Cannabis Grow

These areas are particularly important for adolescents’ processing of social information . The greater activation of these regions during the neurodevelopmental period of adolescence may be the result of substantive neurodevelopmental changes . These changes peak during adolescence, resulting in a relatively greater release of DA during this time frame . Practically, this means that risk taking behaviors, which are inherently exciting, frightening, and fun, may indeed feel much more rewarding during middle adolescence While it is clear that peers take on newfound significance during adolescence and that this shift has the potential to increase risk behaviors, an important body of work is beginning to reveal important caveats to this thesis. First, peer influence is also a powerful motivator for prosocial behavior during adolescence . Relatedly, while neural reward circuits are linked to a variety of risk behaviors during adolescence, VS and vmPFC reactivity to social cues also portend positive, prosocial development . Second, while peers become increasingly important during adolescence, this does not render parents as unimportant. Despite spending less time with parents, connectedness with parents can attenuate the impact of the enhanced reward circuit responses typical during adolescence, serving as a protective force insulating adolescents against risk behavior, stress and even depression . In fact, when compared with peers, parents show a significantly greater impact on adolescent decision-making . Understanding how adolescents navigate not only risk,growing cannabis but also prosocial peer interactions is one of the ultimate challenges for adolescent addiction treatment developers. We believe that this challenge is not insurmountable . Our task is to determine how best to channel youths’ drive and developmentally-unique cognitive systems to help them make more healthy choices. Summary.

These four developmental domains interact dynamically throughout adolescence , and are highly relevant to the adolescent addiction treatment context. For example, an adolescent’s environment can impact the nature and timing of puberty and vice versa; adolescents who look older may be treated differently than same-age adolescents who appear younger . These pubertal changes can alter the social spheres that adolescents are introduced to and experience . Moreover, social experiences, in turn, shape adolescents’ cognitive opportunities and related development early . Considering the interplay of these factors is crucial for understanding adolescent development as well as cultivating impactful programs to prevent and treat adolescent substance use. Throughout the past 3 decades, adolescent addiction treatment has shown some degree of capacity to catalyze and sustain behavior change in adolescents, but overall, results have been underwhelming . More specifically, despite several decades of efforts by experts to identify the best avenues to prevent and reduce adolescent substance use, few youth receive treatment . Of those who do, even when the treatment is grounded in evidence-based approaches and works well for adults, many youth do not show significant long-term changes in their substance use , with 86% returning to use within a year of treatment . As reviewed in Feldstein Ewing , this contrasts with the adult addiction literature, wherein a number of psychosocial interventions have much stronger impact in terms of instantiating and sustaining meaningful behavior change . For example, meta-analyses examining the efficacy of motivational interviewing indicate that in the context of addiction treatment, MI’s effect sizes are notably less robust for adolescents as compared with their impact with adults . At issue is that most of the interventions clinicians use with adolescents are “borrowed” from adult clinical addiction research . Yet the samples and populations utilized in large scale adult addiction studies, such as Projects COMBINE and MATCH , included inherently different populations, such as adults who largely self-referred to treatment. As a result, there is a notable gap between the nature of adults from whom these treatments were derived, and the nature of adolescents that we are trying to implement the same interventions with . Ultimately, better targeting with adolescent neurodevelopment in mind is likely to improve adolescent addiction treatment outcomes.

The poor generalizability of “adult” treatment to adolescents revolves around the significantly different conditions that make interactions within adolescent addiction treatment highly disparate from adults; within treatment sessions, adolescents face inherently different neurodevelopmental issues , disparate sociodevelopmental concerns , are on a different addiction trajectory , and in turn, have different treatment outcome goals than adults . Here, we include a brief overview of the challenges facing adolescent addiction treatment and its reporting, and our recommendations for avenues to improve best practices for clinicians and clinical research in this critical area of adolescent addiction treatment development.Absence of uniformly-agreed upon outcome in the adolescent addiction treatment literature. The current status of the field renders it quite difficult, if not impossible, to compare adolescent treatment outcomes across different treatment approaches . This is in contrast to the adult literature, wherein there are common, widely-agreed upon outcome metrics, such as percent days abstinent or drinks per drinking day . In this brief examination, numerous different categories of outcome variables were reported. The most common included number of substance use days, substance-related consequences, and quantity of substance use. This range of outcomes is likely to reflect a number of issues; one, as observed throughout the adolescent addiction treatment literature, there may simply be different targets for adolescent treatment response. More likely, this reflects that adolescents often show behavior change within one dimension of substance use , while still retaining high scores on another . Of greater concern to adolescent addiction practitioners, variance on outcomes may reflect reporting bias that favors treatment outcomes that withstood the test of statistical significance. One avenue to improve the field may be to report on commonly-agreed upon adolescent treatment outcome measures and do so regardless of statistical significance. We believe that this recommendation, to move toward a core outcome set in the field of adolescent addiction treatment, is highly important, and follows recent relevant initiatives, including the Scottish National Health System’s core outcome work , and the Core Outcome Measure in Effectiveness Trials Initiative . Notably, while these examples serve as excellent models, they have thus far been largely implemented with adult, rather than adolescent, clinical research studies. This fact again highlights the need for identifying and rolling out jointly-agreed upon core outcome metrics for adolescents in addiction treatment.Additionally, even when effect sizes are significant, it is not clear the degree to which reported outcomes are clinically meaningful with adolescent addiction patients. For example, one less drinking day per month may achieve statistically significance, but not a meaningful clinical change in terms of adolescents’ overall health, social, cognitive, and academic outcomes. In the adult literature, clinical impact has been defined as a statistically significant reduction in initial rates or problem scores, or a halving of initial symptoms . As with many other forms of adolescent health risk behaviors , a central measurement challenge is that adolescents engage in substance use sporadically and inconsistently . This makes treatment outcome measurement quite different from adults, whose use is often characterized by heavy, consistent patterns. For example, an adolescent may use alcohol very heavily , but then not drink at all during the initial months of the school year . Another avenue to improve the field is to examine pre-to-post changes in interference in functioning for youth; this represents changes in the degree to which alcohol or other substance use disrupts interactions with peers, with family, with school/other academic,cannabis growing and/or other relevant work/extracurricular obligations. To this end, examining reductions in interference in functioning is likely a more meaningful metric .

Examples of measures that can effectively access and assess this factor include the Rutgers Alcohol Problems Index and the Marijuana Problems Index . Substance substitution? While it is clear that adolescents tend to gravitate toward poly substance, rather than mono-substance, use , many adolescent treatment studies do not report treatment outcomes for non-target substances of abuse. For example, many measured treatment outcomes in alcohol , cannabis , tobacco ; some examine 2 substance categories, but include inconsistent pairings across each study . This is relevant, as many contemporary adolescent addiction treatment teams are trying to disaggregate whether or not youth are “swapping” out one substance for another, particularly in the changing cannabis and opioid landscapes . Our fourth recommendation to the field is our encouragement to explicitly examine and report outcomes across all types of substance use, to ensure that we can disaggregate the differential impacts and interactions that each substance might be having with the developing brain . This is likely to become an increasingly important issue in the field of addiction treatment as researchers move towards a precision medicine lens for understanding the genetic, lifestyle, psychological, social, and other bio-behavioral markers associated with treatment responsiveness . Of course, it should be noted that this point applies equally to adult studies, and to psychosocial interventions for most kinds of behavior disorders. Sadly, adolescent treatment has continued to lag behind advances made in other age groups in the journey towards precision medicine . Summary. Many adolescent addiction treatments appear to have clinically meaningful outcomes, but cross-treatment comparison and interpretation is not truly possible in the literature’s current state. At this time, inconsistent targets and timing obscure careful detection of comparative clinically-meaningful treatment gains . In turn, it is currently quite difficult to access the driving mechanisms and their intersection with potential developmental cognitive factors, and true treatment success in this age group. In turn, we make these recommendations for the assessment of adolescent treatment outcomes, with examples of how each recommendation maps onto relevant neural targets . It will be fascinating to continue to see if and how these developmental neuroscience findings translate to the clinic.While the previous section indicates that existing treatments available to adolescents have had difficulty examining behavior change, it is our position that actively considering the nature of the developing adolescent brain can inform the revision and approach of interventions with this age group. In other words, the developing brain gives us an invaluable perspective regarding what might “work” better in this age group in terms of prevention/ intervention. As summarized in Figure 1, we propose four key neurodevelopmental features of adolescence, and encourage approaching addiction interventions from this foundation as a promising first step in articulating prevention and intervention to the adolescent age group. Compellingly, in largely overlapping neural networks, those four features include: Puberty; Surge of cognitive skills Sculpting out of self; and Changing social landscape. Collectively, consideration of these factors, and their interplay, highlights several important themes to consider in developing novel clinical addiction approaches with this age group. Benefit of a prosocial perspective on prevention and intervention. First, consistent with G. Stanley Hall’s “storm and stress” perspective of adolescence, many existing adolescent-focused prevention and intervention approaches hinge on “problem focused” perspectives in substance use and its resolution. However, this does not play to the nature of the adolescent brain, which is increasingly being recognized as evolving and adaptive . As cited by Ellis , integrating considerations of adolescent neurodevelopment would likely generate more positive treatment outcomes if we took a positive, adaptive-focused perspective that plays to and enhances adolescents’ existing strengths in resilience, natural penchant to cognitive flexibility, and socially-adaptive and prosocial growth. This is an arena that is gaining increasing traction in adolescent addiction contexts . Non-traditional, but potentially highly impactful examples here could include clinical approaches that engage adolescents in helping younger peers, pairing problem users up with more successful youth in the same age group, and engaging adolescents in avenues for more successful positive change in their peer and greater social communities . A relevant point in this examination is that while a handful of emerging studies are beginning to include prosocial, resilience-focused models of adolescent behavior , a careful synthesis of these models has not yet been created. This is a critical avenue for future work, and will likely require not only examination of quantitative, but also mixed method, and qualitative research, as much of this emerging research is still in its inception and early stages of implementation. Maximizing their drastically developing cognitive skills. Adolescents are in the midst of experiencing a surge of new cognitive skills; at the most fundamental level, adolescents’ brains are organized toward and ready for adaptation .

These medical diseases are frequently considered chronic medical conditions, and are included in either the validated Katz chronic disease comorbidity questionnaire 15 or the Hierarchical Condition Category system and obstructive sleep apnea, which is recognized as one of the most prevalent chronic respiratory disorders and therefore included in our analysis. Medical multi-morbidity is defined in the literature as 2 or more chronic diseases . Using the above conditions to study medical multi-morbidity with the NSDUH has been performed in other studies . Drug use was assessed by NSDUH by self-report of cannabis , cocaine , heroin, inhalants, hallucinogens, and non-medical use of prescription medications . Non-medical use of prescription medications was defined as use of a drug that was not prescribed or used for the experience or feeling it caused. Nicotine dependence was defined based on dependence criteria of the Nicotine Dependence Syndrome Scale and alcohol dependence was defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition . SUDs were determined by participant responses to a series of questions that determined if criteria would meet DSM-IV abuse or dependence categories for each drug. While NSDUH is not a diagnostic interview, this method provided a proxy diagnosis.Analyses include all respondents aged 18 and older. We aggregated data from the three separate cohort years into a single cross-section to be able to increase power for examining associations between variables with low prevalence . We first examined bivariable associations between demographic characteristics and number of self-reported conditions . Demographic measures included age , sex, race/ethnicity , education level ,roll bench total family income , marital status . NSDUH only provides pre-coded categorical responses for the age and income variables, and therefore could not be analyzed as a continuous variable.

Tobacco use , nicotine dependence, alcohol use , alcohol dependence, self-reported overall health , and self-reported mental health problems in the past-year were also compared among adults reporting 0, 1, and ≥2 chronic conditions. Bivariable analyses was also performed comparing past-year drug use and diagnosed substance use disorder for adults with 0, 1, and ≥2 chronic conditions. We evaluated correlates of past-year drug use among adults with medical multi-morbidity using binary logistic regression. We first estimated odds of each covariate separately, generating unadjusted odds ratios. We then fit covariates simultaneously using multiple logistic regression. All analyses were weighted as part of NSDUH to account for the complex survey design and to obtain accurate standard errors for estimates at the population level. More detailed information regarding the development of analysis weights are found elsewhere . Since our analyses utilized data from 3 cohorts, we divided the weights by 3 to obtain nationally representative estimates. Stata SE 13 was used for all analyses, and survey commands were utilized to provide accurate standard errors using Taylor series estimation methods . Secondary analysis of this publically available data was exempt for review by the New York University Langone Medical Center Institutional Review Board.The analytic sample included 115,335 respondents. Chronic medical conditions were common among respondents with weighted percentages of 29.5% of adults reporting 1 chronic condition and 17.4% reporting 2 or more conditions, and therefore 46.9% reported at least 1 chronic condition and 53.1% reported no chronic conditions . Most adults with medical multi-morbidity were middle-aged and older adults , while younger adults were more likely to report no chronic conditions . Compared with adults reporting no chronic conditions, adults with medical multi-morbidity were also more likely to be non-Hispanic white, less educated, married, have nicotine dependence, drink alcohol less frequently, report having worse health status, and more likely to have depression, anxiety, and need mental health treatment in the past year . Table 2 presents frequencies and weighted percentages of specific drugs used by 0, 1 or ≥2 chronic conditions. Overall, 16.1% of the full study sample reported past-year use of an illegal drug or cannabis and 2.6% reported meeting criteria for a SUD. The most common drug used in the past-year was cannabis, and cannabis use disorder was the most common SUD.

Non-medical use of opioid analgesics was the next-most common and opioid-related SUD was the second most common SUD. Among all adults with chronic conditions, past-year drug use was reported by 14.8% with 1 chronic condition and 11.6% with ≥2 conditions, and drug use was considerably higher among adults with no chronic conditions . Among adults reporting no chronic conditions, 5.5% used ≥2 drugs, while 4.3% of adults with 1 chronic disease and 3.2% of adults with ≥2 chronic diseases reported use of > 1 drug in the past year . Criteria for past-year SUD was met by 2.4% with 1 chronic condition and 2.1% with ≥2 conditions, and 2.9% of adults with no chronic conditions . In this large, nationally representative survey, we estimated the prevalence of past-year drug use among adults without chronic medical conditions and among those with one and two or more chronic medical conditions . While the prevalence of past year drug use was lower among adults with medical multi-morbidity, compared to adults with no chronic conditions, nearly 12% of adults with multi-morbidity reported engaging in past-year drug use. The lower prevalence of drug use among adults with multi-morbidity may be due to both the fact that younger adults were more likely to engage in drug use and less likely to have chronic medical conditions, and some of those with multi-morbidity may have stopped using drugs because of their morbidities . In alcohol studies for example, the “sick quitter” hypothesis proposes that adults may stop drinking due to medical illness, hospitalizations, or declining health, and therefore this group is not included as individuals with alcohol-related problems even though alcohol may have contributed to their illnesses . A similar phenomenon is likely occurring in observational studies with drug use including our study. However, we did find in multi-variable models that among adults with medical multi-morbidity, adults with alcohol dependence, current tobacco use, and adults with mental health problems were more likely to have engaged in past year drug use,drying rack cannabis and therefore be at particularly high risk for adverse effects of drug use. This emphasizes the importance of including SUDs and poly substance use disorders to the multi-morbidity framework as a distinct clinical profile that necessitates further research to better care for patients with complex multi-morbid disease . The intersection of substance use and medical multi-morbidity is complex. Drug use has a wide array of physiologic effects on the body, that may negatively impact existing chronic medical disease and complicate its management. For example, cocaine use can impact both the cardiovascular and cerebrovascular systems that can lead to stroke, disability or sudden death, particularly among adults with pre-existing disease . Second, substance use can also complicate the clinical management of existing chronic diseases.

Studies have shown decreased adherence to antiretroviral therapy among adults with HIV who engage in active substance use , and poor medication adherence for adults with psychosis who used cannabis . This is particularly relevant for adults with medical multi-morbidity, who often have complex medication schedules that demand careful monitoring and daily management. In addition, the complicated medical care that adults with medical multi-morbidity face places them at risk for medication management mistakes as well as adverse drug effects and interactions . This emphasizes the importance of substance use screening for adults with chronic conditions. In addition, since many adults in SUD treatment often have fragmented primary care , it is also important for SUD treatment providers to screen and ensure medical comorbidities are being managed. In our study sample, cannabis was the most commonly used drug among adults with chronic disease. This is not surprising since cannabis is the most common drug used in the NSDUH study sample and given more positive attitudes and policies related to cannabis use . Cannabis has been used and studied for medical treatment of chronic diseases including HIV, multiple sclerosis, chronic pain, seizure disorder, and other mental health disorders . Although this study cannot distinguish between medical use versus recreational use of cannabis, using cannabis for these types of conditions may partially explain its high prevalence use among adults with chronic disease . The risks of cannabis have not been well-documented, particularly among older adults with medical multi-morbidity who may be at higher risk for negative cardiovascular, pulmonary, and cognitive effects of cannabis use . Further study is needed to better assess the benefits and risks of cannabis use for specific chronic diseases and overall use by adults with medical multi-morbidity.

The statistically significant correlates of past-year drug use among adults with medical multi-morbidity identified in this study include younger age, male sex, low family income , current tobacco use, alcohol use and alcohol dependence, having had a major depressive episode, and having received mental health treatment. The demographic findings are similar to the overall results of NSDUH among the general adult population for illegal drug use including cannabis, such as adults with younger age and male sex having higher rates of use . However, our results are novel in that they identify a potentially very high-risk population of adults with the combination of medical multi-morbidity with poly substance use and/or mental health disorders. The strong association we found with alcohol dependence with concurrent drug use among adults with medical multi-morbidity is alarming. Unhealthy alcohol use itself can cause, exacerbate, and complicate the management of several chronic medical diseases , and therefore the combination of illegal drug use with alcohol use, especially if used concurrently, can be particularly dangerous for adults with existing chronic medical conditions. Further, the co-occurrence of substance use and SUD with mental health illnesses are well documented , and interventions for addressing patients with co-occurring conditions have been developed and studied . The added mental health comorbidity is particularly important as one recent study of 843,584 veterans in the VA system who had at least three visits to a mental health clinic found 30.6% had co-occurring psychiatric and SUDs . The added burden of poly substance use along with mental health problems highlights the need for a syndemic framework for caring for patients with compound multi-morbidity, where the focus is how multiple health conditions are adversely affected by behavioral, psychiatric, biological, and social conditions. The use of the syndemic framework can help identify intervention strategies to reduce harms and improve the management of chronic disease for adults with medical multi-morbidity, poly substance use, and mental health disorders. In practice, one approach using this framework could include the integration of primary care and chronic disease management into SUD and mental health treatment settings and vice-versa. There are important limitations to this study. First, the NSDUH responses are based on self-report and thus are subject to both recall and social-desirability bias; although the survey attempts to limit the latter via ACASI . Second, the NSDUH does not assess when a respondent fifirst experienced or was diagnosed with a chronic medical disease, but asks only for lifetime prevalence. Therefore, participants may have had a chronic medical condition years before being surveyed and that condition may have been resolved and not overlap with past year drug use. However, most of the conditions queried tend to be lifelong. In addition, while the specific chronic diseases chosen in the NSDUH study design were based on expert opinion of medical diseases that are often related to substance use , it does not include many chronic medical diseases that are often asked in clinical research and epidemiological studies to understand the burden of chronic disease in specific populations. It also does not specify “heart disease” further, thus this could include a wide range of cardiac diseases. Therefore, the generalizability of this study is limited to the specific chronic diseases asked of this study sample and how it was asked. Also, assessing DSM SUD criteria via surveys can also be limited as these are not full diagnostic interviews. Finally, our study classifies users of drugs if the individual used in the past-year and therefore does not distinguish between one-time use versus more frequent use, which may potentially have different risks and consequences.

Analyses proceeded in three stages. First, at baseline and 3, 6, and 12 months follow-up, the percent of participants at risk, and the identified preaction stage for those at risk, were determined for each HRB. Second, LCA was employed to discern the common patterns of HRBs at each time point. Third, LTA was used to examine the most likely patterns of HRBs over time. LCA was employed first at baseline, then each follow-up time point for evaluation of structure stability. LCA is a latent variable modeling technique that characterizes homogeneous populations within a larger sample who share common response patterns to categorical indicators . Models of 1–6 classes were fit and standard criteria were used to compare the models. Model selection was based on goodness of model fit, parsimony, and adequacy of the model with respect to the research questions being posed. Four sets of criteria were used for selecting the optimal number of latent classes in factor mixture models as recommended by Muthén and Muthén. First, the Bootstrapped Parametric Likelihood Ratio Test tested for model improvement in each successive model over a model with one fewer class. Second, the Sample Size Adjusted Bayesian Information Criterion and Aikaike’s information criterion were examined, with lower values indicating better model fit. Third, the entropy value, ranging from 0 to 1, measured the clarity of classification. Entropy values that are close to 1 indicate that a model has clearly identified individuals of different types, and it can be a useful summary measure. Finally, the usefulness of latent classes in practice was evaluated by substantive interpretation of the classes in a given model, as well as the parameter estimates including class membership or posterior probabilities and class-specific conditional response probabilities . With LCA,seedling grow rack observations are classified into their most likely latent classes on the basis of the estimated posterior probabilities for the observations. High diagonal and low off-diagonal values in the class classification table indicate good classification. CRPs reflect the probability that an individual within a particular class has a high-risk health behavior.

Based on the patterns of the estimated conditional probabilities, meaningful labels or definitions of the latent classes were made. LTA was then used to examine the extent to which patterns of HRBs at baseline were stable over time, using three analyses . Detailed statistical presentations of the general LTA framework are available in Humphreys and Janson and Reboussin et al.. LTA is a longitudinal strategy that assesses the probabilistic change in class membership over time with categorical latent variables. This analysis extends LCA by assigning transition probabilities, which are conditional probabilities describing the probability of being in a given state at time = t, conditional on the state at time = t − 1. We used an LTA to model the stability of HRBs over the course of 12 months; latent transition probabilities were then used to evaluate how individuals either exhibited the same HRB pattern or changed patterns over 12 months. We hypothesized a priori that the intervention may affect multiple HRBs, because multiple HRBs can occur following interventions aimed at changing a single behavior. Moreover, we found a significant intervention effect on smoking outcomes at 3 months in the RCT. Therefore, the models initially included treatment condition as a covariate. However, because very few participants transitioned between classes over time, adding treatment condition to the model resulted in several empty cells. For example, no participants in the control condition transitioned from substance use risk to low risk between baseline and 3 months. Because adding another parameter resulted in several empty cells, the estimates were unstable and could not be reliably interpreted. Moreover, treatment condition was not a significant covariate in the LCA models . Therefore, all models reported here are without the inclusion of treatment condition. LCA and LTA were conducted with Mplus version 7.4 due to the availability of multiple model fit indices not available in other statistics platforms and the ease of employing randomized starting values. Other analyses were conducted with IBM SPSS Statistics. All available cases were used at each time point.In our sample of young adult smokers, nearly all reported engaging in at least one other HRB at baseline and follow-up, the most prevalent at each time point being diet related. The most prominent patterns of HRBs at four time points highlighted that the more prevalent targets, in addition to tobacco, for behavioral change in young adult smokers are diet-inactivity, sleep habits, and cannabis use.

The 3-class solution that fit the data best at all time points was similar to that found in a sample of adult smokers in a mental health treatment setting, with profiles for a global high-risk group, consisting of substance use and metabolic risks, and one in which risks were primarily metabolic . Three very similar profiles emerged in two very different samples of smokers , suggesting that metabolic and substance use patterns ought to be assessed and ideally addressed through direct treatment or referral, in the context of smoking cessation interventions. HRB patterns in young adult smokers may differ from those of the general young adult population. Prior research in the general young adult population has found that smoking generally clusters with substance use and sexual risk behavior. In the present study, wherein all participants were smokers, likelihood of engaging in condomless sex did not systematically vary with other HRBs. Employing broader measures of sexual risk behavior may have yielded an association between sexual risk behavior and substance use. However, given the similarities in HRB profiles between the present sample and adult smokers with serious mental illness, it is also plausible that the HRB profiles of young adults who smoke differ from those of the general young adult population.There were notable differences in stages of change for different HRBs that could be informative when adding health-related content to smoking cessation interventions for young adults. Given that overall, participants were most ready to change their diet, stress management, sleep, or physical activity, an intervention targeting the metabolic risk group would likely be well-received. Membership in the metabolic risk group at baseline was associated with a greater likelihood of smoking daily and with smoking more cigarettes per day. Young adult smokers with metabolic risk factors may be a group that would particularly benefit from cessation medications. In contrast, motivation to change alcohol and drug use was generally low, suggesting that an intervention targeting the substance use group may need to especially focus on motivational enhancement. Cannabis use was common among participants in the substance use group, and those at risk for cannabis use were least ready to change this behavior compared to all other behaviors. Heavy alcohol use declined from baseline to follow-ups, while cannabis use remained elevated.

Given previous reported differences in stages of change for tobacco and other substance use among young adults, smokers of all types may be less receptive to interventions targeting other substance use than they are tobacco or other HRBs. The substance use group had a higher proportion of males,greenhouse growing racks suggesting that interventions with young adult male smokers may benefit from a focus on enhancing motivation to change substance use. Although class membership was mostly stable over the course of one year, transitions from low risk to metabolic risk were somewhat more frequent than transitions to substance use risk. This finding may reflect a general decline in substance use throughout one’s 20s and underscores the need for intervention on metabolic risk behaviors among the general population of young adult smokers. Given a significant difference in smoking abstinence between treatment and control groups at the 3 month follow-up in the clinical trial, we hypothesized that participants would be more likely to transition to classes characterized by lower risk if they had participated in the Facebook smoking cessation intervention compared to the control condition at each time point. Results showed that classes were very stable over time, with few participants transitioning between them. As such, the model could not be reliably fit when treatment condition was included. This reflects the notable stability of young adults’ patterns of HRBs over 12 months, which may be due to the demanding nature of multiple HRB change and limits of cognitive capacity and self-control, coupled with relatively low readiness to change. Results suggest extended intervention content enhancing motivation and supporting behavior change for a few HRBs is likely needed to create meaningful change in multiple HRBs among young adult smokers participating in any form of smoking cessation intervention. This study recruited a relatively diverse sample of young adult smokers in the USA. Notably, more than one in four participants identified as a sexual or gender minority . This may have been due to the high prevalence of both smoking and social media use among SGM individuals. Moreover, 8.2% of millennials identify as SGM compared to 3.5% of those in Generation X. In this sample, SGM and non-SGM young adults did not significantly differ in smoking cessation rates or other health behaviors, with the exception of physical activity, over time.

Nonetheless, clustering of HRBs may vary by other individual differences , and future research could examine differences in the clustering of HRBs by individual characteristics. Notably, we identified few differences in latent class membership by individual characteristics, suggesting that the HRB profiles in this study have broad applicability to young adult smokers.Study limitations include that the data were self-reported and subject to recall bias. Due to empty cells when additional parameters were included, we were unable to include treatment condition as a covariate or incorporate stage of change into LCA and LTA models. Future research should incorporate these additional characteristics. The sample was neither randomly sampled nor representative, thereby limiting generalization of study findings; however, as an initial investigation, the volume of HRBs among young adult smokers appears high and the patterns stable over 12 months.Over the past century, California has built an extraordinarily complex water management system with hundreds of dams and a vast distribution network that spans the state. This system generates electricity, provides flood protection, delivers reliable water supplies to 40 million people and supports one of the most productive agricultural regions in the world. Yet development of the state’s water management system has come at a price. Damming waterways, diverting water from rivers and streams and altering natural flow patterns have transformed the state’s freshwater ecosystems, leading to habitat degradation, declines of freshwater species and loss of services that river ecosystems provide, including high-quality drinking water, fishing and recreational opportunities, and cultural and aesthetic values. The state aims to accommodate human water needs while maintaining sufficient stream flow for the environment. To support this mission, scientists from the U.S. Geological Survey , The Nature Conservancy and UC have developed new techniques and tools that are advancing sustainable water management in California. At the center of these new advances is the need to understand the natural ebbs and flows in the state’s rivers and streams. Natural patterns in stream flow are characterized by seasonal and annual variation in timing , magnitude , duration and frequency . California’s native freshwater species are highly adapted to these seasonally dynamic changes in stream flows. For example, salmon migration is triggered by pulses of stream flow that follow winter’s first storms, reproduction of foothill yellow-legged frogs is synchronized with the predictable spring snow melt in the Sierra Nevada, and many native fish breed on seasonally inundated floodplains, where juveniles take advantage of productive, slow-moving waters to feed and grow. When rivers are modified by dams, diversions and other activities, flows no longer behave in ways that support native species, contributing to population declines and ultimate extinction. Thus, understanding natural stream flow patterns and the role they play in supporting ecosystem health is an essential first step for developing management strategies that balance human and ecosystem needs. Unfortunately, our ability to assess alteration of natural stream flow patterns, and the ecosystem consequences, is hindered by the absence of stream flow data. California’s stream flow gauging network offers only a limited perspective on how much water is moving through our state’s rivers. In fact, it’s been estimated that 86% of California’s significant rivers and streams are poorly gauged and nearly half of the state’s historic gauges have been taken offline due to lack of funding .

They do, however, cast further doubt on the strength of empirical support for Miron’s intuitively plausible theory. Figure 10 reveals why the added data from the 1990s onward weakens the estimated relationship between prohibition enforcement expenditures and homicide: federal per capita drug prohibition spending has continued to rise despite a steady fall in the homicide rate. A number of more general problems potentially plague the basic regression specifications: the enormous difficulty of drawing causal inferences from national time series data; the possibility that causality runs in both directions; and the omission of state enforcement expenditures and other possible explanatory factors. However, bearing in mind these various provisos, Miron’s analysis is consistent with, and provides a notable supplement to, more targeted analyses—such as the aforementioned study of New York murders—supporting the theory that criminalization does more harm by the systemic crime and violence it creates than good in any toxicologically-induced crime it may prevent.Related problems with our current approach to cocaine are mandatory minimum sentences and the differential treatment of crack and powder cocaine. As discussed earlier, there is a large racial disparity between African-Americans and Caucasians in terms of the percentage imprisoned for drug-related offenses. Much of this racial disparity is the result of mandatory sentences for possession and trafficking of crack which have been far more severe than those in place for powder cocaine. In the early 1990s, over 90 percent of defendants in crack cases were African-American compared with only 25 percent of defendants in powder cocaine cases .Mandatory sentencing laws for drugs generally prescribe a sentence based on the quantity of the drug in question.

Until just recently,indoor grow shelves under federal sentencing guidelines a defendant needed to possess an amount of powder cocaine one hundred times greater than the amount of crack cocaine in order to receive an equivalent sentence.93 Thus a defendant convicted of possessing 50 grams of crack cocaine with intent to distribute faced a mandatory minimum sentence of ten years whereas a defendant would need to possess 5 kilograms of powder cocaine to expect the same sentence. Though President Obama recently signed the Fair Sentencing Act, which is set to reduce the sentencing disparity ratio from to 100 to 1 to 18 to 1 , a significant differential will remain, and some states also have adopted more stringent sentences for crack cocaine than powder cocaine . Differences in state law treatment of the two drugs have the potential to be more important because more prisoners are convicted of crack offenses at the state rather than federal level each year. In addition to the racial disparities created by mandatory sentencing laws, scholars have also noted additional concerns regarding their implementation. First among these is the fact that drug amounts are determined by mixture weight rather than pure weight. This introduces sentencing distortion because drugs sold in the illicit market vary greatly in their purity. For example, the sale of coca leaf, which contains only 2 percent cocaine, is treated the same as the sale of pure powder cocaine in terms of weight, even though 100 grams of coca leaf has the same amount of cocaine as 2 grams of pure cocaine. The focus on weight also prevents a distinction between large-scale dealers, the “kingpins” of the business, and small time dealers. A “kingpin”may operate in such a way that he carries very little of a drug substance on him at any given time and thus when caught in possession with an intent to sell, receives a lighter sentence than one of his subordinates, who carries larger quantities of the substance in order to make frequent sales. Without the mandatory minimum sentences, judges would have more discretion to differentiate between the “kingpin” and the small-time dealer. Mandatory sentences shift power from judges to prosecutors because prosecutors have discretion concerning whether to charge an individual with a crime carrying a given minimum sentence, whereas once the defendant is convicted, under a mandatory sentencing scheme the judge lacks the discretion to reduce a sentence .

Deciding whether it is preferable to grant more power to judges or prosecutors is a judgment call which depends on whether one believes such power should be vested in the executive or judicial branch; however, the shift in power is a clear impact of mandatory minimum sentencing laws. Given the substantial costs of mandatory minimums, are they necessary or cost-justified deterrence mechanisms? Credible evidence suggests they are not. A 1997 empirical evaluation of the cost-effectiveness of mandatory drug sentences found mandatory minimums are less effective at reducing cocaine use than both conventional enforcement and treatment programs . The authors, part of the RAND Drug Policy Research Center, attempted to measure the effects on cocaine consumption of spending an additional $1 million on conventional enforcement, mandatory minimum sentences, or treatment. Looking at the 184,548 drug dealers convicted in state and federal courts during 1990, the authors estimated that were the federal mandatory minimum drug sentences94 applied to all of these dealers, the cost to the public for the additional prison time would be $22.5 billion. According to the model tested in this study, longer sentences influence cocaine consumption by raising the price of cocaine as dealers increase prices in order to offset the increased probability of a longer prison sentence. Using an estimate that a drug dealer must be compensated an additional $37,500 per additional year of incarceration and a cost to the public of $25,000 per year of incarceration, they estimated that each $1 spent on longer sentences will translate into a $1.50 increase in total costs to consumers of cocaine. Thus they found that an additional $1 million spent on longer sentences would increase cocaine prices by 0.004 percent.95 Over a 15 year time horizon given a dealer discount rate of 12 percent and an elasticity of demand for cocaine of 1, they determined that each additional $1 million spent on longer sentences reduces cocaine consumption by 12.6 kilograms nationwide . Given estimated total annual consumption of 291,000 kilograms, this represents a change far less than one-hundredth of one percent. If one assumes the relationship to be linear over this range,indoor garden table every increase in incarceration costs of $1 billion per year might be expected to reduce cocaine consumption by about 4.3 percent.

When evaluating treatment programs, the RAND authors relied on Rydell’s and Everingham’s study of cocaine treatment reporting that 13 percent of cocaine addicts abstain from hardcore cocaine use in the long-run following treatment and that 79 percent abstain during the 0.3 year length of the average treatment program. Given the $1,740 average cost of a treatment program, an extra $1 million could treat 575 heavy cocaine users, resulting in a 16 kilogram reduction in the first year. Over a 15 year time horizon, given that 13 percent of heavy users quit heavy use following treatment, these authors estimated that each $1 million spent on treatment would reduce cocaine consumption by 103.6 kilograms, compared with 12.6 kilograms for longer sentences, making treatment appear much more effective . While the linearity assumption might be more strained over this range, the comparison to the incarceration-increase numbers is revealing: an annual increase of $1 billion in spending on treatment might be expected to reduce cocaine consumption by 35.6 per cent. These findings are in line with Rydell’s and Everingham’s examination of the effectiveness of treatment compared with three other drug enforcement policies: source country control , interdiction and domestic enforcement . The authors found that the cost of crime and productivity loss from cocaine use decreases by $7.46 for every $1 spent on treatment whereas the same figure for source country control is $0.15 per dollar, $0.32 for interdiction, and $0.52 for domestic enforcement. Rydell’s & Everingham’s initial study was criticized for underestimating the decrease in cocaine use stemming from increases in cocaine prices due to source-country control, interdiction, and domestic enforcement. Repeating their study of policy effectiveness in 2000 assuming a more elastic demand for cocaine, Caulkins, Chiesa, and Everingham determined that treatment has a four-to-one advantage over domestic enforcement in reducing the costs of crime and productivity losses. Overall, this evidence on treatment versus severe punishment for those found possessing or dealing cocaine today suggests that mandatory treatment for drug offenders is a more cost effective solution. As with marijuana policy, there appear to be many potential improvements for cocaine policy, even within the regime of criminalization. In the United States—indeed, throughout the world—many individuals are drawn to substances that may harm them greatly. Public policy varies enormously with respect to these substances, partly based on the degree of addiction, the nature of harms, and historical experience. Though sugar, saturated fat, and high fructose corn syrup impose enormous health costs, regulation to discourage consumption of them is virtually non-existent; in fact, corn subsidies in particular have been criticized for perversely incentivizing poor diets. In contrast, tobacco and alcohol are subject to considerable regulation while remaining legal, and a host of drugs ranging from heroin and cocaine to methamphetamine, ecstasy, LSD, and marijuana are banned by state and federal law. Tobacco imposes high costs on a large proportion of users because the addiction is powerful and the health cost of decades of use will likely be great. Nonetheless, consumption rates tend to be high because the health costs are temporally distant, and governments tend not to prohibit consumption because current productivity and parenting ability are not discernibly impaired. Interestingly, perhaps the greatest domestic success in reducing consumption of harmful substances came for this lawful product, engineered largely through tax hikes via the settlement of tort litigation against the tobacco companies. Other harmful recreational substances vary in terms of addictiveness and the ability of large numbers of users to enjoy them sporadically and without substantial health cost or productivity impairment for work and parenting. But for sizeable percentages—perhaps 10 percent for marijuana users, 15 percent for alcohol and cocaine users, and almost 25 percent for heroin users —the personal and social costs are dramatic and substantial. It is largely to reduce these costs to this minority of users that governments have banned, and tried to keep as many people as possible away from marijuana, cocaine, methamphetamine, and heroin . Estimates placing the economic costs of illegal drug abuse at levels roughly comparable to those costs for alcohol and tobacco underscore that there are no easy choices when it comes to drug policy. Aggressive efforts to limit consumption through a tough penal approach tend to restrain the costs from drug use while unleashing the high costs of enforcement and incarceration in a context of increased violence centered around the criminal gangs that run the drug trade. Conversely, legalization of alcohol and tobacco drastically reduces enforcement costs with respect to these substances while keeping the costs of consumption high. A cost-minimizing approach to drug policy might move us away from a punitive approach to control of the currently illegal drugs, while entailing aggressive measures to prevent underage consumption and constrain demand. On the other hand, while thorough consideration of policy toward legal drugs is beyond the scope of the present inquiry, comparisons of their toxicological effects and social costs with those attributable to such illegal drugs as marijuana and cocaine suggest that more vigorous pursuit of demand-restraint policies for alcohol and tobacco may result in a reduction of the social costs of those drugs. At some point, insights from social science and medical testing may be refined enough, and widely enough disseminated, to enable potential users to secure better advance notice regarding their particular susceptibility to the serious consequences of drug and alcohol abuse. At present, many individuals find out the hard way, at great cost to themselves and society. Despite the problem of moral hazard, greater treatment seems to offer a more cost effective method for dealing with these abusers than criminal penalties. Our analysis has also underscored that optimal drug policy is likely to differ from one drug to another, since, for example, the impact of government policies—current and hypothetical—may be substantially different for an extremely prevalent drug with relatively mild toxicological effects, such as marijuana, than for a far less common, but more addictive and dangerous drug, such as cocaine.

Attorneys for prosecution, whereas for 97.5 percent, drug trafficking was the most serious offense .In terms of drugs involved for defendants actually convicted of federal drug offenses, 30.6 percent involved marijuana, 22.4 percent involved crack cocaine, 21.5 percent involved cocaine powder, 12.5 percent involved methamphetamine, 7.8 percent involved opiates, 0.5 percent involved hallucinogens, and 4.8 percent other substances . Unsurprisingly, the percentage of incarcerated drug offenders serving time for possession appears to be significantly greater in state as opposed to federal prisons. Analyzing data from the 2004 Survey of Inmates in State and Federal Correctional Facilities, Mumola and Karberg report that in 2004, 5.3 percent of drug offenders in federal prisons and 27.9 percent of drug offenders in state prisons were incarcerated for possession. The authors found that of drug offenders held in state prisons, 61.8 percent reported that cocaine or crack was involved in their offenses, and the analogous figures were 18.6 percent for stimulants, 12.7 percent for marijuana or hashish, 12.2 percent for heroin and other opiates, 2.2 percent for depressants, and 1.7 percent for hallucinogens.One must interpret these data with caution. First, just 20.7 percent of drug offenders in state prisons reported having no prior criminal history . Second, given the pervasiveness of plea bargaining and the evidentiary ease of prosecuting possession relative to other offenses,vertical hydroponic system the percentage of convicts incarcerated in state prisons whose most severe offense truly is possession remains somewhat illusive.

Locking up approximately half-a-million drug offenders has a direct budgetary cost in the billions each year—approximately $6.6 billion for state drug prisoners and perhaps that sum over again for federal prisoners and convicts serving time in jail.In addition to the costs of incarceration borne by government and prisoners, a large toll falls on the families of those incarcerated, partly in terms of lost incomes, many of which were lawful ones . Fifty-nine percent of male state and federal inmates in prison for drug possession or trafficking have minor children, whereas in the general prison population, only fifty-one percent have children, indicating an additional cost stemming from high incarceration rates in the form of children with absent fathers . There is also a startling racial disparity in imprisonment for drug charges. In state prisons, African-Americans account for 38.6 percent of prisoners overall and 45.1 percent of prisoners convicted of drug offenses , though they represent just 13 percent of the U.S. population .There is also evidence that a substantial portion of racial profiling problems result from the targeting of drug sellers through criminal enforcement efforts, which could be greatly reduced under a less punitive drug policy. With the aim of devising rational drug policies based on practical experience rather than predominantly ideological concerns, countries throughout Europe are experimenting with drug policy in a variety of ways. In general, European countries have less punitive—and more harmreduction oriented—approaches to drug policy than the United States. The Action Plan adopted by the German government in 2003 to deal with Germany’s drug problem is representative of this approach, claiming: “The ‘Action Plan on Drugs and Addiction’ advocates a realistic drug policy. It responds more to the concrete reality of life than to any ideological principles. Every addict must have access to appropriate therapy options” .

The plan encompasses both legal and illegal substances, recognizing that far more Germans suffer substance abuse problems related to tobacco and alcohol than illegal drugs . Portugal has become the poster child of European drug reform following its July 1, 2001 decriminalization of formerly illicit substances.Rather than handle drug possession and use as a criminal matter, the police in Portugal give a civil citation to those caught using or possessing a quantity of drugs less than the average amount sufficient for ten-day use by one person. As Greenwald notes, these civil citations instruct recipients to appear before a “dissuasion commission” within seventy-two hours. The dissuasion commission, which is designed to avoid all appearances of a criminal tribunal, is made up of a lawyer and two members of the medical profession, and it may order those caught with drugs to pay a fine or undergo a course of treatment. Greenwald reports, however, that fines are a last resort designed to be suspended except for addicts and repeat offenders, who can have their fines suspended as well, if they agree to treatment .Even European countries that have not followed the extreme depenalization approach of Portugal have experimented with less punitive and more treatment-oriented drug policies. In Switzerland, for example, cannabis use remains a criminal offense . However, Switzerland experimented with a regime of open sales of small quantities of illicit drugs, such as heroin, in Zurich’s Platzspitz . This experiment lasted only five years, from 1987-1992, because the park became unsightly and was viewed as an embarrassment by the city. Instead of resorting to strict punitive measures for drug use, Switzerland then instituted a heroin maintenance program that allowed heroin addicts to receive daily heroin shots supervised by a nurse in a clinical setting. Switzerland has since expanded this program due to evidence that crime rates and unemployment rates among participants drop during participation . Similar programs have been instituted with encouraging results in Vancouver, Canada, and the Netherlands . But the trend toward decriminalization of drugs is not universal: the United Kingdom has gone in the other direction in recent years, at least with respect to marijuana, by increasing the maximum penalties for marijuana use.

Gordon Brown’s government decided to reclassify cannabis from a Class C drug to a more serious Class B drug, resulting in a maximum penalty of fourteen years of imprisonment for marijuana supplying, dealing, producing, and trafficking, and five years for possession . However, while the potential for such penalties exists, the British Home Office describes the “likely” enforcement steps: for a first possession offense police will issue a warning, for a second they will issue a Penalty Notice for Disorder , and for a third, they will arrest the individual . Thus, even in one of Europe’s strictest drug regimes, arrests and criminal punishment are reserved for repeat offenders. While many European countries have more liberal policies toward drug possession, they generally continue to have strict penalties for drug trafficking―though these are appreciably less severe than their counterpart American punishments. As the European Monitoring Centre for Drugs and Drug Addiction puts it, “over the past ten years, most European countries have moved towards an approach that distinguishes between the drug trafficker, who is viewed as a criminal, and the drug user, who is seen more as a sick person who is in need of treatment” . For example, in spite of their relatively liberal policies toward drug users, the maximum drug trafficking penalty in the Netherlands is, nominally at least, 16 years . Even in Portugal,cannabis grow set up drug trafficking remains a criminal offense because it involves possession in excess of the average dose needed for ten days of personal use . Relative to America, Europe has focused more on helping rather than punishing problem users, while still attempting to disrupt large-scale drug networks. Europe is not the only region of the world to have largely eliminated or reduced the penalties associated with possessing and using certain drugs. Latin America has also trended toward decriminalization in recent years. The Argentine Supreme Court decriminalized possession of small amounts of marijuana in August of 2009 . The court based its ruling on the grounds that it is unconstitutional to punish adults for private use of marijuana if that use does not harm anyone else .In declaring unconstitutional a law that provided for sentences of up to two years for drug possession, the court also opened the door for possible decriminalization of other substances, because the specific law overturned was not limited to marijuana.

Lower courts might expand the ruling to other drugs. Following the court ruling, the chief of the Argentine cabinet praised the decision for challenging an American-style war on drugs by ending “the repressive policy that the Nixon administration invented” . A few days prior to the Argentine court ruling, Mexico enacted decriminalization legislation specifying that individuals in possession of small amounts of marijuana, cocaine, heroin, and methamphetamine will not be criminally prosecuted . The new Mexican regime is similar to the Portuguese decriminalization in that those caught by police possessing a small amount of drugs will be encouraged to seek treatment . After being caught three times with drugs, the user will be required to attend treatment. Unlike the prior presidential administration, which sharply criticized earlier attempts by Mexico to decriminalize drugs, President Obama’s drug czar, Gil Kerlikowske, said that the Administration would evaluate the new Mexican law using a “wait and see” approach . In recent years both Brazil and Ecuador have also signaled that they may follow the path of Argentina and Mexico toward decriminalization . Taken together, these developments reflect the dissatisfaction many Latin American governments have with America’s punitive war on drugs: a war that was started in large part to combat drug production and trafficking emanating from Latin America. While it is too soon to tell what effects the Argentine and Mexican reforms will have on use rates in those countries, we will show in subsequent sections that the European experience casts doubt on prohibitionist fears that drug use will inevitably jump sharply. The social costs of recreational drug use in America have been staggering and unabated. According to the ONDCP’s most recent estimate, the economic cost of illegal drug use in the United States in 2002—including lost productivity, health effects, and crime-related costs such as policing expenditures and incarceration—was $180.9 billion, having grown at an average rate of 5.3 percent annually since 1992 .The costs of two legal drugs—alcoholand tobacco—are of a similar order of magnitude. The most recent comprehensive estimate of Harwood puts the annual economic cost of alcohol use at $184.6 billion in 1998.Rice estimates the annual economic cost of smoking in 1995 was $138 billion. Placing these figures in constant 2008 dollars provides a set of crude estimates of current annual social costs of alcohol , tobacco smoking , and illegal drugs .Commentators have rightly pointed out that such cost figures give a misleading impression of precision, ignore the benefits of drug use,and provide scant direction for actual drug policy.We offer these cost estimates for a crude sense of the scale of the problems under the current regime and as a reference point from which to examine the various types of costs associated with drug use—their relative magnitudes, who causes them, and who bears their burdens. It is also worth noting, however, that while such aggregate figures aspire to capture the domestic costs of illegal drugs, the costs imposed on foreign countries by the combination of America’s exceptionally large demand for illegal drugs coupled with its severe attempts at prohibition are also high and growing. Organized criminals from the Taliban in Afghanistan to drug cartels in Colombia and Mexico are enriched by America’s drug consumption and prohibition policy, with many highly unpleasant consequences. The current American administration has shown some signs of appreciating the magnitude of the role played by American drug demand in fostering crime in foreign countries. Following the recent wave of increasingly deadly gang violence near the Mexican-American border, Secretary of State Hillary Clinton surprised the media by candidly admitting that American drug consumers support crime in Mexico fueled by drug profits .Consideration of these foreign costs might bring total social costs of illegal drugs to equal or exceed those of alcohol. While steps toward legalization of currently illegal drugs would likely increase consumption, estimates vary about the extent of this change and how its concomitant costs would compare with gains from decreased law enforcement costs, productivity and other gains from reducing the levels of incarceration, and potentially substantial decreases in the crime and violence stemming from decreased profitability and scope of black markets.Though our best guess is that moving towards legalization would substantially reduce crime, it is possible that aregime shift to depenalization or legalization would increase toxicologically-induced crime and thereby offset expected decreases in black market crimes.

Again we find that individuals living in states that have statutorily removed jail sentences as penalties for possession of up to an ounce of marijuana are statistically less likely to report jail as the maximum penalty and more likely to report fines as the maximum penalty. However, again we see that the actual difference in knowledge across states is small. Interestingly, the “Don’t Know” rates in Table 3 correspond relatively closely to the “Don’t Know” rates in the 1976 and 1980 Monitoring the Future Survey data in Table 2 . There is no indication that citizens perceive themselves to be better informed in one period than in the other. Our study finds significant associations between the maximum penalty specified in state marijuana laws and people’s knowledge of those maximum penalties. But the associations are very small in magnitude. Citizens in decrim states are only about 27 % more likely to believe the maximum penalty for possessing an ounce of marijuana is a fine or probation . About a third of citizens in each type of state believes the maximum penalty is a jail sentence. The modest magnitude of these knowledge effects helps to clarify why decriminalization effects are fairly weak and inconsistent. The answer appears to be that people are not oblivious to their marijuana laws, but their awareness is pretty tenuous. It appears that people were more aware of their state penalties in 1980 than today . Why? One possibility is that it is the publicity surrounding a change in law, drying racks rather than the law’s actual implementation, that produces differences in citizen perceptions by state. In the 1970s, the initial decision to decriminalize marijuana was a matter of active public debate. MacCoun and Reuter report that in the 1970s, 22 out of 22 New York Times op-ed essay on drug legalization or drug decriminalization mentioned marijuana; only 15 out of 37 did so in the 1990s.

We believe that recent changes in these penalties have received far less publicity; only those interested in obtaining this information search it out. Another possibility, again not mutually exclusive, is that there has been erosion over time in knowledge of what may have been, in the 1970s, a real policy change. Indeed, research in other policy areas have shown that the impact of a policy is usually seen within a one to three year period following the policy’s adoption/effectiveness date . Given that many of the depenalization policies examined here occurred well before 2001, time may have decayed people’s knowledge or awareness of the laws. Thus the data we present here create a new puzzle for the decriminalization literature. Early studies failed to find an effect of decriminalization in an era in which citizens clearly recognized a difference in policies across states. But some recent studies have appeared to find a decriminalization effect in a more recent era, when citizens can just barely detect a difference in state laws. This is not how perceptual deterrence is supposed to operate. Either some studies are misestimating decriminalization effects , or we do not adequately understand the psychology of deterrence. South Africa has one of the highest rates of incarceration globally , most commonly among young men under the age of 35 years. Over 97% of SA’s prison population are men. Violence is pervasive with about half of SA men between the ages of 12 and 22 years old admitting to committing at least one criminal offence, and it is almost always a violent offense. Reports include assaults of others at school , in public places and at home , with 92.9% of the victims reporting that they knew the identity of the perpetrator. In addition, 30% of young men report committing physical or sexual violence against their partners. The current study complements previous research focusing on young men’s management of alcohol and drug behaviors as the primary source of criminal acts by considering both individual- and community-level factors associated with ever being arrested in SA.

Substance use is associated with higher rates of township violence and is linked to 75% of homicides, 67% of domestic violence, and 30% of hospital admissions. Gaining access to drugs often involves stealing, selling, or bartering goods and services–each acting as a pathway to engagement in criminal activity. Almost a third of young SA men report symptoms of alcohol dependency; half report recently using cannabis ; and a third recently used methamphetamine. The rates of substance dependence among SA offenders range from 10%–48% for males. In a SA study , 45% of arrestees tested positive for cannabis , methaqualone , opiates, cocaine, amphetamines, and/or benzodiazepines, with cannabis and methaqualone being the most prevalent. Those arrested for housebreaking and drug/alcohol offenses and those arrested at least once before are also more likely to test positive for drugs. Thus, substance use is hypothesized to be associated with ever being arrested among young men living in SA township neighborhoods. The high rates of substance use, violence, and incarceration in SA have been linked to unemployment, lower incomes, fewer years in school, younger age, being unmarried, and having mental health issues. More recently, attention has been focused on the mental health of individuals who come into contact with the criminal justice system. As with many other countries, SA experiences high rates of neuropsychiatric disorders in its prison system. Thus, we expect these individual factors and mental health risks to be associated with arrests. Research often focuses on youth’s background and risk behaviors to understand acts leading to arrests. Yet, globally it has been demonstrated that community disorder, poverty, and infrastructure are linked to rates of crime. Social disorganization theory suggests that where an individual lives has a substantial impact on their propensity to commit crimes or engage in criminal or delinquent behavior. Specifically, in contexts where there are few social bonds, crime and delinquency thrive. However, it is unknown whether these social processes operate in the same way across different disadvantaged neighborhoods. Shaw and McKay’s classic study in Chicago reports an ecological model of crime by mapping thousands of incidents of juvenile delinquency.

The results show that high rates of poverty and ethnic heterogeneity, combined with high levels of residential stability and single-parent households, are indicators of high criminality. Further, neighborhoods characterized by poverty and negative familial factors have been found to contribute to high arrest rates among youth in the U.S. and adolescent anti-social acts in Mexico. However, the applicability of social disorganization theory in SA is unclear. The applicability of Western criminological theories to urban SA has been previously demonstrated, but there are important differences in the ecological dynamics of violent crime across differing cultures. In SA, with the socio-political history of violence and oppression, the socioeconomic conditions of communities are likely to be important contributors to high criminality. However, there are limited data in post-apartheid SA. Research with individuals needs to take into account differences in the properties of the groups in which they belong.In some cases, neighborhood-level differences have been found to be significantly associated with outcomes, even when there is no significant variability between neighborhoods. These data suggest that assessing how individual and neighborhood effects are reciprocal and inherently related would lead to a better understanding of neighborhood effects on arrests. However, the relationship between arrests and individual- or community-level risk factors is far less studied in low and middle-income countries , such as SA. In township neighborhoods, infrastructure , limited opportunities for employment, gang activity, and the presence of small, informal and often illegal bars may function to exacerbate or dampen an individual’s risk of being arrested. The aim of this cross-sectional study is to examine arrest rates across low-resource township neighborhoods in Cape Town, SA. A community sample of young Black-African men was recruited from five areas within two Cape Town townships. Townships are peri-urban settlements that vary significantly in years since establishment, infrastructure,cannabis drying and access to services. In post-apartheid SA, townships experienced a major influx of migrants in search of work, which resulted in the rapid development of new informal township neighborhoods. Accordingly, the older township communities are more formalized with access to municipal roads, a police station, and community clinics.

However, the newly established communities are more informal, where the large majority of the residents are living in informal housing , with limited access to basic services. Thus, community-level factors such as less formal housing, fewer household sources of water , and food security, as well as greater number of bars and violence are hypothesized to be associated with having a history of arrests. This study examines: the individual- and community-level factors associated with a history of arrests among young men living in the townships of Cape Town, SA; and whether these factors are significant predictors of contact with the criminal justice system. The data used in this article are part of the baseline assessment for a larger longitudinal study investigating the impact of a public health intervention to improve the health of young men living in low-resource communities. Understanding the associations between individual factors, the community context, and having a history of arrests is important to plan interventions which seek to reduce the risk of arrests and re-arrests among young men in SA.Community clusters containing approximately 450–600 households, including roughly 50 young men per neighborhood, were identified in two Cape Town townships: Khayelitsha and Mfuleni. In each community, a random spot was selected, and recruitment proceeded house-to-house in concentric circles until a sample of 50 young men aged 18 to 29 was reached. If other household members reported that there was a male that slept there at least four nights a week, the interviewer returned repeatedly to solicit voluntary participation of the young man. Potential participants were only excluded if they were unable to understand the recruiter, appeared to be actively hallucinating, or lacked capacity to consent. Individuals who appeared to be under the influence of alcohol or drugs during the time of recruitment were enrolled in the study, but were only interviewed once they were no longer intoxicated. Assessments were conducted at a local research facility in the township, with transport provided. Of the 994 men approached, 5% declined to participate or were identified but could not be found later to complete the assessment . The final sample included 906 young men aged 18–29 years old .First, individual-level and community-level risk factors were compared based on the history of arrests across all neighborhoods. As a first step, chi-squared analyses were used to identify significant covariates between young men who have been arrested and those who have not. From these identified covariates, the most relevant variables were entered into a multiple predictor logistic regression to identify individual- and community- level risk factors associated with arrests. Odds ratios with 95% confidence intervals are presented. To identify communities with a high versus low number of arrests, we compared the proportion of men who have ever been arrested in each neighborhood. Since less than half of the young men reported ever being arrested, a reasonable split of 25% of the communities with the highest arrest rates was compared with 25% of the communities with the lowest arrest rates . We then compared measures of individual- and community-level risk factors between the communities with a high and low number of arrests using chi-squared analyses . This enabled analysis between neighborhoods by comparing aggregate measures of community-level risk factors. All analyses were conducted using SPSS Statistics 23.Substance use and gang involvement are the strongest predictors of ever being arrested. Substance use is consistently linked with high levels of violence and gang involvement. Most notably, young men using methamphetamine are three times more likely to be arrested than young men who abstained. Alcohol users are almost twice as likely to report ever being arrested compared to non-drinkers. This is consistent with previous studies in both HIC and LMIC –including SA –that link substance use with arrest history and incarcerations. This is one of the first studies to examine both individual and community characteristics linked to contact with the criminal justice systems among SA men. While poverty is widespread in these peri-urban settlements in Cape Town, this classification of resources may not be sufficient to explain the high rates of criminal activity among different SA communities, especially if more than half of young men report engaging in substance use or risky delinquent acts.

Whilst the aim of our study was to examine the relationship between baseline features and subsequent outcome , it is important to note that there are limitations with this approach. First, we did not account psychosocial stressors and other confounding factors/events that may have occurred in the time between baseline and follow-up. Indeed, it is possible that stressor cortisol concordance at follow-up does in fact distinguish between CHR subgroups, but that our measure at baseline was too distal to outcome. Second, CHR individuals are at elevated risk for a wide range of psychiatric disorders, particularly depression and anxiety , and so worsening of prodromal symptoms/transition to psychosis is only one of several potential outcome measures, all of which will inevitably involve more false negatives the shorter the follow-up period. Indeed, a recent study suggested that well-established risk factors are better at predicting poor functioning in CHR populations than transition to psychosis . The extent to which stressor-cortisol concordance at baseline is associated with other non-psychotic disorders and functioning at follow-up is therefore warranted.We assessed HPA axis function using basal salivary cortisol collected in the laboratory, as it is more reliable and, unlike home sampling methods, unlikely to be influenced by confounding factors such as exercise . However,grow cannabis in containers meta-analytic evidence indicates that the effect of chronic stress on cortisol varies across cortisol measures; whilst diurnal cortisol, afternoon/evening cortisol, and the CARi are elevated following chronic stress, basal morning levels are lower and the diurnal rhythm appears to be flatter . Employing alternative cortisol measures might therefore reveal different patterns of stressor-cortisol concordance across CHR individuals and controls.

Indeed, using a home sampling procedure, Cullen and colleagues reported a negative correlation between the CARi and negative life event distress in at-risk children with a family history of schizophrenia but a positive correlation in typically-developing children , whilst a study of adults found that diurnal cortisol was associated negatively with stressful life event exposure in first-episode psychosis patients, most of whom were receiving antipsychotic medication, but positively in controls . Thus, employing multiple measures of cortisol may be more informative than basal cortisol alone and enable the identification of dissociated relationships in at-risk individuals/psychosis patients and healthy controls. Cortisol output is not, however, the only method of assessing HPA axis function. In addition to endocrine measurement neuroimaging can be used to determine pituitary and hippo campal volume and density, distribution and/or affinity of glucocorticoid/mineralocorticoid receptors. The latter is particularly important as these receptors mediate the effects of glucocorticoids on cellular targets. As a related point, future studies are warranted to investigate glucocorticoid sensitisation , in CHR youth, as this may have implications not only for the HPA axis, but also the immune system , and dopamine levels . Thus, studies employing multiple methodological approaches, including genetic profiling, neuroimaging, and endocrine measurement, may be needed to adequately investigate the extent to which individuals at-risk for psychosis are characterised by increased HPA axis sensitivity. Our findings have other implications for future research examining HPA axis responsivity in at-risk individuals. First, we observed that the lapse-of-time between completion of stress measures and cortisol collection moderated stressor-cortisol concordance . Whilst we anticipated this pattern for daily stressors occurring within the past 24 hours, the findings for life events and childhood trauma were not predicted as these events did not occur on the day of measurement. It is plausible that reporting these events in the research environment is itself a stressful experience for some participants, and that it elicits a cortisol elevation and thus a relationship between stressor exposure and cortisol.

These findings highlight the importance of adjusting for the interaction between stressors and timelapse between assessments when examining stressor-cortisol concordance. Second, participant sex was identified as a potential confounder. The updated neural diathesis-stress model noted sex differences to be an important area for future research , whilst it was beyond the scope of the current study to explore whether sex modified the degree of stressor-cortisol concordance, future studies should investigate this possibility. Finally, whilst we defined CHR outcome status on the basis of attenuated positive symptoms and transition to psychosis, as noted above, there has been recent acknowledgement of the need to examine a broader range of outcomes, including, levels of social and role functioning, non-psychotic disorders, and negative symptoms . Future studies might therefore examine whether stressor-cortisol concordance is associated with these outcomes at follow-up.The nation’s health care system faces a mandate to improve quality in multiple dimensions, including those identified by the Institute of Medicine: safety, timeliness, effectiveness, efficiency, equitability and patient centeredness. Expenditures and gaps in health care delivery in general are not evenly distributed throughout the population; only 5% of the population account for half of all health care spending, and quality varies considerably across conditions and settings. An effective response to the quality mandate will require a focus on subgroups of patients who have severe or multiple health conditions associated with significantly higher costs and poorer outcomes.In the National Comorbidity Survey Replication, more than 68% of adults with a behavioral disorder report having at least one general medical disorder, and 29% of those with a medical disorder had a comorbid mental health condition. Research has documented the high rates of psychiatric comorbidity among specific medical conditions, such as HIV, diabetes, asthma and chronic medical illnesses. Conversely, studies have reported high rates of medical comorbidity among patients with psychiatric illness.

The co-occurrence of behavioral and medical conditions leads to elevated symptom burden, functional impairment, decreased length and quality of life, and increased costs. For patients with comorbid behavioral and medical conditions, problems with quality of care occur when they are treated in a primary care and/or specialty mental health setting. Even more concerning, premature mortality is elevated two- to four‐ fold. In response to these findings, care delivery models have been developed for patients with comorbid medical and psychiatric conditions. The most effective have been collaborative care approaches that use a multidisciplinary team to screen and track mental health conditions in primary care. These models build on the Chronic Care Model. Yet, even as these models are promoted, the gaps in our knowledge about cooccurrence may have important implications for how these collaborative models are structured. Research to date on the prevalence of co-occurring medical and psychiatric conditions has focused on national surveys, specific illnesses, disease-focused clinics or claims data for health insurance populations, such as Medicaid or Medicare. To our knowledge, no prior study has focused on a large patient population with a predominately employment-based insurance that receives treatment in an integrated health care system. Examining cooccurrence in this setting addresses several important gaps in the existing literature. First, employed patients in an integrated health care system represent an important and distinct sub-population of patients receiving care in the delivery model promoted by health care reform. Second, this kind of system generates encounter-based rather than claims-based data. The data are generated from all clinical departments within a comprehensive system of care. To address these gaps, pot for cannabis we examine the prevalence of behavioral health conditions in a large integrated health system that primarily serves patients with employment-based insurance. We compare the burden of medical co-morbidity and chronic diseases among those health plan members with a behavioral health condition to matched members without. This provides the opportunity to examine the robustness of the behavioral and medical disease nexus in this sub-population compared to the other sub-populations more commonly studied.Institutional review board approval was obtained from the Kaiser Research Foundation Institute for this retrospective database-only study. Initially, all KPNC patients aged 18+ with any behavioral health diagnoses in 2010 were identified. Automated clinical databases were used to identify all outpatient visits , hospitalizations and emergency department visits at KPNC facilities between January 1, 2010, and December 31, 2010, where a patient had a BHD. The BHDs used for this study included both mental health and substance use disorders: depressive disorders, bipolar spectrum disorders, anxiety disorders, attention deficit hyperactivity disorders , autism spectrum disorders, personality disorders, substance use disorders, dementia, schizophrenia spectrum disorders and other psychoses [see Appendix A for International Classification of Diseases, Ninth Revision codes relevant to this paper]. These categories were selected based on collaborations with NIMH’s Mental Health Research Network and KPNC’s Regional Mental Health leadership.

The first mention for each BHD during the study period was included, so patients in the sample could have multiple BHDs over the 1-year study period . The prevalence rates of BHDs were examined among all adult KPNC patients. Patients insured by Medicare or Medicaid were excluded from the study.Compared to the matched controls, each of the most prevalent BHDs [depression , anxiety , substance use , bipolar spectrum disorder and ADHD ] had significantly more patients with any medical comorbidities based on the ICD-9 categories. This was true across all ICD-9 categories examined, with the exception being Diseases of the Circulatory system and Neoplasms for the ADHD comparison, which did not differ . We had similar findings with respect to the burden of chronic conditions. With the exception of ADHD and bipolar disorder, each specific BHD had a significantly higher prevalence of each chronic condition compared to the matched controls. Those with ADHD diagnoses did not significantly differ in the chronic conditions related to the circulatory system , Parkinson’s disease or osteoporosis. However, there were significant differences between those with ADHD and their matched controls for the other chronic conditions. Patients with bipolar disorder did not significantly differ from their matched controls in prevalence of end-stage renal disease or osteoporosis. We also examined the Charlson Comorbidity Index and found that patients in the SubBHD sample had a significantly higher average Charlson Comorbidity index compared to their controls across all conditions examined .BHDs are highly prevalent in this health system. Fifteen percent of members with a health plan visit in 2010 had at least one BHD. By far, the most common were depression and anxiety. Moreover, among patients with BHD, psychiatric comorbidity is common; of those patients with a BHD, 28% had multiple BHDs. Among the five most prevalent BHDs, the proportion of patients with at least one psychiatric comorbidity ranged from 42% to 60%. Finally, those patients with a BHD carry a disproportionately high medical disease burden.With only a few exceptions, the proportion of BHD patients with a medical illness, including common chronic conditions, was significantly higher than non-BHD patients. Risk of 10-year mortality as calculated by the Charlson Index was found to be significantly higher for those with any of the BHDs examined compared to their matched controls. Thus, similar to studies of other patient sub-populations, BHDs in a largely commercially insured, employment-based health system are common and associated with a higher burden of chronic medical disease. At the same time, these results differ from some of the other large data sets. For example, disabled Medicaid beneficiaries have a prevalence rate of psychiatric illness ranging from nearly 29% to 49%, depending on whether the claims-based data include pharmacy data in addition to diagnostic codes . This higher proportion likely reflects both a more vulnerable patient population and methodology that better captures patients being treated for a psychiatric illness without an associated diagnostic code. In that same data set, the Medicaid aged population’s prevalence of psychiatric illness more closely mirrored the prevalence in this study: 10.4% or 35.9% . Our encounter data were based on ICD-9 coding and therefore are more similar to the diagnosis-only data from the Medicaid data set. The US National Comorbidity Survey Replication, which is based on a face-to-face household survey, reported a 26% any Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, disorder prevalence, and others have reported around 30%. Given that fewer than half of people with a psychiatric illness receive treatment, these estimates are fairly consistent with the 15% prevalence observed in this study, which includes a patient population more representative of the general US population but only captures those who made a health plan visit. This study has several limitations. As with other studies that have used claims-based data, our study only captures patients with behavioral health disorders noted in their visits during the study period. This methodology is vulnerable to under-estimation especially with regards to behavioral health disorders.

Future work could potentially benefit by further extending the present work to other health outcome variables such as ART medication adherence and HIV symptoms among persons with HIV/AIDS. It also may be advisable to explore how distress tolerance and emotion dysregulation relate to a broader array of symptoms and disorders. Finally, the present study was a secondary analysis from a larger study exploring the role of cannabis use among an HIV/AIDS population . Thus, as with any secondary analysis of data, it would be important to replicate and extend the findings in the future with an a priori research investigation. Overall, the results of the current study highlight the importance of perceived distress tolerance and emotion dysregulation in terms of elevated rates of anxiety and depression symptoms among an HIV/AIDS sample. specifically, findings indicated that perceived distress tolerance was significantly related to greater depressive and anxiety symptoms and that emotion dysregulation mediated this association. The present findings therefore suggest that emotion dysregulation may be important in better understanding the link between distress tolerance and certain negative emotional symptoms among people living with HIV. Indeed, it is possible that targeting emotion dysregulation among HIV? persons via strategies aimed at increasing self-efficacy over the ability to regulate affective states and gaining further control over affect-driven behaviors could be an integral step in efforts to promote greater degrees of psychological health among HIV? individuals.

Brain tissue volume losses in the frontal, temporal and select sub-cortical regions of individuals with alcohol use disorders have consistently been reported with volumetric magnetic resonance imaging , cannabis grow equipment and so have deficiencies in executive skills, learning and memory, processing speed, visuo spatial skills and working memory . Today, more than half of individuals with AUD who present for treatment also chronically abuse illicit substances . Substance use disorders have adverse effects on brain biology and function separate from those of AUD . Comorbid alcohol and substance use disorders have also been associated with brain morphological abnormalities. Liu et al. reported smaller normalized gray matter and white matter volumes of the prefrontal lobe in poly substance abusers abstinent from substance use for more than 2 weeks compared to controls. Tanabe et al. also reported smaller GM volumes of the bilateral medial orbito frontal cortex in long term abstinent individuals dependent on two or more substances compared to controls. As in AUD, the neurobiological abnormalities in individuals with PSUD are accompanied by cognitive deficiencies, particularly in visual and verbal memory, attention, psychomotor speed, visuomotor skills, problem solving and abstraction abilities . Thus, brain morphological abnormalities appear to occur in somewhat similar brain regions with similar neurocognitive deficits in AUD with and without comorbid substance abuse. To determine potential unique group differences, there is the need to directly contrast the magnitude and spatial distribution of structural brain abnormalities and their associated neurocognitive abnormalities in AUD with and without comorbid substance abuse. Directly contrasting structural brain abnormalities and their neurocognitive correlates in AUD with and without substance abuse will help design more efficacious treatment strategies tailored to individuals with PSUD or AUD. In this context, we showed recently that one-month-abstinent treatment-seeking PSUD individuals have prefrontal metabolite concentrations that were uniquely different from those of alcohol dependent individuals at similar abstinence duration, reflecting neuronal and glial dysfunction partly related to neurocognition . This quantitative volumetric magnetic resonance imaging study, contrasted differences in total and regional GM, WM and sub-cortical tissue volumes as well as ventricular and sulcal cerebrospinal fluid between abstinent alcohol dependent individuals without current illicit substance dependence and those with current psychostimulant dependence .

The functional relevance of our MRI measures was assessed by correlating them with neurocognitive measures. Since ALC recover brain tissue volume significantly but not completely within their first month of sobriety , while individuals with PSUD show regional GM tissue volume deficits even after many weeks and years of abstinence , we tested the following hypotheses in treatment seeking individuals after one month of abstinence from alcohol and other substances: PSU have smaller lobar GM, WM and sub-cortical tissue volumes as well as larger CSF volumes than light-drinking controls and ALC and in PSU and in ALC, smaller lobar GM and WM volumes correlate with worse measures of working memory, processing speed, visual-spatial learning and memory and auditory-verbal learning and general intelligence.Treatment-seeking PSU and ALC were recruited from the San Francisco VA Medical Center and Kaiser Permanente. For statistical reasons, we reduced our large ALC cohort to 40 individuals by matching them on age, education, smoking status and drinking variables to the smaller PSU group. Both ALC and PSU participants completed the structured clinical interview for the Diagnostic and Statistical Manual of Mental Disorders Axis I Disorder Patient Edition, Version 2.0 . Prior to enrollment, male participants consumed more than 150 alcoholic drinks per month for at least 8 years; females consumed more than 80 drinks per month for at least 6 years. PSU individuals were diagnosed with both alcohol dependence and dependence on at least one psychostimulant, with or without nicotine dependence and cannabis use disorder. All PSU met DSM-IV dependence criteria for at least one illicit substance and 10.5% met criteria for cannabis use disorder. specifically, 12 PSU met criteria for cocaine dependence and 2 of these were either abusing or dependent on cannabis; 2 other PSU met criteria for both cocaine and methamphetamine dependence ; and yet 2 others were dependent on both cocaine and opiates ; 2 other PSU met criteria for methamphetamine dependence only and 1 for opiate dependence only . In the PSU group, 32% were non-smokers, including 2 ex-smokers.

All ALC participants met DSM-IV criteria for alcohol dependence with or without nicotine dependence. 15 ALC participants were non-smokers, including 6 ex-smokers; the proportion of smokers did not differ among ALC and PSU.All ex-smokers among ALC and PSU individuals had stopped smoking for at least 5 years before the study. Within the ALC group, 2 individuals were currently abusing cannabis, while 3 had past cannabis abuse, currently in full remission. In addition, one ALC participant each showed past dependence on cocaine, amphetamines, or opioids, but all were currently in full remission. Thus, while the ALC participants were “clean” alcohol dependent individuals, the PSU participants were all dependent on alcohol and 84% on cocaine; only about 11% in both groups had a current or past cannabis use disorder diagnosis. Other non-substance-related inclusion and exclusion criteria were described previously . All ALC and PSU participants were tested daily with breathalyzers for alcohol consumption and randomly for substance use during outpatient treatment to ensure sobriety during the one-month-abstinence period. Twenty-seven non-substance-using LD, without histories of medical or psychiatric conditions known or suspected to influence brain structural outcome measures were recruited from the local community. Twenty-one of the LD individuals were never-smokers,mobile grow system and the proportion of non-smokers in the LD group was not significantly different from that in ALC or PSU .Within one day of the MR study, participants completed standardized questionnaires for alcohol withdrawal , depression and anxiety symptomatology . Alcohol consumption over lifetime was assessed with the lifetime drinking history . From the LDH, age of onset of heavy drinking [defined as consuming >100 alcoholic drinks per month or >80 drinks per month ] was derived and the average number of alcoholic drinks consumed per month over 1 year, 8 years before enrollment and over lifetime estimated. For PSU, substance use history was assessed with an in-house questionnaire based on the Addiction Severity Index , NIDA Addictive Drug Survey , drinking history, and Axis I disorders Patient Edition, Version 2.0 . The questionnaire probed for information on phases of drug use for each substance that a participant had a current or past disorder diagnosis on. The variables recorded included age of first and last use, number of total lifetime phases, duration of individual and total lifetime phases , frequency and quantity of use during each phase, and route of administration. Another variable recorded was money spent per day on a substance, which was then converted to one metric, using catchment area-specific conversion norms. Thus, monthly averages for grams of the substances over 1 year prior to enrolment and over lifetime were estimated.

To evaluate the nutritional status and alcohol-related or other hepatocellular injury, laboratory tests for serum, pre-albumin, alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase were obtained within three days of each MR scan. The values of these variables in the liver and the white blood cell counts were not significantly different between the groups. Table 1 shows demographics, alcohol consumption and select blood variables for LD, ALC and PSU.The neurocognitive domains and constituent measures evaluated were as follows : Executive skills: Short Categories Test, color-word portion of the Stroop Test, Trail Making Test part B, Wechsler Adult Intelligence Scale 3rd Edition Similarities, Wisconsin Card Sorting Test- 64: Computer Version 2-Research Edition non-perseverative errors, perseverative errors, and perseverative responses. Fine Motor Skills: Grooved Pegboard Test. General Intelligence: Ward-7 Full Scale IQ; based on WAIS-III Arithmetic, Block Design, Digit Span, Digit Symbol, Information, Picture Completion, and Similarities subtests. Learning and memory: Auditory-verbal: California Verbal Learning Test-II Immediate Recall trials 1–5 , Short and Long Delay Free Recall . Visuospatial: Brief Visuospatial Memory Test-Revised, Total Recall and Delayed Recall . Processing speed: WAIS-III Digit Symbol, Stroop Color & Word, WAIS-III Symbol Search Trail Making Test-A. Visuospatial skills: WAISIII Block Design; Luria-Nebraska Item 99. Working memory: WAIS-III Arithmetic, WAIS-III Digit Span. The raw scores for all neurocognitive measures were converted to age-adjusted or age-and-education-adjusted standardized scores. The standardized scores were then converted to z-scores for all measures. For the Luria-Nebraska Item 99 ratio, raw scores were converted to z-scores based on the performance of 27 non-smoking light drinking controls , since there are no published procedures available for this measure.Whole brain three-dimensional T1-weighted coronal images and two dimensional T2-weighted oblique-axial images were acquired on a 1.5 Tesla magnet , using Magnetization Prepared Rapid Acquisition Gradient Echo imaging sequence and spin-echo imaging sequence , respectively. After re-alignment of each subject’s T1- and T2-weighted images, the expectation maximization segmentation method was applied to the T1-weighted images to segment the brain into WM, GM, and CSF after coregistration and correction for bias field intensity variation of the T1-weighted images. The major lobes and sub-cortical regions were then parcellated by overlaying the tissue maps on a reference atlas containing landmarks of 36 brain regions , including the regions reported here. The volumes of total cortical GM and total lobar WM were calculated by summing the respective GM and WM values from the major lobes. The intracranial volume was estimated by summing all regional tissue and CSF volumes.Multivariate analysis of variance assessed group differences on age and education, differences between ALC and PSU on drinking and smoking severities, days of abstinence, anxiety and depression symptoms, as well as basic clinical laboratory measures . Multivariate analyses of covariance examined group differences on ICV-normalized volumes of 4 GM regions , 4 WM regions , 5 sub-cortical regions and 5 CSF regions separately . For total and regional cortical GM volumes and CSF, only age was a significant predictor of group variances and was therefore used as a covariate. For total and regional lobar WM volumes, only the body mass index contributed significantly to the variances and therefore was the only used covariate. Neither age nor BMI was a significant predictor of sub-cortical tissue volume variance. Participants’ cigarette smoking status was not a significant predictor of any tissue volume; however, this exploration has to be treated with caution, as the proportion of smokers in the LD sample tended to be smaller than in the patient groups. Given our a priori hypotheses, all MANCOVAs of tissue volumes were followed-up with post hoc analyses as well as pairwise and univariate t-tests . Also because of our a priori hypothesis, we did not correct for multiple comparisons. The reported p-values for GM are 1-tail, but those for WM and CSF volumes are 2-tail, because PSU unexpectedly had greater WM and smaller CSF volumes than LD and ALC. Although ALC and PSU did not differ significantly on the frequency of medical and psychiatric co-morbidities, these comorbidities were controlled for in all group comparisons. Effect sizes for pairwise comparisons were calculated using Cohen’s d .

However, most programs allowed tobacco use in designated outdoor areas, such as front or back porches, program parking lots, or other specified areas.Data collection was conducted by research staff during site visits in 2019. All adult clients enrolled in each program on site visit days were eligible to complete the survey.Research staff reviewed study information with clients in small groups. Those interested to participate were given an iPad with a pre-populated research ID number, reviewed the study information sheet on the iPad, and clicked “Agree” to complete an online Qualtrics™ survey. Informed consent was obtained from all individual participants included in the study. The self administered survey took approximately 30 minutes and participation was anonymous. Participants received a $20 gift card for study participation. Study procedures were approved by the institutional review board of the University of X. There were 682 clients enrolled in the participating programs at the time of the site visits and 562 completed the survey, giving an 82% participation rate. Current use of tobacco products was self-reported. Current cigarette smokers were those who reported having smoked ≥100 cigarettes during their lifetime and currently smoked at the time of survey. Current users of each of the other tobacco products were those who had reported using [e-cigarette, cigar/cigarillo,cannabis grow tent and smokeless tobacco during the past 30 days.There were 5 participants selecting “Don’t know” for current use of alternative tobacco products and their responses were coded as missing.

Based on their reports on use of four tobacco products, participants were categorized into one of three categories: poly tobacco users , dual tobacco users , and single tobacco users . Nicotine dependence was measured using the Heaviness of Smoking Index ,a 6- point scale based on two self-report items . The HSI has demonstrated reliability and validity as a measure of nicotine dependence severity, with internal consistency of 0.63,and the correlation with the Fagerstrom Test for Nicotine Dependence score of 0.94.Quitting experience was measured by two items. Having past-year quit attempts was assessed by the item “In the past year, did you quit smoking voluntarily for at least 24 hours?” Participants also reported whether they had ever used e-cigarettes to try to quit smoking. Intention to quit smoking was assessed by the item “Are you seriously thinking of quitting smoking?” with response options including “Yes, in the next 30 days”, “Yes, within the next six months but not in the next 30 days;” and “No”.Intention to quit smoking was defined as seriously thinking of quitting in the next 30 days . Past 30-day use of blunt or spliff was assessed by the item “Have you mixed tobacco and marijuana and smoked them together in the past 30 days?” Perceived physical and mental health assessments were based on the Centers for Disease Control and Prevention’s Healthy Days Measures.As defined by the CDC, the term “physically unhealthy days” was used to describe a number of days when physical health was not good, and was assessed by the item “Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” Likewise, the term “mentally unhealthy days” was used to describe a number of days when mental health was not good, and was assessed by the item “.

Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” Demographic characteristics included age, gender, race/ethnicity, and educational attainment. Age was self-reported. Gender was self-reported as male, female, transgender, gender queer, and other. Since there were 10 participants identifying as transgender and gender queer, these individuals were combined into the “Other” group. Race/ethnicity was measured by combining two items: race and ethnicity . Due to small numbers of Non-Hispanic Asian/Pacific Islander and American Indian/Alaskan Native participants , these subgroups were collapsed into “Other/Multi-race”, resulting in only four groups in the analysis . Educational attainment was measured as less than a high school education, high school or GED equivalent, and greater than a high school education. Participants were asked about reasons for entering treatment programs with answer options including treatment for SUD, for both SUD and a mental health condition, or for other reasons. Those with a SUD problem, or SUD and mental health problem, were asked about primary drug for treatment and/or mental health diagnosis, respectively. Persons reporting treatment for other reasons were most often referred by criminal justice sources. If they did not disclose a SUD or mental health reason for treatment, they were not asked for primary drug or mental health diagnoses. In addition, participants reported their time in treatment . Participant characteristics were summarized for the total sample and by tobacco use pattern . Since the outcome was a three category variable, we employed multi-nomial regression modeling with single tobacco use as the referent outcome and cluster–robust standard errors to account for clients nested within each treatment program.A multivariate model included all independent variables and controlled for demographic variables and time in treatment. The analysis used complete cases. Multi-collinearity among the independent variables was examined by Variance Inflation Factor and the results showed no presence of multi-collinearity. All tests were two-tailed with a significance level of α less than 0.05. Statistical analyses were performed using STATA version 15 .Table 2 describes tobacco use patterns among the total tobacco users and among each tobacco use subgroup. Of the total sample, 32.6% reported single tobacco use, 18.9% reported dual tobacco use, and 14.0% reported poly tobacco use. Most tobacco users were using cigarettes . Among the alternative tobacco products, e-cigarettes were the most commonly used in the past 30 days , followed by cigars/cigarillos , and smokeless tobacco .

Among tobacco users, the top three common patterns were exclusive cigarette use , dual use of cigarettes and e-cigarettes , and dual use of cigarettes and cigars/cigarillos . Compared to single tobacco users, dual and poly users had greater HIS scores and higher proportions of ever using e-cigarettes for quitting smoking and past 30-day use of blunts/spliffs. Using cross-sectional data from 562 clients in residential SUD treatment programs in California, we found that 65.5% of the sample were using any tobacco, with multiple tobacco use being equally prevalent as single tobacco use . Our estimate of multiple tobacco use is much higher than that in the general population 6,24 and also higher than the previous estimate from a national sample of people in SUD treatment.This study adds to current evidence identifying multiple tobacco use as an emerging public health issue among people in SUD treatment. Notably, our observation that proportion of dual users was higher than that of poly tobacco users has not been reported in previous research in SUD populations. In addition, consistent with previous studies among SUD samples,we found that e-cigarette was the most common alternative tobacco product used and dual use of cigarettes and e-cigarettes was the most common pattern of multiple tobacco use. However, our study is among the first bringing up the popularity of cigars/cigarillos and its dual use with cigarettes, calling for greater attention to this dual use pattern among people with SUD. This study extends existing literature by indicating factors associated with dual and poly tobacco use among people in SUD treatment. Current tobacco users who had ever used ecigarettes for quitting smoking were more likely to be both dual and poly tobacco users. In addition, among those who reported ever using e-cigarettes to quit smoking, 54% were still using e-cigarettes at the time of our survey . Although the efficacy of e-cigarettes as cessation aids is unclear,25 current evidence indicated that e-cigarette use was related to greatersmoking26 and the escalation of poly tobacco use over time,and thus,grow lights for cannabis may not be an effective aid for long-term cessation.Aligned with this evidence, our study suggested that current tobacco users in SUD treatment who are unsuccessful in quitting by using e-cigarettes may continue using multiple tobacco products. In our study, past 30-day use of blunt/spliff was strongly associated with poly tobacco use. Since use of cigars/cigarillos was prevalent in our sample, it is possible that participants were using cigars/cigarillos for blunt use. This finding may partly reflect our recruitment in California – a state having legalized recreational cannabis use since 2018 and considered the largest cannabis market in the US.Co-use of tobacco and cannabis is even higher than tobacco use alone among California’s young people.In the general population, cannabis use is associated with persistent cigarette smoking, high nicotine dependence, and low cessation among cigarette smokers.Likewise, a study among the SUD population found that ever users of blunt/spliff were less likely to plan to quit in the next 30 days.Given the increasing legalization of cannabis use in the US, our finding and other emerging evidence points to a need to address co-use of tobacco and cannabis and its potential impacts on tobacco use and cessation among people in SUD treatment.

Consistent with previous research among the general population and the SUD treatment population, we found that poly tobacco users had greater nicotine dependence and dual users had fewer days of good mental health as compared to single tobacco users. Mental health is frequently comorbid with SUD, and mental health problems is also linked to multiple tobacco use.This highlights a need to address the intersection of multiple tobacco use and mental health in SUD treatment programs. While prior studies in the SUD population found that multiple tobacco users were more likely to have past-year quit attempts,we did not find differences in having quit attempts and intention to quit smoking between dual or poly tobacco users and single tobacco users. More research is needed to better understand quitting intention, quit attempts, and cessation outcomes among dual and poly tobacco users in SUD treatment. Our study has implications for efforts to address tobacco use in the SUD population. As dual and poly tobacco use is highly prevalent among clients in SUD treatment and may confer additive risk compared to cigarette smoking alone, SUD treatment programs need to screen and assess the use of alternative tobacco products to better provide cessation supports for quitting cigarette smoking as well as quitting other types of tobacco use. Dual and poly tobacco users may comprise distinct groups, given that their use of multiple tobacco products is associated with greater nicotine dependence, co-use with cannabis in the form of blunt/spliff, and mental health problems, and thus, tailored interventions or multi-component interventions may be needed to address multiple health risks simultaneously. Particularly, interventions targeting dual use of cigarettes with e-cigarettes or cigars/cigarillos should be provided since they may place people in SUD treatment at risk for increased negative health effects and continued tobacco use rather than quitting.Although in-door smoking is prohibited in residential SUD treatment programs in California, clients may still smoke cigarettes and use other tobacco products outdoors. Tobacco free grounds policies, which ban use of tobacco products on treatment program grounds, should be adopted to reduce tobacco use among clients in SUD treatment.16 Study limitations include reliance on cross-sectional data, which precludes causal inference. Second, self-reported data in this study may have been susceptible to some degree of recall and social desirability bias. Third, the generalization of study findings is limited by the convenience sampling strategy, the inclusion of residential SUD programs only, and the fact that all programs were located in California. Finally, we could not explore reasons for dual and poly tobacco use patterns as well as context of using alternative tobacco products due to the small sample size and original measures. Future research should investigate mechanisms underlying tobacco use patterns among people in SUD treatment to find the best ways to treat tobacco use in this population. In conclusion, this study revealed high prevalence of dual and poly tobacco use among people in SUD treatment, and suggested that SUD treatment programs should address use of other tobacco products as well as cigarette smoking among their clients. In addition, interventions for dual and poly tobacco users should address use of e-cigarettes, cigars/cigarillos, and blunt/spliff as well as mental health to improve cessation outcomes and reduce tobacco related health disparities among this population.Cigarette smoking is strongly associated with alcohol use , and smoking is especially common among heavy drinkers and binge drinkers .

In our multivariate analysis that included RASS and CAM-ICU findings, age did not continue to be a predictor of WS . However, the RASS and delirium findings, when added to the model, significantly increased the model fit. That is, we found that they are both related to WS. From a conceptual and clinical perspective, it could be important for providers to recognized agitation/restlessness and delirium when caring for ICU patients being weaned from opioids and/or benzodiazepines.The final model also showed that cumulative opioid dose amounts prior to weaning were associated with development of WS, although the number of days that patients received opioids was protective. In our study, as expected, days on opioids and cumulative opioid dose were strongly correlated . The nature and the strength of the relationship between these two variables could be the reason behind our findings: in the multivariate regression analysis, while holding the cumulative opioid dose constant, the “days on opioid” variable showed a slightly protective odds ratio for WS . That is, given the same cumulative dose of opioid, patients with longer duration on opioid had lower odds of developing WS.Another explanation for this finding is that there are several factors that can cause differences in opioid tolerance, the precursor to WS, at the opioid receptor level.In addition, genetic differences in opioid receptor synthesis and variable opioid receptor affinity, the difference in type of opioid administered, and the use of continuous versus intermittent administration may be influential factors . Use of multi-modal analgesia may help to counteract development of WS through reduction of opioid amounts administered to the patient . However,cannabis grow tray further research is warranted on time versus amount differences in opioids and their risk for WS. This study has several strengths. The assessment of WS was done using a prospective approach two times a day for 72 hours or more. Furthermore, in the absence of an instrument validated to measure opioid and benzodiazepine WS in ICU adults, we developed a checklist using several reliable sources: the DSM-5 criteria, International Classification of Diseases, 10th Edition criteria WS, and symptoms identified in previous adult WS studies.

Therefore, the checklist had content validity. Furthermore, given that our study was exploratory in nature, we were able to conduct several analyses by constructing various models between patient- and clinical-related factors and the probable presence of WS. Our study has notable limitations. Consistent with other WS studies in ICU , our sample was small. In addition, the TICU did not have a protocol for daily sedation interruption or a pain management protocol. Therefore, there was a large degree of variability in the opioid and/or benzodiazepine weaning process between patients; this could have influenced differences in WS development. In addition, we were unable to evaluate some symptoms on the checklist in patients with RASS –3 to –5 such as hallucinations, delusions, illusions, dysphoria, nausea, insomnia, and delirium. Also, the intensity of the probable WS sign and symptoms was not evaluated. Our checklist has not yet undergone a formal validation process and reliability testing. Interrater or intrarater reliability was not possible because only one person performed all measures and the occurrence of WS was not constant between measurements. Finally, the signs and symptoms on our checklist are not specific for WS; thus, we could not rule out other conditions associated with these signs or symptoms. Further research on the psychometric characteristics of our checklist is warranted.Social media is now a common source of health-related information. This includes user-generated conversations about a variety of topics, with an emerging field focused on better understanding tobacco and alternative and emerging tobacco and electronic nicotine delivery system related knowledge, attitudes, and behaviors. User generated social media conversations can be assessed to better understand how health behaviors are changing closer to real-time.This approach introduces certain advantages over traditional survey methodology including faster identification of emerging trends. However, methods to appropriately code social media content for specific health-related topics remain underdeveloped, particularly in the context of characterizing transitions in behaviors that change over time. Twitter is a micro blogging social networking platform that allows users to tweet 280-character messages, which can then be retweeted, favorited, and shared across a network of online users. Users can form online communities by interacting with other users who share similar beliefs, interests, and opinions about topics.This includes users who initiate, use, and transition between different tobacco and ATP and ENDS products.

In fact, Twitter has specifically become a platform for sharing information about electronic cigarettes a nicotine delivery device commercially available only in the past decade.Evidencing growing popularity of vaping behavior, studies have shown that online searches for electronic cigarettes have increased. However, increased uptake of different types of e-cigarettes , particularly among youth and young adults, has not been without controversy. Ongoing concerns about the long-term health impact of nicotine consumption, e-cigarette-related adverse events and mixed evidence about the efficacy of ATPs as cessation devices, continues to generate public health and patient safety concerns. These concerns are accentuated when trying to assess the interaction of use behavior between traditional combustible tobacco products and ENDS. Understanding the pathways of transition of tobacco and ATP use—including what products users initiate on, why they switch between products, and unique health harms related to dual-use —is still a relatively underdeveloped area of study. Hence, the objective of this study was to examine Twitter user conversations to characterize users’ conversations in relation to transition of use associated with ENDS, with a focus on developing an inductive coding approach specific to characterizing transition of use knowledge, attitudes, and behaviors.To identify themes in our full corpus of tweets, we used an unsupervised machine learning approach called the Biterm Topic Model designed to detect patterns in data and summarize the entire corpus of tweets into distinct highly correlated categories. BTM is used to sort short text into highly prevalent themes without the need for predetermined coding or training and has been previously used for exploration of key public health topics. For each topic, BTM generates the top 20 words that represent the topic cluster. These topics were then reviewed and selected to identify clusters of Twitter conversations relevant to vaping and transition of use. Using BTM, we are able to identify “signal” topics based on the BTM output and eliminate irrelevant topics. BTM topics were first generated after applying keyword filters and were included for further analysis if they were pertinent to vape and vaping behavior, topics were excluded if they contained irrelevant topics or appeared to correlate with non-user generated conversations.We then extracted all the posts from the select vaping BTM topics and manually coded the content of tweets in these topics to ensure relevance to user-generated tobacco and ENDS use behavior. Posts were excluded as signals if they were: news related and not organically user-generated content; not written in English; and retweets, the tweets that were retweeted counted as only one tweet. However, all tweets, replies, and tweets containing photos or videos were included to assess additional contextual information in addition to content analysis of text of tweet. Transition of use was classified as switching from one tobacco or ATP/ENDS product to another.Tweets and any associated URLs/hyperlinks were aggregated into a table and imported into Atlas.ti qualitative software for content analysis. A first iterative, inductive analysis of the data was conducted to identify thematic areas and classify tweets into codes with code descriptions. Tweets were read for identification of thematic areas in the dataset,vertical grow system for sale then coded based on thematic areas of interest. Codes and coding descriptions were developed and modified iteratively throughout the coding process. A second analysis of the dataset was undertaken to expand the codebook to include subcodes. Subcodes and subcode descriptions were created and modified iteratively during a second round of data coding. Once a coding scheme was developed, the data were coded, extracted, and reviewed to assess the validity of the coding scheme by a second coder . Te final coding scheme and distribution of codes is presented in Fig. 1 and Table 1.Data was collected from the Twitter public API stream and included publicly available tweets that were filtered for posts with geolocation/geotagged information.

As the study did not involve human subjects, involved no interactions with online users, and only used publicly available data that was further de-identified for research purposes, ethics, and IRB approval was not required and twitter users were not consented into this study. Any user identifiable information was removed from the study results. This study explored user-generated conversations occurring on Twitter in relation to tobacco and ATP/ ENDS use, with a specific focus on transition of use between these highly addictive products. We observed that this subset of Twitter users actively tweeted about their experience using tobacco and ATPs/ENDS, representing powerful information about this behavior that is influenced by a changing landscape of new and emerging nicotine products. Te majority of tweets reviewed related to tobacco and ATP/ENDS use and behavior characteristics, including users asking about tobacco/ATP/ENDS products, how to quit, observations of tobacco/ATP/ENDS use behavior, opinions about products and vaping , sharing knowledge about tobacco/ ATP/ENDS products, and specific characteristics of use Close to half of all conversations discussed transition of use behavior, including users actively discussed the types of tobacco/ATP/ENDS products used and switched between, as well as provided reasons for product use change. A wide variety of tobacco/ATP/ ENDS products were mentioned, including combustible tobacco products , chewing tobacco, different types of e-cigarettes and cannabis smoking products. Transition was observed between different products and within specific product classes , with some users self-reporting poly tobacco and poly-substance behavior . Users expressed various sentiment about different products including how products could act as substitutes for others, what products made them feel better, attempts to quit use of one product by switching to another, and issues related to cost and access. Some users stated that cannabis vaping products helped them with cessation of nicotine addiction. Based on these preliminary results, Twitter appears to enable robust conversation and sharing of information related to tobacco and ATP/ENDS use and can act as a digital forum for smokers and vapers to accumulate knowledge, share experiences, and actually lead to potential behavior change associated with nicotine use and addiction.Since early March of 2020, the COVID-19 pandemic and the mitigation strategies put in place have had dramatic impacts on mental health and well being around the world . The public health response to COVID-19, such as social isolation and quarantines, resulted in several unintended consequences that increased the risk of anxiety and depression, as well as substance misuse and overdoses . The rise in substance misuse is particularly alarming, given that prior to the pandemic, in 2019, 60% of the United States population aged 12 and older used substances, including tobacco, alcohol, and illicit drugs, and an estimated 20 million had a substance use disorder . In addition to SUDs, one in five U.S. adults had a mental illness, which often co-occurs with SUDs . The prevalence of mental health symptoms continued to increase throughout the pandemic while disruptions in health care services resulted in unmet mental health care needs for many . At the same time, many individuals may have been reluctant to seek treatment for SUDs due to stay-at-home orders and worries about contracting COVID-19 . The increased rate of substance misuse raises several concerns regarding its impact on people who use drugs and overwhelmed health care systems. A rapid and drastic rise in drug overdoses and overdose-related deaths led to an emergency health advisory by the Centers for Disease Control and Prevention . Moreover, people with SUDs have been at increased risk of COVID-19 infection and poorer health outcomes . In turn, COVID-19 may increase the risk of overdose in PWUD . Furthermore, the pandemic exacerbated long-standing social and health disparities among under served and vulnerable populations. Not surprisingly, pre-existing disparities in mental health conditions and substance misuse have only widened during the pandemic . Among those who may be disproportionately affected by the pandemic are people living with HIV , a socially vulnerable population over represented in U.S. minorities . Indeed, COVID-19 infections correlate with social vulnerability and with county-level HIV prevalence .