenhouse which produces most of the harmful particles in smoke. The study discovered that the vaporizer delivered similar amounts of THC as traditional smoking, but with less variance. They state that they used one of the multiple heat settings on the device because, by their calculations, it was the most efficient. Not all users may be able to tolerate that temperature setting, so it would be worthwhile to see if the delivery method remains passably effective at other settings. The examiners pointed out that they only studied THC delivery and, while this is the most studied and well understood cannabinoid present in marijuana, it may not be wholly responsible for marijuana’s therapeutic effect. Consequently, research comparing the delivery of the other compounds is necessary. Uritsky, McPherson, and Pradel10 ran an online survey of hospice workers to determine attitudes towards medical marijuana in the industry. While they found that a majority of responders support medical marijuana, they highlighted several potential flaws with their own research. They only surveyed workers for one company, which may attract employees with particular viewpoints based on its policies. Because the survey was run through a website, responders could have submitted answers multiple times by using different computers. Additionally, a high proportion of workers are either volunteers or unlicensed, so their support might be simple personal opinion rather than the result of research and knowledge about the issue. The questions in the survey seemed appropriate for what the researches sought to discover. Perhaps the imprecision of survey-taking, in general, caused more problems than anything the researchers did.Marijuana, a mix of dried flowers of the cannabis plant, is used by between 7.5% and 9.4% of the United States population. With increasing legalization for recreational and medical use, concern about its possible health effects is rising. Heart health is a special concern, since case reports from the early 2010s suggest that marijuana may trigger heart attacks in healthy adults without significant coronary atherosclerosis. Some retrospective studies in France and the USA explore the possible association between marijuana use and cardiovascular incidents around the same time and found recent marijuana use raised myocardial infarction incident risk nearly five-fold for a one-hour period after use ,, but most patients in this study were predisposed to cardiovascular disease . In contrast, larger observational studies in the USA, Sweden and Belgium published between the late 90’s and the late 2010’s found no association between marijuana use and incident CVD . We know marijuana can have both pro-atherogenic effects, from activating the Cannabinoid receptor type 1 , and anti-atherogenic effects, by activating CB2. Previous analyses of the Coronary Artery Disease Risk of the Young study, a longitudinal study with over 5,000 participants and up to 30-year follow-up in the USA, found that cumulative marijuana use was not associated with markers of sub-clinical atherosclerosis like coronary and abdominal calcium score, but that tobacco cigarette smoking was associated with increased risk of these outcomes. Since CARDIA follows a relatively young cohort into early middle-age, participants may be too young to exhibit signs of CVD. Marijuana could also be associated with increased risk of future CVD non-atherosclerotic in origin. A potential increase in future risk of CVD could be captured by studying the association between marijuana use and electrocardiograms , as observational studies following over 1,000 participants in the USA over more than 10 years suggest. So far, only a few, small experimental studies , mainly from the late 1970s, examined the cellular mechanism that might connect marijuana use and abnormalities in ECGs. Some identified associations between marijuana use and these ECG abnormalities: P-wave axis abnormality; atrial flutter; atrial fibrillation; transitory 2nd grade atrioventricular block; premature ventricular contraction; elevated ST-segments; T-wave axis abnormality; and, decreased or increased R-R interval , and signs of Brugada pattern. Because of the limited size of participants, findings of these studies are inconsistent, differed according to sex and race, and, in most cases, could not be reproduced between studies. We set out to explore potential associations between current and cumulative marijuana use and ECG abnormalities in a large black and white cohort, followed over two decades. We used data from the CARDIA study. CARDIA is a cohort of 5,115 black and white women and men, aged between 18 and 30 years at baseline, from four study sites in the USA followed over 30 years. The study strove for equal distribution of race, sex, education, and age at each site.

Among cancer patients taking prescription opioids, opioid prescribing patterns are associated with the risk of opioid overdose death. Medicinal marijuana has been shown to have analgesic properties, and specifically for cancer patients, has demonstrated relief from adverse effects of therapy like nausea and anorexia, with few reports even suggesting antineoplastic activity. Recent research among Medicaid beneficiaries suggests that medical and adult-use marijuana has the potential to lower opioid prescriptions. As of 2016, approximately 60% of the US population now resides in states with legalized use of medicinal marijuana, which highlights increasing public support given its promising medical benefits. A cross-sectional survey of adult cancer patients in Washington State showed that nearly a quarter of patients reported active cannabis use. Classification of marijuana as a Schedule I controlled substance , however, makes large-scale clinical studies challenging. While marijuana use appears to be quite promising in the management of chronic and neuropathic pain, there are associated adverse effects including the potential for addiction, impairment of memory and judgement, and the potential to exacerbate psychiatric illness including depression and anxiety. There is limited population-based or epidemiologic data on marijuana and other substance use specifically in patients with cancer. The primary objectives of this study were to examine the associations between cancer and marijuana use as well as between cancer and prescription opioid use in a population-based setting. We also sought to examine trends in marijuana and opioid use over a 10-year period given the evolving legislation for marijuana legalization and dynamic temporal changes in prescription opioid use. We compiled population-based datasets from the US National Health and Nutrition Examination Survey ,commercial grow setup a survey designed to assess the health and nutritional status of non-institutionalized adults and children in the US.

This nationally representative, biennially administered survey interviews 10,000 individuals per two-year cycle about demographic characteristics , substance use, and medical conditions. We compiled five biennial datasets from 2005-2014 and included all respondents aged 20-60 years , which includes all respondents that were asked to report on a cancer diagnosis and marijuana use . Respondents missing a definitive ‘yes’ or ‘no’ response to cancer diagnosis were excluded. Table 1 summarizes the NHANES variables considered in the analyses. Respondents were grouped by reported diagnosis of cancer. For respondents with multiple cancer diagnoses, primary cancer site was defined as the first site reported. Demographic variables of interest included age, gender, race, education, self-reported health status, low income, and insurance coverage. Age was analyzed as a continuous variable. Race was categorized as non-Hispanic white, non-Hispanic black , Hispanic, and other. Education was dichotomized as less than college-level education versus college-level education or beyond. Self-reported health status was dichotomized as “good” versus “poor” . Low income was categorized as annual household income of less than $20,00031 versus $20,000 and above given the average federal poverty line for a family of four from 2005-2014. Insurance coverage status was categorized as covered or not covered. Current marijuana use was defined as use within the past 30 days and recent marijuana use as use within the past year. Prescription opioid use was defined per the Prescription Medication subsection of the survey on use of prescription medications during a one-month period prior to the survey date and included the following generic drug names: morphine, hydrocodone, codeine, oxycodone, fentanyl, dihydrocodeine, hydromorphone, meperidine, and methadone. Additional substance use variables included cigarette smoking, binge alcohol use, and illicit drug use. Cigarette smoking was defined as having smoked at least 100 cigarettes in a lifetime.

Binge alcohol use was defined as drinking an average of more than 5 drinks/ drinking day in the last year for men and more than 3 drinks/drinking day for women. Illicit drugs included cocaine, heroin, and methamphetamines . Current illicit drug use was defined as use within 30 days. The primary explanatory variable of interest was diagnosis of cancer, while the primary outcome variables were marijuana use and prescription opioid use. Other associated variables explored included previously-described demographic variables and other substance use including alcohol, smoking, and current illicit drug use. Given the potential for poly substance use in this cohort,we also investigated the relationship between our primary outcomes of marijuana and opioid use. Propensity score matching was performed to compare respondents with cancer to controls . A 1:2 matching was performed based on a nearest-neighbor matching algorithm with a caliper width of 0.1 of the propensity score with age, gender, race, education, and self-reported health status as co-variables. These demographics were chosen to better estimate the association between cancer diagnosis and marijuana and prescription opioid use, especially given the tendency of NHANES to over sample certain groups . Cancer respondents and propensity score matched controls were compared for primary outcome measures of current marijuana use and prescription opioid use using Pearson chisquare tests for categorical data and independent sample t-tests for continuous data . Univariable and multi-variable logistic regressions were used to evaluate significantly associated variables of marijuana and prescription opioid use among both cancer and non-cancer matched controls . Demographic and substance use co-variables that were not significant at level P<0.10 on multi-variable analyses were removed via backward stepwise elimination from the final multi-variable logistic regression models 36. Conditional logistic regression models were used when analyzing the propensity score matched cohort to account for the matched pairs.

Logistic regressions were used to investigate trends in marijuana and opioid use over the 10- year time-period for all NHANES respondents as well as cancer respondents, and to investigate differences in these trends between respondents with cancer and matched controls by using an interaction term of year and cancer diagnosis. Survey sampling weight, strata, and clusters were accounted for in any analysis of non-propensity score matched cohorts . Two tailed P<.05 was considered significant for all analyses. All statistical analyses were done using SAS v9.4 . In an era of rapidly evolving marijuana legislation and a growing opioid epidemic, it has become critically important to understand and quantify current substance use patterns. To our knowledge, this is the first population-based analysis of the prevalence of marijuana and prescription opioid use in people with a cancer diagnosis. Among cancer respondents, 8.7% and 40.3% reported using marijuana in the last 30 days and one year, respectively. This contrasts with a recent survey of cancer patients in Washington State which found that 24% used cannabis in the last year and 21% in the last 30 days. While cancer respondents in this study self-reported more current and recent use of marijuana than non-cancer matched controls, cancer was not significantly associated with current marijuana use. This may be in part because our data do not specify medicinal versus recreational marijuana use, the former being more associated with managing cancer-related symptoms, including pain. Among cancer patients surveyed in Washington State, active users reported using cannabis most frequently for pain. Also, we analyzed years 2004-2015, so perhaps with future datasets reflecting the evolving role of marijuana in oncology18 and broadening legalization, the association of cancer and marijuana use may change. Nearly 14% of cancer respondents reported prescription opioid use in the last month,vertical grow racks for sale and cancer diagnosis was the only variable significantly associated with opioid use. Indeed, opioid analgesics are critical to the management of moderate to severe cancer-related pain,and we cannot draw conclusions regarding the association between cancer status and opioid misuse from this analysis presented here. However, it is becoming increasingly important to identify risk factors for opioid misuse, such as younger age and higher pain levels, which have previously been identified among cancer patients being treated for pain. We did find that insurance status trended towards a significant association with opioid use, likely reflecting access to a prescribing provider. A previous study found that uninsured and low income adults had a higher prevalence of prescription opioid misuse and substance use disorders. While there are no randomized trials of marijuana compared with prescription opioids for cancer-related pain, patients are increasingly reporting the use of cannabis as a substitute for prescription opioids.

Oncology patients may have apprehensions about opioids including fear of dependence and potential side effects. Indeed, the most commonly reported motivation for opioid misuse is pain relief, yet these fears introduce potential barriers to effective cancer pain management. Medical marijuana legalization has been associated with a 23% reduction in hospitalizations related to opioid dependence or abuse, suggesting that if patients are in fact substituting opioids with marijuana, this substitution may reduce the risks of opioid-related health problems. However, most large-scale randomized trials of marijuana use for pain are limited to non-cancer pain17, and there may be potential adverse effects of marijuana use that should be considered. We found an increase in the proportion of marijuana users between 2005-2006 and 2013-2014 with a significantly increased likelihood of 5% each two-year study period among all survey respondents. This finding reflects increased US support of marijuana legalization and changes to local and state legislation over this decade. In 2005, 36% of the population supported marijuana legalization; in 2014, 51% of Americans were supportive. Between 2005-2014, seven states legalized medical marijuana, while four states and Washington, DC legalized marijuana for recreational use. By November 2014, nearly 175 million people lived in areas where recreational or medical marijuana were fully legal or decriminalized. This phenomenon is particularly relevant for oncology, as prior studies have shown that legalization is an important factor in cancer patients’ decision to use cannabis. Given the current opioid epidemic with sales of opioid pain relievers quadrupling between 1999 and 2010, it is interesting that there was no significant increase in the proportion of respondents using prescription opioids between 2005-2006 and 2013-2014. This outcome echoes a recent Centers for Disease Control report, which found that recent annual opioid prescribing rates actually decreased by 13.1% between 2012 and 2015, yet still remained three times as high compared to 1999. A recent observational study over a 6 year period found that doses of opioids prescribed to cancer patients had decreased. These recent decreases suggest heightened awareness among physicians and all patients about the risks associated with opioid pain relievers. The increase in marijuana use measured in this study in the context of stable opioid use highlights the significance of increasing marijuana usage between 2005-2006 and 2013-2014. This study has several limitations. Given the cross-sectional study design, our findings are associations and not indicative of a causal relationship between cancer and marijuana or opioid use. Future studies that further investigate these relationships should consider investigating additional clinical characteristics not accounted for here but previously shown to predict opioid abuse, such as number of opioid prescriptions, number of opioid prescribers, early opioid refills, and psychiatric diagnoses51. Second, data currently available from NHANES does not include results beyond 2015. Thus we are unable to capture time and prevalence trends after some of the most recent legislative changes in marijuana legalization and responses to opioid epidemic. With NHANES data we cannot discern between medicinal and recreational marijuana use. The cancer variable for our analysis is not confirmed with medical records but instead is self-reported and subject to recall bias. Thus, we do not have additional information about respondent cancer status that may impact substance use and it is possible that these data may not be generalizable to all cancer patients with a verified diagnosis. However, NHANES data has been used to investigate cancer in other studies. Finally, we defined opioid use based on filling a prescription within the last 30 days, which may be an under representation of total opioid use. While the complex, multistage probability sampling method of NHANES data collection introduces statistical challenges, our analysis effectively accounts for confounding variables via propensity score matching and multi-variable analyses. Ultimately, while the NHANES data is self-reported and subjective to sampling bias , we are able to investigate the outcome of substance use in this representative population otherwise not previously documented. Currently, medical marijuana is legal in 25 states and Washington DC, with retail marijuana legalised in four states and Washington DC. On 1 January 2014, Colorado became the first state to legally sell retail marijuana to people 21 years or older. Shifting regulations have been accompanied by technological innovations, including electronic vaporisers for tobacco and marijuana. These developments are likely to transform use of these substances, especially among young adults.

A primary limitation is that the parent study was designed to focus on tobacco rather than marijuana use, and thus assessment of the latter was less detailed. However, it is important to note that robust relationships emerged despite this limited assessment. Relatedly, the items assessing use of specific tobacco products did not allow us to separate use of traditional cigars and cigarillos, and so these were grouped into the “OTP” category. Because these products are commonly used as “blunts” to smoke marijuana, being able to differentiate their use may provide additional important information. Moreover, our assessment of marijuana use was limited to frequency and did not capture quantity of use nor the extent to which use of marijuana and tobacco products was simultaneous. Another limitation is that the sample was composed of 18-24 year-old California residents who were non-daily cigarette smokers at baseline, and may not generalize to other populations with differing levels of social and legal acceptance of tobacco and/or marijuana use. Previous research has indicated that young adults who are intermittent cigarette smokers are the most likely to engage in co-use, the issues are particularly relevant for this group . However, future research examining whether these associations differ in other settings would make a valuable contribution. A final limitation is that this study did not examine mechanisms that might explain the association between tobacco and marijuana use. Cancer and AIDS patients experience weight loss and tissue wasting due to increased metabolic demand and decreased nutritional intake . These complications are important indicators of patient prognosis and may directly result in death . To prevent adverse outcomes related to malnutrition, various treatments have been utilized including corticosteroids, metoclopramide, and progestational agents .

Another appetite stimulant, medicinal marijuana,plant grow trays has been at the center of controversy regarding its therapeutic effect, route, dose, and side effects . Not only has medicinal marijuana been shown to relieve pain, anxiety, and depression, but also, studies among HIV patients reported appetite stimulation and weight gain as the primary reason for medicinal marijuana use . The Food and Drug Administration approved the use of dronabinol, the oral form of THC, for the treatment of anorexia in AIDS patient, but since THC is not water soluble, smoking marijuana remains the most efficient delivery method for THC . Seconds after the first puff of a cannabis cigarette, THC is detectable in the plasma whereas oral administration of THC results in detectable plasma levels within one to two hours . THC may be taken orally in fat containing food or dissolved in suitable pharmaceutical oil, but the absorption remains delayed and variable because of gastric acid degradation and the first pass liver effect. . Due to the potential benefits for cancer and AIDS patients and the recent discovery of the endocannabinoid system, medicinal marijuana’s role in appetite stimulation has been an active area of research. In 1997, researchers initially found that THC did not produce acute appetite stimulation in the rat , but further studies disproved this previous hypothesis. Today, THC is known to bind to cannabinoid receptors located in the brain and may play a critical role in the leptin pathway, a critical system for appetite stimulation. This paper will explore the current knowledge of medicinal marijuana and its role in appetite stimulation.For many years, the effects of THC on the brain remained a mystery. The first major step in understanding the mechanism of THC was brought about by Matsuda et al with the discovery of cannabinoid receptors. Further research identified two cannabinoid receptors, CB1 and CB2, which are coupled to G inhibitory proteins . Activation of these Gi proteins inhibits adenylate cyclase with subsequent inhibition of AMP’s conversion to cAMP. Due to their role as neuromodulators at axon terminals, cannabinoid receptors are hypothesized to be presynaptic rather than postsynaptic .

CB1 receptors are located on neurons in the brain, spinal cord, peripheral nervous system, and some peripheral organs and tissue whereas CB2 receptors are located primarily in immune cells . More specifically, CB1 receptors are located in axons and nerve terminals . The frontal regions of the cerebral cortex, basal ganglia, cerebellum, hippocampus, hypothalamus, and anterior cingulated cortex of the limbic forebrain contain a high density of CB1 receptors . After the identification of cannabinoid receptors, the endogenous ligands for these receptors known as endocannabinoids were discovered. . Of the three arachidonic acid derivatives known as endocannabinoids, N-archidonyl–ethanolamine or anandamide has been the most extensively studied thus far . These endocannabinoids are released locally on demand and are rapidly inactivated by an enzyme, fatty acid amide hydrolase, which provides a possible pharmaceutical target for the modification of cannabinoids and their effect on the brain . Multiple studies have aimed to describe the role of cannabinoids in appetite stimulation. The endocannabinoid anandamide was proven to stimulate food intake in rats, and the CB1 antagonist rimonabant also known as SR141716 suppressed food intake, which resulted in decreased body weight in adult non-obese rats . In a related study, rimonabant was given to diet-induced obesity model mice, and the suppression of appetite and food intake was significant . Further research on mice demonstrated that CB1 knockout mice were significantly leaner than CB1 mice, which helped researchers conclude that endogenous cannabinoids are important in both feeding and peripheral metabolic controls . In an attempt to understand more precise mechanisms of CB1, one study discovered a relationship between ghrelin and CB1 antagonists. Ghrelin, a peptide hormone secreted by the fundus of the stomach, stimulates hunger. Rats that were treated with CB1 receptor antagonists, rimonabant and oleoylethanolamide, demonstrated a decreased level of ghrelin . Research has revealed that endocannabinoids may play an integral role in the leptin pathway, which may be the key to understanding their role in appetite stimulation.

Leptin is the main signal in which the hypothalamus senses nutritional state and modulates food intake. In one study, a defective leptin signaling pathway resulted in increased levels of hypothalamic endocannabinoids which points to a strong association between the leptin signaling pathway and the endocannabinoid system . One mechanism in which leptin decreases feeding is through the inhibition of neuropeptide Y production. Further, neuropeptide Y may be related to the endocannabinoid system. One study proved that the administration of SR141716, a CB1 antagonist, eliminated neuropeptide Y-induced overeating and reduced ethanol and sucrose intake in CB1 wild type mice . Although marijuana may prevent cachexia associated with AIDS and cancer, health care providers must consider the side effects associated with smoking marijuana. Similar to the toxicities associated with cigarettes, smoking marijuana leads to cellular dysplasia and subsequent increase risk for the development of pulmonary malignancy . A different inhalation pattern of marijuana smokers results in a 50% increase exposure to procarcinogen benz-alpha-pyrene and carboxyhemoglobin compared to cigarette smokers . In addition, researchers have identified alveolar macrophage damage as a result of marijuana use . Since a large proportion of CB1 receptors are located in the brain,custom grow rooms marijuana users have been thought to experience neurologic side effects. Unfortunately, many studies have yielded conflicting results of both neuroprotective and neural damaging actions . One systematic review found that marijuana use was associated with lower education attainment and increased utilization of illicit drugs, but a relationship with psychological health problems could not be proven . Although statistics did not prove or disprove this relationship, the evidence points in the direction of marijuana’s negative impact on psychosocial functioning and psychopathology . Marijuana may adversely affect learning, memory, and psychomotor and cognitive performance . In addition, marijuana may influence various forms of impulsivity , driving ability , and flying ability . One phenomenon associated with increased marijuana intake is “cannabis psychosis” which can present with delusions, grandiose identity, persecution, auditory hallucinations, and blunting of emotion . In addition, marijuana use may exacerbate existing psychotic illness . Smoking marijuana may be detrimental to AIDS and cancer patients. First, smoking marijuana may cause hypotension and tachycardia, a stressful response on the body . In addition, these immuno compromised patients may be exposed to life threatening microbes such as Klebsiella, Enterobacter, Group D Streptococcous, Salmonella, and Shigella, which have been cultured from marijuana . Since AIDS patients are treated with anti-retroviral therapies, researchers explored the potential impact of cannabinoids on indinavir and nelfinavir and found no significant impact of marijuana on the efficacy of these drugs . The first written account of medicinal marijuana took place in China in the 5th century BC , and with ongoing research of cannabinoid receptors and endocannabinoids, the therapeutic actions of marijuana are becoming clearer.

Medicinal marijuana has been a controversial topic for many years which is characterized by the petition in the 1970s to convert marijuana from a schedule I drug to a schedule II drug and the support of rescheduling and appeal by the Drug Enforcement agency in the 1980s . In 1996, California proposition 215, the Compassionate Use Act, passed and stated “Patients and caregivers may possess or cultivate medical marijuana for medical treatment” . This vague statement that legalized marijuana enraged the government and health care providers because of the new stereotypes regarding the safety of marijuana and the lack of regulation. As a result, the federal government attempted to eliminate medicinal marijuana indirectly by prohibiting physicians to discuss medicinal marijuana with the consequence of losing prescription writing privileges . In addition, the definition of pharmaceutical grade marijuana and its production has been an area of active debate. The heterogeneous population of medicinal marijuana fails to meet a consistent standard of composition and quality . Solving this problem would require pharmaceutical companies to successfully develop a synthetic cannabinoid derivative . In the modern patient-centered health care system, health care providers must acknowledge the current research and make evidence based decisions on the benefits of medicinal marijuana as a treatment for cancer and AIDS related weight loss. Fifteen years ago, the existence of cannabinoid receptors was unknown, but research has painted a clearer picture of the hypothalamic CB1 receptors’ role in appetite stimulation. Despite the controversy of medicinal marijuana, continued research in this field has opened new avenues for treatment and prevention of the nation’s biggest health care problem, obesity. Understanding the cannabinoid receptors’ role in appetite suppression and its link in the leptin pathway may allow future physicians to treat and prevent obesity . Obesity is a significant risk factor for deadly diseases such as atherosclerosis, hypertension, and diabetes, and further research in medicinal marijuana’s role in appetite stimulation may be the key to curing an obese nation. Although the amount of information regarding medicinal marijuana is vast, there are many areas that need further research for more effective use among patients. First, double blind randomized control trials in humans are needed to truly assess the effectiveness of marijuana in appetite stimulation. Many studies on rats and mice have produced a working scientific basis for medicinal marijuana, but human trials are necessary to assess potential benefits and adverse effects in patients. Further, a risk/benefit analysis of medicinal marijuana is needed. Medicinal marijuana is often disputed as a treatment based on its side effect profile, but terminally ill cancer and AIDS patients might be willing to increase their risk for lung cancer in the long term to achieve an immediate improvement in quality of life. With a target population of immuno compromised patients, research on alternative delivery methods need to be employed to decrease the risk of infection associated with marijuana smoking. Finally, a logistical study on the most effective and safest mechanism for distribution of marijuana in the population must be conducted. With this information, marijuana can be utilized safely to allow sick patients to engage in one of the most essential actions in life, eating. The concurrent or sequential usage of multiple drugs during adolescence is a critical public health problem, spawning a large literature focusing on whether usage of one substance leads to usage of others. The study of interdependence in adolescent substance use yields insight into potential patterns regarding which drugs are used sequentially or concurrently. As these risk behaviors co-occur and accumulate over time for certain individuals and social groups, there is potential to concentrate risk and negative sequelae among these concurrent users making concurrent users a high risk population that may be in need of prioritized and targeted intervention.