Chronic use of cannabis has been linked to psychological and physical health consequences, including increased risk for psychiatric disorders , decline in cognitive function, impairment in learning and coordination, reduced educational and 3 Clear and convincing evidence indicates that there are multiple studies of a treatment and that the large majority of studies are of high quality and consistently find that the treatment is either effective or not effective. workplace outcomes, and lung inflammation/chronic bronchitis. It is not clear to what extent cannabis use increases the risk of mortality related to these health consequences. It is estimated that 2,782 Californians are seen in EDs and 543 are hospitalized for cannabis related issues each year. For many patients with SUD, attitudinal barriers are the most significant barrier to treatment initiation and persistence. The stigma of SUD and the ability to acknowledge an SUD affect patient desire to seek care, even more so for those who have co-occurring psychiatric conditions. Many people with SUD believe they can solve the problem themselves. Another barrier for patients participating in treatment specifically using CM is the requirement to travel to the provider’s office, sometimes up to two or three times a week. This can cause more of a burden for patients who do not have flexible schedules and those who are living in areas with a shortage of providers administering CM programs. However, when CM is administered as an adjunctive component of psychosocial treatments in the context of intensive outpatient programs , patients are already traveling to attend therapy the required two to three times per week.Some interventions in proposed mandates provide immediate measurable impacts while other interventions may take years to make a measurable impact . When possible, CHBRP estimates the long-term effects to the public’s health that would be attributable to the mandate. As there is no research that examines longterm impacts of CM for SUDs treatment on health care utilization, it is not possible to estimate the long-term health and cost impacts of SB 110.

CHBRP anticipates the demand for treatment of SUDs would continue as relapsed patients reattempt abstinence and first-time initiators would join the pool of patients seeking care. However,hydro tray limited patient readiness for SUD treatment and limited number of providers remain significant barriers to care. To the extent that SB 110 results in an increase in SUD treatment with CM, and the extent to which this leads to long-term abstinence, it is possible SB 110 would contribute to reductions in substance use– related morbidity and mortality, such as cardiovascular disease, cancer, HIV, and hepatitis C. Stimulants are a class of drugs that includes prescription medications to treat ADHD as well as drugs such as cocaine and methamphetamine. Repeated misuse of stimulants can lead to psychological consequences, such as hostility, paranoia, psychosis, as well as physical consequences of high body temperatures, irregular heartbeats, and the potential for cardiovascular failure or seizures . The DSM-5 characterizes stimulant use disorder as a pattern of stimulant use that results in significant impairment or distress. People meeting at least two of 11 specified criteria within a 12-month period are diagnosed with mild, moderate, or severe substance use disorder depending on the number of criteria met . Stimulant use disorder prevalence in the United States is relatively low compared with other substances, with 0.2% of the population reporting a prescription stimulant use disorder, 0.4% of the population reporting a methamphetamine use disorder, and 0.4% of the population reporting a cocaine use disorder . In California, it is estimated that 33% of all admissions to state- and county-contracted SUD programs are for stimulant use disorders – representing nearly 50,000 admissions annually . The pattern seen in California and other western states, where stimulants such as methamphetamine are the leading cause of overdose deaths, is significantly different from the pattern seen in eastern states, where opioids such as fentanyl are the leading cause of overdose deaths . For example, in Region 9 , the leading cause of death from overdose was methamphetamine , followed by heroin , fentanyl , and cocaine . Across the United States the leading cause of overdose death is fentanyl , followed by heroin , cocaine , and methamphetamine . In 2018, 1,954 Californians were seen in EDs for amphetamine overdose and 536 Californians were seen in EDs for cocaine overdose . The rise in methamphetamine use in California is particularly concerning due to the increased use of fentanyl in methamphetamine and, as a result, an increase in fentanyl-related overdoses.

An estimated 3,035 deaths are from stimulant use disorder in California each year . There are disparities in rates of stimulant use and stimulant use disorder by gender and sexual orientation. Women tend to start using stimulants at a younger age and are more sensitive to the effects of cocaine and methamphetamine, and can become more dependent on methamphetamine than men . Sexual orientation is also a predictor, with gay, lesbian, and bisexual men and women having higher odds of stimulant use as their heterosexual counterparts . Cannabis, also known as marijuana, is the most commonly used psychoactive drug in the United States, after alcohol . Acute effects of cannabis use include nausea, vomiting, and abdominal pain, while chronic impacts include cognitive impairment, pulmonary disease, and sleep disturbance. Chronic use of cannabis has been linked to psychological and physical health consequences, including increased risk for psychiatric disorders , decline in cognitive function, impairment in learning and coordination, reduced educational and workplace outcomes, and lung inflammation/chronic bronchitis . It is not clear to what extent cannabis use increases the risk of mortality related to these health consequences . It is estimated that 2,782 Californians are seen in EDs and 543 are hospitalized for cannabis related issues each year . Although 19.4% of Californians 12 years and older report cannabis use in the past month, only 2.1% report having cannabis use disorder . Research suggests that 9% of adults who use cannabis will become dependent, and that this increases to 17% in individuals who initiate cannabis use during youth . In California, it is estimated that 15% of all admissions to state- and countycontracted SUD programs are for cannabis use disorders – representing more than 22,000 admissions annually . To date, 36 U.S. states have medical cannabis laws and, of these, 14 states have recreational cannabis laws, including California . Research estimating the impact of recreational cannabis laws with cannabis substance use disorders indicate that these laws have increased the proportion of the population experiencing cannabis use disorders . This research looks at early adopters of recreational cannabis laws and does not include data on California which legalized recreational use in 2016. The impacts of legalization of recreational cannabis use in California on the rates of cannabis use disorder have not yet been established in the literature. The rates of cannabis use disorder vary by gender and race/ethnicity. Although women tend to be less likely to use cannabis or have a cannabis use disorder than men, women with cannabis use disorder are more likely to experience more severe withdrawal symptoms when attempting to quit .

Further, cannabis use disorders are more common in Blacks, Native Americans, and Mixed-Race adults . Treatments for SUD include residential, inpatient, and outpatient care using behavioral therapy, counseling, and/or prescription medication. Mutual help groups also support those with SUDs to establish and maintain sobriety. CM is used as a stand-alone treatment or as an adjunct to typical treatments for SUD and is described in detail below. Health care professionals note that relapse is common during the recovery process for many patients, with approximately 40% to 60% of patients returning to alcohol or drug use within one year of treatment, and when relapse occurs it is important for patients to work with their provider to resume or modify the treatment plan .CM is a type of behavioral therapy in which individuals are “reinforced,” or rewarded, for evidence of positive behavioral change or achievement of specified goals . Based on principals of behavioral analysis, CM has been assessed in the context of substance use treatments and typically consists of monetary-based rewards or vouchers to reinforce abstinence from the target drug and to promote medication compliance and treatment attendance . CM has been utilized as part of treatment for SUDs, including stimulants, opioids, marijuana, alcohol, and tobacco, and is often included as an adjunct to a specific SUD treatment such as cognitive behavioral therapy , medicationassisted therapy , and community reinforcement approach, although it is also used as a standalone treatment. Much of the research on the effectiveness of CM for SUD treatment has focused on stimulant use disorder. This is in part due to the rising increase in prevalence of and mortality related to stimulant use as well as due to the lack of effective treatments for stimulant use disorder . CM relies on detection of the target substance’s metabolites to biochemically confirm abstinence from use. Metabolites can be detected via urine, saliva, blood plasma, and breath samples . Urine is most often used to detect metabolites from stimulants, cannabis, and non-synthetic opioids . Detection of opioid use in urine can be complicated by the use of MAT,planting table which may produce metabolites that are similar to those produced by opioid use . Tobacco use can be monitored through either carbon monoxide levels in breath or cotinine levels found in saliva, plasma, or urine. If nicotine replacement therapy is being used as part of therapy, the best way to accurately measure tobacco use is through breathalyzers. Alcohol use can also be monitored via breath, saliva, plasma, or urine, but the relatively short length of time that ethanol can be detected in urine can be prohibitive for outpatient treatment programs that only monitor patients twice per week . Detection method is selected based on characteristics of the target drug, costs of the detection method, feasibility, and acceptability to the patient . Since urinalysis involves self-collection of the specimen, patients are often monitored in order to ensure the sample has not been tampered with. This may cause additional discomfort and reduce the acceptability of the CM treatment to the patient. It can also be an additional burden for treatment programs to have trained staff members on hand to observe the sample collections. However, CM is not the only reason that treatments programs use urinalysis; they often do so anyway if they require abstinence to remain engaged in treatment or simply to monitor progress.

Therefore, some programs may need to make adaptations to their existing protocols whereas others may already have processes in place for monitored urinalysis collection.The goal of CM is to modify behaviors related to substance use. Common CM goals include substance abstinence, treatment attendance, and/or medication compliance . As noted by Prendergast et al. , the duration of the behavior modification also varies. Substance abstinence: Abstinence from the target substance is typically measured through collection of urine samples in order to capture all potential substance use within that week . Analysis of the urine sample is conducted either on-site through a purchased on-site test kit or sent out to an outside lab for analysis. The results from the on-site test kit can be ready within two to five minutes, whereas results from an outside lab can take between three and five days. It is preferred to conduct the analysis on site to ensure provision of immediate rewards for substance abstinence; immediacy of rewards is a defining element of behavior therapies such as CM. Treatment attendance: CM can also be employed to increase attendance and participation in SUD treatment. SUD treatment clinics typically have attrition rates of 80% or higher, particularly among outpatient mental health treatment centers. Through utilizing reinforcers or rewards that are contingent on attendance, attendance rates may improve across a variety of treatment settings . Medication compliance: The treatment of some SUDs includes FDAapproved medications. CM can target adherence to a medication regime to improve compliance through rewards for directly supervised ingestion of medications . Note: The CM specified in SB 110 is primarily used for stimulant and cannabis use disorders, for which there is no MAT. Therefore, the results described in the Medical Effectiveness section and the models estimated in the Benefit Coverage, Utilization, and Cost Impacts section do not include results related to medication compliance.

Future work should draw on longitudinal data to examine the ways in which the strength of associations for PRS changes with the age of the target sample. Third, UKB was a large portion of the discovery sample for each of the GWAS used to create PRS. To the degree that UKB is biased, each of the PRS in these analyses will also reflect that bias. Finally, these analyses examined the marginal influence of PRS, independent of environment. Processes of gene-environment interaction are well documented in alcohol misuse. Incorporating environmental information along with PRS in a methodologically rigorous manner will be an important next step in developing clinically predictive algorithms. Polygenic scores are becoming better powered and starting to explain non-trivial portions of variance. We examined the current state of PRS for substance use, with a focus on AUD. Each of the PRSs analyzed here were associated with AUD. However, the maximum variance explained by any single score was still small . Individuals at the top of the PRS continuum had elevated rates of multiple substance use problems, but these differences across the PRS continuum are unlikely to be of broad clinical use in their current state. As GWAS discovery samples become larger and we are better able to model the complex relationship between genotype and phenotype, polygenic scores may eventually be useful in a clinical setting.Caffeine reduces fatigue and increases concentration and alertness, and athletes regularly use it as an ergogenic aid.Trained athletes seem to benefit from a moderate dose of 5 mg/kg, however, flood tray even lower doses of caffeine may improve performance. Some groups found significantly improved time trial performance or maximal cycling power, most likely related to a greater reliance on fat metabolism and decreased neuromuscular fatigue, respectively.

Theophylline, a metabolite of caffeine, seems to be even more effective in doing so. The effect of caffeine on fat oxidation, however, may only be significant during lower exercise intensities and may be blocked at higher intensities. Spriet et al.found that ingestion of a high dose of caffeine before exercise reduced muscle glycogenolysis in the initial 15 min of exercise by increasing free fatty acid levels which inhibits glycolysis and spares glycogen for later use. Caffeine’s effect of inhibition of glycogen phosphorylase has also been shown in vitro as well as its effect on increasing HSL activity. The effect of caffeine on adipose triglyceride lipase has not been studied and warrants investigation. Following caffeine administration prior to and after the onset of cycling, Ivy et al. found that plasma free fatty acid levels were increased 30% compared to placebo. This action might be mediated by inhibition of the enzyme phosphodiesterase, thereby yielding higher levels of cAMP, which has been identified as important molecule for glycogen metabolism and lipolysis. Phosphodiesterase inhibition has been observed only at high concentrations. When direct Fick measurements were applied, Graham et al. did not find altered CHO or fat metabolism, at least in the monitored leg. Further research is needed to evaluate the effect of caffeine on lipolysis, especially during higher exercise intensities. Augmented post-exercise recovery by increased rates of muscle glycogen resynthesis has been observed. Pedersen et al. found higher rates of muscle glycogen accumulation after the co-ingestion of caffeine with CHO during recovery in highly trained subjects. This might, at least in part, be mediated by the activation of AMP-activated protein kinase as it is involved in the translocation of glucose transporter 4 to the plasma membrane. This mechanism enables the cell to take up glucose from the plasma and store it as glycogen.

Not only does caffeine impact endurance, it has also been reported to benefit cognitive function and fine motor skills. While the performance enhancing effects of caffeine in moderate-to-highly trained endurance athletes are quite clear and well documented, its effects on anaerobic, high-intensity tasks are less well investigated. Whereas caffeine supplementation did not yield significant performance increases in a Wingate test in untrained subjects, Mora-Rodriguez et al.report that caffeine ingestion of 3 mg/kg could counter reductions in maximum dynamic strength and muscle power output on the morning thereby increasing muscle performance to the levels found in the afternoon. Especially with regard to anaerobic performance caffeine’s adenosine receptor blocking effect in the CNS may be important. A possible explanation for the diverging effect of caffeine on anaerobic performance is that caffeine seems to benefit trained athletes who show specific physiological adaptations whereas performance gains in untrained subjects might be lost or masked by a high variability in performance. It has been shown that coffee, by containing phenolic compounds such as chlorogenic acids, elicits metabolic effects independent of caffeine. These compounds may have the potential to antagonize the physiological responses of caffeine. The question therefore remains whether ingesting the same amount of caffeine via a food source is as effective as ingesting isolated caffeine in the form of a tablet. As mentioned above, the performance enhancing effect of caffeine is very clear. Only a few studies, however, have shown a positive effect of coffee on performance. Whereas some studies found enhanced performance after coffee consumption, others did not. One of the earlier works by Costill et al. reported increases in time trial performance of competitive cyclists only in the coffee trial group but not in the decaffeinated coffee trial. Graham et al. studied exercise endurance in runners after ingestions of a caffeine or placebo capsule with water or either decaffeinated coffee, decaffeinated coffee with added caffeine or regular coffee. The authors found that only caffeine significantly improved running time to exhaustion at 75% VO2max but neither did regular coffee or decaffeinated coffee plus caffeine. Based on these results, the authors speculated that some component in coffee possibly interfere with the ergogenic response of caffeine alone.

This is in opposition to Hodgson et al.who looked at time trial performance in trained subjects after administration of caffeine , coffee , decaffeinated coffee and placebo one hour prior to exercise. The authors report similar significant increases of ~5% in time trial performance in both the caffeine and the coffee supplemented group with no effects in the decaf or placebo group. The authors conclude that coffee consumed 1 h prior to exercise, at a high caffeine dose improved performance to the same extent as caffeine. One reason for the disparity of the two studies mentioned above might be the different performance tests used. Whereas Graham et al. used a time to exhaustion test which reportedly can exhibit a coefficient of variation as high as ~27%, Hodgson et al. used a time trial which have been shown to be more reproducible. It has also been speculated by Hodgson et al. that due to lower statistical power, Graham et al. were not able to detect a difference between caffeine and coffee ingestion on performance. At this point, both coffee and caffeine exhibit a performance enhancing effect. Further research will hopefully extend our understanding on this issue. Another reason for the widespread use of caffeine within the exercise community might be its small but significant analgesic effect, possibly mediated by augmenting plasma endorphin concentrations. It is also established that caffeine reduces the rate of perceived exertion during exercise, suggesting that athletes are able to sustain higher intensities but do not perceive this effort to be different from placebo conditions. Some studies used caffeine-naïve whereas others used caffeine-habituated subjects. There seems to be a higher increase in plasma adrenalin in caffeine-naïves compared to caffeine habituated subjects after caffeine ingestion. However,grow table no differences between habitual caffeine intake and 1500 m running performance or force of contraction could be observed. For both caffeine-naïve as well as caffeine-habituated subjects, moderate to high doses of caffeine are ergogenic during prolonged moderate intensity exercise. Although there is clearly the need to study caffeine habituation further, the differences between users and non-users do not seem to be major.From 1962 to 1972 and again from 1984 to 2003 caffeine was on the WADA banned list, with a concentration >12 μg/ml in the urine considered as doping. Caffeine has been demonstrated to be ergogenic at doses lower than those doses that result in a urine concentration of 12 μg/ml, and higher doses appear to exhibit no additional performance-enhancing effect. During the second banned period, many athletes tested positive for caffeine. The sanctions ranged from warnings up to 2 year suspensions . Since 2004, caffeine has been removed from the prohibited list, however, it is still part of WADAs monitoring program in order to monitor the possible potential of misuse in sport. According to WADA, one of the reasons caffeine was removed from the Prohibited List was that many experts believe it to be ubiquitous in beverages and food and that having a threshold might lead to athletes being sanctioned for social or dietary consumption of caffeine. Furthermore, caffeine is metabolized at very different rates in individuals and hence urinary concentrations can vary considerably and do not always correlate to the dose ingested.

In addition, caffeine is added to a wide range of popular food products such as coffee, tea, energy drinks and bars, and chocolate.In general, nicotine has a psychostimulatory effect on the CNS at low doses via enhancing the actions of norepinephrine and dopamine in the brain. At higher doses, however, nicotine enhances the effect of serotonin and opiate activity, exerting a calming and depressing effect. Nicotine-induced stimulation of the sympathetic nervous system leads to increased heart rate and blood pressure, cardiac stroke volume and output and coronary blood flow. Although the results are conflicting and some authors report increases in cutaneous blood flow and skin temperature, others report a decrease in cutaneous blood flow and subsequent decline in skin temperature associated with nicotine consumption. These differences in cutaneous blood flow are possibly related to differences in nicotine administration. Both snus and nicotine gums enable nicotine to diffuse across the mucous membranes and are taken up by the bloodstream or, when inhaled, diffuses across the alveolar membrane of the alveoli, and enters the bloodstream. Although the amount of nicotine inhaled is lower than with conventional cigarettes, the use of electronic cigarettes is becoming more and more popular. However, the amount taken up by smokeless tobacco users tends to be much greater than by smoking. Once in the bloodstream, nicotine is quickly delivered to the brain, where it interacts with neural nicotinic acetylcholine receptors . It is metabolized by the liver cytochrome P450 enzyme system and has a half-life of approximately 2 hours. Upon binding of ACh or its exogenous ligand nicotine, the ion channel is opened and causes an influx of sodium and calcium . This local increase in intracellular Ca2+ can alter cellular functions. A mechanism termed Ca2+-induced Ca2+ release can further boost intracellular calcium upon activation of nAChR. In vitro experiments using human neutrophils showed a dose-dependent rise in intracellular Ca2+ levels of 700% over baseline at a concentration of 10-2 M nicotine. In numerous pathways, Ca2+ acts as an intracellular messenger, setting the stage for nAChRs as potent candidates to influence a variety of Ca2+-dependent neuronal processes, such as neurotransmitter release, synaptic plasticity or gene transcription.While it is clear that smoking can lead to the development of respiratory, cardiovascular, and skin diseases as well as a number of tobacco-related cancers there are other forms of application such as the use of alternative smokeless tobacco , which is gaining popularity among athletes as it bypasses the respiratory system. Snus and cigarette consumers show similar peak blood nicotine levels after use with a tendency for higher cotinine levels in the former. Nicotine activates the sympathoadrenal system, which leads to increased heart rate, contractility, vasoconstriction and a rise in blood pressure and the level of circulating catecholamines during light exercise. Nicotine also increases muscle blood flow and lipolysis due to enhanced circulating levels of norepinephrine and epinephrine as well as direct action on nicotinic cholinergic receptors in adipose tissue. The effects exerted by nicotine may be beneficial in a wide variety of sports and it is suggested that nicotine is abused by athletes. According to Marclay , cumulative exposure to nicotine metabolites were found in 26% – 56% of urine samples that were subjected to screening for tobacco alkaloids. After correcting for exposure to second-hand smoke, 15% of the athletes were considered active nicotine consumers.

No history of drug overdose or recent illness was obtained. Upon arrival to the ED, the patient was obtunded , but would occasionally follow commands. Her Glasgow Coma Score was eight, scoring two points for eye-opening response, two points for verbal response, and four points for motor response. Vital signs revealed blood pressure of 135/81 millimeters of mercury , pulse 124 beats per minute , rectal temperature of 99.6 degrees Fahrenheit , and 97% pulse oximetry on room air. Physical exam revealed dilated pupils of six millimeters , normal neck exam, normal lung sounds, a soft and non-tender abdomen, and normal heart sounds. A neurological exam revealed rigidity in both lower extremities with a sparing of rigidity in the arms. Deep tendon reflexes showed sustained clonus in both feet, and the presence of hyper-reflexivity in the patella tendons bilaterally but with normal reflexes in the upper extremities. Lab results showed a normal complete blood count, normal creatine kinase, normal comprehensive metabolic profile, normal arterial blood gas, normal prolactin level,and a urine drug screen positive for THC. Electrocardiogram showed sinus tachycardia, and a non-contrasted head computed tomography was normal. Serotonin syndrome was considered in the differential diagnosis. After pediatric critical care and pediatric neurology consultation, one oral dose of cyprohepatidine 4 mg was administered. The patient was admitted to the pediatric intensive care unit. Magnetic resonance imaging of the brain was normal, and an electroencephalogram showed no epileptic activity. The patient rapidly improved and was discharged the following day. Prior to discharge, the patient admitted to “dabbing” about 30 minutes prior to arrival to the hospital. The same patient returned to the ED the following night with a similar presentation, once again associated with dabbing. Over time, improvements in antiretroviral therapy have lengthened lifespan and reduced HIV transmission among people living with HIV. Findings that ART adherence can suppress viral load and reduce HIV transmissibility during condomless sex have led to prioritizing treatment as prevention as a key strategy to prevent HIV transmission by PLH.

However, high rates of substance use and depression among PLH remain key barriers to successful implementation of TasP in the U.S. and other similar settings. However, empirical studies on the associations of substance use,indoor plant table depression, and achieving undetectable VL have not been adequately assessed in low- and middle-income settings. Non-injection substance use is the most common form of substance use among PLH, with 40–70% reporting the use of alcohol, cannabis, non-injection stimulants , and/or opioids. In general, PLH who use substances are less likely to access ART, are found to have lower ART adherence, are less likely to achieve viral suppression, and are more likely to have faster disease progression compared to non-substance using PLH. Moreover, this population may be the most likely to engage in condomless sex, making it critical to understand how to improve their HIV care outcomes. Aside from behavioral risk, and the reduced ART adherence associated with substance use, emerging research indicates that substance use may have pathophysiological effects on HIV disease progression. For example, stimulants have been linked to increased HIV replication—in peripheral blood mononuclear cells and in mouse models. When examining the effects of substance use on VL or other HIV outcomes, it is also important to investigate the contribution of depression as it is a highly prevalent comorbid condition. Depression is a more common comorbidity to substance use among PLH than the general population, and is the most common psychiatric health condition among PLH—affecting 20–33% of adults in HIV care. In terms of HIV clinical outcomes, depression is thought to lower ART adherence and reduce the likelihood of sustained viral suppression. Studies indicate that depressive symptoms may also affect HIV disease progression above and beyond sub-optimal ART adherence by reducing individuals’ responsiveness to ART, decreasing CD4+count, and increasing HIV VL. Depressive symptoms and substance use are prevalent among PLH and likely contribute substantially to the lack of sustained viral suppression. Despite the high prevalence of substance use and depressive symptoms among PLH, most research examining depression, substance use, and HIV disease outcomes has been conducted in the U.S..

There is little information on the type and patterns of non-injection substance use, on the prevalence of depression, and on how these common comorbidities affect viral suppression among PLH in low- and middle-income settings. There is reason to think that the association between substance use and viral load detectability may operate through decreased ART adherence and increased co-morbidity with depression among PLH. Previous research have linked substance use—including alcohol, cocaine, heroin, methamphetamines, and other stimulants—to decreased ART adherence, although these studies took place in the U.S.. A recent systematic review focused on ART adherence among those who engaged in substance use in low- and middle income countries found sub-optimal adherence to treatment, however this review solely focused on injection drug use. In addition, a study that examined active drug use on ART adherence and viral suppression found that depression appeared to mediate the association, although the finding was only significant for HIV-infected women and not HIV infected men. Moreover, based on the minority stress theory—which posits that sexual minorities have adverse health outcomes as a result of heightened stress from prejudice and stigma based on their sexual minority status—it is thought that men who have sex with men may have greater substance use and depressive symptoms than heterosexual men. This greater comorbidity prevalence is hypothesized to magnify the association between substance use, depression, and viral load detectability. This is likely the case for men in low- and middle-income settings, such as Thailand and Brazil, where HIV prevalence is much greater among MSM compared to the general adult population at 9.2% and 10.5% , respectively. Although less research has been conducted among men who identify as heterosexual in international contexts, in Brazil they comprise the largest proportion of men infected with HIV and as many as 70% receive late HIV-diagnosis. Furthermore, non-injection substance use often affects MSM and heterosexual men at greater rates than women, potentially exacerbating the effects of substance use on HIV outcomes via ART adherence and depression in low- and middle-income settings.

This study aims to address this gap in research by conducting a secondary data analysis focused on MSM and heterosexual men using HPTN 063 data, a longitudinal observational study of HIV-positive individuals in HIV care in Zambia, Thailand, and Brazil. First, we described the type and pattern of non-injection substance use and prevalence of depressive symptoms among men infected with HIV at baseline. Second, we examined the effect of non-injection substance use on ART adherence and HIV VL undetectability, testing ART adherence as a mediator of the association between substance use and HIV VL undetectability. Third, we examined the effect of non-injection substance use on depressive symptoms and VL undetectability, testing depressive symptoms as a mediator of the association between substance use and HIV VL undetectability. Then, we tested whether there was evidence of effect modification due to sexual orientation, on the association between substance use, mediators , and HIV outcomes. For all analyses, we stratified by unique country context.Data were collected via HPTN 063, a multi-site, longitudinal observational cohort study of people living with HIV at high risk for sexual transmission in HIV care in Africa , Asia , and South America . Recruited participants included HIV-infected heterosexual men, heterosexual women, and men who have sex with men . Structured interviews were conducted every 3 months over the course of 12 months, collecting data on socio-demographics, behavioral risk, substance use, mental health, and ARV adherence. HIV clinical variables were extracted from patient flews.The HPTN063 study design has been described in detail in previous publications.Plasma HIV–RNA VL was extracted from medical records at baseline and each follow-up visit and recorded if a current VL was documented. VL was then dichotomized . Non–injection substance use was measured as the number of self-reported use days and included stimulants, cannabis, and alcohol. Stimulant use was measured as the number of days that non-injection cocaine , methamphetamine, and ecstasy use were reported in the prior 3 months. Cannabis was measured as the number of days that marijuana and hashish were reported in the prior three months. Alcohol misuse was measured using the 10-item alcohol use disorders identification test . Example items include how many drinks containing alcohol one has on a typical day and how often one is not able to stop drinking once started. AUDIT score was dichotomized into alcohol misuse versus no alcohol misuse. Polysubstance use was measured as the total number of non-injection substances reported used in the past 3 months , including stimulants, cannabis, and alcohol misuse, plant growing stand and was treated as a continuous variable . Depression symptoms were measured using the Center for Epidemiologic Studies Depression Scale. Example items ask how often during the past week participants had a poor appetite or felt depressed. CESD score was dichotomized into severe depressive symptoms versus not severe depressive symptoms . ART adherence was measured using the self-reported question on adherence ability, “in the last 3 months, on average, how would you rate your ability to take all your antiretroviral drugs as your doctor prescribed?”.

Instructions provided prior to the interview normalized ART non-adherence. Participants were provided with a response card with Likert response options, ranging from very poor to excellent. This single-item, self-report adherence measure has been found as valid and reliable in prior research. Due to small cell size, ART adherence ability in Thailand was recoded into two levels . For Brazil, ART adherence ability was missing on too many participants to warrant inclusion in this analysis and the dichotomized variable of taking ARTs was used in place. The self-reported measure asked, “In the last 3 months, have you taken antiretroviral drugs?” Socio–demographic variables included in our analysis were age group and education .Data analysis began with descriptive statistics at baseline of the total sample and of heterosexual men versus men who have sex with men on non-injection substance use, depression, HIV outcomes, and socio-demographics. The Chi square statistic test was used for categorical variables, and t-statistic test for continuous variables, to detect statistically significant differences between groups . Next, we described the type and number of self-reported non-injection substances used in the prior 3 months at baseline stratified by country and sub-group to understand poly-substance use in our sample . Then, generalized linear mixed models were applied with the logit link function for longitudinal binary outcomes to estimate the odds ratios of non-injection substance use on having an undetectable HIV VL adjusting for covariates, age and education . The mediators, ART adherence and depression, were also estimated as an outcome of non-injection substance use using GLMM and mediation was controlled for when estimating the effects of non-injection substance use on undetectable HIV VL. GLMMs with the logistic link function with a random intercept and compound-symmetric covariance were used to account the correlations of observations between visits within individuals. All analyses were stratified by country. For each model, an interaction term of substance use and sub-group was included to test for statistically significant differences between MSM and heterosexual men in the associations between substance use and ART adherence, depressive symptoms, and undetectable VL.Table 1 shows the baseline characteristics of participants in the total sample stratified by study site and heterosexual men versus MSM. In Thailand, 43% of the total sample reported alcohol misuse. In the past 3 months, individuals, on average, reported using stimulants for zero days , cannabis one day , and used one non-injection substance , with no significant difference by sub-group. Twenty-two percent of the total sample had severe depressive symptoms, with no significant difference by sub-group. In terms of HIV outcomes , 82.4% reported good/very good/excellent adherence ability, with MSM reporting significantly better adherence ability than heterosexual men . Seventy-seven percent of the total sample presented an undetectable VL at baseline, with no significant differences by sub-group. The median CD4+ count at baseline was significantly lower among heterosexual men compared to MSM . In Brazil, 34% of the total sample reported alcohol misuse. In the past 3 months, individuals, on average, reported using stimulants for 4 days , cannabis for 5 days , and used one non-injection substance , with no significant difference by sub-group. About half of the sample in Brazil had severe depressive symptoms, with no significant difference by sub-group.

Despite high AR exposure levels , both studies reported very low numbers of animals dying primarily from AR exposure. Nevertheless, AR poisoning may significant impact mortality rates in Californian fisher populations , with increasing prevalence from 2007 to 2014. AR contamination is not limited to mammals. It was also documented in northern spotted owl and barred owl populations, likely through secondary poisoning from predation on contaminated rodents . Despite some limitations due to small sample sizes , these studies draw attention to a potential ecological threat posed by illicit cultivation methods. Far less is known about application of chemicals in legal growing operations, which vary greatly by region and country. While some ARs are illegal or heavily restricted in the United States, various other pest-control methods have been reported for cannabis . In the US, due to the crop’s federally illegal status, no commercially available pesticides have been approved for use on cannabis . In Canada, 25 pesticide and fungicide compounds have been approved for legal use on cannabis.Wang, et al. measured biogenic volatile organic compounds emitted by cannabis plants grown under conditions mimicking greenhouse cultivation. Results suggested BVOC emissions from indoor cultivated cannabis in Colorado could contribute to ozone formation and particulate matter pollution. The authors acknowledged limitations due to small sample sizes, sub-optimal growing conditions, and a focus on only 4 out of 620 reported cannabis strains. In a follow-up study, Wang, et al. estimated terpene emissions and regional ozone impacts from indoor cannabis cultivation facilities in Colorado using the Comprehensive Air Quality Model. Results predicted increases in hourly ozone concentrations which may have consequences for regional air quality. This approach was limited by reliance on estimates and assumptions in the absence of data regarding emission capacity of most cannabis strains, number of plants and plant biomass. Nevertheless, preliminary findings indicated that concentrated indoor cannabis cultivation could influence ozone pollution through BVOC emissions from terpenes,seedling grow rack particularly in areas where nitrogen oxides are not the limiting factors in ozone formation.

Surface- and ground-water pollution from the cannabis industry, including from soil erosion, pesticide and fertilizer in run-off, chemical processing or waste disposal operations, is a likely risk . Nevertheless, we found no peer-reviewed study quantifying the impacts of cannabis cultivation on water quality, although current pilot projects in California are underway. We did find an academic book series and five peer-reviewed publications documenting the effects of pollution from cannabis consumption on water quality. These studies used THC-COOH concentrations in sewage systems, presumably originating from human consumption, as a proxy. Evidence of THC-COOH presence was found in both raw and biologically treated wastewater across major European cities as well as in raw wastewater in the US . Concentrations of chemical compounds derived from cannabis were lower in treated than in raw wastewater. Nevertheless, accumulation of these compounds may contribute to waterway contamination downstream from wastewater effluent discharges in urban areas, although likely to a lesser extent than other illicit drugs . While these studies primarily aim to document urban cannabis consumption, they also point towards potential contamination issues impacting downstream freshwater ecosystems. Our current understanding of the consequences of wildlife exposure to cannabis-related chemicals remains limited. Parolini, et al. sought to bridge this gap through experimental exposure of zebra mussels to concentrations of cannabis active compounds Δ-9-THC and THCCOOH. Results showed that prolonged exposure could contribute to oxidative and genetic damage in the mussels. Still, given the lack of knowledge regarding actual Δ-9-THC and THCCOOH concentrations in aquatic ecosystems, and the lack of documentation of the compounds’ effects on mussels or other organisms in the wild, it is difficult to draw broader conclusions about potential environmental risks posed by exposure to active compounds in cannabis for aquatic organisms. Because there are environmental trade-offs across production methods, it is important for policy makers to consider the potential unintended consequences of policy decisions. For example, in California, stringent water-use regulations for outdoor production may incentivize cultivators to turn to alternative indoor production methods. While this shift may alleviate water-stress in sensitive ecosystems, it may also increase the carbon footprint of cannabis by encouraging energy-intensive indoor production.

Identifying and understanding trade-offs within and across systems is thus important, and cannabis regulation should be comprehensive in order to prevent impacts from being displaced from one pathway to another. The emerging literature on cannabis and the environment already provides useful insights to guide policy. Still, the majority of studies reviewed here were individual case studies, mostly geographically centered in Northern California. There is a tremendous need for similar studies to be carried out across different biophysical, socioeconomic, historical and cultural contexts, both to confirm the generalizability of these results and to avoid exporting environmental problems from the developed to the developing world. We expect that continued liberalization worldwide will provide expanded geographic scope for this work for years to come, and researchers should be ready to act on this expansion. Most of the literature reviewed here relies on observational or model-based methodologies . While these approaches provide insights, experimentation is fundamentally needed to understand basic agroecological functions and processes governing cannabis cultivation. Trials quantifying the energy footprints, water use, and nutrient requirements of different cultivation and management methods are also needed to improve the efficiency of production systems. Given increased liberalization trends, we expect to see a normalization of cannabis-related research. Scientists should be encouraged to carry out a range of experiments to bolster scientific capacity to assess the environmental impacts of an expanding cannabis sector. Additionally, as regulations around cannabis cultivation are implemented, long-term studies are needed to understand how these regulations affect cannabis cultivation practices. Cannabis cultivation may lead to additional environmental impacts, which remain scientifically undocumented to our knowledge. For instance, solid waste management of materials originating from cultivation, packaging, or other production processes, will need to be addressed. Life-cycle assessments of the cannabis sector could provide information on how to minimize such waste and more generally increase the efficiency and sustainability of cannabis production processes. Other potential areas for future research include odor pollution risks in communities where increased cannabis production has led to farms being sited near residential areas, cross pollination issues between cannabis and hemp , alternative cannabis farming or transportation efficiency. These topics, and many others, should make the study of cannabis’ environmental impacts a rich field for discovery for many years to come.

Traditionally, cannabis has been cultivated remotely and at small scales. Legalization is altering this through cultivation expansion, shifts toward urban areas, and increased size of production facilities , which may in turn affect the environmental impacts of the industry. The intensification of cultivation activities at large-scale facilities may magnify negative impacts. Conversely, economies of scale may increase the efficiency of larger facilities which may have broader capacities to invest in sustainable production processes. Larger facilities are also less likely to be located in remote sensitive areas than historical smaller farms, but may lead to land use trade-offs with other forms of agriculture. Continued diligence by policy makers and consumers is needed to ensure that the move towards industrialization is not a move away from sustainability – and researchers must continue to document shifts in the industry and their environmental impacts. In conjunction with legalization, social and ecological certification schemes could increase environmental performance of the industry. Emerging programs such as Sun and Earth Certification or planned appellation designations in California constitute first steps in this direction. By contributing to consumer awareness and providing incentives for growers to produce in sustainable ways, these programs may pave the way for the development of a more sustainable cannabis sector. In many ways, the question of how to best produce and consume cannabis while protecting the environment echoes larger debates about the environmental impacts of agricultural production in general. Current discourse on the optimal ways to address shifts in the cannabis sector touches upon fundamental sustainability framings such as land sparing vs. land sharing, intensification vs. expansion, technology-driven agriculture vs. agroecology,greenhouse growing racks the role of smallholder farmers vs. industrial-scale facilities. Policy makers working with cannabis have strong interests in developing effective regulations following legalization and are also dealing with regulatory “blank slates”. This may equip them with a novel combination of increased freedom and institutional capacity to test and evaluate the effectiveness of multiple policy approaches. Ultimately, failures and successes of environmental regulations for cannabis may lead to important lessons-learned for agriculture more broadly. Marijuana smoking was prevalent in this adolescent sample of tobacco smokers: 80% reported past month marijuana use and more than a third smoked marijuana daily. Notably, among adolescent tobacco smokers who also smoked marijuana, the frequency of marijuana use was associated with greater levels of nicotine addiction on all three major scales used in studies with adolescents plus the ICD-10. Moreover, models incorporating age, frequency and years of tobacco smoking with marijuana accounted for 25-44% of variance in adolescent nicotine dependence. Interestingly, CPD was only minimally associated with the frequency of marijuana use and made minimal contribution to the model since associations with the mFTQ were similar after removing the question about CPD.The finding that with the exception of drive and priority, the other sub-scales of the NDSS were not significantly associated with marijuana frequency was not surprising since most of these adolescent smokers were light and intermittent tobacco users and dimensions of dependence such as stereotypy and tolerance become more prominent as teens develop more regular and established patterns of smoking . However, despite relatively light tobacco use, the drive sub-scale, which measures the compulsion to smoke, and the priority sub-scale, which measures the preference of smoking over other reinforcers, were associated with marijuana use. It is possible that since both marijuana and tobacco share common pathways of use, smoking cues for one substance may trigger craving for the other, and thus reinforce patterns of use.

As such, tobacco and marijuana may serve as reciprocal reinforcers. Some limitations of this brief include the relatively small sample size and the lack of detailed information on the timing of the initiation of marijuana use with regard to cigarette smoking. Future studies will need to examine how the proximity of marijuana use to cigarette smoking affects the degree of nicotine addiction. For example, examining whether concomitant use impacts the level of nicotine addiction more than smoking marijuana separately from tobacco. The sample largely consisted of light smokers, which reflects adolescent smoking in the US. That we found such a strong association between marijuana use and nicotine addiction in this group of relatively light tobacco smokers is notable, and reinforces the relevance of the association.Large-scale prospective cohort studies of those at risk for or living with HIV have been instrumental in investigating research questions that could not otherwise be accomplished through smaller studies. A number of cohorts have been established going as far back as the start of the HIV epidemic in the mid-1980s. Some of the cohorts such as the multi-center AIDS cohort study were set up as a single study across multiple sites, implementing the same protocol with standard data collection tools, while other studies such as the North American AIDS Cohort Collaboration on Research and Design were designed as a collaborative in which 25 cohorts collect and integrate a common set of core information. Smaller cohorts have an important role in addressing questions in sentinel populations. In the absence of a common data collection effort such as those in MACS and NA-ACCORD, strategies that allow us to compile data across these individual studies can help us achieve comparable effects, increasing the impact of the data collected. Te Collaborating Consortium of Cohorts Producing NIDA Opportunities was established in 2017 by the National Institutes of Health/National Institutes of Drug Abuse to stimulate the use of NIDA longitudinal cohorts and to address high priority research on HIV/AIDS in the context of substance use . Tis consortium includes nine different cohorts located in the United States and Canada. All cohorts were established before the consortium was established, with the oldest having started in 1988 and the newest in 2015. All cohorts focus on HIV and substance use, however the target population, participant sampling strategies, as well as data collection tools differs for each of these cohorts.

Using a set of 2006 reviews as a starting point, the work group noted weaknesses, highlighted gaps in knowledge, identified data sets to investigate possible solutions, encouraged or conducted analyses to fill knowledge gaps, monitored relevant new publications, and formulated interim recommendations for proposed changes. The work group elicited input on proposed changes through commentary , expert advisers, the DSM-5 web site , and presentations at over 30 professional meetings . This input led to many further analyses and adjustments. The revisions proposed for DSM-5 aimed to overcome the problems identified with DSM-IV, thereby providing an improved approach to substance use disorders. To this end, the largest question was whether to keep abuse and dependence as two separate disorders. This issue, which applies across substances , had the most data available. Other cross-substance issues included the addition or removal of criteria, the diagnostic threshold, severity indicator, course specifiers, substance-induced disorders, and biomarkers. Substance-specific issues included new withdrawal syndromes, the criteria for nicotine disorders, and neurobehavioral disorder associated with prenatal alcohol exposure. Additional topics for consideration involved gambling and other putative non-substance related behavioral addictions. This article presents the evidence that the work group considered on these issues and the resulting recommendations.The DSM-IV criteria for substance abuse and dependence are shown in Figure 1. Dependence was diagnosed when three or more dependence criteria were met. Among those with no dependence diagnosis, abuse was diagnosed when at least one abuse criterion was met. The division into two disorders was guided by the concept that the “dependence syndrome” formed one dimension of substance problems, while social and interpersonal consequences of heavy use formed another . Although the dimensions were assumed to be related , DSM-IV placed dependence above abuse in a hierarchy by stipulating that abuse should not be diagnosed when dependence was present.

The dependence diagnosis represented a strength of the DSM-IV approach to substance use disorders: it was consistently shown to be highly reliable and was validated with antecedent and concurrent indicators such as treatment utilization, impaired functioning, consumption,flood table and comorbidity . However, other aspects of the DSM-IV approach were problematic. Some issues pertained to the abuse diagnosis and others pertained to the DSM-IV-stipulated relationship of abuse to dependence. First, when diagnosed hierarchically according to DSM-IV, the reliability and validity of abuse were much lower than those for dependence . Second, by definition, a syndrome requires more than one symptom, but nearly half of all abuse cases were diagnosed with only one criterion, most often hazardous use . Third, although abuse is often assumed to be milder than dependence, some abuse criteria indicate clinically severe problems . Fourth, common assumptions about the relationship of abuse and dependence were shown to be incorrect in several studies . The problems pertaining to the DSM-IV hierarchy of dependence over abuse also included “diagnostic orphans” , the case of two dependence criteria and no abuse criteria, potentially a more serious condition than abuse but ineligible for a diagnosis. Also, when the abuse criteria were analyzed without regard to dependence, their test-retest reliability improved considerably , suggesting that the hierarchy, not the criteria, led to their poor reliability. Finally, factor analyses of dependence and abuse criteria showed that the criteria formed one factor or two highly correlated factors , suggesting that the criteria should be combined to represent a single disorder. To further investigate the relationship of abuse and dependence criteria, the work group and other researchers used item response theory analysis, which builds on factor analysis, to better understand how items relate to each other. Item response theory models indicate criterion severity and discrimination . The results from these analyses are often presented graphically , where each curve represents a criterion. Curves toward the right indicate criteria of greater severity; steeper slopes indicate better discrimination .Table 2 lists the 39 articles on the item response theory studies that were examined or conducted by the work group, which include over 200,000 study participants. Two main findings arose, with similar results across substances, countries, adults, adolescents, patients and non-patients. First, unidimensionality was found for all DSM-IV criteria for abuse and dependence except legal problems, indicating that dependence and the remaining abuse criteria all indicate the same underlying condition. Second, while severity rankings of criteria varied somewhat across studies, abuse and dependence criteria were always intermixed across the severity spectrum, similar to the curves shown in Figure 2. Collectively, this large body of evidence supported removing the distinction between abuse and dependence.

Substance use prevalence, attitudes, and norms vary across groups, settings, and cultures . Therefore, the work group examined the studies listed in Table 2 in detail for evidence of age, gender, or other cultural bias in the DSM-5 substance use disorder criteria. Such differences are identified in an item response theory framework by testing for differential item functioning . With the exception of legal problems, the criteria did not consistently indicate differential item functioning across studies. Even where differential item functioning was found , no evidence of differential functioning of the total score was found. Thus, consistent gender or cultural bias was not found, although the extent of the changes proposed for DSM-5 criteria for substance use disorders suggested that there would be value in additional research using different analytic strategies to examine whether gender, age, or ethnic bias exists in the criteria.Support for craving as a substance use disorder criterion comes indirectly from behavioral , imaging, pharmacology , and genetics studies . Some believe that craving and its reduction is central to diagnosis and treatment , although not all agree . Craving is included in the dependence criteria in ICD-10, so adding craving to DSM-5 would increase consistency between the nosologies. Item response theory analyses of data from general population and clinical samples in the United States and elsewhere were used to determine the relationship of craving to the other substance use disorder criteria and whether its addition improved the diagnosis. Craving was measured using questions about a strong desire or urge to use the substance, or such a strong desire to use that one couldn’t think of anything else. Across studies, craving fit well with the other criteria and did not perturb their factor loadings, severity, or discrimination. Differential item functioning was generally no more pronounced for craving than for other criteria. In general population samples , craving fell within the midrange of severity . In clinical samples, craving was in the mid-to-lower range of severity, likely because of high prevalence .Using visual inspection to compare item response theory total information curves for the DSM-5 substance use disorder criteria with and without craving produced inconsistent results . Using statistical tests to compare total information curves, the addition of craving to the dependence criteria did not significantly add information . However, when craving and the three abuse criteria were added, total information was increased significantly for nicotine, alcohol, cannabis, and heroin, although not for cocaine use disorders . Clinicians expressed enthusiasm about adding craving at work group presentations and on the DSM-5 web site. In the end, while the psychometric benefit in adding a craving criterion was equivocal, the view that craving may become a biological treatment target prevailed. While awaiting the development of biological craving indicators, clinicians and researchers can assess craving with questions like those used in the item response theory studies .The studies in Table 2 and others demonstrate that the substance use disorders criteria represent a dimensional condition with no natural threshold. However, a binary diagnostic decision is often needed. To avoid a marked perturbation in prevalence without justification, the work group sought a threshold for DSM-5 substance use disorders that would yield the best agreement with the prevalence of DSM-IV substance abuse and dependence disorders combined. To determine this threshold, data from general population and clinical samples were used to compute prevalences and agreement between DSM-5 substance use disorders and DSM-IV dependence or abuse,indoor plant table examining thresholds of two or more to four or more DSM-5 criteria . As shown, prevalence was very similar, and agreement appeared maximized with the threshold of two or more criteria, so it was selected.

Another recent large independently conducted study further supported this threshold . Concerns that the threshold of two or more criteria is too low have been expressed in the professional and lay press , at presentations, and on the DSM-5 web site . These understandable concerns were weighed against the competing need to identify all cases meriting intervention, including milder cases, for example, those presenting in primary care. Table 3 shows that a concern that “millions more” would be diagnosed with the DSM-5 threshold is unfounded if DSM-5 substance use disorder criteria are assessed and decision rules are followed . Additional concerns about the threshold should be addressed by indicators of severity, which clearly indicate that cases vary in severity. An important exception to making a diagnosis of DSM-5 substance use disorder with two criteria pertains to the supervised use of psychoactive substances for medical purposes, including stimulants, cocaine, opioids, nitrous oxide, sedative-hypnotic/anxiolytic drugs, and cannabis in some jurisdictions . These substances can produce tolerance and withdrawal as normal physiological adaptations when used appropriately for supervised medical purposes. With a threshold of two or more criteria, these criteria could lead to invalid substance use disorder diagnoses even with no other criteria met. Under these conditions, tolerance and withdrawal in the absence of other criteria do not indicate substance use disorders and should not be diagnosed as such. DECISION: Set the diagnostic threshold for DSM-5 substance use disorders at two or more criteria.In DSM-IV, six course specifiers for dependence were provided. Four of these pertained to the time frame and completeness of remission, and two pertained to extenuating circumstances. In DSM-IV, the specifiers for time frame and completeness of remission were complex and little used. To simplify, the work group eliminated partial remission and divided the time frame into two categories, early and sustained. Early remission indicates a period $3 months but ,12 months without meeting DSM-5 substance use disorders criteria other than craving. Three months was selected because data indicated better outcomes for those retained in treatment at least this long . Sustained remission indicates a period lasting $12 months without meeting DSM-5 substance use disorders criteria other than craving. Craving is an exception because it can persist long into remission . The work group noted that many clinical studies define remission and relapse in terms of substance use per se, not in terms of DSM criteria. The work group did not do this in order to remain consistent with DSM-IV criteria, and because the criteria focus on substance-related difficulties, not the extent of use, for the reasons discussed in the section on adding criteria. In addition, a lack of consensus on the level of use associated with a good outcome complicates substance use as a course specifier for the disorder. The extenuating circumstance “in a controlled environment” was unchanged from DSM-IV. DSM-IV also included “on agonist therapy” . To update this category, DSM-5 replaced it with “on maintenance therapy” and provided specific examples. DECISION: Define early remission as $3 to ,12 months without meeting substance use disorders criteria and sustained remission as $12 months without meeting substance use disorders criteria . Update the maintenance therapy category with examples of agonists , antagonists , and tobacco cessation medication .Substance use and other mental disorders frequently co-occur, complicating diagnosis because many symptoms are criteria for intoxication, withdrawal syndrome, or other mental disorders. Before DSM-IV, the non-standardized substance-induced mental disorder criteria had poor reliability and validity. DSM-IV improved this via standardized guidelines to differentiate between “primary” and “substance-induced” mental disorders. In DSM-IV, primary mental disorders were diagnosed if they began prior to substance use or if they persisted for more than 4 weeks after cessation of acute withdrawal or severe intoxication. DSM-IV substance induced mental disorders were defined as occurring during periods of substance intoxication or withdrawal or remitting within 4 weeks thereafter. The symptoms listed for both the relevant disorder and for substance intoxication or withdrawal were counted toward the substance-induced mental disorder only if they exceeded the expected severity of intoxication or withdrawal.

Given the constellation of elevated risk-taking and inferior executive functioning, marijuana using teens may be at greater risk than non-users for antisocial and safety risk behaviors, thus increasing the possibility of negative personal, social, legal, or occupational consequences .One limitation of the present study was that, given the intercorrelations between various substances of abuse, it was not possible to determine whether elevated risk-taking is a direct consequence of marijuana or any other substance use. Further, elevated risk taking may predate substance use. However, one might speculate that substance use exacerbates a premorbid tendency toward risk taking, placing the user at greater risk for harmful consequences . Because we studied a community sample of marijuana users , the differences between the non-using controls and marijuana users may be attenuated relative to clinical samples of marijuana users. We also acknowledge that, given the number of comparisons made, the risk of type-I error is increased. Given the sample size, we were not able to examine the presence of gender differences, and this is an area for future research.In addition, we used a food reward because the participants started the study when they were less than 18 years old, and we had to adjust reimbursement for study participation to protect this initially underage sample from possible coercion. Although Gonzalez et al. , as well as the originators of the BART task used monetary rewards for BART performance , the current sample had a higher average number of pumps , suggesting that a food reward was a sufficient motivator in this sample. This study used a version of the BART that required manual pumps for each balloon and did not provide feedback after each trial . According to Pleskac et al., this manual BART may be biased due to psychomotor demands . They further explained that the average adjusted pumps score is biased because it excludes responses that ended in an explosion ; therefore, it is an underestimate of the number of pumps the participant would have completed if the balloon had not popped. Given that we are examining the risky behavior that would lead to increased pumps and popped balloons, the average adjusted pumps may not be the optimal estimate of risky behavior. This may partially explain why marijuana users and non-using controls did not differ on this score. A newer automatic BART avoids biases by informing participants of the optimal number of pumps ,indoor garden table allowing them to numerically input pumps , rather than tapping the space bar 85 times, and providing trial-by-trial feedback . Future studies should consider the automated BART to maximize behavioral variability. Additionally, we excluded recent users to reduce residual effects of substance use; however, it is possible that cannabis users who did not complete the abstinence protocol may have produced a different pattern of results. Thus, risk-taking behavior may be examined over the first few weeks of abstinence to determine how behavior changes when substance use is stopped.

We were not able to examine the precise role of various substances on BART performance; therefore, the role of alcohol and other drug use in risk-taking should be further explored. Future studies may also examine physiological measures of marijuana levels prior to and throughout the abstinence period. Given that self-reported externalizing behavior was not correlated with BART performance, we did not pursue this variable further; however, future studies may consider the role of externalizing behavior on risk-taking and BART performance.Marijuana and other substance use during adolescence and young adulthood is concerning because this is a critical time of continuing brain development . The primary structure involved in executive functions and impulse control is one of the last cortical structures to mature . A review by Gowin et al. suggests that individuals with substance use disorders show alterations in the prefrontal cortex and in adjacent areas involved in executive functions and risk/reward processing , and these alterations have been associated with greater risk-taking on behavioral measures and elevated levels of substance use. Our finding of elevated risk-taking among marijuana users is in agreement with their hypothesis that substance users have impaired risk processing that may result from under-activation of areas responsible for evaluating risks and/or an over-activation of reward processing centers . Marijuana users’ poorer executive function , while not correlated with the current measure of risk-taking, may reflect a weakness in flexibility of thinking that could also lead to deficiencies in effectively integrating and organizing information. In their review of prefrontal cortex function and addiction, George and Koob described the prefrontal cortex as highly modulated with a variety of subsystems, and a dysfunction of any of these subsystems could explain the individual differences in self-regulation and vulnerabilities to substance use and/or addiction. Consistent with Romer et al. , our findings also suggest that risk-taking is not always associated with executive dysfunction, and that there may not always be a linear relationship between the various executive cognitive functions and more emotionally driven risk or reward processing. In light of the current and previous findings, clinicians should consider that dysfunction within one or more prefrontal executive subsystems may be responsible for behavior leading to or resulting from problematic substance use, and that risk-taking may not necessarily imply deficient executive functioning. While some risk-taking in adolescence is important as youth evolve into independent adults, continued research on the neurobehavioral mechanisms for maladaptive risk-taking can help us to understand why some youth progress to regular substance use or develop substance use disorders.

Tobacco dependence is prevalent among individuals in Medication Assisted Treatment for opioid use disorder. Methadone maintenance patients have been the most extensively studied, and between 84% and 94% of them report they are current smokers.One group reported a 90% current smoking rate in a sample that included patients receiving methadone or buprenorphine MAT for opioid use disorder.Two groups compared the smoking status of patients with opioid use disorder receiving either methadone or buprenorphine; both found similar rates in both groups, with over 90% of the patients reporting current smoking.Most patients in treatment for opioid use disorder have lower educational and socioeconomic status than the general population, and higher smoking rates are associated with lower status.Opioid administration may make smoking cessation difficult, as increases in both methadone dose and buprenorphine dose are related to increased smoking.Stress is related to cigarette smoking,and individuals with opioid use disorders often lead stressful lives.Nevertheless, 44–80% of methadone maintenance clients report wanting to quit smoking cigarettes.Randomized controlled trials of treatment for cigarette smoking in patients receiving MAT for opioid use disorder have been reported. The earliest study compared cognitive–behavioral therapy alone to CBT plus a 20% methadone dose increase .Post treatment cigarette abstinence rates were 0 in the dose increase plus CBT condition and 18% in the CBT alone condition. At follow-up, one participant in the control condition was abstinent from cigarettes and none in the experimental condition. In a second study,participants received 12 weeks of nicotine replacement therapy and were assigned to one of four conditions: NRT-only, relapse prevention + NRT, contingency management + NRT, or relapse prevention + contingency management + NRT. During treatment, contingency management participants showed higher abstinence rates than those who did not receive contingency management. At 6- and 12-month follow-up visits, there were no differences between conditions. Sigmon et al. found that extending contingent reinforcement for abstinence increased extended abstinence rate over non-contingent reinforcement.Stein et al. randomized 383 patients to either advice only or an experimental condition.Abstinence rates did not differ between conditions at either 3 months or 6 months . A fourth study recruited 225 cigarette smokers from methadone maintenance and other drug and alcohol treatment clinics.Participants were randomly assigned to 12 weeks of CBT+NRT or to treatment as usual. Smoking abstinence rates were 10–11% during the five-week treatment period in the CBT+NRT condition, and “negligible” in the control condition. At 13- and 26-week follow-up, differences between the two conditions were non-significant and ranged from a low of 0 in the control condition at week 13 to a high of 5.7% in the experimental condition at week 26.

Effects of nonnicotinic pharmacotherapy on cigarette abstinence in patients receiving MAT for opioid use disorder have been studied. Efficacy of 6 months of varenicline treatment compared to placebo and to combined NRT did not indicate significant differences, with low rates in all conditions . Nahvi et al. found differences favoring varenicline over placebo at 12 weeks in methadone maintenance clients, although rates were low and differences were not maintained after drug treatment ended.In the combined data from two studies, Sigmon et al. found no effect for bupropion treatment.In summary, cigarette smoking rates in patients receiving MAT for opioid use disorder are high, although these patients report a desire to quit smoking. Interventions generally considered effective in other populations have not been successful in effecting long-term abstinence in patients receiving MAT for opioid use disorder, and abstinence rates are low. In the current study, we compared an extended innovative system intervention with a standard treatment control to increase cigarette smoking abstinence in buprenorphine treatment patients. The E-ISI had two components modeled after a similar intervention used successfully in a study of smokers in treatment for depression.It included the Expert System intervention, a motivational tool that is designed to intervene with individuals who may not be willing to make the commitment to quit smoking cigarettes.In the current study, we offered an extended, intensive treatment that provided extended NRT,grow rack as well as the opportunity to receive varenicline, and an extended cognitive behavioral intervention . The E-CBT has produced high and stable long-term abstinence rates in three treatment studies in the general population.The study was conducted in the Integrated Buprenorphine Intervention Service operated under the San Francisco Department of Public Health . All IBIS patients received their maintenance drug through a single central pharmacy. The study was approved by the University of California San Francisco Institutional Review Board and written informed consent obtained after verification of study eligibility. Clinic data indicated that 83% of the IBIS patients smoked cigarettes. To be eligible for services through IBIS, patients must have been 18 years of age or older, have had a diagnosis of opioid use disorder, resided in San Francisco City or County, and be eligible for treatment through the SFDPH system of health care. Patients dependent on benzodiazepines or alcohol, who had an uncontrolled medical or psychiatric condition, who had a pain syndrome requiring opioid analgesics, or who were pregnant or planning to become pregnant were treated elsewhere in the SFDPH system. Potential participants needed to have smoked ≥5 CPD for the last week, and, in order to insure a degree of stability, to have been in IBIS for at least 3 months. They did not need to want to quit smoking. Patients with contraindications for NRT were excluded. Patients with a history of Schizophrenia or Bipolar Disorder in their medical record, or diagnosed with these disorders on the Mini International Neuropsychiatric Inventory,were not eligible. Potential participants with current major depressive disorder, or who reported a suicide attempt within the last year, were not eligible to receive varenicline but were eligible for NRT and E-CBT. Participants were recruited via flyers at the clinic or were approached by the research staff to solicit participation. Research staff routinely reviewed medical records of patients who had been in treatment at IBIS for at least 3 months,and approached those patients. All participants had clearance from IBIS staff to participate.At each assessment, participants reported CPD and were queried about other smoking treatments used, if any. An expired-air carbon monoxide sample was obtained and a urine sample for anatabine/anabasine assays.At the follow-up assessments, participants were coded as abstinent if they reported not smoking within the past 7 days, had expired CO levels <5,and anatabine/anabasine levels <2.The primary outcome variables were 7-day self-reported cigarette abstinence biochemically verified by CO and anatabine and anabasine assays at months 12 and 18. A questionnaire with demographic, smoking history, and smoking behavior questions was also administered at baseline. At all assessments, we also administered the Profile of Mood States,the Fagerström Test for Cigarette Dependence ,the Medical Outcomes Scale, Short-Form ,the Drug and Alcohol severity and Psychiatric severity scales of the Addiction Severity Index,the Thoughts About Abstinence Questionnaire,the Minnesota Nicotine Withdrawal Scale,a questionnaire that assessed Stages of Change,and questions about life-time and 30-day cannabis use that are part of the Addiction Severity Index .

The dispensary provided electrically heated cannabis flower vaporizers and dab rigs for use.Smoking of cannabis and tobacco were not permitted.We monitored PM2.5 in the dispensary for 38 days and 16 hours.During business hours, the average PM2.5 concentration was 84 µg/m3,with a standard deviation of ± 124 µg/m3 , an inter quartile range of 16-111 µg/m3 and a median of 47 µg/m3.When the business was closed, the average PM2.5 concentration was 3 ± 7 µg/m3, the IQR of 1-4 µg/m3 and the median was 2 µg/m3.When examined in two-hour intervals, the median PM2.5concentration was highest between 5:00 and 7:00 PM, at 76 µg/m3.The average PM2.5 concentration outdoors was 6 ± 4 µg/m3 during business hours and 6 ± 5 µg/m3 when the business was closed.The dispensary gravimetric data yielded a photometer calibration factor of 0.57.Our data show a clear association between the consumption of cannabis and elevated PM2.5 concentrations in the dispensary.The average PM2.5 concentration when the business was open was 28 times higher than when the business was closed, the median concentration was 23.5 times higher and peak daily particle concentrations corresponded with the busiest hours.The PM2.5 concentrations in this cannabis dispensary are similar to those observed in indoor spaces where smoking is permitted.These findings are some of the first field measurements of PM2.5 emissions from cannabis flower vaporizers and dabbing of cannabis concentrates.In a space with similar ventilation and consumption activity, it is likely that dabbing and vaporizing would create lower PM2.5 concentrations than smoking, because smoking decomposes the cannabis more completely, creating more side stream smoke.Most of our data are from TSI Sidepak laser photometers, which are factory-calibrated to NIST standard A1 test dust.To deliver accurate measurements of any other aerosol, a specific calibration factor is required.As of this writing, there are no published calibration factors for aerosols created by vaporizing cannabis flower or dabbing cannabis grow set up concentrates and little is known of their properties.

The gravimetric data from the dispensary yielded a calibration factor of 0.57, but variation was high because there were only seven daylong samples.We therefore used the well-validated calibration factor for secondhand cigarette smoke to adjust our data.It is unlikely to yield inflated values and if the true calibration factor is higher, that does not affect our finding that on-site consumption was associated with strong and consistent increases in PM2.5.As of February, 2022, over 78 million Americans are known to have been infected with COVID-19, and over 925,000 have died . Many remain unvaccinated, and highly contagious variants of the virus have appeared since the start of the pandemic, so greater knowledge of risk factors for COVID-19 infection and poor outcomes remains critical. Demographic risk factors for COVID-19 infection include non-Hispanic Black or Hispanic race/ethnicity ; males may also be at higher risk of infection . Risk factors for hospitalization or death include older age , obesity , non-Hispanic Black or Hispanic race/ethnicity , male sex , and medical conditions such as diabetes, neoplasms, cardiovascular, respiratory and kidney disease . Substance use disorder is also a potential risk factor for COVID-19 infection and poor outcome. SUD may increase risk for infection due to homelessness, or to SUD-associated decision-making traits that could increase COVID-19 exposure through preference for in-person interactions over maintaining social distance. Among those infected, SUD could increase the risk for poor outcomes through effects on the immune, respiratory, or cardiovascular systems , indirect effects of medical conditions commonly co-occurring with SUD , or delays in seeking medical care . To study the relationship of SUDs and COVID-19 outcomes, large-scale electronic health record databases are needed to evaluate if SUD is associated with COVID-19 outcomes over and above the potentially confounding effects of other medical conditions that are associated with both SUD and COVID-19 outcomes. To our knowledge, five US studies have examined SUD and COVID- 19 in EHR databases . Two studies addressed infection risk. One found that SUD was associated with a positive COVID-19 test after adjustment for demographics, but did not further adjust for medical conditions. The other found an unadjusted association of SUD with COVID-19+, but not after adjustment for medical illnesses that could confound the relationship by increasing the likelihood of COVID-19 testing among those in treatment for other conditions. Four studies examined SUD and hospitalization among COVID-19+ patients . Across several studies, SUD was associated with hospitalization before and after adjustment for medical conditions. Three of these studies also found associations of SUD with aspects of intensive care, e.g., ICU admissions.

These studies also addressed mortality , as did a study of social, behavioral and substance use risk factors for mortality in COVID-19+ veterans . In the study without adjustment for medical conditions, SUD predicted increased COVID-19 mortality . Three studies found that SUD increased the unadjusted but not adjusted risk of COVID-19 mortality . In the study of veterans , substance use was inversely related to COVID-19 mortality, although the association was attenuated after adjusting for medical conditions . Therefore, findings were mixed. The range in SUD prevalence in these studies did not appear to account for their findings. The Veterans Health Administration is the largest U.S. integrated healthcare system, treating approximately 5.5 million veterans each year. EHR data are aggregated across the entire VHA system. Using a retrospective cohort design, we investigated relationships of SUD to COVID-19+ and COVID-19 outcomes among veterans treated at the VHA in 2019, including whether SUD was related to COVID-19+ up to 11/1/2020, and among the COVID-19+ sub-sample, whether SUD was related to hospitalization, ICU admission, and death. SDR COVID-19 diagnoses came from two sources. One was a record in the EHR of a VHA SARS-CoV-2 real-time reverse transcription polymerase chain reaction or antigen test. The other source was electronic chart notes. To provide COVID-19 testing as widely as possible to VHA-eligible veterans , the VHA covered the costs of COVID-19 rRT-PCR or antigen test conducted by outside providers. Coverage of community care generally requires advance VHA authorization, resulting in a chart note in the electronic medical record. “Natural language processing of these notes was used to detect positive COVID-19 tests conducted outside the VHA . Natural language processing involves computerized analysis of human language to extract information, for example, from text notations in electronic records. Note that natural language processing is a widely-used strategy in medical research . The SDR thus included positive and negative tests conducted at the VHA, and information about positive outside tests. To create a consistent time frame, an index date variable was created to indicate the date of the first positive COVID-19 test or inpatient admission date closest to the first test within the 15 days prior to the test. We included COVID-19 tests from 02/20/20–11/01/2020.Substance disorders examined separately included cannabis , cocaine , opioids , stimulants and sedatives . We also created an ‘any SUD’ variable by combining these, two additional categories too rare to examine separately , and ‘other’ SUD , used when the specific substance is unknown. ICD-10-CM codes indicating remission were excluded. For exploratory post hoc analyses of the mortality results, we created 2 additional SUD exposure variables. First, since SUD treatment could mitigate SUD effects on mortality through increased clinical monitoring and hospitalization at an early stage of COVID-19 illness that prevented later mortality , we created a 3-level SUD variable incorporating SUD treatment: no SUD ; any-SUD without SUD specialty treatment; and any-SUD with SUD specialty treatment. Treatment was indicated by any 2019 visits to a VHA SUD specialty care setting. Second, since SUD could affect mortality only in patients with severe SUD and having multiple poly substance problems is an indicator of severity , we created a 3-level variable indicating no SUD , 1 SUD, or ≥ 2 SUDs. ICD-10-CM medical conditions associated with poor COVID-19 prognosis included cardiovascular disease , respiratory disease , diabetes , HIV , neoplasms , and kidney disease . Obesity is not associated with SUD but is associated with COVID-19 mortality and is prevalent in VHA patients , so we included body mass index , calculated from height and weight measured at the date closest to the index date. We also included ICD-10-CM mental disorders, as these may be related to SUD and to COVID-19 infection or poor prognosis , including bipolar , depressive , psychotic , and post traumatic stress disorders . Mental disorders in remission were excluded. Heavy drinking can impair lung functioning and increase risk for respiratory disease ,grow rack systems while smoking elevates risk for poor COVID-19 outcomes . Because both are associated with SUD,we also controlled for alcohol and nicotine use disorders . All diagnoses were from the 2019 EHR. Demographic characteristics included sex , age and race/ethnicity . The ‘other’ category included American Indian/Alaskan Natives and patients with multiple race/ethnicities.

Multi-variable logistic regression modeled the association of 2019 any SUD diagnoses with 2020 COVID-19 outcomes, producing odds ratios , adjusted odds ratios and 95% confidence intervals . We also modeled the association between 2019 substance-specific disorders and 2020 COVID-19 outcomes. When analyzing the substance specific disorders, to account for multiple testing and potential increased risk of Type I error , we used an alpha level of 1%, calculating and reporting 99% confidence intervals. We evaluated any SUD and substance-specific disorders in separate models. We conducted analyses in 4 stages. The first was unadjusted for covariates, providing simple associations. The second adjusted for demographics . The third further adjusted for medical conditions . The fourth further adjusted for mental, alcohol and nicotine use disorders to identify the unique effect of these additional, fully-legal SUDs on COVID-19 outcomes. We also conducted 2 sets of post hoc exploratory analyses of mortality of mortality. In one, we replaced the binary any-SUD exposure variable with the 3-level variable incorporating information on SUD treatment. In the other, we replaced the binary any-SUD exposure variable with the 3-level SUD severity variable. These models were run using the same four adjustment stages described above. In over 5.5 million veterans treated by the VHA in 2019, we investigated the relationship of substance use disorders to testing positive for COVID-19, and among those positive, the association of SUD with hospitalization, ICU admission, and mortality. Analyses were conducted without adjustment, and with adjustment for demographic characteristics and medical conditions that increase the risk of poor COVID-19 outcome.Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to an ICU, and 5.84% died. SUDs were robustly associated with increased odds of hospitalization regardless of adjustments. After adjustment, no SUDs were associated with ICU admission. In contrast, in unadjusted results, any SUD and cannabis, cocaine, and stimulant disorders were inversely associated with mortality. However, associations became attenuated after adjustment and were no longer significant. Previous studies of SUD and COVID-19 infection yielded conflicting results: one study without adjustment for medical conditions found a strong positive association , while another study with such adjustments found no association . While our results varied somewhat depending on the adjustments made in each model, in the final fully-adjusted models, all substance-specific SUDs were positively associated with COVID-19+ except cannabis use disorder. In studies of this relationship using EHR data, results may depend on factors influencing whether a COVID-19 test is conducted. VHA patients with SUDs other than cannabis use disorder may have been more likely to be tested because many of them were in SUD treatment, potentially providing closer monitoring of their medical status than others. Such monitoring and testing could help detect disease early and prevent further spread, but it complicates interpretation of findings on risk factors for infection. Since 2020, the VHA has greatly expanded its COVID-19 testing capacity . Future studies should re-examine the relationship of SUD to infection using data from a broader sector of the VHA patient population. Among VHA patients who tested positive for COVID-19, any SUD and all substance-specific SUDs were robustly associated with inpatient hospitalization before and after adjustments. These findings are consistent with several other studies . Taken as a whole, SUD appears to be associated with more severe COVID-19 requiring hospitalization even accounting for patients’ co-existing medical and psychiatric conditions. This is also consistent with our findings on ICU admission and with other studies showing that SUD is associated with greater intensity of treatment among those hospitalized .

Social factors, systemic factors, and familial discrimination contribute to increased risk of poverty, housing insecurity, health disparities and social isolation especially among the LGBTQ community’s most vulnerable.More LGBTQ adults have no health insurance compared to 12% of non-LGBTQ adults with greater disparities in LGBTQ of color , transgender adults , and transgender adults of color.Overall, LGBTQ populations are at greater risk of COVID-19 exposures and at risk of both economic and health complications than non-LGBTQ people.Moreover, a study of 2,732 cisgender gay and other men who have sex with men from 103 countries reported loss of employment due to the pandemic , inability to receive COVID-19 related financial benefits and reduction or cutting of meals completely during the pandemic.Another study of 1,051 MSM found that 47.1% experienced problems with buying food, 17.3% with paying for rent, 32.4% with decreased work hours, and 19.1% reported losing a job due to COVID-19.Overall drug use around the world has risen from 210 million in 2009 to 269 million in 2018 with 192 million people reporting cannabis use.17 Within the United States, about 46% of the US population reported past year cannabis use in 2019.This includes about 12 million individuals 18 to 25 years of age and 33 million adults 26 or older reporting past year cannabis use in 2019.Cannabis is composed of two main components, Δ9-tetrahydrocannabinol , the psychoactive element, and cannabidiol , the non-psychoactive element.Cannabis is predominantly dried flower that can be smoked as cigarettes , or with pipes, water pipes , cannabis vaporizers , e-cigarettes for cannabis extracts, and rigs for cannabis extracts.Other modes of cannabis use include edibles, topicals, and sprays.However, smoked cannabis is the most popular mode of use in the US.Salles et al.found people smoked 13 joints per week on average prior to the pandemic compared to 9.75 joints during the pandemic.However, this study was limited by small sample size and did not encompass all forms of cannabis use behaviors.On the other hand, Brenneke et al.and Assaf et al.reported slight increases in cannabis use frequency during the pandemic, but overall use was comparable to pre-pandemic levels.It was also reported that mode of cannabis use stayed the same pre-pandemic and during the pandemic with most respondents reporting a method for cannabis inhalation , vaporizing plant, wax/dab, and/or vaping oil/concentrates.Data for this dissertation was collected by the COVID-19 Cannabis Survey.The COVID- 19 Cannabis Survey, supported by US National Institute on Drug Abuse and Semel Charitable Foundation, is an anonymous cross-sectional web-based survey of respondents who use cannabis and cannabidiol in the United States.Participants were included in the survey if they were 18 years of age or older, living within the United States, and indicated non-medical drying rack cannabis, cannabis for medical use, or CBD use in the last 12 months.The survey was sent out in August – September 2020 where 2,000 respondents were recruited.After exclusion criteria were applied, 1,883 respondents were eligible and completed the survey.Recruitment was based on a convenience sample of respondents on internet-based platforms including Reddit, Bluelight , Craigslist, and Twitter.

The study advertisement stated the following: “Have you used cannabis or CBD in the past year? Participate in a UCLA survey ”.Duplicate responses or “ballot stuffing” was restricted by limiting one response for each unique internet protocol address.The survey took approximately 20 to 30 minutes to complete, and participants were remunerated $5 for completing the survey.In this single survey, cannabis and CBD behaviors were assessed at two, 3-month time periods.Participants were initially asked about their non-medical/medical cannabis and CBD use behaviors three months before the COVID-19 pandemic.A reference date of January to mid-March 2020 was deemed as 3 months before the pandemic; hereby noted as before the pandemic.Following questions before the COVID-19 pandemic, respondents were asked about non-medical/medical cannabis and CBD use behaviors in the past three months at the time of the survey.These questions were a proxy for non-medical/medical cannabis and CBD use behaviors during the COVID-19 pandemic; hereby noted as during the pandemic.Questions on non-medical cannabis, medical cannabis, and CBD were asked separately.Data from this survey include cannabis and CBD frequency of use, reasons for changing cannabis use during the COVID-19 pandemic, sharing behaviors of prepared cannabis and cannabis-related paraphernalia, education, sex, age, sexual orientation, and geographical location.This study received institutional review board approval from the University of California, Los Angeles.All respondents received online informed consent.This study examines changes in reported sharing of prepared cannabis and cannabis related paraphernalia during the COVID-19 pandemic – risks of concern for increasing transmission of this respiratory infection.Our study found an overall decrease in sharing of prepared cannabis and cannabis-related paraphernalia during the pandemic across all levels of sharing with the largest decreases observed among those reporting a higher frequency of sharing before the pandemic.Moreover, sharing of cannabis between the two time periods varied differently by sex and frequency of cannabis use before the pandemic.Those who self-identified as female had a larger percent change in no sharing of cannabis during the pandemic compared to males.Those who self reported ≥weekly cannabis use before the pandemic also had a larger percent change in no sharing during the pandemic compared to those who used cannabis ≤ monthly.Moreover, a smaller proportion of those reporting no sharing obtained their cannabis from a friends or family most of the time compared to those reporting any sharing while more respondents reporting no sharing indicated increased cannabis use because of time at home compared to 69% of those reporting any sharing.Thus, we hypothesize that these reductions in sharing of prepared cannabis and cannabisrelated paraphernalia may have served as a risk mitigation strategy for COVID-19 infection, may have been a consequence of not seeing others, or both.

Sharing of paraphernalia for cannabis, tobacco, and crack cocaine use has previously been demonstrated as a risk factor for respiratory infections such as COVID-19.For instance, a cluster of tuberculosis cases has been linked to sharing of a cannabis pipe.Sharing of tobacco water pipes has also been shown to increase the risk of respiratory bacterial and viral infections.In the early stage of the pandemic, the World Health Organization and other researchers highlighted potential risks with COVID-19 transmission through sharing of tobacco and e-cigarette smoking and recommended not to share.Likewise, implementation of safe crack use kits to reduce sharing of smoking paraphernalia for COVID-19 prevention was advocated in the United Kingdom.To the best of our knowledge, no risk mitigation strategies or official public health messaging toward sharing of prepared cannabis and cannabis-related paraphernalia during the COVID-19 pandemic have been suggested in the United States.However, education around sharing smoking paraphernalia and distribution of safe crack use kits have previously been shown to reduce sharing.Thus, public health messaging and education toward sharing practices of prepared cannabis and cannabis-related paraphernalia like “Puff, Puff, Don’t Pass,” may be useful for risk mitigation of COVID-19 infection especially during future COVID-19 peaks.Messaging may also be useful with other respiratory infections such as during the cold and influenza season.Additionally, this study identified variables associated with sharing of cannabis during the pandemic.First, those reporting ≥weekly cannabis use during the pandemic had lower odds of sharing compared to those who used cannabis ≤ monthly after adjusting for age, sex, sexual orientation, education, and state’s cannabis regulation status.This may be the case as more respondents reporting ≥weekly cannabis use reported increasing their cannabis use because of time at home and social distancing measures compared to 46% and 38% of those reporting ≤ monthly respectively.Second, as age increased, the odds of any sharing during the pandemic decreased.Thus, one hypothesis is that older individuals may not share as a risk mitigation for COVID-19 given increased risk of COVID-19 severity among older groups.On the other hand, younger adults may have a lower risk perception toward COVID-19 and thus lower acceptance of COVID-19 mitigation strategies.Moreover, US Census region, was associated with increased odds of sharing prepared cannabis and cannabis-related behaviors during the pandemic.Specifically, those from the Midwest, Northeast, and South had higher odds of sharing compared to those from the West.This may be the case given differences in COVID-19 state policy and public health messaging between these regions.Overall, there were 1,112 respondents who reported non-medical cannabis use in the past twelve months.Of which, 340 self-identified as sexual minority compared to 752 as non-sexual minority.SM individuals were primarily Hispanic/Latino/a/x and non-Hispanic White compared to non-SM individuals who were primarily nonHispanic White.

A larger proportion of SM individuals had a high school or less than high school degree compared to non-SM individuals.Finally, there were more SM individuals from the Northeast compared to non-SM and less SM individuals from the South compared to 34.31% of non-SM individuals.During the pandemic, 43.82% of SM individuals reported daily/weekly non-medical cannabis use in the past three months compared to 62.10% of non-SM individuals.Meanwhile, more SM individuals reported any sharing of prepared cannabis or cannabis-related paraphernalia during the pandemic compared to 72.75% of non-SM individuals.Most SM and non-SM respondents reported an inhalation method as the most frequent mode of cannabis use during the pandemic.Tobacco, alcohol,commercial greenhouse supplies and other substance use was reported by 60.00%, 47.65%, and 36.47% SM respondents respectively compared to 57.05%, 52.13%, and 27.39% among non-SM respectively.Changes in non-medical cannabis frequency of use was similar for SM and non-SM as 24.71% of SM and 24.87% of non-SM had increased use.Changes in cannabis frequency of use was similar among SM and non-SM women and SM and non-SM men.However, there was a difference in changes of sharing among SM compared to non-SM where 26.91% of SM had decreased sharing compared to 34.80% of non-SM respondents.This difference was greater among men where 19.66% of SM had decreased sharing compared to 36.11% of non-SM men.Frequency of cannabis use during the pandemic was associated with sexual orientation, age, education, tobacco use, and alcohol use.The odds of daily/weekly cannabis use among SM respondents was 0.49 times that of non-SM respondents in the unadjusted model.After adjusting for age, education, tobacco use, and alcohol use, the odds of daily/weekly cannabis use among SM respondents was 0.55 times that of non-SM respondents.Ten year increases in age and alcohol use were positively associated with daily/weekly cannabis use during the pandemic.However, tobacco use and education were negatively associated with daily/weekly cannabis use during the pandemic.Among men, the odds of daily/weekly cannabis use during the pandemic was 0.39 times that for SM respondents compared to non-SM men after adjusting for age, education, tobacco use, and alcohol use.Sharing of prepared cannabis and cannabis-related paraphernalia during the pandemic was associated with sexual orientation, age, education, and tobacco use.In the unadjusted model, the odds of any sharing were greater for SM individuals compared to non-SM individuals.After adjusting for age, education, and tobacco use, the odds of any sharing during the pandemic among SM respondents was 1.60 compared to non-SM respondents.Ten-year increases in age and education were negatively associated with any sharing whereas tobacco use was positively associated with any sharing during the pandemic.Among men, the odds of any sharing were 2.19 times higher for SM men compared to non-SM men after conditioning on age, education, and tobacco use.This study presents findings on frequency of non-medical cannabis use and sharing of prepared cannabis and cannabis-related paraphernalia during the COVID-19 pandemic among SM and non-SM individuals.First, SM and non-SM individuals reported similar increases in frequency of cannabis use during the pandemic , a finding comparable to Slemon et al.Nevertheless, SM respondents were less likely to report daily/weekly cannabis use during the pandemic compared to non-SM individuals.The point estimate was even larger for SM men compared to non-SM men.Previous literature has highlighted that SM individuals are more likely to use cannabis and to have cannabis use disorder compared to their non-SM counterparts.However, studies reporting this have only looked at any cannabis use with minimal literature examining the characteristics of cannabis use behaviors, particularly among those who report some level of cannabis use.Thus, the findings from this study extends the context in the literature of more specific use behaviors by looking at frequency of use among those who use cannabis.This study suggests that among those who use cannabis, non-SM may use it at higher frequencies than SM.On the other hand, use of tobacco and other substances were higher among SM respondents in our study population which may substitute frequency of cannabis use.Second, SM respondents were more likely to report sharing of cannabis during the pandemic compared to non-SM respondents with higher odds for SM men compared to non-SM men.