Hemp Growing

A systematic search for articles involving substance use and HIV risk behaviors among justice-involved youth published from January 1, 2013 to March 2, 2018 was conducted in PubMed, PsycINFO, Embase, Web of Science, Sociological Abstracts, Social Services Abstracts, and Google Scholar. The search combined four concepts: incarceration, youth, substance use, and HIV risk behaviors. A search strategy was developed in collaboration with a clinical librarian using an iterative process that involved testing search terms, keywords, and controlled vocabulary, including MeSH and Emtree terms, for each of the search concepts and examining the relevance of corresponding search results. The search strategy was peer reviewed by a second librarian using Peer Review of Electronic Search Strategy guidelines. Detailed search strategies for each database can be found in Appendix Table 1. The literature search yielded 858 articles. After excluding duplicates, 532 articles were screened for inclusion based on title and abstract and 478 were eliminated because of their irrelevance to the topic. Additionally, eight publications from the search results that addressed the intersection of HIV, substance use, and juvenile justice were excluded because of the study design; this included one systematic review of HIV/STI -prevention interventions for detained and delinquent youth, three studies examining intersecting risks of HIV, substance use, and justice involvement among HIV-positive youth, two studies of large existing adolescent health databases,greenhouse tables and two retrospective studies of at-risk populations examining how a history of juvenile justice involvement and/or incarceration may increase HIV risk. A total of 46 articles were therefore included in the final review. Inclusion criteria were as follows: was a peer-reviewed article; included a US-only population; youth were in 10–-18 -year -old age range at time of study enrollment ; participants were actively justice-involved at time of study enrollment ; data were collected on HIV/STI and substance use, which included any illicit substances for minors, including nicotine.

A team of three reviewers assessed and summarized findings of the final 46 articles. One reviewer selected a random sample of articles to review for quality assurance. Any disagreements in the abstract review were resolved after a consultation and detailed examination of the study. Across the 26 cross-sectional studies reviewed, 38% included community supervised samples. Findings suggest that, among any justice-involved youth, substance use confers enhanced risk for engaging in HIV- risk -related behaviors. Many studies used global measures of substance use, collapsing drug use into one broad category. Studies have found that drug use is associated with unprotected sex , number of sexual partners, drug use during sexual activity, and increased rates of STIs.For example, one study found that attention-deficit- hyperactivity disorder was associated with sexual risk behavior for youth with conduct problems, but that cannabis use completely accounted for this association. A cross- sectional brain fMRI imaging study focusing on alcohol detected activation of brain regions associated with risky decision-making, riskier peer norms, and number of days endorsing sex while using alcohol among a sample of community-supervised justice-involved youth. Studies examining multiple substances find differential effects of these substances on HIV risk. One cross-sectional study by Gillman and colleagues found that detained youth who reported frequently used only cannabis were less likely to engage in risky sexual behavior and reported greater intention to use condoms compared with those who frequently used alcohol and cannabis or just alcohol alone. In this study, alcohol use alone appears to increase the likelihood of risky sexual behavior among juvenile detainees than either alcohol and cannabis or just cannabis alone. Several cross-sectional studies identified critical social ecological factors associated with enhanced risk for substance use and HIV, such as gang violence, dating violence victimization, and a history of child maltreatment. Several studies also included psychiatric risk factors, such as depression. The majority of cross-sectional studies focused on individual-level factors associated with HIV and substance use risk and, as stated above, several studies included an examination of larger socioecological factors.

Notably, few studies incorporated interpersonal and or parent or family-level factors associations with risk.Of the ten studies published, 80% centered on intersecting risk of HIV and substance use over time among community-supervised justice -involved youth, such as truant court-involved youth, youth presenting to a court intake center youth on probation, and youth in the juvenile drug court. These studies assessed and supported a wide variety of individual-level factors associated with substance use and HIV risk, including callous- unemotional traits, incidence of pregnancy, clustering of problem behaviors, and history of sexual coercion. Several longitudinal studies used global measures of any substance use and others included measurement of single substances, such as alcohol, marijuana and cigarettes. The three studies that focused on following detained youth highlighted contextual risk factors for substance use and HIV such as racial disparities, community trauma, and child welfare involvement. The Northwestern Juvenile Project was a seminal study that followed detained youth starting in 1998 for 14 years . Abram and colleagues found that multiple sexual partners and unprotected vaginal sex remain prevalent at the 14- year follow-up but that risk varied by sex and race, with African American and Hispanic males most at -risk. Among females, non-Hispanic white youth were at greatest sexual risk; the authors attribute this to their higher rates of substance use disorder relative to African American and Hispanic women. Ramaswamy and colleagues examined cigarette smoking among a 16–-18- year -old re-entry population of Black and Latinx men to ascertain risk factors associated with nicotine use prior to detention and 1 year post re-entry; high rates of smoking were reported at follow-up and associated with foster care history and number of prior arrests; only use prior to detention was also related to a greater number of sexual partners. Lastly, Puja Seth and colleagues followed 188 African American female detained adolescents over 6 months and found that community trauma at baseline was a significant risk factor for future unprotected sexual activity and marijuana use as well as post traumatic stress disorder symptoms.Of the ten published intervention studies, with the exception of one within-subject design, all were RCTs.

All interventions were conducted with community supervised justice -involved youth; however, two interventions began in detention and then continued with youth post-release. Two intervention trials published two sets of papers with the same intervention just at different stages. Interventions ranged with respect to format and intensity, ranging from brief to more intensive. For example, Bryan and colleagues recently demonstrated the efficacy of a single 3-h substance use and sexual risk -reduction intervention session for detained youth in reducing STI incidence at 12- month follow-up. In a pilot study of adolescents recruited from a juvenile drug court , participants were randomized to either a 6-month-long, sexual risk – reduction protocol with an emphasis on contingency management and family involvement or a treatment-as-usual arm; preliminary results demonstrated that adolescents randomized to the intervention exhibited slower increases in sexual behavior over the study period. Likewise, Donenberg and colleagues randomized youth on probation to a 6- month sexual risk -reduction program or a time-matched health promotion program ; in a moderator analysis, high-risk adolescents reported significantly lower risk behaviors than controls at the end of the trial. Five interventions incorporated parent training or family-based approaches. Efficacy of the interventions was variable. For instance, Perrino and colleagues randomized community-dwelling, Hispanic youth with a history of delinquency to either a multifaceted, family-based intervention or a community-practice control; youth in the intervention arm were significantly less likely to endorse internalizing symptoms at 6 to 12 months following initial assessment. This effect was especially pronounced among youth with worse caregiver-youth communication at baseline, which suggests that the internalizing-reducing effect of the intervention may have been mediated by improved communication. Another study that focused on drug court-involved youth, randomized to either a family-focused intervention for substance use disorders or usual services for 1 year, no clinically significant treatment-attributable effects were detected for substance use, sexual risk behaviors, and HIV- risk behaviors. The authors posited that treatment effects may have been obscured by interventions that were implemented to all study participants by the juvenile drug court. In a study from our group, juvenile drug court-involved adolescents and an involved caregiver were randomized to either a five-session,growing cannabis indoors family-based substance use and HIV/STI- risk -reduction arm or a psycho education-only arm. At 3 months, results suggested that the intervention was associated with increased motivation to change marijuana use, lead to a decrease in marijuana use, and decrease in risky sex over time. Dembo and colleagues tested a brief intervention for truant youth and families that found a two – session youth-only intervention to be superior to that which added a parent-only session in robustly decreasing recidivism at 12 and 24 months; increased rates of recidivism were noted among youth with more substance use and sexual- risk behaviors at baseline. Both interventions that began in detention and continued post-release included a first, single session while in detention and then delivered the remainder of the intervention while community-supervised. DiClemente and colleagues tested a gender responsive intervention uniquely tailored for African American detained girls and designed to reduce new STIs, increase safe-sex practices, and improve psychosocial markers. Participants were randomized to Imara, which included three individual and four phone-based sessions, or time-matched psychosocial intervention; 3 months post-intervention, participants in the intervention arm reported more frequent condom use self-efficacy, STI-HIV knowledge, and condom-use skills. However, groups did not differ on new STI cases, condom use, or number of sexual partners.

Similarly, Rowe and colleagues randomized drug-involved, detained youth in two sites to either Multidimensional Family Therapy or enhanced treatment as usual; both groups demonstrated reduced rates of unprotected sex acts and STI incidence from intake to 9 months. Of note, youth in both conditions received structured HIV/STI prevention but only those in MDFT received family-based HIV/STI prevention after release. Only in one of the two sites, the MDFT group demonstrated a lower frequency of sex acts and unprotected sex compared to the usual-care group at 9- month follow-up. Our review illustrates recent advances in our understanding of how substance use is interrelated with HIV risk behaviors for justice-involved youth. Over the past 5 years, the number and scope of studies examining and addressing intersecting risks among this population have increased. Necessarily, the field has expanded its focus to include juvenile justice populations outside of youth in detention settings. Specifically, there have been a proliferation of studies focused on one particular subset of this at-risk population: community-supervised justice-involved youth . By including community-based juvenile justice samples, the field is beginning to identify distinct risks, expanded juvenile justice settings, and new frameworks to develop, test, and implement critical substance use and HIV -prevention intervention programs. The field has observed an increase in the proportion of longitudinal studies examining risk in this population, yet the majority of studies continue to be cross-sectional. The lack of longitudinal studies limits the field’s ability to ascertain how risk develops over time; a critical component of HIV-prevention efforts among a subset of youth that are at great risk for future substance use and adult criminal justice involvement . There has also been a marked increase in the number of published interventions, but overall there is still a dearth of available evidence-based behavioral treatments. Programs that target substance use and HIV risk behaviors in tandem are sorely needed; as are interventions that tailor HIV -prevention content to substance -using youth in the justice system. In addition, intervention effects remain short-term and thus effects of interventions into emerging adulthood remain unknown. The Northwestern Juvenile Project and Leve and colleagues conducted studies following justice -involved youth from adolescence into critical adult HIV risk windows. Leve and colleagues’ study followed justice-involved girls post- intervention, but intervention effects were not the focus of the longer-term follow-up study. As such, we are very limited in understanding how interventions developed and delivered in adolescence impact HIV risk into adulthood. Evidence examined as part of this review suggests that substance use promotes HIV risk behaviors for justice-involved youth populations. With one exception, the cross-sectional literature underscores that substance use is associated with engaging in a variety of sexual risk behaviors including inconsistent condom use, number of sexual partners, and history of STIs.

On average, young adults perceive marijuana as less harmful to health, less addictive, and more socially acceptable than tobacco , and are less ready to quit marijuana than cigarettes . While a few studies have found that marijuana users were less likely to quit smoking than non-users , others have found no significant differences in smoking outcomes between marijuana co-users and non-marijuana users . Previous research focused on general adult populations, collected data in-person, and was conducted before the advent of widespread changes in marijuana legalization and social norms . It is unclear whether and to what extent marijuana use interferes with smoking cessation and related outcomes among young adults in an era of rapidly shifting laws and attitudes regarding marijuana. It is particularly important to study young adults in this context, because they are less likely to seek smoking cessation treatment and are more likely to use marijuana than are older adults. Moreover, due to the stigma around marijuana use and its illegal status in many states, collecting data online may be a useful strategy to improve accuracy of self-reported marijuana use and to further examine its relationship with smoking cessation. Lastly, marijuana use has become increasingly accepted in society and increasingly common among cigarette smokers . Given the widespread availability and acceptability of marijuana among young adults, current tobacco smokers may experience more difficulty quitting than those surveyed in previous decades. As such, this study uses data from a randomized controlled trial of the Tobacco Status Project , a smoking cessation intervention for young adults delivered on Facebook, to examine differences in smoking outcomes between marijuana users and non-marijuana users. Participants were young adult smokers who reported smoking 100+ cigarettes in their lifetime, currently smoking 1+ cigarettes per day 3+ days per week and using Facebook 4+ days per week, and who were English literate. Recruitment consisted of a paid Facebook ad campaign from October 2014 to July 2015 . Clicking on an ad redirected participants to a confidential eligibility survey. Eligible, growing cannabis indoors consented participants were randomly assigned to one of two conditions: 1) the Tobacco Status Project intervention, or 2) referral to the National Cancer Institute’s Smoke free.gov website .

Participants in both conditions were included in all analyses except treatment engagement and perceptions . TSP included assignment to a private Facebook group tailored to participants’ readiness to quit smoking, daily Facebook contact with study staff, weekly live counseling sessions, and six additional Cognitive Behavioral Therapy counseling sessions for those ready to quit. Study staff posted once a day for 90 days and participants were asked to comment on the posts. Post content varied by readiness to quit smoking and included strategies informed by the Transtheoretical Model and the U.S. Clinical Practice Guidelines for smoking cessation .Nicotine dependence was assessed using the 6-item Fagerström Test of Cigarette Dependence , scored on a scale of 0 to 10, from low to heavy dependence. Daily smoking at baseline was measured with the item, “On average, how many days in a week do you smoke cigarettes ?”. Responses were recoded into daily smoking or non-daily smoking . The Smoking History Questionnaire assessed early smoking as well as usual number of cigarettes smoked per day. The Stages of Change Questionnaire was used to categorize participants into one of three stages of change based on their readiness to quit smoking at baseline. Alcohol is another substance commonly used by young adults, and use of alcohol can co-occur with tobacco and/or marijuana . Hence, we measured alcohol use for possible inclusion as a covariate in the models, using the item, “Have you consumed alcohol in the past 30 days?” .Current marijuana use was measured at each time point using the Staging Health Risk Assessment , based on the Transtheoretical Model stages of change and the Healthy People 2020 goals for the United States . The item read: “Marijuana is also called pot, weed, and grass. Are you planning to stop using marijuana?” . Participants were categorized as marijuana users if they indicated recent use on the staging item. All others were categorized as non-marijuana users. Participants in the intervention group reported their perceptions of the intervention at treatment end by rating their agreement with 7 items. Items addressed whether the intervention was easy to understand, gave sound advice, gave participants something to think about, and helped them to be healthier, as well as whether they used the information, thought about the information, and would recommend the intervention .

Responses were coded as disagreement or agreement . Engagement was measured by the number of Facebook comments an individual posted during the 90-day intervention, including comments on posts and during live counseling sessions . First, marijuana users and non-users at baseline were compared on baseline demographic and smoking characteristics. Second, differences in reported smoking outcomes between users and non-users during the follow-up period were analyzed using a series of generalized estimated equations with binary distributions and logit link functions for dichotomous variables and a multi-nomial distribution with a logit link function for the ordinal variable . Longitudinal analyses controlled for intervention group and adjusted for baseline stage of change , baseline average cigarettes per day, sex, alcohol use, and age participants began smoking regularly. The first two covariates were determined a priori and the latter were selected based on the observed baseline differences between marijuana users and non-marijuana users. Because all participants were smokers at baseline, longitudinal analyses only included data from the three follow-up points . Largely due to attrition, there were 493 missing data points across all three time points on the abstinence variable, 498 on the reduction variable, 489 on the quit attempts variable, and 502 on the readiness to quit variable. GEE analyses are relatively robust to missingness, and a participant’s data could still be included in the analyses if they were missing one or more time points. Third, chi-square tests for independence were used to compare marijuana users’ and non-marijuana users’ perceptions of the intervention. An independent-samples t-test was used to compare treatment engagement between marijuana users and non-marijuana users in the treatment group.This study showed longitudinal patterns of marijuana use, point-prevalence abstinence from smoking, and reduction in smoking among young adults participating in a digital smoking cessation intervention trial. Most importantly, results showed that young adult smokers who coused marijuana were less likely to reduce their cigarette smoking or to have been abstinent from smoking than were those who did not use marijuana; however, they did not differ in readiness to quit smoking or likelihood of having made a quit attempt.

Although smoking marijuana in addition to cigarettes increases young adults’ likelihood of negative physical effects , smoking marijuana may make quitting cigarettes more difficult in part by perpetuating the habit of smoking. Quitting smoking requires breaking associations or cues between the behavior of smoking and other contextual factors . Young adults commonly use marijuana in conjunction with cigarettes . Thus, continuing to use marijuana may hamper cigarette smokers’ efforts to change their behavior. Indeed, results showed that marijuana users were less likely to have recently abstained from smoking or reduced their smoking over a 12-month period. On the other hand, growing indoor cannabis use status was consistently unrelated to readiness to quit smoking at baseline and during the followup period. Moreover, users and non-users did not significantly differ in the likelihood of making a quit attempt over 12 months. Results are consistent with research showing that young adult marijuana users do generally view quitting smoking as important , but have less ability to follow through with a complete abstinence goal despite a desire to quit smoking . Overall, our finding that marijuana users are less likely to report recent abstinence or reduction in smoking is consistent with extant literature suggesting that marijuana users are less likely to be successful at quitting smoking . Encouragingly, marijuana users and non-marijuana users participating in the digital smoking cessation arm of the intervention did not differ in their perceptions of the intervention or their engagement in it. This suggests that young adults who use marijuana were receptive to the content and digital platform of the smoking cessation intervention. The intervention content included minimal information on the potential harmful effects of marijuana use. Future intervention content could highlight the negative effects continued marijuana use may have on quitting smoking, and could serve as a resource for young adults who want to quit using one or both substances. The variables most strongly and consistently associated with smoking outcomes over time were baseline stage of change for quitting smoking and marijuana use. Both should be assessed to inform treatment efforts with young adult smokers. Strengths of this study include multiple smoking-related outcomes, a 12-month longitudinal design, and a focus on young adults . This study had a few notable limitations. First, outcomes were self-reported. Our group has previously demonstrated the reliability and validity of young adults’ online self-reported tobacco and marijuana use , as well as the accuracy and limited bias of self-reported point prevalence abstinence in the present sample . Therefore, we opted to use self-reported abstinence, which had a much higher response rate. Second, current marijuana use was categorized into use versus non-use. It is possible that the relationship between marijuana use and smoking outcomes differs by heaviness of marijuana use, which our survey item did not assess. Although past research has shown that readiness to avoid marijuana use is significantly correlated with past 30 day marijuana use , future research should include a more detailed measure of marijuana use.

The measure of alcohol use was similarly nonspecific, and a more detailed measure may yield different results. Moreover, up to twice as many of the participants indicated being abstinent for 7 days at each follow-up than identified as being in action/maintenance for having quit smoking. This was especially true of participants who were not using marijuana concurrently, as reflected in the significant difference in point prevalence abstinence and reduction between marijuana users and non-marijuana users. This finding may be due to the sample including non-daily smokers, and/or the young adult age of the participants. Based on self-report, 5-10% of the sample refrained from smoking for at least one week, yet were not committing to quitting. Future research could include more nuanced measure of marijuana use and measures of smoking specific to non-daily cigarette smokers. The opioid epidemic in the United States has seen an almost sixfold increase in overdose deaths since 1999, a rise that parallels a 30% increase in the suicide rate between 2000-2016.While the association between opioid use and suicidal ideation has been documented, the impact of opioid use on the current escalation in suicides is unclear.It is likely that suicides caused by opioid overdose are under reported and that many opioid overdose deaths classified as “undetermined” by coroners are suicides.Treating individuals with opioid use disorders must include attention to suicide risk. The 99.4% increase in opioid-related visits to emergency departments between 2005-2014 represents an opportunity for EDs to encourage patients already in a vulnerable period to make behavioral changes.This includes offering medication-based treatment with methadone and buprenorphine to OUD patients. Studies show that EDinitiated buprenorphine decreases opioid use and increases engagement in outpatient substance use treatment.A limitation of these studies is that OUD patients with SI have University of Maryland School of Medicine, Department of Psychiatry, Baltimore, Maryland been excluded, despite some evidence that buprenorphine might reduce SI.We present observational data on 14 OUD patients with SI who presented to the ED for treatment. Our goals were to explore the feasibility of starting buprenorphine in these patients in the ED, and to determine whether ED-initiated buprenorphine treatment would be associated with improvements in SI and engagement in outpatient substance use treatment. This study was approved by the medical school’s institutional review board. The 14 patients presented to the ED of a tertiary care hospital between July 2012–August 2018. All met criteria for OUD and reported current SI or a suicide attempt at their index ED visit. All were evaluated by a psychiatrist and offered buprenorphine with a referral to an outpatient substance use treatment program.

Interestingly, though marijuana tweets exhibited the highest proportion of positive tweets, they also exhibited the highest proportion of neutral tweets and the lowest proportion of tweets with negative sentiment. This finding may suggest relative homogeneity regarding marijuana attitudes, possibly as a consequence of debate regarding marijuana legalization during this time period. As the majority of all sentiment-containing tweets were positive, results from this study may suggest that outreach efforts to raise awareness about the health risks of tobacco and ATPs on college campuses may have limited resonance. However, these preliminary data also suggest discrepancies in sentiment between tobacco products, as well as differences in sentiment toward smoking across California universities. Therefore, policymakers and health promotion advocates should consider tailoring policy implementation and health communication for specific college students in California based upon evidence of latent receptivity toward anti-tobacco approaches and existing community sentiment toward smoking behaviors as detected in this study. Furthermore, future studies should more explicitly assess user reaction and sentiment to debate, communication and implementation of state-level policies that both legalize and restrict use of tobacco and smoking products, as well as how these macro policies interact with campus-specific smoke free policy perceptions for different tobacco, marijuana, and e-cigarette product categories. For example, actionable insights based on preliminary findings from this study indicate that users generally express more positive sentiment about tobacco use and smoking behavior. This may necessitate the use of campus-based health promotion and education activities that focus on reducing appeal of these products, such as restricting any form of marketing and promotion in or near campus communities. This should be coupled with broader state legislation to further restrict marketing and promotion that targets young adults and college communities. Further, perceived penalties for violating smoke- and tobacco-free campus policies may also impact compliance based on socioeconomic factors. For example, one user from UC Riverside tweeted, “other places might be more lenient,planting racks but UCs have a shitty tobacco and smoking policy and I got caught and now it’s over” [emphasis added to denote correction of misspelling].

Hence, data-driven approaches to assess receptivity and the impact enforcement has on smoking behavior should be built into smoke free program implementation iteratively. Importantly, the breakdown of smoking-related tweets between numerous college campuses as detected in this study presents challenges with respect to whether the distribution of tweet characteristics accurately reflects distributions in the underlying college populations. Nevertheless, similar work has been conducted which presents correlational evidence between characteristics of geospatially-specific social media posts and characteristics of populations in those areas . Furthermore, as over half of college students in California are between the ages of 18 and 24 , academic and demographic distributions of tobacco consumption within colleges may be the consequence of socioeconomic disparities in childhood and potential effects of these disparities on attitudes about smoking among parents, high schools, and/or neighborhoods that warrant further study . Results from our study are limited in generalizability, though complement work by others on examining the impact of tobacco free policies on US college campuses. This includes a recent study from 2020 of small colleges in Massachusetts that found that a college with a smoke-free policy had significantly more anti smoking attitude than a control campus, but did not have lower rates of smoking itself . Relatedly, a separate earlier study from 2005 that analyzed undergraduates in Texas found that campuses with preventive education programs had lower odds of smoking, whereas designated smoking areas and cessations programs were associated with higher odds of smoking . Collectively, these prior studies and our own work helps to better characterize knowledge, attitudes and behaviors of college campus communities toward smoking, as well as the smoke-free policies attempting to discourage smoking, which in turn should aid in the development of more targeted approaches to educate college-aged populations about the health harms of tobacco and also enable better implementation of anti-tobacco policies in these critical populations.This study was exploratory in nature and collected social media messages for which latitude and longitude coordinates could be collected from the Twitter API, but this data collection methodology is limited to collecting messages from Twitter users that enabled geolocation, a specific limitation to generating a more generalizable dataset on Twitter as it is estimated that only 1% of all tweets are geocoded .

Hence, the dataset used in this study after filtering for keywords was small and likely biased, limiting the generalizability of results. This method of data collection may have introduced bias in the types of tweets collected, thereby limiting the generalizability of findings as the majority of Twitter users do not geolocate their posts. Potential sampling biases for Twitter include oversampling for certain geographic areas , filtering for specific features , and the limitations of the Twitter public streaming API in lieu of other data collection approaches . Future studies should examine the use of multiple Twitter APIs to generate a more representative Twitter dataset and compliment findings with other traditional sources of data to generate findings that are more robust and generalizable, as well as use complementary Twitter and social media datasets made publicly available by other researchers. specific to identification of Twitter users and conversations associated with colleges and universities, using keyword searches, and selecting accounts affiliated with higher education should be explored in future studies. Also, inclusion criteria required tweets to be posted from college campuses, which would not have accounted for variability in smoking related tweets from off-campus housing or areas/neighborhoods at the borders of campus properties where college students may reside. Furthermore, though the study design permitted searches of the Twitter API to return different volume of tweets for different keywords, there was a smaller number of original keywords for substances containing marijuana/cannabis than those for e-cigarettes or products containing tobacco due to our purposeful filtering for tobacco and alternative tobacco product keywords . Additionally, the majority of tweets analyzed for this study were from 2015, a period prior to major public scrutiny about default privacy settings for location sharing on Twitter . Finally, this study is an ecological study and should therefore be considered hypothesis generating and not generalizable to individuals on college campuses until further studies among individuals confirm these correlational findings. Adolescence has been argued to be the only developmental period bookended by highly disparate events . It commences with biology, defined as the onset of puberty,sub irrigation cannabis and concludes via social construct, typified by the achievement of “independent functioning” such as obtaining a job, completing training, and beginning a family . While the ages for this window vary widely throughout the globe, most agree that the central work of adolescence largely encapsulates ages 13-18 . Across cultures and societies, it is during this precise developmental period that substance use is most often initiated , with peak age of first misuse, and related problems following soon thereafter .

Given that adolescents have goals, cognitions, and social contexts distinct from adults, it is critical that clinicians and researchers consider adolescent substance use through a neuro developmental lens. In the present review, we engage a developmental neuroscience framework to characterize the prevalence, and related health and safety implications of substance use within this age group; identify the nature of the adolescent brain, including characteristic features of this phase of neuro development relevant for adolescent substance use treatment; and provide an overview of current adolescent addiction interventions and avenues to improve clinical treatment and clinical research efforts for adolescents. We then conclude by examining the intersection between the nature of the developing brain and adolescent substance use, and utilize that information to inform alternative routes and highlight promising future directions for substance use treatment in this critical age group. Due to the inherently poly substance-using nature of this age group , we focus this review primarily on the three most frequently used substances by adolescents: alcohol, cannabis and tobacco . We have not integrated examination of prescription pain/opioid misuse here due to its relatively recent history within adolescent addiction clinical research and treatment settings, and the related dearth of empirical adolescent opioid treatment research in this area . The rates and patterns of adolescent substance use have maintained historical consistency throughout the past several decades.Noted in top United States surveys , despite the legal age being 21 years, alcohol is highly accessible for American youth, with most accessing alcohol through peers or other individuals . In turn, it is no surprise that half of American 14-year-olds have consumed alcohol. This rises to 75% by age 18, with half drinking to intoxication . Relevant to potential neurotoxicity and health impact , 20% of adolescents start drinking by age 13 . Interestingly, U.S. surveys reflect a current 10-year low in youth drinking. Yet, rates of youth alcohol use and related problems still remain consequential, in terms of safety and health impact . One currently debated reason for the decline in adolescent alcohol use in the U.S. revolves around recent changes in cannabis legislation . For example, treatment providers in states where cannabis is medically and/or recreationally legal are observing some degree of youths’ increasing preference for cannabis over alcohol . Presently, one quarter of U.S. youth report cannabis use by age 14, with 8% starting by age 13. Of relevance, rates of cannabis use have recently begun to approximate adolescent alcohol use patterns, with half of U.S. teens now using cannabis by age 18 . This reflects a 22% rise in adolescent cannabis use during the past decade . These startling trends raise the question of whether increased recreational and medical legalization of cannabis are contributing to observed increases in adolescent use . Unfortunately, requisite data needed to identify causal relationships between changes in cannabis legislation and adolescent use are not yet available, and thus far, evidence has been mixed regarding effects on both adolescent consumption and perceived harmfulness. Further, effects may vary substantially across states/contexts . Active efforts by scientists and practitioners in recreationally and medically legal areas are needed to disaggregate directional impacts between public policy legislation and adolescent use . For example, in 2017, national survey databases indicated adolescents reported significant drops in their estimations of potential harm and disapproval of cannabis use ; how this aligns with patterns of adolescent cannabis use and intersects with alcohol use is a critical, and increasingly pressing, empirical public health question that has direct implications for addiction treatment providers working with adolescents in this field . In terms of tobacco, one quarter of American youth have tried tobacco, with 7% initiating use before age 13; this rises to a third of youth by age 18, with 10% smoking 10+ cigarettes/day . National data reflect that American adolescents are moving away from tobacco use, as represented by a 71% drop in past 11 year cigarette use . Part of this may be due to teens’ transition to electronic vapor products , a recent newcomer to the world of adolescent substance use. Adolescents report vaping nicotine and cannabis , preferring flavor-based cartridges , contentiously marketed toward children . Surveys reflect 20% of U.S. youth have vaped an e-liquid containing nicotine and/or cannabis by age 14, rising to one third by age 18 . For many clinicians, the most pressing public health concern around adolescent substance use is not that it represents an entry point into a life-long course of protracted addiction. Rather, the more imminent concern for parents and providers is that adolescents will make a risky choice while trying to obtain, use, and/or dispose of substances that result in consequences that cannot be undone , and that render their life trajectory much more difficult . One of the central challenges in this area is that experimentation with substance use is so typical among adolescents that for decades it has been interpreted as largely “normative”, and to some degree to-be-anticipated, and some argue, even developmentally appropriate . While the initial part of this trajectory has been interpreted as largely “harmless” in terms of behavioral impact, conferring a degree of social advantage, for some, during high school , the nature of substance use shifts for many during this period.

This form of engagement with government agencies and the broader public helps define the agenda early in the policy-making process , although quantifying the degree to which our research contributed to policy outcomes such as SB 88 is difficult. The future impact of our work on environmental flow management remains unclear, but early engagement with state and federal agencies through the Environmental Flows Work group suggests that our flow modeling tools and data will have an important role in future policy development. Recognizing there are likely other applications for our modeling tools, we have been working to make the data available to the public. Model predictions have now been generated for every stream in California, including values of mean monthly, maximum and minimum monthly flows and confidence intervals for California’s 139,912 stream segments in the National Hydrography Database . The dataset is being hosted by The Nature Conservancy at rivers.code for nature.org, where it can be accessed and downloaded through an application programming interface . A more dynamic spatial mapping tool has been developed to explore the data in individual rivers, watersheds or regions. An online interactive visualization tool is also available that allows a user to select one or several stream gauges and generate the corresponding hydrograph of observed and expected monthly flows . An immediate next step for this project is to expand the natural flows dataset to include predictions of additional stream flow attributes that are relevant to environmental water management. This will support the Environmental Flows Work group’s goal of defining ecological flow criteria in all rivers and streams of the state and can help inform a variety of programs including, for example, water transactions and stream flow enhancement programs. Other direct applications of the natural flows data may be in hydro power project relicensing,greenhouse rolling racks which requires consideration of environmental flow needs. In addition, under the Sustainable Groundwater Management Act , groundwater sustainability agencies are required to avoid undesirable results including depletions of interconnected surface water that have significant and unreasonable adverse impacts on beneficial uses of the surface water.

Because environmental flow criteria have not been established for most streams in California, GSAs are rightfully confused as to the standards they are expected to meet. Statewide environmental flow criteria may help to define management targets required for SGMA implementation. Looking to the future, society will continue to face challenges in balancing environmental protections with the demands of a growing population. Tools that make use of long-term monitoring data and modern computing power, such as the models described here, can help inform policy and management intended to achieve this balance.In the U.S., use of prescription pain relievers , also known as prescription opioids and opioid pain relievers, has been increasing dramatically. Worldwide, prescriptions of PPRs have almost tripled since 1990, and the U.S. is a factor in this rise, as it has the highest per capita consumption of PPRs in the past ten years . This increase has become dangerous, as opioid use carries risks that include addiction, sedation, respiratory depression, overdose and death . Between 1999 and 2010, deaths attributed to PPRs rose five times among women and 3.5 times among men . Of all prescription drug OD deaths in the U.S. in 2013, 71.3% involved PPRs . PPRs and marijuana are biologically linked; like PPRs, marijuana induces analgesia, acts on some of the same brain regions, and partly exerts its effects via opioid receptors . This connection is especially relevant due to the changing legal status of marijuana. As of August 2016, 24 states and Washington D.C. had legalized medical marijuana. Between 2007 and 2012, the number of past month marijuana users rose from 5.8 to 7.3% 2013, and between 2001 and 2013, past year adult marijuana use increased from 4.1 to 9.5% in the U.S. . Further, legalization of medical marijuana has been associated with increased odds of marijuana use among adults , though no consistent association has been determined among youth/young adults .The relationship between different substances can be impacted by 1) change in cost of a substance, 2) policy alterations that influence availability of a substance, 3) shifts in legal consequences of using a substance, and/or 4) the psychoactive/pharmacological effects of a substance . More U.S. states are legalizing medical marijuana , and marijuana shares some psychoactive/pharmacological effects with PPRs.

The substitution theory postulates that there is a substitution effect, whereby an increase in marijuana use coincides with a decrease in the use of other substances – in this case, PPRs . There are logical reasons why individuals would opt to use marijuana instead of PPRs. With the new legal status of medical marijuana, individuals can access it through medical dispensaries and enjoy a lower legal risk if they live in a state where it is legalized. Individuals also report switching to marijuana for pain control because when compared to prescription drugs, marijuana has fewer side effects and withdrawal symptoms . Studies supporting the substitution effect have demonstrated that either increases in the use of marijuana or the legalization of medical marijuana is associated with reductions in opioid use, hospitalizations for opioid dependence/abuse, PPR ODs, and opioid OD mortality . In contrast to the substitution effect, there may be a complementary effect, where an increase in marijuana use is associated with an increase in the use of PPRs . In support of this theory, researchers using National Survey on Drug Use and Health data found a positive association between marijuana and increased use of PPRs . In another study, researchers focused on individuals who were prescribed long-term opioid therapy and found that those who also used medical marijuana presented with greater risk of misusing prescription opioids. Additionally, a prospective cohort study using the National Epidemiologic Survey of Alcohol and Related Conditions data determined that use of marijuana was associated with a greater risk of using non-medical prescription opioids three years later . However, in these studies, researchers did not analyze how co-use of other substances would impact the direction and/or strength of the relationship between marijuana and opioids/PPRs. To determine if there is either a substitution or a complementary effect between marijuana use and PPR use, co-use with other substances needs to be studied. Additionally, there is a strong positive association between nicotine use and PPR use. When compared to non-smokers, tobacco smokers experience more intense and longer lasting chronic pain, as well as a higher frequency of PPR use . Studies have demonstrated an interaction between nicotine and opioids that is associated with an increase in the total consumption of the two substances and contributes to other effects of the drugs . The relationship between the use of these two substances has a basis in the biological connection between them, as the endogenous opioid system is an underlying mechanism for several behavioral outcomes related to nicotine .

Like marijuana, nicotine is involved in anti-nociception via endogenous opioid system mediation, suggesting that nicotine is used for the self-medication of pain ; and in fact, nicotine heightens the anti-nociceptive effects of both opioids and marijuana . Several studies have documented common use patterns among tobacco, marijuana, and opioids/PPRs . For example, a prospective study of NESARC data demonstrated that early-onset of smoking cigarettes increased the odds of beginning opioid use and that frequency of both cigarette and marijuana use increased the odds of beginning opioid use, re-initiating opioid use after previously stopping, and continuing opioid use among current users . Thus, the three substances share anti-nociceptive actions mediated by the endogenous opioid system, and evidence indicates that marijuana and nicotine use predict opioid use among adults. From 2003 to 2012, NSDUH data revealed a significant increase in the co-use of marijuana and tobacco . Further,vertical grow smoking tobacco is significantly associated with cannabis dependence . Given the national trend toward marijuana legalization, co-use is likely to increase. Cigarette smokers and marijuana users are a crucial population to study, as nicotine and marijuana share mechanisms of action with each other and with opioids, and use of each substance has been shown to be associated with use of opioids/PPRs . However, whether there is an association between prevalence of marijuana and PPR use among current smokers has not been determined. The present study addresses this gap by using the Tobacco Attitudes and Beliefs Survey II to investigate the relationship between marijuana use and PPR use among current cigarette smokers. This study examines 1) the likelihood of PPR use by marijuana use and 2) the frequency of marijuana use and current PPR use. Findings may help elucidate whether marijuana use is associated with PPR use, and if so, whether marijuana is used as a substitute or complement to PPR use. This is a cross sectional analysis of data from the TABS II, a web-based longitudinal survey of U.S. adult former and current cigarette smokers, aged 24 years old and older. The survey included topics such as individuals’ use of tobacco, tobacco-related products, marijuana, and other substances including PPRs. The present analysis used demographic data from Wave 1 from August 2015 .

Wave 3 data were collected in August 2016 and included survey items on marijuana use and new items on PPR use . Surveys were administered by Qualtrics, which uses a combination of online panels to establish national samples from which survey participants can be randomly selected. Qualtrics invited potential participants to take the survey via an email notification and offered them a $10 incentive to complete each survey wave. For Wave 1, 2,378 individuals clicked on the survey link, and 819 went on to complete the survey, yielding a completion rate of 34.4% . Current smokers were included in the current analysis. The TABS II project was approved by the UCSF Institutional Review Board. Results suggest that adult current cigarette smokers have differential use of PPRs depending on their use of marijuana. Those who were current and ever marijuana users were over 2–3 times more likely to have used PPRs in the past 30 days, respectively, when compared to cigarette smokers who never used marijuana. Results support the findings of previous studies that addressed a possible complementary effect of marijuana use with PPR use. Novak, Peiper, and Zarkin analyzed NSDUH data in 2003 and 2013 and found that greater marijuana use was associated with more frequent PPR use. An analysis of NESARC data found higher levels of marijuana and cigarette use predicted initiation, re-initiation, and sustained opioid use ; and another study using NESARC data determined that marijuana use was associated with an elevated risk of using non-medical prescription opioids three years later . Two Swedish teams found similar results. One study found a positive association between use of marijuana and unauthorized use of PPRs . In a re-analysis of a Swedish national household survey, non-medical PPR use was associated with both frequent cigarette smoking and marijuana use . Studies with adolescent and young adult samples found non-medical use of PPRs is associated with marijuana use . Though longitudinal studies are needed to make definitive conclusions about the nature of the relationship between marijuana and PPR use among cigarette smokers, the interface among biological effects of PPRs, marijuana, and nicotine could influence the strength and direction of this relationship. For one, PPRs and marijuana share anti-nociceptive effects, the two substances act on some of the same brain regions, and THC partly exerts its analgesic influence by relying on opioid receptors . Nicotine additionally interacts with the opioid system, and the systems have almost identical influences in key pleasure-sensing areas of the brain . Therefore, the behavioral responses to nicotine use and withdrawal are likely affected by the opioid system . As with marijuana and opioids, nicotine has antinociceptive actions . Consequently, the interconnected neural activity and biological effects of nicotine, marijuana, and opioids could play a role in the relationship between PPR and marijuana use among cigarette smokers. Another explanation for the higher likelihood of current PPR use among ever and current marijuana users in cigarette smokers could be that some participants had used marijuana and/or PPRs to reduce pain symptoms. Epidemiologic and prospective cohort studies point to a relationship between smoking and chronic pain, with smokers having a greater likelihood of developing chronic pain disorders than non-smokers . And the most frequently reported reason for adult misuse of PPRs in 2015 was to alleviate physical pain .

E-liquid marketing is focused on promoting flavors , and is known to enhance appeal, and intentions to use more than advertisements for non-flavored products . Youth also believe that advertisements for flavored e-liquids target individuals similar to their own age . Findings from this study highlight that flavors are one of the key topics of discussions related to e-liquids on Twitter, which supports FDA’s recent announcement about the need to regulate sale of flavored e-liquids, and e-cigarette products . Person Tagging, or one Twitter user directly communicating to another about e-liquids, was also a common topic in this study, consistent with prior research . This finding demonstrates that Twitter users communicate their experiences with e-liquids with their friends and followers on Twitter. In other words, posts classified under ‘person tagging’ consistently used ‘@Person’ tags to involve others in conversations about e-liquids. Cannabis was a common topic in the present study. Compared to smoking cannabis, motivations for vaping cannabis, in general, include better taste, lower perceived health risks, and stronger drug impact in the form of higher concentrations of cannabinoids . Mixing nicotine and cannabis in vaporizers, is also an emerging trend, although additional research is warranted to examine the prevalence of this behavior . The present study contextualizes discussions of cannabis grow system use within the larger discourse about e-liquids given limited evidence about the prevalence or health effects of vaping cannabis . Future work should explore topics of conversation at the intersection of nicotine and cannabis. The composition of e-liquids was discussed on Twitter in 2018. Awareness of e-liquid composition requires a nuanced understanding of the proportions of its constituents. Varying proportions of e-liquid VG/ PG content in combination with puff topography , and device architecture, determine e-cigarette users’ experiences and nicotine delivery .

E-liquid composition has direct implications for appeal, and user experience, and potentially for the maintenance of longer-term use. For instance, producing bigger vape clouds is known to drive e-cigarette product appeal . Older, regular smokers typically desire a throat hit that is similar to their regular combustible cigarettes . In this evolving e-liquid product landscape, it is possible that knowledge of e-liquid composition allows users to adapt to and choose from the wide variety of these products. Conversations at the intersection of flavors and e-liquid composition, may potentially enhance the appeal of e-cigarettes. Such knowledge may be transmitted to Twitter influencing product preference, and may educate new users about ways to initiate, and maintain, product use. The health risks of nicotine were also discussed suggesting Twitter users are concerned about the health consequences of nicotine. These types of messages may be amplified by public health practitioners to clarify the consequences of nicotine on adolescent development or other consequences. While health risks of nicotine were a common topic in the present study, the use of e-liquids to quit smoking combustible cigarettes was rarely mentioned. Similar to prior research utilizing Twitter , conversations about e-cigarette use and related products seldom mention cessation.The committee graded the strength of the evidence of the effectiveness of cannabis and cannabinoids in several therapeutic areas. We also explained in Chapter 151 the barriers to conducting research with cannabis, which are particularly relevant to a discussion of therapeutic effects. In truth, most of the literature we reviewed was not actually assessing the cannabis plant, but pharmaceutically derived compounds, especially tetrahydrocannabinol alone as dronabinol or nabilone. There are an increasing number of trials investigating nabiximols, which is a whole plant extract. Some studies looked at cannabidiol alone, but none of the studies on the pharmaceutical cannabidiol preparation, Epidiolex, was yet published in the literature we reviewed. This agent has shown promise in epilepsy patients.

Studies of the whole plant, usually smoked or vaporized, were few. So, when you ask what conclusions we drew regarding the therapeutic uses of cannabis, it would probably be more correct to ask about the therapeutic uses of cannabinoids; and then recall the strength of the evidence that qualified our confidence in the validity of the conclusion. I can say that the committee concluded there is conclusive or substantial evidence that cannabis or cannabinoids are effective for the treatment of chronic pain in adults, nausea and vomiting related to cancer chemotherapy, and symptoms of spasticity associated with multiple sclerosis. Again, this means that we found strong evidence in good-quality systematic reviews or meta analyses to support these conclusions. Interestingly, these were quite similar to the results reported in the IOM 1999 report.2 We found moderate evidence that cannabinoids may be beneficial in sleep disorders associated with several chronic illnesses. Evidence supporting the use of cannabis or cannabinoids for appetite stimulation, improving anxiety, and symptoms of Tourette syndrome was felt to be limited by our review of existing literature. For all the other conditions or symptoms included in the analysis, we found only limited or no evidence to support or refute that cannabis or cannabinoids have benefit. Included in our list were conditions or diseases for which states allow patients to access cannabis. As mentioned, we also found no evidence of benefit in epilepsy, although recently completed and ongoing clinical trials are supporting the benefit of pharmaceutical grade cannabidiol for refractory seizures. One of my mantras on the committee was that the absence of evidence of effectiveness does not equate to evidence of the absence of effectiveness! We were quite selective and stringent in reviewing only the highest quality clinical trials and restricted our grading of evidence by using the careful criteria that the committee agreed upon. This coupled with the significant barriers that exist to conducting clinical trials of the potential therapeutic benefit of the plant material leave us with a handful of strong conclusions on therapeutic benefits of cannabis.

Most of the conclusions are based on studies of approved pharmaceutical products or those under current clinical investigation. Hopefully, the future will allow for larger, longer, high-quality studies of plant-based medicine preparations to be conducted and provide us with much needed information. As an oncologist, I am faced daily with patients asking me if they can forego conventional cancer therapies and treat their malignancy with cannabis. There is absolutely no data in the published literature to support the use of cannabis or any cannabinoid as a treatment for cancer. The committee decided to veer from the mandate to only include clinical trials in the report to be able to say something about cannabinoids and cancer. So, a review of 34 preclinical studies of cannabinoids in brain tumors was included. Although there is an increasing and impressive body of evidence that cannabinoids may have some anticancer activity in cell culture and animal studies,hydroponics rack system the only clinical trial in cancer patients reported to date involved infusion of tetrahydrocannabinol by a catheter into brain tumor recurrences in nine patients. Hence, we concluded there was insufficient evidence to support or refute the conclusion that cannabinoids are an effective treatment for cancers.There are some specific areas that might have benefitted from more precision in the summary conclusions. For example, the substantial statistical association between cannabis use and schizophrenia might suggest a causal link association. In fact, there has been no increase in diagnosis of schizophrenia in western societies during the five decades since recreational cannabis use became prevalent. A more cautious interpretation of the cannabis–schizophrenia association may be that different elements of psychopathology travel together, perhaps reflflecting influence of some common vulnerability factor or factors, with problematic substance use being one of those indicators. Regarding cannabis and persisting brain injury, my view as someone who has looked at neurocognitive consequences of several substances of abuse, leads me to the conclusion that the evidence for lasting effect of cannabis on the brain is very inconclusive. The meta analyses on neurocognitive performance in adult cannabis users that adjusted for recency of use basically found no associations, and similarly, the brain imaging reports have been contradictory. Even data on human neuro developmental consequences are quite fragmented. So, in that sense the report might have hewed closer to data by interpreting the strength of the associations more cautiously or explaining the pitfalls better.Regarding schizophrenia, the report concluded that there was substantial evidence to support an association between cannabis use and development of psychotic disorders. This was one of the strongest conclusions in the report. The strength of this conclusion arose from the large number of studies examining this issue as well as evidence for a dose–response relationship in which more frequent cannabis use was associated with higher risk of developing a psychotic disorder. All of the studies reviewed found some degree of increased risk with no significant findings to the contrary. It is still not clear whether cannabis use causes schizophrenia and, as pointed out by Dr. Grant, alternate hypotheses could explain this strong and consistent association.

Regarding anxiety disorders, the report concluded that there was moderate evidence for an association between cannabis use and an increased incidence of social anxiety disorder.Also, the report concluded with moderate evidence that cannabis use is associated with a small increase in the risk of developing depressive disorders, but no evidence to support or refute an association between cannabis use and symptoms of depression. It is not clear whether cannabis use is causally linked to the development of social anxiety or depression.Several studies have investigated the possible association with cannabis and lung and upper aerodigestive malignancies over the years. It makes sense that an inhaled plant material that many equate with tobacco should raise a concern for the possibility of an increased risk of tobacco-related malignancies. We reviewed two publications each comprised of analysis of six studies that both failed to show a statistical association between the use of cannabis and the development of lung cancer. One could question how this could be so when there is such a clear link between tobacco smoking and pulmonary neoplasia. First, no one smokes 20 to 40 cannabis cigarettes a day. Second, cannabis has anti-inflammatory, antioxidant, and some believe antitumor qualities which tobacco does not. Many of the older studies suggesting a link between cannabis smoking and lung cancers did not control for tobacco use. We also reviewed another analysis of nine case–control studies of head and neck cancers that also was suggestive of no increased association of cannabis use with those malignancies either. The only possible link that we noted was limited evidence of a statistical association between current, chronic, or frequent cannabis use and nonseminomatous germ cell testicular tumors. As an oncologist, I am not sure I see biologic plausibility in that association other than the fact that cannabis use and testicular cancer are two things common to young men.The committee concluded that there is substantial evidence for an association between cannabis use and increased risk of motor vehicle accidents. This conclusion was based primarily on a 2016 meta analysis of 21 case–control or culpability studies across 12 countries and included an impressive sample of nearly 240,000 participants. The findings demonstrated that cannabis use, as assessed by self-report and the presence of tetrahydrocannabinol metabolites in blood, saliva, or urine, was associated with a 20% to 30% higher odds of a motor vehicle crash. In addition, the magnitude of the association was in the low to moderate range. An important aspect of the study was the magnitude of effect was weakened when accounting for alcohol intoxication. A limitation of these findings is the difficulty in determining the proximity of cannabis use relative to motor vehicle crashes based on the presence of tetrahydrocannabinol and its metabolites in biological samples, like urine or plasma, since they can be detected long after use in heavy cannabis users. However, studies by the Huestis laboratory investigating the effects of acute cannabis exposure on performance in a driving simulator agree with these findings.These controlled studies demonstrate that smoking cannabis significantly impairs psychomotor skills needed for safe driving.Four recommendations were put forth to support and improve a cannabis research agenda. The first recommendation was to address research gaps. These gaps included prioritized research areas to assess the short- and long-term effects of cannabis and cannabinoids specifically related to clinical and observation research, health policy and economic research, and public health and public safety research. The second recommendation included suggestions to improve research quality by developing a set of research standards and benchmarks that can yield high-quality cannabis research.

The weight of auxiliary evidence suggests, however, that this correlation primarily reflects factors other than a causal relationship of crime induction through intoxication. First, the psychopharmacological effects of marijuana are relatively modest compared to the effects of alcohol, cocaine, and other illegal drugs, and do not suggest, a priori, that intoxicated users are driven to violent, antisocial activity with great frequency. Second, it is clear from the sheer size of marijuana’s user base that most users do not resort to non-possessory crime at all—while intoxicated or otherwise. Third, some empirical evidence suggests that the enforcement of marijuana criminalization may not work even as a “broken windows” policing strategy,57 much less as a direct measure preventing supposedly toxicologically induced crime. A recent analysis of marijuana in public view arrests across 75 police precincts in New York City from 1989 to 2000 concluded that “there is no good evidence that this “reefer madness” policing strategy contributed to the decline in the sorts of serious crimes that are of greatest public concern in New York City” .On the contrary: while an initial panel data analysis offered some support for the idea that these misdemeanor marijuana arrests contributed to reductions in violent crime, when the authors restructured their regression model to control for mean reversion, the coefficient on MPV arrests became statistically significant in the opposite direction—suggesting that “an increase in MPV arrests over the period translates into an increase in serious crime—not,flood and drain tray as the broken windows theory would predict, a decrease in serious crime” . In considering the merits of criminalization, it is also important to remember that even within a system of criminalization, there is much leeway regarding the severity and nature of prohibition enforcement.

Moreover, there is significant historical and cross-country evidence to help understand how consumption and costs might change under a less punitive criminal regime. While it is always difficult to isolate the impact of a drug policy, and one must always be wary in generalizing from the experience of other countries to today’s America, there is evidence, albeit somewhat conflicting, suggesting that depenalization and even decriminalization of marijuana may not lead to significant increases in use. It is often said that in the 1970s, eleven states “decriminalized” marijuana . These states significantly reduced penalties for simple possession of marijuana, in some cases implementing a narrow form of the regime we call depenalization.On the other hand, a recent study finds that because other states have also reduced penalties for marijuana possession, “[so called] decriminalized states are not uniquely identifiable based on statutory law as has been presumed by researchers over the past twenty years” . The same study also finds, however, that the demand for marijuana among young people is sensitive to variation in penalties. A still more recent study traces the research—which began with studies finding little to no effect but now has become more mixed—and offers two possible explanations for the conflicting findings: the effect of legal variation is different across age groups; and the historical time period may matter . Moreover, the authors find that a reason for minimal effects of depenalization may be that many individuals are unaware of the changes in their state’s marijuana law.Another reason why use rates might not respond to decreased penalties is the extremely low likelihood of being arrested for illegal drug possession: reviewing the data, Boyum and Reuter estimate that in 1999, the “risk of being arrested for marijuana possession, conditional on using marijuana in the previous year, was about 3 percent; for cocaine the figure was 6 percent” .

To the extent that individuals predisposed to illegal drug use also exhibit lower risk aversion and higher discounting of future welfare than the rest of society, they are especially unlikely to find psychologically salient—or change their behavior as a result of— risks characterized by low probabilities and high costs, such as possible arrest for possession. Probably the most famous example of marijuana reform comes from the Netherlands. There, the 1976 “Opium Act” ushered in the de facto decriminalization of possession of small amounts of cannabis for personal consumption and a system of tolerated sale in “coffee shops” that in some sense resembles a form of highly but peculiarly regulated legalization. Under the latter system, registered coffee shop owners that adhere to certain guidelines may, without being targeted for prosecution, possess up to 500 grams of cannabis and sell it in quantities of 5 grams or fewer . The Dutch experience with this controlled form of drug use provides insight into what could happen if the United States were to move down a path toward depenalization, decriminalization, or even legalization of marijuana. MacCoun and Reuter report that since the 1976 reform, the number of “coffee shops” has increased steadily so that there now may be between 1200 and 1500 such venues in Amsterdam; on the other hand, van der Gouwe, Ehrlich, and van Laar report a decrease in the number of officially tolerated coffee shops from 1999 to 2007. Marijuana use in the Netherlands increased during the 1980s and early 1990s as the “coffee shops” became more widespread. However, there is no evidence for the existence of the so-called “gateway effect” discussed earlier. Notably, there was no increase in use rates of heroin, which is traditionally the most widely used hard drug in the Netherlands, or of cocaine, in spite of the corresponding crack crisis in the United States . Indeed, the European School Survey Project on Alcohol and Other Drugs conducted a quarter-century after de facto decriminalization and emergence of the coffee shop system in the Netherlands found that only 28 percent of Dutch school children surveyed reported smoking cannabis compared with 38 percent in France, whose politicians have been harshly critical of the Dutch approach.Also, as we note in Figure 1 below, data from the World Health Organization World Mental Health Surveys indicate that when measured in terms of lifetime cannabis use, the United States has a much higher rate of those over age 18 who have ever used cannabis compared with the Netherlands .

One of the goals of the Dutch scheme involves separating cannabis sales from sales of other illicit drugs in the hopes that cannabis users will not come into contact with sellers of drugs like heroin, thus stopping marijuana users from moving to more serious drugs. Manja Abraham reported that for users over age 18, 48 percent of cannabis purchases took place in coffee shops, whereas relatives and friends supplied 39 percent of cannabis used . While this demonstrates that a large informal cannabis market exists, only 3.7 percent of users reported obtaining cannabis from a stranger and 5 percent from a home dealer, someone who advertises cannabis sales and delivers them to the home, legally or illegally, depending upon the amount delivered. Among experienced users of cannabis , 54 percent reported purchasing cannabis most often in a coffee shop compared with 32 percent for less-experienced users . This suggests that while a large percentage of sales occur outside of the state-sanctioned coffee shops, the heaviest users obtain their cannabis through regulated channels or from people they know, rather than participating in a clandestine market of dealers. The lack of transactions with dealers who are otherwise unrelated to the individual is important because it is such transactions that bring an individual into contact with the black market and its associated crime and violence. Evidence from Portugal and Australia also suggests that depenalization need not lead to substantial increases in marijuana use or its associated problems. In the period since decriminalization,hydroponic tables canada drug use in Portugal has not spiked, nor has the country been besieged by drug tourists . In fact, Portugal continues to have among the lowest rates of cannabis and cocaine use in the European Union, and its rates remain far below their counterparts in the United States . Room et al. have pulled together a handful of studies comparing changes in use rates in Australian jurisdictions covered by schemes involving civil penalties for small cannabis offenses with changes in use rates for the rest of Australia still subject to the country’s standard criminal penalties for marijuana possession. On the whole, these analyses offer little if any evidence to suggest that use rates increased more in civil penalty jurisdictions than elsewhere. In the United States, medical marijuana laws have begun to create a subsystem that, under our taxonomy, would be considered a form of decriminalization verging on a highly regulated form of legalization. Medical marijuana laws have introduced a mechanism that allows patients to grow and use marijuana for medical purposes without facing the prospect of state prosecution, while still allowing the states and the federal government to continue prohibiting the large-scale cultivation, distribution, and ordinary possession of marijuana. Fifteen U.S. states have provisions allowing for some type of medical marijuana; however, these subsystems of decriminalization differ from state to state.

For example, in Colorado, a constitutional amendment providing for medical marijuana included the requirement that patients using medical marijuana possess a registry identification card issued by the state, and it provided for the establishment of a confidential state registry for this purpose.In California, probably the best-known example of a medical marijuana regime in the United States, the Compassionate Use Act of 1996 simply declares as one of its purposes: “to ensure that patients and their primary caregivers who obtain and use marijuana for medical purposes upon the recommendation of a physician are not subject to criminal prosecution or sanction.”This act did not create a mandatory registry program for patients using medical marijuana. Rather, in 2004, California introduced a voluntary Medical Marijuana ID card, administered by the county governments.While California’s medical marijuana dispensaries have been the focus of several news stories since the Obama Administration announced that agencies in charge of enforcing federal drug laws would no longer raid such dispensaries , the legal status of dispensaries remains questionable, and it would be misleading simply to say that California legalized the “sale” of medical marijuana . The Compassionate Use Act did not provide for sales through such dispensaries, and the expanded codification of medical marijuana in California occurring in 2003 provided only for multiparty growing of marijuana in collectives and cooperatives.California’s Attorney General has indicated that for dispensaries to operate legally in California, they must operate as a non-profit, only sell to members of the collective, verify members’ status as qualified patients or primary caregivers, only acquire marijuana from qualified members, and only cultivate and transport amounts required to meet the needs of the collective’s members . The California courts have also placed limits on the ability of individuals cultivating and selling marijuana to avoid prosecution for possession and sale of the drug by claiming to be the “primary caregiver” of multiple patients. The California Supreme Court has held that a patient’s primary caregiver must establish such status “based on evidence independent of the administration of medical marijuana,” and that growth and supply of medical marijuana alone are insufficient to establish oneself as a primary caregiver. The California Supreme Court has also held that employers can fire medical marijuana patients who test positive for marijuana as a result of a urinalysis, because the drug remains illegal at the federal level, and nothing prevents employers from terminating employees who use illegal substances.Thus, while medical marijuana states like California have decriminalized marijuana possession and use for medical marijuana patients, users still face repercussions such as loss of employment and certain limitations on purchases of marijuana that would presumably be reduced or eliminated in a legalization regime. From a cost-minimization perspective, the primary expected benefits of legalization over depenalization would be even more substantial reductions in government expenditures on drug control, new tax revenues to offset remaining government spending, the potential for increased government control over product standards and labeling information, and substantial reductions in drug-related crime costs. Government regulation of labeling and product standards could help mitigate the problems of increased potency and user uncertainty regarding whether the drug taken has been laced with, or partly replaced by, other harmful ingredients the consumer did not intend to use—such as PCP. As noted earlier, Miron estimates that the tax revenues from legalized marijuana would indeed be substantial—somewhere between $2.4 and $6.2 billion.

We found that that social impairment is corrected in two distinct mouse models by increasing anandamide activity through FAAH inhibition. This correction raises the immediate question of whether increasing anandamide activity in these mice is prosocial per se or simply anxiolytic. This question is particularly important in light of the known roles of endocannabinoids in stress modulation.In our case, we found that the prosocial action of anandamide is unlikely to be due to general anxiolysis, because FAAH inhibition did not alter BTBR performance in the elevated plus maze when tested in the dim lighting conditions of the social approach test. This result is in line with previous reports showing that FAAH inhibition results in anxiolytic-like effects only under ‘‘high-light’’ conditions.While this phenotype does not exclude more nuanced forms of social anxiety, it does support the idea that the modulation of stress reactivity and social behavior can be dissociable. In line with this notion, CB1 over expression in the medial prefrontal cortex alters social interactions without overtly changing the anxiety-related phenotype.In contrast to socially impaired mice, parallel experiments in normal mice indicated that increasing anandamide does not alter social approach. One explanation could be technical—namely, that the social approach test has a ceiling effect or is unable to capture more subtle qualities of social interaction.Indeed, the task is typically used as a screening tool rather than a continuous scale measure of sociability. Another explanation could be biological—that signaling systems in a healthy brain are able to compensate for endocannabinoid enhancement in a way that socially impaired brains cannot. These explanations are in line with previous reports of different social situations in which faah / mice demonstrated increased direct reciprocal interactions,as well as URB597-treated juvenile rats engaging in more social play.

Therefore, expanded investigation is warranted into how anandamide contributes to different social contexts and the qualities of social interactions. Nevertheless, our set of results suggests that the prosocial action of FAAH blockade is selective for social impairment in certain contexts,growers equipment which may be therapeutically advantageous for the spectral nature of ASD. Based on our results and the available literature, we can reasonably speculate on two possible scenarios improved by anandamide signaling that may underlie social impairment in BTBR and fmr1 / mice. First, oxytocin-driven anandamide activity in the NAc, which we previously demonstrated to be important for social reward,may be impaired in these mice. Consistent with this idea, BTBR mice were found to have abnormal oxytocin expression in the hypothalamus.BTBR mice were also reported to be deficient in conditioned place preference to social interactions.46 However, because social conditioned place preference is a relatively new construct, and the learning impairments in these mice make interpretation problematic, further support from the literature is lacking. A second possible scenario is that anandamide might correct an imbalance of excitatory and inhibitory neurotransmission in the cortex, which has been postulated to underlie ASD.Enhancing GABAergic activity in BTBR mice ameliorates their social impairment, and negative allosteric GABA modulation in C57Bl6J mice recapitulates social impairment.This suggests that a loss of balance between inhibitory and excitatory activity might contribute to social impairment. A simplified view of this result orients us to interpret our findings as indicating that anandamide could modulate such balance. This view is consistent with the presence of CB1 receptors on presynaptic terminals of both glutamatergic projection neurons and GABAergic interneurons.In conclusion, the present study provides new insights into the role of endocannabinoid signaling in social behavior and validates FAAH as a novel therapeutic target for the social impairment of ASD.The United States stands out among developed nations for both its extremely punitive illegal drug policy and the high percentages of its population that have consumed banned substances—particularly marijuana and cocaine. The war against the millions of Americans who use and sell these drugs has cost taxpayers billions of dollars each year and contributed substantially to America’s globally unmatched incarceration rate .

Yet it has failed to displace America from among the world leaders in use rates for illegal drugs even if escalating punitiveness may have contributed to declines in U.S. drug consumption from its peaks in the late 1970s and 1980s. To locate America’s illegal drug policy globally and along a spectrum of potential alternatives, it is helpful to consider three broad approaches governments may take toward drugs: legalization—a system in which possession and sale are lawful but subject to regulation and taxation ;criminalization—a system of proscriptions on possession and sale backed by criminal punishment, potentially including incarceration ; and depenalization—a hybrid system, in which sale and possession are proscribed, but the prohibition on possession is backed only by such sanctions as fines or mandatory substance abuse treatment, not incarceration.All three of these approaches have been implemented in the practices of various governments around the world, though to greater and lesser extents. Nearly all countries have criminalized a consistent set of proscribed substances including marijuana, cocaine, heroin, and methamphetamine; most have also legalized other drugs such as alcohol and tobacco; and some have adopted policies of depenalization for substances whose sale, and to some degree possession, remains prohibited.We begin our analysis in Section II by attempting to define America’s illegal drug problem, first sketching consumption patterns, current policy, and the social costs of illegal drugs under America’s basic regime of criminalization. Because America’s illegal drug policies are an integral part of the context in which those costs arise—and many of those costs, such as those associated with incarceration, would not exist but for America’s current policies—we consider current policies and social costs in tandem, distinguishing costs that stem from criminalization and costs that flow from psychopharmacological effects of drugs on their users. Following this overview, we focus in Section III on the particular cases of marijuana and cocaine. For both marijuana and cocaine, we analyze three potential regimes—criminalization, depenalization, and legalization. We also address the two most significant sources of social costs from cocaine: crime and incarceration.

Cocaine has been an especially acute problem in America, with the prevalence of this drug and its derivative, crack, providing the impetus for the escalation of the War on Drugs in the 1980s and Plan Colombia in the 1990s. We restrict our discussion to these two drugs partly because one of our principal contentions is that analysis of illegal drug policy from a perspective of minimizing social costs requires great focus on the varying burdens of individual drugs given their different toxicological and inherent criminogenic effects, and their distinct patterns of consumption and distribution. Under U.S. criminalization of marijuana, a large number of people are arrested and otherwise punished for possession of a substance that is routinely consumed in today’s developed world and is—by various expert accounts and along many measures—less dangerous to users and society than cigarettes or alcohol. This policy not only consumes criminal justice resources and crowds out other valuable social spending, it also creates hard-to-quantify costs in other forms: diminished respect for the law, loss of faith in government warnings about the serious dangers posed by more harmful drugs, and a morally arbitrary arrest lottery undermining the principle that like offenders be treated equally. On the other hand,plant benches cocaine is substantially more dangerous than marijuana and under criminalization it is much more socially costly in the aggregate, notwithstanding far lower rates of use. The costs of cocaine under criminalization overwhelmingly stem from crime, violence, and incarceration. The differing nature of the costs of criminalization for marijuana and cocaine is important because it suggests that the effect of a regime change would be different for marijuana than for cocaine. Depenalization and legalization could both potentially reduce perhaps the foremost cost of marijuana criminalization: the extremely high number of arrests for possession, and the concomitant burdens they impose on the criminal justice system’s resources and individual arrestees—many of whom are otherwise law-abiding.6 Legalization, to a much greater extent than depenalization, would reduce the costs of black-market violence and lengthy incarceration for sellers that weigh so heavily in the overall costs of cocaine. On the other hand, economic theory suggests that reductions in sanctions through depenalization or legalization would lower costs both implicit and explicit , and thereby increase demand and use. By more substantially reducing costs and government disapproval, and by potentially enabling advertising, legalization would be expected to lead to higher levels of consumption than under a regime of depenalization. The possible exception to this claim would be if legalization were accompanied by a sufficiently comprehensive taxation regime that would restrain consumption by maintaining a high enough price to the consumer. The psychopharmacological effects of cocaine are markedly more harmful than those of marijuana, and the costs per additional user would be higher for cocaine than marijuana. Moreover, marijuana consumption is much higher than cocaine consumption so the offsetting effect of tax revenues on the social costs of cocaine would be much less significant than for marijuana. In sum, legalizing cocaine would pose greater risks and offer greater potential rewards than legalizing marijuana: the decreases in certain categories of costs and increases in others would be much more substantial for cocaine than for marijuana. Not surprisingly, much of the debate over illegal drug policy and potential reforms hinges on two contentious questions. First, by how much would the prevalence and intensity of a drug’s use rise under a different regime?Second, would reductions in other social costs—particularly through lower rates of crime and criminal justice enforcement costs—outweigh the costs of increased consumption? Our nation’s experience with alcohol regulation is instructive. During Prohibition—a regime of decriminalization or extreme depenalization—alcohol consumption was suppressed to a degree that noticeably lowered the cost of alcohol abuse.

These gains, however, appear to have come at a high cost in terms of crime, which fell sharply after Prohibition ended. While criminal gangs no longer cause mayhem over alcohol distribution, alcohol abuse does lead to belligerence and crime as well as many other social costs ranging from impaired productivity and increased motor-vehicle deaths to higher levels of child abuse and neglect. The U.S. has vastly more alcoholics than drug addicts in part because we have allowed a free market coupled with extensive advertising to promote alcohol consumption, with taxation levels that are well below social costs. Conjectures from some sources that similarly free markets for cocaine could increase today’s relatively small number of cocaine addicts to levels beyond the current number of alcoholics are offered in support of the current war on drugs. Opponents counter by pointing to the enormous criminal violence—here and abroad—that this war has generated, as well as the 500,000 incarcerated Americans whose lost freedom and productivity are among the greatest casualties of the war on drugs. The stakes are high for illicit drug policy, yet unfortunately we must continually choose its contours with a less than ideal evidentiary base. Legalization would almost certainly reduce crime, but such a prospective gain must be weighed against the increase in the costs of substance abuse that would likely follow. The murder and violence of illegal drug dealing, and the hundreds of thousands of ruined lives of prison inmates must be assessed against increased motor vehicle deaths and potentially millions of lives impaired by addiction. These are not pretty or easy choices, and to a significant extent the consequences of various drug policy regimes will depend upon the specifics of design and implementation. Our effort here is directed toward clarifying the trade offs by exploring, in the contexts of marijuana and cocaine, the question of which regime—and what set of policies within that overarching framework—would minimize the total cost to society.8A recurring pattern in the distribution of consumption across users holds for a variety of recreational drugs: a small percentage of users account for a large percentage of consumption. This pattern is found for alcohol consumption in the United States , as well as for cocaine use. For example, one study found that the top 22 percent of users account for 70 percent of cocaine consumption . The top heaviness of the distribution of cocaine use among consumers is believed to have increased from the early 1980s when consumption was nearly evenly split between light users and heavy users .

As the contributions illustrate, there are important future avenues to consider. specifically, the articles in the Special Section provide recommendations for future MOBC research to improve the feasibility of experimental manipulation, collect and analyze data with greater temporal resolution, maximize the use of existing data sets to extract valid and reliable information to inform clinical practice, consider the importance of pretreatment behavior change, and examine multiple levels of analysis. Greater use of experiments, which is advocated by both the MOBC and Science of Behavior Change initiatives, would move the field a step closer to garnering convergent evidence for multilevel mechanisms of drinking behavior change. The use of biofeedback to modify emotion regulation is one such approach. Also, future trials can incorporate data collection methods, such as ecological momentary assessment to assess person-level change in the natural environment, that allow greater attention to be paid to how behaviors vary with time, how behavior outside treatment occurs, and how pretreatment behavior change affects later change processes. Each of these components of change is likely important for a more complete understanding of MOBCs. Moreover, as the world becomes more connected via mobile devices and applications, ecological momentary assessment and other intensive momentary data collection methods are becoming increasingly affordable, sophisticated, and user friendly. Finally, there remains a need to examine existing data from clinical trials that have already been conducted with high methodological rigor, even when no main effects of treatment were observed. Analyses of extant data sets can additionally examine theory-based, cannabis square pot pretreatment moderators of MOBCs or changes in potential mechanisms during treatment. In conclusion, significant strides have been made in understanding how and under what conditions individuals change addictive behavior.

The question is deceptively simple, whereas the multifaceted nuances in behavior change require rigorous and complex design, sophisticated analytic methods, and strong theoretical rationale. This Special Section touches on a number of these important advances that can inform the emerging science of behavior change. Early U.S. studies generally found that decriminalization had no statistically significant impact on use in the United States. These studies focused on the years immediately following the passage of decriminalization statutes in eleven states. The most ambitious analysis found no significant association with use in both cross-sectional and longitudinal comparisons, using micro-level data from the late 1970s Monitoring the Future Survey of High School Seniors . Other state-specific analyses found similar null results . Studies examining the early years of decriminalization in several Australian jurisdictions also failed to find an effect on prevalence in simple cross-sectional and longitudinal comparisons . Drawing on a sparse set of cross-sectional and longitudinal indicators, MacCoun and Reuter argued that Dutch decriminalization in the 1970s had no measurable impact on levels of use over the first decade, though they tentatively attributed a later increase in prevalence to the rapid expansion of the number of commercial retail coffee shop outlets for cannabis. Only one study has suggested an effect of state decriminalization during the 1970s. Model analyzed the effect of marijuana decriminalization on drug mentions in hospital emergency room episodes using data from the 1975-1978 Drug Abuse Warning Network. Her analyses showed that cities in states that had decriminalized marijuana experienced higher marijuana ER mentions and lower other drug mentions than nondecriminalized cities. Model did not estimate a demand function directly, but her results implied that under decriminalization, drug users might have substituted marijuana for hard drugs. More recent statistical analysis have generated mixed findings, with some studies showing no effect while others showed a positive and statistically significant effect. For example, DiNardo and Lemieux found no effects of state marijuana decriminalization using state-level aggregated data from the 1980-1989 Monitoring the Future Survey.

They estimated log-linear and bivariate probit models of the likelihood of using alcohol and marijuana, so unlike previous models, their model considered the possible relationship between alcohol and marijuana use. Thies and Register found no significant impact of decriminalization in their analysis of data on young adults from the 1984 and 1988 National Longitudinal Survey of Youth . They estimated logit and to bit specifications of the demand for marijuana, binge drinking, and cocaine and included cross-price effects in all of the regressions. Finally, Pacula found no significant effect of decriminalization policy in her two-part model specification of the demand for marijuana using data from just the 1984 NLSY. Her model differed from that of Thies and Register in that it included additional proxies for the price of marijuana and other substances. Saffer and Chaloupka also found a significant decriminalization effect in individual level prevalence equations for past year and past month use of marijuana, alcohol, cocaine, and heroin using data from the 1988, 1990, and 1991 National Household Survey on Drug Abuse. Unlike other analyses, Saffer and Chaloupka’s work controlled for various measures of the monetary price of legal and illicit drugs in addition to controlling for whether a state had a formalized decriminalization policy. Additional analyses finding evidence of a statistically positive association in nationally representative samples of youth and young adults in the United States include Williams et al , DeSimone and Farrelly and Pacula et al . Another possibility is that the period in which the policies were evaluated may matter. This inconsistency in years evaluated may be generating differences due to cohort effects or unidentified policy changes that are not captured fully in the analysis. Cohort effects are likely to exist due to the fact that public awareness of specific policies generally declines over time as we move farther away from the period in which the policy was discussed or adopted. There are a number of other unidentified policy changes that could also be occurring during the time period, such as changes in enforcement practices associated with marijuana offences.

For example, Reuter, Hirsch field and Davies find that one third of those arrested for marijuana possession in three major Maryland counties spend time in jail pre-trial, even though almost none receive a sentence involving incarceration. Thus there may be variations over time in the extent of pre-trial detention that affect perceived penalties even though not targeted at marijuana use. Murphy conducted an analysis of FBI records and showed that 7 out of the 11 states that chose to decriminalize marijuana during the late 1970s ranked in the lowest 21 states in per capita marijuana possession arrests before they enacted their decriminalization law. Two states, Mississippi and North Carolina, were among the top 23 states in per capita arrests before their policy change. Murphy’s analyses of changes in arrest patterns before and after the reform took place suggests that the statutory change had little impact on arrest patterns for any of these states. But survey data from that period, examined below, suggests that youth perceived significantly lower penalties following the legal change, and this shift only occurred in those states changing their laws. We will present evidence that these perceptual differences across states have largely vanished, suggesting either that “decriminalization” and “nondecriminalization” states no longer differ in their actual enforcement patterns, or that citizens no longer perceive the difference – perhaps due to the lower salience of the change over time. Much of the confusion about decriminalization involves terminology. The term “decriminalization” is often seen by the public as a synonym for “legalization.” But this is a mistake; decriminalization refers to penalties for marijuana possession,trim tray and does not imply any change in the legal status of marijuana sales. Also, “decriminalization” literally implies a removal in the criminal status of marijuana possession offences; however, many jurisdictions that are recognized as having decriminalized marijuana in fact merely reduce the penalties associated with possession of specified amounts. In many ways, the term marijuana “depenalization” is a more useful term for describing the diversity in liberalizing policies that have arose across and within countries . Decriminalization, nonetheless, remains the more common term in policy debates. In addition, progress in understanding the effects of marijuana laws has been hindered by an over-reliance on a crude dichotomous “decriminalization” indicator. Recent research demonstrates that this simple dichotomy is quite inadequate for uniquely identifying real differences in the criminal treatment of low-level marijuana offenders in the United States.

Table 1 summarizes statutory penalties in effect as of January 2001 for first time marijuana possession offenders caught in possession of small amounts of marijuana for all fifty states and the District of Columbia . The correspondence between the “decriminalization” label and actual policies is quite variable. Seven states that had actually removed the criminal status of minor possession offences , were not formally recognized as decriminalized states. Five states that are widely recognized as having decriminalization statutes maintain the status of marijuana possession offences as a criminal charge. Some states allow a minor marijuana possession charge to be removed through a formal process called expungement. Many of the states that have expungement provisions are not known as decriminalized states, and only three of the five so-called decriminalized states retaining the criminal status of minor marijuana possession offences allow for the removal of the criminal charge upon completion of mandated punishment. It is also important to note that the decriminalization statutes do not remove criminal penalties for smoking marijuana in public, which has always constituted an important source of possession arrests. In addition to this conceptual confusion, there is empirical uncertainty about the effects of marijuana laws on enforcement patterns. Pacula et al. examined the relationship between state marijuana statutes and actual enforcement during 1991-2000. They report a 264% increase in marijuana possession arrests across all states, mostly occurring between 1991 and 1995. Between 1991 and 2000, there was a dramatic increase in variation across states, with the range increasing from about 30 arrests per 10,000 in 1991 to 110 arrests per 10,000 in 2000. More importantly, by 2000, states that had eliminated the criminal status of possession offences involving amounts of one ounce or less of marijuana did not have systematically lower arrests per capita than those states retaining the criminal status. More than half of the states that do not consider small marijuana possession offences a criminal offence still had per capita arrest rates greater than the national average and they still experienced a significant increase in arrests during the 1992-1995 time period. One interpretation is that these arrests do not reflect simple possession of marijuana but that many are the result of bargaining down from more serious offenses, such as marijuana distribution . But if these arrest rates do correspond to actual legal risks for marijuana possession, then it is puzzling that recent studies find a consistent and statistically significant effect of the simple decriminalization dummy indicator on use even after controlling for enforcement . Because decriminalization is a severity-based intervention, these results may explain those studies failing to detect reliable decriminalization effects. But as we have seen, those studies operationalized decriminalization using an imprecise and somewhat misleading dichotomous indicator. It is also possible that perceptual deterrence studies conducted within a criminalization regime understate the potential effects of decriminalization . The sanction certainty dimension may have important threshold effects.Also, the mere fact that an act is illicit may influence behavior independently of the magnitude of the legal threat. There may be similar discontinuities for the sanction severity dimension. Human judgment is notoriously susceptible to range and anchoring effects . Statutory maxima are an example of the kind of “worst case scenarios” that people tend to weight disproportionately . Whether decriminalization might have a larger than expected effect depends, in part, on whether citizens actually know something about their state’s marijuana laws. Various lines of evidence suggest that citizens may have distorted or biased beliefs about sanctioning threats , but very little work has been done to empirically investigate whether this is true with respect to drug laws. Relatively few studies have ever measured the accuracy of citizens’ beliefs about legal sanctions. Early studies found that the general public tends to exaggerate the risks of arrest and punishment for many crimes . But in accordance with the availability heuristic , personal experiences play an important role in shaping perceived risks.

One recent 20-year comparison of daily tobacco versus marijuana smokers showed only increased risk for periodontal disease with cannabis, whereas tobacco users had expected increases in lung, cardiac, and metabolic risk factors. Cannabis appears to have some anti-inflammatory properties; according to the National Academies report, there is insufficient evidence to support any conclusions about the impacts of cannabis on other immune functions. Interesting public health data suggest that there may be significant trends toward decreases in opioid overdose deaths in states with legalized cannabis,fewer overall traffic deaths after legalization, and reductions in pre-versus-post legalization Medicare expenditures on prescription analgesics, sedative-hypnotics, anxiolytics, and other agents.A separate but important consideration is whether a standard risk-benefit analysis makes sense for palliative care patients when contemplating cannabis. Risks of overgeneralization aside, most palliative care patients are NOT young people with unlimited life prospects who are early in their school years or social developmental trajectories, are NOT climbing career ladders, are NOT parenting small dependent children, or operating heavy industrial machinery. Thus, I would argue that this brief summary of safety risks should, in the palliative care clinical setting, be balanced against the exigencies of attempting to help patients achieve symptom relief in the context of serious illness, particularly if they are facing difficult symptoms not responsive to conventional treatments.In addition to those scientific and public policy matters outlined above, there are many other uncertainties facing the palliative care clinician and his/her patient contemplating cannabis grow racks. Chief among them is what the patient actually receives when he/she purchases medical marijuana at a dispensary: a recent small study of marijuana edibles showed accurate labeling in only 17%.The majority of products were ‘‘overlabeled’’ , while 23% were ‘‘underlabeled’’ . Geographic differences were noted as well, with Los Angeles dispensaries showing a significant inclination to underlabel.

FDA has recently published a report of its analysis of CBD products purchased over the internet, which showed most of the products to contain little or no active ingredient.These findings undermine a fundamental element of physician practice, namely the ability to identify and recommend specific, reliable doses of compounds. It should also be noted that under most of the state laws, physicians are not prescribing medical marijuana at all. Instead, they are asked to endorse, attest, or certify that in their professional judgment the patient has a disorder for which medical marijuana may have efficacy. This, too, is unfamiliar territory for many of us.Originally formulated over twenty years ago, and recently updated, the neural diathesis-stress model proposes that the hypothalamic-pituitary-adrenal axis is the central physiological mechanism linking psychosocial stress to the onset and exacerbation of schizophrenia and related psychotic disorders . A central tenet to this model is that individuals with increased vulnerability for psychosis are more sensitive to the effects of psychosocial stressors due to abnormalities within the HPA axis which in turn contribute to dopaminergic and glutamatergic abnormalities that eventually trigger expression of psychotic illness . In support of the model, accumulated evidence indicates that patients with psychosis exhibit elevated basal cortisol relative to healthy controls , but a blunted cortisol awakening response [CAR ], the latter thought to represent a distinct HPA axis component, independent of stress-induced cortisol secretion . More recently, these features have been reported among individuals who are at increased risk for psychosis due to clinical features and/or genetic liability . Moreover, at-risk individuals who later develop full psychosis show even greater increases in basal cortisol and pituitary volume , suggesting that increased HPA axis activity may signal risk for worsening illness. In parallel with this research, studies show that at-risk individuals report greater exposure and sensitivity to a range of psychosocial stressors, including major life events, childhood trauma, and minor daily stressors . However, there has been a paucity of studies examining the concordance between psychosocial stressor exposure/distress and HPA axis function; as such, the extent to which individuals on the psychosis spectrum exhibit ‘abnormal’ HPA axis responses to psychosocial stressors is unclear.

That is, the increases in basal cortisol observed in those with, and at-risk for, psychosis may represent either a ‘normal/ adaptive’ response to the high levels of psychosocial stressors reported in these populations , or hyperresponsivity of the HPA axis , characterised by an increase in cortisol greater than that expected in a healthy individual . Alternatively, the elevated basal cortisol levels observed may be partially independent of psychosocial stress exposure/distress , and instead reflect individual-level factors such as genetic predisposition to HPA axis hyperactivity or metabolic abnormalities , the latter being more common among individuals at clinical high-risk for psychosis , who present features consistent with the prodromal phase of illness. Two recent studies of at-risk individuals support the ‘increased concordance’ hypothesis: Using the experience sampling method, siblings of psychosis patients showed more pronounced increases in salivary cortisol in response to unpleasant events relative to controls , whilst a further study reported a stronger association between retrospectively-reported stressful life events and basal cortisol in CHR youth compared to controls . In contrast, lower cortisol responses during psychosocial stressor tasks have observed in CHR individuals and young adults with high schizotypy traits relative to controls; a pattern consistent with that observed in patients with chronic schizophrenia . Together, these findings tentatively suggest that naturally-occurring psychosocial stressors are associated with greater cortisol increases in at-risk individuals compared to healthy controls, whereas the response to experimentally-induced psychosocial stressors is blunted. However, the degree to which HPA axis responses to laboratory-based stressor tasks are relevant to psychosis aetiology is unclear.Unlike studies using experimentally-induced stressor tasks, the lapse of time between stressor exposure and cortisol measurement may be considerable. Whilst elevations in cortisol levels following stressor exposure appear to decrease over time , early life events and trauma exposure are associated with HPA dysregulation later in life, suggesting long term effects of stress exposure . A related issue is that stress measures and cortisol samples may not be collected on the same day, particularly when studies have large assessment batteries spanning several days.

It is possible that day-to-day variations in perceived stress might influence both retrospective reporting of stressful events and cortisol levels, such that greater concordance is observed when measures are collected on the same day. However, to our knowledge, this has yet to be investigated. Determining the extent to which HPA axis responsivity in at-risk youth predicts clinical outcome is important, as such work might ultimately help to identify individuals at increased risk of illness progression by virtue of being more sensitive to the effects of psychosocial stress, enabling targeted interventions. Utilising data from the North American Prodrome Longitudinal Study 2 [NAPLS 2, ] we investigated whether psychosocial stressors, basal cortisol levels, and stressor-cortisol concordance at the baseline assessment differed across healthy controls and CHR subgroups defined on the basis of their clinical presentation at the two-year follow-up . Based on previous studies, we hypothesised that CHR youth who later converted to psychosis would show greater exposure and distress in relation to psychosocial stressors, elevated basal cortisol, and higher stressor-cortisol concordance relative to healthy controls; we also anticipated that CHR non-converters would be intermediate to CHR converters subgroups and healthy controls on these measures. In all analyses we controlled for a range of potential confounders ,cannabis grow system and additionally explored the effect of lapse-of-time between assessments on stressor-cortisol concordance.NAPLS 2 is a consortium of eight research sites examining CHR youth, the aims and recruitment methods for which are detailed elsewhere . Briefly, CHR subjects were help-seeking individuals who met criteria for one or more prodromal syndromes: attenuated psychotic symptoms; brief intermittent psychotic symptoms; or substantial functional decline combined with a first degree relative with a psychotic disorder, or schizotypal personality disorder in individuals younger than 18 years. Prodromal syndromes were assessed using the Criteria of Prodromal Syndromes , based on the Structured Interview for Prodromal Syndromes [SIPS ], conducted by clinically-trained interviewers; psychiatric diagnoses were determined via the Structured Clinical Interview for DSM-IV . CHR individuals who had met criteria for an Axis I psychotic disorder were not eligible for inclusion; treatment with antipsychotic medication was permitted provided that full psychotic symptoms were not present at the time of medication commencement. Healthy controls were recruited from the community and had no personal history or first-degree relative with psychosis and did not meet criteria for any prodromal syndrome. All participants were aged between 12 – 35 years at recruitment. Exclusion criteria for both groups included substance dependence in the past six months, neurological disorder, or full-scale IQ < 70. Non-psychotic psychiatric disorders were permitted in CHR and healthy control groups .Ethical approval was provided by Institutional Review Boards at each NAPLS site , all participants provided informed consent or assent. The current sample includes 662 participants for whom variables of interest at baseline and clinical status at follow-up were available. At baseline, participants provided information on sociodemographic factors and potential confounders, completed stress measures, and collected saliva samples.

Baseline assessments were completed over two or more visits. Where possible, saliva was collected on the same day as daily stressor, life event and childhood trauma measures . However, in some in cases , the baseline assessment was interrupted that lead to a substantial delay in the completion of all measures. In such instances, the remaining baseline measures were collected when the participant was able to return and complete the schedule, with clinical assessments repeated to confirm CHR status. All participants were included in the analysis which accounted for timelapse between assessments. Prodromal symptoms were assessed via the SIPS at 12- and 24-month follow-up assessments and used to categorise CHR subgroups [see Table 1 for details ].Participant date of birth, sex, and ethnicity were assessed via self-report, the latter was subsequently collapsed to a four-level variable . Cannabis use was assessed via a structured interview . For the purposes of the current investigation we created a binary variable indexing current use . Details of all prescribed psychotropic medications were obtained at the baseline assessment via self-report, pharmacy records, and/or medical records. Binary variables were created for current antipsychotic use and current psychotropic use , irrespective of type, dose, or data source.The 58-item, Daily Stress Inventory , was used to determine the presence of minor stressors occurring within the past 24 -hs. Participants indicated whether they experienced each stressor and the level of distress elicited by each endorsed stressor . Total distress scores were then divided by the total exposure score to obtain an average distress per item score . Life events were assessed via the Psychiatric Epidemiology Research Interview Life Events Scale , modified to exclude life events of lesser relevance to youth . The 59 events can be classified as independent or dependent . Interviewers recorded how often each of the 59 events had occurred in the participant’s lifetime and the associated level of distress ; participants could report multiple exposures to the same event , where the maximum occurrence for any single life event in the NAPLS cohort was four. An average life event distress score was derived by dividing the total distress score by the total exposure score . Participants additionally completed the Childhood Trauma and Abuse Scale , a semi-structured interview examining experiences of physical, sexual, and psychological abuse, and emotional neglect, occurring prior to age 16 . Each trauma type was scored as absent/present with a binary variable indexing any form of trauma derived.At the research session, participants provided three saliva samples with a mean salivary cortisol value subsequently derived when two or more samples were available . The median time of collection for the three samples was 1107 h , 1207 h , and 1300 h , respectively. The mean cortisol value, which is highly correlated with area under the curve values , was computed to provide consistency with previous publications . Participants were instructed to avoid consumption of caffeine, alcohol, or dairy products after 1900 h on the day before sampling; individuals who reported non-compliance with these instructions were not excluded as previous analyses performed on a subset of the cohort found no association with these variables and cortisol levels .

Utilization of emergency department resources are 50% to 100% higher for patients with SUD compared with patients without SUD.In addition to acute medical emergencies, ED use may be indicative of poor health, unmet service need, or inappropriate use of health care.To date, studies have found most SUD-related ED visits are associated with alcohol,and frequently document ED-based treatments have focused on alcohol to the exclusion of other drugs.Yet, ED visits associated with the misuse of opioids and marijuana are common, and considerable SUD-related ED visits involve concurrent or other drug use.In addition, alcohol and opioid use disorders are among the most severe SUD diagnoses in terms of their negative impact on health, and evidence continues to emerge about the adverse health effects associated with marijuana use disorder.Thus, the study of ED trends among patients with alcohol, marijuana, and opioid use disorders is important.High rates of SUD-related clinical emergencies and associated ED visits are a persistent barrier to improving health outcomes in this population.Thus, a study that seeks to identify how patients with alcohol, marijuana, and opioid use disorders use ED resources is important, to potentially inform more specific ED-based treatment efforts . This study examined ED trends across patients with alcohol, marijuana, and opioid use disorders, and controls, over time in a large integrated health care system in which all patients have insurance coverage to access health care. Using electronic health record data, we aimed to determine the odds of having an ED visit each year from 2010 to 2014 for patients with alcohol, marijuana, and opioid use disorders relative to controls without these conditions; evaluate differences in ED use between controls and those with alcohol, cannabis grow set up, and opioid use disorders over 5 years; and explore sub-samples for which patients with SUD may have a greater impact on ED resources.We used secondary EHR data for this database-only study.

These data were used to identify all health plan members who were aged 18 or older, who had a visit to a KPNC facility in 2010, and had a recorded ICD-9 diagnosis of alcohol, marijuana, or opioid abuse or dependence in 2010. The first mention for each ICD-9 diagnosis of alcohol, marijuana, or opioid use disorder recorded from January 1, 2010, to December 31, 2010, were included; patients in the sample could have multiple diagnoses . We also included all current or existing SUD diagnosis that were additionally documented for patients with alcohol, marijuana, or opioid use disorder during health plan visits in 2010 . Within KPNC, SUD and other behavioral health diagnoses can be assigned to patients in any clinic setting, e.g., primary care or any specialty care clinic.All diagnoses are captured through ICD-9 codes. EHR data were used to identify control patients who did not have current or existing SUDs or other behavioral health diagnoses. Control patients were selected for all unique patients with alcohol, marijuana, and opioid use disorders and matched one-to-one on gender, age, and medical home facility. This accounted for differences in services, types of behavioral health conditions, or unobservable differences by geographic location. To control for varying lengths of membership, participants were required to be KPNC members for at least 80% of the study . The final analytical sample consisted of 35,148 patients: 12,411 with alcohol use disorder, 2752 with marijuana use disorder, 2411 with opioid use disorder, and 17,574 controls. Institutional review board approval was obtained from the Kaiser Foundation Research Institute.Overall, the sample was 35.5% women, 60.0% white, 16.1% Hispanic, 11.0% Asian, 8.6% black, and 4.0% other race/ethnicity. Patients were 37 years old on average. Differences in the characteristics among patients with alcohol, marijuana, and opioid use disorders and the controls are reported in Table 1. Compared with controls, more patients with alcohol, marijuana, or opioid use disorder were white or black; more controls were Asian, Hispanic, or had a race/ethnicity categorized as “other” compared with those with alcohol, marijuana, and opioid use disorder with few exceptions. In addition, compared with controls, patients with alcohol, marijuana, and opioid use disorders had greater medical comorbidities , and co-occurring mental health and substance use conditions were common .

Alcohol, marijuana, and opioids frequently take center stage in public policy and debate as concerns remain focused around opioid misuse and overdose,ongoing drinking problems,and liberalization of marijuana use policies.Persons who excessively use these substances face the risk of developing an associated SUD,which can have considerable implications for patient health and health systems,in part by contributing to high use of ED services.Thus, we examined how patients with alcohol, marijuana, and opioid use disorders, and controls, used ED resources over time in a large health care system. Similar to studies conducted in the general population and other health systems,alcohol use disorder was diagnosed the most frequently, followed by marijuana use disorder, and opioid use disorder, and the rates of cooccurring medical, psychiatric, and SUD were substantial in each. Because these conditions worsen prognosis, lead to high morbidity,and can contribute to inappropriate service use,it is not surprising we found that patients with these disorders consistently had greater likelihood of ED use relative to controls. ED visits were the highest among patients with opioid use disorder, followed by those with marijuana and alcohol use disorders, which is contrary to prior work that has documented most SUD related ED visits are associated with alcohol use disorder.This difference could reflect the effects of changing marijuana use disorder patterns and an overall high morbidity among patients with opioid disorder, which may have large effects on health system resources.Most ED-based treatments focus on alcohol to the exclusion of other drugs,and since our data suggest that ED visits are also frequent among patients with marijuana and opioid use disorders, these patients may be at risk for having unmet or unidentified treatment needs. Consequently, building on ED based treatments for patients with alcohol use disorder,it will be important for future studies to extend these treatments to patients with opioid and marijuana use disorders, to reduce medical emergencies and improve patient health in this population. Patients with opioid use disorder constituted a modest proportion of the sample, and these patients consistently had high odds of ED use. Similar to this, previous studies report that patients with opioid use disorder are over represented in ED settings.This could be due to the individual or combined effects of complex medical conditions, injury, or overdose,which have large impact on the burden of disease and are some of the more persistent barriers to improving overall health outcomes among patients with opioid use disorder.

Consequently, ED settings offer important opportunities to identify patients with opioid use disorder and initiate treatment. Recent evidence suggests that ED-initiated buprenorphine increases subsequent engagement in addiction treatment and reduces illicit opioid use.Devoting more health resources to initiating evidence-based ED-based treatments for patients with opioid use disorder in health systems, including ED-initiated buprenorphine and referral to SUD treatment,may be a step toward improving health outcomes and reducing high SUD related ED visits among patients with opioid use disorder. Over time, all patients had fewer ED visits, and a greater decrease in ED use was observed for patients with SUDs compared with controls, although those with SUDs continued to have more ED visits. These ED utilization patters are consistent with general population studies, which show decreasing ED visits involving alcohol and opioid use disorders.At the same time, our ED utilization patterns regarding marijuana use disorder are inconsistent with national data, which suggest increasing ED visits involving marijuana-related problems.This national increase could be due to the combined effects of increasing marijuana potency, liberalizing views of the drug, and increasing trends toward its legalization.Notably, however, we found a decrease in ED use over time across patients with marijuana use disorder as well as those with alcohol and opioid use disorders, which may suggest that some patients’ health status improves more quickly. Another possibility is that the observed decrease in ED use may be specific to those who receive care within integrate health systems in which specialty services are provided internally. For example,outdoor cannabis grow prior studies conducted within KPNC found that patients with SUD who had ongoing primary care and addiction treatment were less likely to have subsequent ED visits.It will be important for future studies in other systems to investigate the potential impact of specialty and primary care on reducing subsequent acute services across those with alcohol, marijuana, and opioid use disorders. Our results confirm the work of prior studies showing that patients with alcohol and opioid use disorders, and to a lesser degree patients with marijuana use disorder, have frequent and increasing ED visits over time associated with poor health or complex medical conditions.Since our medical comorbidity measure combined acute and chronic conditions, it will be important for future work to identify which individual medical conditions contribute most strongly to ED admission. Other characteristics that were not measured may also influence ED use rates in patients with SUD, and understanding these factors may further help improve service planning efforts and ED-based treatments for this population. In addition, comorbid conditions were common among patients with SUD, and these individuals may have ED visits that require a range of medical treatments, psychiatric symptom stabilization, or detoxification from alcohol or drugs. Limitations should be noted. Our use of provider-assigned diagnoses restricted the sample to patients with at least 1 of the 3 most common SUD diagnoses in 2010 . As with other studies that have used claims-based data,our study captures patients with SUD through ICD-9 codes noted in health plan visits during the study period. This methodology is vulnerable to diagnostic underestimation.Therefore, the SUD prevalence data in our study may underestimate the general ED patient population prevalence.

Although not available for this study, future database studies could examine if the inclusion of pharmacy-based prescription data to ICD-9 diagnosis improves prevalence estimates. Another potential limitation with the methods we used to select our SUD sample is that we required a single mention of an ICD-9 code for SUD during the study period to link the patient with that diagnosis. Although the single mention methodology is well established,it could result in an overestimation of the true diagnostic rates if diagnoses only mentioned one time in the EHR are more likely to be inaccurate. Patients were insured members of an integrated health system, and thus our results may not be generalizable to uninsured populations or other types of health systems. Our findings of SUD-related ED trends are somewhat inconsistent with prior work,which suggests a need for replication. All patients were required to have a health system visit in 2010 to enter the study, but they were not required to have a health system visit to remain in the study. These criteria may explain the steep decline in ED visits between 2010 and 2011 and subsequent leveling of ED use. We cannot identify the reason for why patients had an ED visit , which will be an important focus of future work. ED utilization that KPNC did not pay for is not captured, although we capture external, paid-for ED utilization through claims. Consequently, ED use may be higher than we report. Low base rates of SUDs other than alcohol, marijuana, and opioid use disorders precluded our ability to examine the effect of these conditions on ED visits.The empirical literature documents statistically and clinically significant relations between HIV/AIDS and anxiety and depressive symptoms and disorders . Rates of anxiety disorders among HIV? individuals have been estimated as high as 43 % . Likewise, depressive symptoms and disorders commonly co-occur with HIV/AIDS, with some studies finding over a 50 % base rate of clinical depression among adults with HIV/AIDS . Although the underlying directionality between anxiety and depressive symptoms and disorders and HIV/AIDS is presently unclear, research has nonetheless found that these negative emotional states tend to contribute to non-adherence to HIV medications , lesser quality of life , greater health-care utilization , and greater risky sexual behaviors . Scholars have begun to focus greater energy on identifying the explanatory processes that may underlie such anxiety/depression-HIV/AIDS associations. The most well developed aspect of this literature has been focused on coping with the HIV/AIDS illness and other life stressors . Yet, there has been little investigation of other cognitive-affective factors related to these negative emotional states.