Hemp Growing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We used linear mixed-effects models to assess changes in continuous outcomes over time: post-bronchodilator FEV1, post-bronchodilator FVC, SGRQ total score, CAT score, and HRCT metrics. Linear mixed-effects models, specifically proportional odds models, were used to assess changes in respiratory symptoms over time. In assessing whether marijuana use among tobacco smoking participants without COPD at baseline increased the risk of subsequent development of COPD, the primary outcome was time to development of airflow obstruction, defined by a post-bronchodilator FEV1/FVC<0.70.We used zero-inflated negative binomial models to compare the rate of exacerbations between CMSs, FMSs, and NMSs. Exacerbations were classified as moderate , severe , and total . To assess dose response relationships, the same models were used with the primary predictor of interest being categorical joint-year history at baseline.A consort diagram describing the derivation of the study cohort is shown in Figure 1. At enrollment, CMSs, when compared with NMSs, tended to be younger and more often current tobacco smokers, men, and Black .They also had fewer exacerbations during the year prior to enrollment, had a better FEV1, less frequent airflow obstruction, and less emphysema and air trapping, but had similar levels of respiratory symptoms. Similar findings were noted in comparison of FMSs with NMSs. Due to incomplete reporting, calculating the cumulative lifetime amount of marijuana use in joint years was not possible for all participants, so that the number of those classified by joint-year category is lower than that of the total analysis sample. Among those with the heaviest marijuana use , directionally similar baseline differences were noted in age, sex, the proportion of Black participants,industrial rolling racks and current tobacco-smoking status compared to those with 0 joint years, as were found in comparison between CMSs and FMSs with NMSs . The estimated rates of change in continuous outcomes by baseline marijuana-smoking status are shown in Table 3A.

While numerically higher annual rates of FEV1 and FVC decline and higher rates of worsening CAT and total SGRQ scores were found comparing CMSs with NMSs, these differences were neither clinically nor statistically significant . Similar rates of change in these parameters were found on comparison of FMSs with NMSs. Estimated rates of change in continuous outcomes between joint-year-based categories were similar across all joint-year groups and between groups . Estimated annualized FEV1 decline during follow-up by marijuana joint years stratified by former and current tobacco-smoking history were similar, irrespective of tobacco smoking status . Estimated participant-specific yearly changes in odds for worsening respiratory symptoms during follow-up compared to the baseline visit by baseline marijuana status and baseline joint years are shown in e-Table 9A and B and e-Figures 1 and 2 in the online supplement, respectively. The odds over time of more cough and sputum, but not more wheeze or dyspnea, were significantly higher in CMSs compared to FMSs or NMSs , while no significant differences were found across the different joint-year categories that included both CMSs and FMSs . Estimated yearly changes in CAT and SGRQ scores were not significantly different across marijuana-smoking status and joint-year categories as shown both in Tables 3A and B in the online supplement, respectively, and e-Tables 5 and 6 in the online supplement, respectively. Our analysis showed nominally less emphysema, air trapping, and functional small airways disease progression without statistical significance among CMSs compared to NMSs. Similarly, a comparison between NMSs, FMSs, and CMSs showed no significantly different changes in HRCT metrics, except for unadjusted increased total tissue volume loss among FMSs compared to NMSs . No difference in tissue volume loss between CMSs and NMSs was found.

Estimated rates of change in HRCT metrics were generally similar across all joint-year groups , except for a higher rate of increase in PRMf SAD on comparison of those with ≥20 joint years versus 0 joint years , with a between-group difference 0.393 when unadjusted for multiple testing . Estimated yearly rates of 1 or more total or severe exacerbations during the first 365 days or the total follow-up period by baseline marijuana-smoking status and marijuana joint years are shown in Table 4 A and B and e-Figures 3 and 4 in the online supplement. While rates of total and severe exacerbations were numerically lower among both CMSs and FMSs versus NMSs during the first follow-up year, and severe exacerbation rates were slightly higher among CMSs versus NMSs during the total follow-up period, none of these differences were statistically significant . Estimated rates of total and severe exacerbations were numerically higher among those with ≥20 versus those with 0 joint years during the first follow-up year. During the total follow-up period, rates of total exacerbations, but not severe exacerbations, were slightly higher among those with ≥20 versus those with 0 joint years. However, none of these between-group differences were statistically significant .Estimated hazard ratios for the development of COPD during follow-up by baseline marijuana-smoking status and joint years among participants without spirometric evidence of COPD at baseline are shown in Table 5 and e-Figures 5 and 6 in the online supplement. The odds of developing COPD by spirometric criteria were lower among CMSs and FMSs versus NMSs, as well as among those with ≥20 versus those with 0 joint years, although these differences were not statistically significant.The increasing prevalence of marijuana smoking among adolescents and adults,including aging adults,in the wake of a growing number of states legalizing marijuana use underscores the need to better understand the impact of marijuana use on lung health. This need is particularly evident among adult tobacco smokers in their mid- and older life who have been understudied previously.

The current analysis of the pulmonary consequences of marijuana smoking in the SPIROMICS cohort of current and former tobacco smokers with or at high risk of developing COPD is a longitudinal extension of a cross-sectional analysis of the baseline findings in the same cohort.10 While the latter cross-sectional study failed to identify deleterious effects of concomitant marijuana smoking on lung function or baseline structural radiographic abnormalities when compared with the effect of tobacco smoking alone, it could not answer the question of whether marijuana drying racks affects changes in these outcomes over 1 to several years of follow-up. In addition, the current study overlaps to some extent with a recent longitudinal analysis focused mainly on the trajectory of lung function in SPIROMICS participants limited to those with ≥3 spirometry visits.By including all those participants with ≥2, rather than only ≥3, spirometry visits at least 1 year apart, the current study has the advantage of including in the analysis larger numbers of CMSs and FMSs, most importantly of those heavy MSs with ≥20 joint years, in an effort to achieve greater statistical power in examining the influence of marijuana smoking on lung function decline. Furthermore, the current study examined changes in respiratory symptoms and HRCT metrics during follow-up that were not included in the previous report. Our study revealed trends toward higher rates of decline in post-bronchodilator FEV1 and worsening CAT and SGRQ scores among CMSs compared with NMSs and contrastingly, smaller rates of change in percentage of emphysema and functional small airways disease. However, none of these differences were statistically significant. Similarly, when we compared different categories of lifetime cumulative amounts of marijuana smoking, no significant differences were noted in rates of change in lung function, CAT or SGRQ scores, or HRCT metrics, except for an increase in PRMfSAD among the heaviest marijuana-smoking category in comparison to those with 0 joint years. It is noteworthy that significantly higher odds of worsening cough and sputum were noted among CMSs in comparison with both NMSs and FMSs, but not between FMSs and NMSs. The latter finding is consistent with previous data showing a significant reduction in symptoms of chronic bronchitis after cessation of marijuana smoking. Although some numerical differences were noted in rates of exacerbations across marijuana-use status and joint-year categories, none of the between-group differences were statistically significant. Finally, while the probability of subsequently developing COPD among tobacco smokers without COPD at baseline was lower among CMSs and FMSs compared with NMSs, as well as between the heaviest marijuana smokers versus those with no history of marijuana smoking, none of these differences reached statistical significance.

Taken together, the aforementioned data failed to demonstrate that marijuana smoking of any lifetime cumulative amount had a demonstrable effect on changes over time in clinical outcomes relevant to COPD, including respiratory symptoms, health status, HRCT metrics, or frequency of exacerbations. Our failure to find any impact of even heavy marijuana smoking on lung function decline in ever-tobacco smokers with or at risk of COPD differs substantially from the findings of Tan et al.The authors demonstrated a dose-response effect of marijuana on lung function decline in the CanCOLD study subcohort with a significantly greater rate of decline in FEV1 only among those with ≥20 joint years compared to those who never used marijuana . Surprisingly, in the same study, among those with ≥20 joint years of marijuana smoking, the rates of FEV1 decline were very similar for CMSs and FMSs, compared to NMSs. In contrast, the average rate of FEV1 decline among the heaviest former tobacco smokers was substantially lower than that of the current tobacco smokers. Since tobacco smokers with COPD have a substantial reduction in the rate of FEV1 decline after sustained smoking cessation,34 the disparate findings of Tan et al15 comparing the impact of quitting marijuana with that of quitting tobacco is surprising. The absence of a difference in the rates of decline between their current and former marijuana smoking participants, most of whom were dual smokers of marijuana and tobacco, may be a reflection of the impact of continuing tobacco smoking among those who had quit using marijuana rather than of an enduring effect of marijuana among the quitters. It is also noteworthy that the number of SPIROMICS participants who were particularly heavy marijuana smokers  was almost 3 times higher than the number of CanCOLD participants with a heavy marijuana smoking history , suggesting that our analysis of the impact of heavy marijuana use on lung function decline had greater statistical power. Finally, while the reference control group in our analysis of FEV1 decline in relation to marijuana smoking consisted of NMSs with a history of at least 20 pack years of tobacco smoking, the reference group in the analysis reported by Tan et al was comprised solely of never smokers of either substance. Thus, our aim was to examine whether marijuana smoking had an impact on the progression or development of COPD in current or former smokers of tobacco who already had COPD or were at increased risk of developing COPD, while Tan et al evaluated whether marijuana smoking led to an accelerated decline in lung function in a population of whom 43% were nonsmokers of tobacco.Our findings are also at odds with the results of another recent study by Winhusen et al.Using data from electronic health records of patients treated in an integrated health care system located in Northeast Ohio, the authors reported a significantly greater risk for COPD, defined using International Classification of Diseases, 9th and 10th revisions’ codes, among persons with a diagnosis of cannabis use disorder compared to propensity-matched controls in a subgroup of patients with a diagnosis of tobacco use disorder . These findings imply an additive effect of cannabis on top of tobacco use. However, limitations of the latter study include misclassification of COPD in the absence of spirometry data, suggested by the relatively young average age of the authors’ analysis population versus ours , as well as the absence of data on the route of cannabis administration and the intensity and duration of its use. The marked disparity of these results with ours underscores the need for additional study. The possibility of a dose–response impact of marijuana exposure is suggested by our finding of a significantly larger effect of ≥20 joint years on PRMfSAD in comparison with 0 joint years , consistent with a deleterious effect of heavy marijuana use on small airways. The latter observation is consistent with the recently reported finding in a New Zealand birth cohort at age 45 years of an association of lifetime cannabis use, adjusted for tobacco pack years, with pre-bronchodilator peripheral airways resistance and reactance using impulse oscillometry.

We also adjusted for the current use of each of these products/substances to address the potential confounding in all the models except for any use of the three. For example, we coadjusted for the use of e-cigarettes and marijuana in the model regressing the odds of using cigarettes. A jackknife method based on design-based replicate weights was used to estimate variances and significance values of regression coefficients. The same types of analyses were conducted separately for use of different types of products/substances. All analyses were implemented using SAS 9.4 . In 2018, 1.66 million California young adults, ages 18 to 25, were currently using at least one form of cigarette, e-cigarette, or marijuana: 314,000 smoked cigarettes, 682,000 used e-cigarettes, and 1.3 million used marijuana. There was no statistically significant change in cigarette use between 2017 and 2018 . In contrast, there was escalating use of e-cigarettes and marijuana. Between 2017 and 2018, current e-cigarette use climbed by 4.8% and current marijuana use rose by 4.6% among young adults. The proportion of young adults currently using any of these products/substance increased by 5.5% between 2017 and 2018 . Table 1 presents descriptive analyses of the current use of cigarettes, e-cigarettes, marijuana, and any use of the three by age, gender, race/ ethnicity, income , psychological distress, urban/ rural residence, and region of residence. Young adults aged 18–20 were smoking cigarettes at significantly lower rates than other young adults aged 21–25 . Underage use was substantial for e-cigarettes and marijuana. About 17% of underage young adults were current e-cigarette users. About 27% of underage young adults were current marijuana users. A wide and significant male–female difference was seen in e-cigarette use , vertical farming system with male e-cigarette use nearly doubled female e-cigarette use. Any use of cigarettes, e-cigarettes, or marijuana was also significantly higher for males than females.

Young adults who were white have higher rates of cigarette and e-cigarette use than those who were Latino. Approximately 27% of young adult Latino, whites, and Asians used marijuana. Only e-cigarette rates differed significantly by income: young adults with incomes at or below 200% FPL- used e-cigarettes at lower rates than young adults with incomes greater than 200% FPL. Young adults with psychological distress had higher rates of use of cigarettes, e-cigarettes, marijuana, or any use of the three.From 2017 to 2018, California saw an increase in e-cigarette and marijuana use among young adults, while cigarette smoking remained flat. Psychological distress was observed to be associated with cigarette, e-cigarette, marijuana use, or any use of the three. Using cigarettes, ecigarettes and marijuana were also found mutually correlated. California’s trends in cigarette and e-cigarette smoking are parallel to those observed nationwide . What stands out in our findings are several aspects. One is that the percentage of California young adults using marijuana increased to 28.5% from 2017 to 2018 while the national rate remained to be 22% for both years Another finding is that in 2018, those young adults who were using each of these products/substance also significantly increased the odds of using cigarettes, e-cigarettes, or marijuana than their counterparts. Importantly, we found that severe psychological distress was significantly associated with the use of cigarettes and marijuana. Although many tobaccos and recreational cannabis use policies restrict sales to young adults under age 21, underage use is considerable– about half of the young adults were current e-cigarette users and more than half a million or 40% of current marijuana users were underage. Our findings that cigarette smoking rates remained flat between 2017 and 2018, but e-cigarette smoking and marijuana increased could be possibly explained by the current policy changes related to the cigarette tax increase and recreational marijuana legalization in California.

The finding that the smoking rates would remain flat is expected since the CHIS 2017 data were collected after the cigarette tax increase in April 2017. Studies have found that marijuana policy could inadvertently affect cigarette and marijuana use and this spillover effect poses challenges to tobacco cessation . Similar to our findings, other studies have also shown that cannabis and e-cigarettes uses have increased among youth, and these trends will likely continue as e-cigarettes remain to gain popularity and cannabis legalization policies proliferate . Our findings that the use of tobacco is positively associated with the use of marijuana or vice versa among young adults are consistent with other studies . There are several explanations for this association. One is that tobacco and marijuana use support and reinforce the use of each other Research has shown that tobacco use is associated with initiation and dependence on other substances, such as marijuana . Longitudinal studies that examined tobacco use before marijuana use generally supported a gateway sequence and progression, in that case, people smoked tobacco first, then marijuana . Additional studies have shown a “reverse gateway effect,” that those who used marijuana were at increased risk of initiating tobacco . Another explanation for the concurrent use of cigarettes, e-cigarettes and marijuana is that tobacco and marijuana use can co-occur via the same devices for both tobacco and marijuana . Studies showed that concurrent users were more likely to use e-cigarettes and blunts to administer marijuana. Vaporizers are increasingly popular among young people. Many youths replace nicotine with marijuana in battery-powered vaporizers . Another way is through the use of “blunts,” or rolling up marijuana in a cigar or cigarillo shell. Research has shown that ’smoking’ was found to constitute a social construct within which the use of cigarettes, cigars, and blunts was somewhat interchangeable among the youth . Tobacco and marijuana, taken in combination, potentially raise the likelihood of dependence on these substances and problems associated with their use.

For example, one study of University of Florida college students who used both cigarettes and marijuana found that 65% had smoked both substances in the same hour; 31% reported they smoked tobacco to prolong and sustain the effects of marijuana, and 55% had friends who engaged in these behaviors . Our findings that psychological distress was significantly associated with smoking cigarettes or using marijuana were supported by previous studies . Studies showed that adolescents and young adults with mental health problems were at high risk for tobacco and marijuana use, compared to those without such problems . Studies also showed that affective disorders and psychological distress were more common among those who smoke than those who do not smoke and among cannabis-dependent participants . Daily cannabis use was significantly more common among persons with serious psychological distress and was increasing in this group, as well as among those without . Lower quit rates among those with serious psychological distress are one factor that could contribute to the higher prevalence of smoking in this group . A study using the 2008–2016 National Survey on Drug Use and Health showed that quit rates among individuals with past-month psychological distress were approximately half than quit rates of those without psychological distress and had not increased over the past decade . Adults with depression or psychological distress had a lower quit ratio overall,vertical farming racks but were equally or even more likely to make quit or self-regulation attempts . One study’s findings suggest an increase in psychological distress among those who smoke over time may be due to the fact that as smoking has declined, thus those with psychological distress are comprising a greater proportion of those remaining to smoke. . Given that our study is cross-sectional, the direction of the association between substance use and mental health could not be established. If substance use is an antecedent to psychological distress, our estimated effects of psychological distress on smoking cigarettes and marijuana use may be biased upward. A few longitudinal studies provide causal evidence that smoking or marijuana use increased with psychological distress. For instance, a study using longitudinal data showed that smoking uptake was associated with an increase in psychological distress . Another birth cohort study that tracks youth longitudinally from before marijuana onset also reinforced that early-onset and chronic marijuana use was associated with a greater risk of psychiatric disorders . Data from a cohort study with an 8-year follow-up in the general population in Stockholm County also showed cannabis use was associated with an increased risk of psychological distress eight years later in Sweden women .

Regardless of the causal direction, to protect the health and well-being of young adults, decision-makers need to consider both the mental health and substance use behavior implications of less restrictive substance use policies. California laws banned sales of cigarettes, e-cigarettes in 2016, and marijuana to young adults under 21 years old. Though underage young adults had lower odds of smoking cigarettes than older young adults, the underage use was substantial for e-cigarettes and marijuana.The studies did show that California law reduced illegal sales to youth under 18 . Researchers from UC Davis used data from the 2012–2019 Behavioral Risk Factor Surveillance System and observed that although the trends of ever and current smoking did not change significantly before and after California’s T21 policy, while there was an 8% annual decrease of daily smoking before the policy and a 26% annual decrease after the policy among underage in California . Our study and others showed that underage use could still be an issue due to limited knowledge of such laws and other influencing factors . A study found that the knowledge of the minimum legal age was inversely associated with the intention to use tobacco among youth. Educational campaigns to raise awareness and support for MLA among youth may improve the impact of MLA policies . The strength of this study is that it is based on CHIS data, which is the largest state health survey in the nation, and it collects extensive information for assessing the health and health behaviors of adults, adolescents, and children in California. Each year, CHIS surveys over 20,000 households. Also, from 2016 to January 2018, California implemented a series of policies, including prohibiting the sale of tobacco products and e-cigarettes to persons under 21, a cigarette tax increase, and recreational marijuana legalization. All these state-level policy changes make California a natural experimental ground for studies on tobacco and marijuana use behaviors and risk factors associated with smoking behaviors among young adults. It is worth noting that the findings in this study are subject to some limitations. First, data were self-reported, which might have resulted in recall and social desirability biases. Specifically, we were unable to examine whether decriminalization and legalization of adult marijuana use affected self-reporting bias; that is, respondents might have felt more comfortable reporting marijuana use as it became legal in California. Second, the survey does not include institutionalized populations and persons in the military in its sample, so the results might not be generalizable to those populations. Lastly, as noted, it is based on cross-sectional data, it is difficult to determine the direction of the relationships we estimated, for instance, if cigarette use caused marijuana use or vice versa. The adverse consequences of illicit drug use on users’ physical and psychological health have been examined extensively. Substance abuse has been found to be associated with reduced cognitive abilities , educational attainment , as well as undesirable labor market outcomes such as unemployment , employment mobility and lower wages . Studies that specifically focus on marijuana-use and labor market outcomes have yielded similar findings , where regular cannabis use is associated with poor school performance, higher dropout rates , and lower levels of educational attainment – an important factor that facilitates subsequent labor market outcomes including occupational status and income . Despite the growing number of studies investigating the relationship between substance abuse and labor market outcomes, however, a closer examination of the empirical evidence reveals a surprising lack of concurrence among their findings. Using data from both the 1980 and 1984 waves of the National Longitudinal Survey of Youth , Gill & Michaels , examine the effects of substance abuse on wages. After accounting for what they refer to as “self-selection” effects, the authors conclude that users of illicit drugs receive higher wages than their non-drug using counterparts.

The association between marijuana use and the prevalence of diabetes has also been examined in the National Health and Nutrition Examination Survey III population. The researchers reported that marijuana use was associated with a lower odds of diabetes after adjustment for manifold demographic, lifestyle and clinical characteristics. The study population was restricted to individuals aged 20– 59 years; excluded 25% of the potential analysis population because of missing laboratory data; and, when examining age-stratified analyses , found the association was restricted to the older age stratum. A recent meta-analysis of eight independent replications from NHANES and the National Surveys on Drug Use and Health found a summary adjusted OR of 0.7 for the association of current marijuana use and prevalent diabetes; however, both marijuana use and diabetes status were ascertained via self-report. These associations might result from the self-exclusion of unhealthy individuals who frequently use marijuana from study participation, resulting in an underestimation of diabetes cases in marijuana users, and from reverse causation where individuals with diabetes abstain from marijuana use in older age because of concerns about and awareness of their health status. Recent analysis of NHANES 2005–2010 participants found marijuana use to be associated with lower levels of fasting insulin and HOMA-IR, and a decreased WC compared with individuals who reported never using marijuana, after adjustment for relevant covariates; however, no association was found between current marijuana use and fasting glucose, HbA1c or BMI. In a small study of otherwise healthy individuals, after matching cannabis users to non-users by sex, ethnicity, age and BMI, adipocyte insulin sensitivity was found to be higher in cannabis users compared with non-users; however, skeletal muscle insulin sensitivity, insulin secretion,clone rack fasting insulin and glucose, and HbA1c were not different between the two groups. Significant differences in diet quality between the two groups were noted, and the effect of tobacco use on the results is unknown.

Previous epidemiological research has cited animal models investigating the potential mechanisms underlying the metabolic effects of marijuana. Potential anti-inflammatory effects and improved metabolism by actions through the cannabinoid receptors have been suggested to reduce the progression of type 1 diabetes, improve beta cell function and decrease weight in mouse models. However, no models have assessed this association in healthy mice, and these studies administered cannabis/cannabidiol via ingestion or intravenously. The mode of administration and the dose should be considered when extending these results to public health studies, as the most common modes of consuming cannabis among the general population are cigarettes, pipes and bongs, in which the user inhales the chemical compounds in smoke form and the quantity consumed varies from user to user. Given the potential of marijuana smoke to increase the production of reactive oxygen species and oxidative stress, any potential anti-inflammatory benefit might be countered by detrimental oxidative effects from intake by smoking. Research on the prospective evaluation of marijuana use on metabolic health is scant. It is unclear how marijuana use could place an individual at increased risk for prediabetes yet not diabetes. This is a potential study limitation, and may reflect a spurious prediabetes association. Similarly, it is possible that it is an artefact arising from our exclusion criteria disproportionately affecting those with higher levels of marijuana use and greater potential for the development of diabetes. Individuals excluded from our analysis generally had higher levels of marijuana use and less favourable levels of traditional diabetes risk factors and were, historically, more likely to develop diabetes. Alternatively, the effect of marijuana use might have a more noticeable impact on glucose metabolism in the prediabetes range compared with the diabetes range, when traditional diabetes risk factors are far less favourable and might dominate over any effect of marijuana. This might explain the differing results in the linear trend of fasting glucose level at censoring. There are plausible ways to reconcile the seemingly contradictory tendencies between this prospective analysis , animal and cellular models, and prior cross-sectional findings in which current marijuana use coincided with a lower prevalence of prediabetes and diabetes.

We speculate, for example, that some people in ill health might choose to quit marijuana as a result of a physician’s recommendation to abstain from tobacco and other substances or a general concern for their health, or because of more complicated associations between poor health, income and drug access. This speculation awaits confirmation. In addition, previous work has not accounted for the use of other illicit drugs. While illicit drug use per se might not cause a decline in metabolic health, it might be an indicator of the propensity to use drugs or overall deleterious health behaviour, or cause declines in overall health.Recreational stimulant use is a growing concern among young adults, with 4.4% and 5% to 35% of college students endorsing cocaine and recreational amphetamine  use, respectively, and 16% of cocaine experimenters developing dependence within 10 years . To develop cost-effective prevention and intervention strategies, it is crucial to identify ultra–high risk recreational users. However, little is known about bio-behavioral markers forecasting trajectory of occasional stimulant use to stimulant use disorder . Previous stimulant use research is predominantly cross-sectional, comparing individuals with chronic stimulant use with healthy individuals; although findings from these studies highlight brain disruptions related to drug use, they cannot disentangle whether disruptions preceded or were a result of chronic use. Young adulthood is a period of increased independence, often providing more opportunities for risky behavior such as drug experimentation. Risky behavior can be defined as actions that may be subjectively desirable but are potentially harmful and is typically quantified in young adults by their degree of substance use, unprotected sex, health habits, and crime engagement . Risk taking often occurs in clusters of maladaptive behaviors, suggesting underlying impairments in decision making . Decision making involves several brain processes, including learning, inhibition, and outcome assessment, specifically appraising positive or negative valence of choices . Functional magnetic resonance imaging research indicates that individuals with SUD show impaired decision making associated with altered brain activation in executive control and reward processing regions . Decision making is thought to involve a cooperative relationship between an impulsive system activated by immediate rewards and aninhibitory control system. Through learning, the control network allows individuals to resist immediate attraction to rewards in favor of longer-term advantageous outcomes .

In SUD, bio-behavioral indices of risk taking suggest an underlying imbalance between the control and impulsive systems. The control system integral to decision making comprises prefrontal cortex , theorized as responsible for learning the relationship between stimuli and outcome, working memory, and inhibiting behavior . SUD samples exhibit frontal lobe impairments associated with compromised decision making and increased risk behavior . For example, cocaine abusers exhibit dorsolateral PFC hypoactivation during response inhibition and prediction of uncertain outcomes ; in cocaine dependence, orbitofrontal cortex and DLPFC attenuation are linked to reduced ability to differentiate between variable monetary gains . Similarly, methamphetamine users inaccurately process success or failure of available options, a pattern associated with orbitofrontal cortex/DLPFC hypoactivation . Working in conjunction with frontal regions is striatum, an area associated with reward processing , selecting and initiating actions , and learning . During the Iowa Gambling Task , healthy individuals show stronger striatal activation to wins than to losses ,4×8 tray grow but amphetaminedependent individuals demonstrate hypersensitive striatal responses to rewards . Cocaine and methamphetamine users also exhibit striatal hyperactivation but frontal hypoactivation during risky decision-making tasks such as the Iowa Gambling Task and the Balloon Analogue Risk Task that is linked to riskier behavioral performance .Evidence from fMRI studies has led researchers to theorize that frontal lobe and striatum form a functional circuit with insular cortex and anterior cingulate cortex ; these regions coordinate to integrate emotional and autonomic information about rewards into goal-oriented behavior . ACC is proposed to be involved in emotion and behavior management based on its neural connections to both the emotion processing limbic system and the cognitive control center, PFC . Insula is proposed to play a role in interoceptive processing, wherein individuals integrate physiological cues to differentiate between risky and safe decisions and transform these cues into conscious feelings and behaviors . ACC and insula hypoactivation is evident in chronic stimulant users in response inhibition and error monitoring during decision making . Evidence for aberrant activity in key components of the PFC-limbic network has led researchers to suggest that weakened ability to accurately process information about options and control behaviors leads to favoring choices that offer immediate, rather than delayed, rewards . Cross-sectional studies of occasional stimulant users report decision-making impairments that parallel findings in stimulant-dependent individuals, including 1) weakened inhibitory control and reduced cognitive flexibility ; 2) neuropsychological impairments in executive functions ; and 3) frontal, striatal, and insular attenuation during a Risky Gains Task paired with reduced ability to differentiate between safe and risky decisions . Several research groups have recognized limitations of cross-sectional addiction research and have shifted toward a longitudinal approach to understand the transition to problematic substance use . Structural MRI studies show that decreased brain volume in frontocentral regions at age 14 years predicts binge drinking at age 16 and that frontostriatal regions are linked to heightened stimulant use in OSUs 1 to 2 years later . However, fMRI has been less applied to predict the development of SUD.

The current longitudinal study used follow-up clinical and drug use data from OSUs 3 years after an fMRI scan to determine whether baseline behavioral and blood oxygen level–dependent responses during the RGT 1) differentiated young adults who became problem stimulant users from those who desisted from stimulant use during the 3-year interim and 2) predicted cumulative baseline and follow-up stimulant and marijuana use across OSUs, regardless of clinical status , to address concerns regarding significant rates of marijuana and stimulant co-use . Analyses compared BOLD activity related to specific task requirements: decision contrasts compared BOLD activity during risk-taking choice trials versus safe choice trials; outcome contrasts compared BOLD activity on trials where each subject took a risk and subsequently earned a win or a loss. Categorical hypotheses were tested based on prior bio-behavioral findings in stimulant- dependent individuals: 1) PSUs would exhibit riskier task performance than DSUs; 2) PSUs would show greater striatal BOLD signals than DSUs to outcomes, particularly in response to risky wins; and 3) PSUs would exhibit lower PFC, insular, and cingulate BOLD signals during decision making. Because dimensional analyses were exploratory, no a priori hypotheses were tested.The University of California, San Diego, Human Subjects Review Board approved the study protocol. Participants were recruited through newspapers, internet ads, and fliers mailed to college students. Figure 1 demonstrates participant recruitment and categorical/dimensional data analysis protocol. A total of 1025 individuals were phone screened, and 184 OSUs meeting study criteria provided written informed consent to participate. OSU inclusionary criteria were as follows: 1) within the last 6 months, two or more separate occasions of cocaine or prescription amphetamine use without a prescribed purpose; 2) no lifetime stimulant dependence; 3) no lifetime stimulant use for medical reasons; and 4) no drug treatment interest. Participants completed three sessions: 1) a baseline diagnostic interview to determine lifetime psychiatric diagnoses and current drug use patterns , 2) a neuroimaging session completing the RGT , and 3) a follow-up interview session 3 years later to determine changes in drug use and clinical diagnoses . The current study includes data from OSUs who completed all three sessions . No OSU reported using methamphetamines at baseline;all baseline stimulant use was of cocaine and prescription stimulants.Three hypotheses were tested. First, consistent with the prediction that PSUs would exhibit riskier task performance than DSUs, PSUs more frequently made a risky decision following a win compared with DSUs, while DSUs more frequently made a safe decision following a risky win. This pattern supports previous findings that PSUs are more reactive to rewards . Second, although it was predicted that PSUs would show greater activation in reward processing striatal regions to risky wins than to risky losses when compared with DSUs, our results demonstrated the opposite effect, with PSUs exhibiting lower striatal BOLD signals across outcomes than DSUs. However, this finding is consistent with a longitudinal study of sensation-seeking adolescents in which striatal hypoactivation predicted future problematic drug use; the authors theorized that lower striatal activity may lead to a compensatory mechanism in which one seeks out increased risk to gain greater stimulation, thereby balancing reward center hypoactivation .

Colorado, Washington state, Alaska and Washington, D.C., don’t allow home delivery of marijuana. Oregon, California and Nevada do, but services are not universal Colorado, Washington state, Alaska and Washington, D.C., don’t allow home delivery of marijuana. Oregon, California and Nevada do, but services are not universal. Colorado Governor John Hickenlooper stated one of chief concerns surrounding marijuana delivery services; that “delivery service offers more opportunity for that marijuana to get into the hands of kids.” . Another key concern for delivery services is enforcement. Many are based in cities where marijuana businesses are not permitted and it is impossible to monitor how often they deliver to cities in which MMDs are banned; even though some delivery business have put protocols in place that allow them to identify areas where delivery is prohibited and refuse to deliver to those jurisdictions , they represent only a handful of the hundreds of the businesses available to choose from for marijuana delivery. For example, neither medical or recreational marijuana business are currently legal in the cities shown in the screenshot below, but dozens of marijuana delivery businesses are based there and ready to service those regions. The results presented in Chapter 6 refuted Hypothesis 1.2, and established that dispensary bans do not have a direct effect on high school students’ marijuana use when controlling for student and school characteristics known to be associated with adolescent marijuana use. This diverged from findings from the trend analysis which found that over time a more restrictive dispensary policy in Los Angeles was followed by decline in lifetime marijuana use among the City’s 9th and 11th grade students. Before concluding that dispensary bans had no relevance to adolescent marijuana use, however, I investigated if a more complex relationship was masking an association.

By investigating indirect effects,drain trays for plants I hoped to learn identify indirect mediators of a relationship between dispensary bans have an impact on adolescent marijuana use, for example if their effect is dependent on them having a significant effect on another variable that has a significant influence on student marijuana use. This kind of hypothetical relationship is called indirect mediation . Often, the researcher’s interest switches to the variable with the direct effect once it is identified, but in the case of civic policies regulating dispensaries, learning more about these dependent relationships could also elucidate the mechanisms by which restrictive city regulations on legal, adult-use products might be effective in preventing substance use among adolescents. For example, if these analyses had demonstrated that the density of dispensaries was significantly correlated with adolescent marijuana use by city, policies that strictly limit the number of dispensary bans could pursued in lieu of dispensary bans. Recent studies have demonstrated that dispensary density is positively associated with higher prevalence of use and more frequent use among adults but their influence has not been studied among youth. Prevention research supports the idea that more convenient access to substances that are legal for adults, such as tobacco or alcohol, often has the end result of creating easier access for youth . This finding implies that youth living in or attending school in a city that allows dispensaries might obtain cannabis more easily or more often from adults in their social network. Considering that adolescents report older relatives and the illicit market as their primary sources of cannabis , a dispensary ban making access less convenient for adults could have the additional effect of making it less conveniently obtained by teens. The number of dispensaries in a community makes sense as a measure of convenience of access but could also be a marker for ineffective enforcement if it is larger than the number a city allows. Dispensary bans were significantly negatively associated with lower density of dispensaries, among the cities of LA County , which supported H2.2.

This means that the average city with a dispensary ban had less dispensaries operating there the average city that allowed dispensaries. I expected the number of dispensaries in a city to be positively correlated with the prevalence of marijuana use among students but instead found that there was not a statistically significant association . This finding refuted H2.2 and ruled out the rate of dispensaries per 10,000 residents as an indirect mediator that carries the effect of dispensary bans on students’ rates of lifetime and recent marijuana use. Included as a measure of the actual exposure to dispensaries in communities, the number of dispensaries per 10,000 had surprisingly little influence on the outcomes of interest for this study. As youth are not able to access these storefront outlets directly, the presence of dispensaries in their city may have little impact on the availability of marijuana within their social circles. That the number of dispensaries in a community normalized by population had no effect on high school students’ marijuana use was in line with research indicating that adolescents generally do not get marijuana directly from dispensaries, but rather from social sources like relatives or friends. I hypothesized that a greater number of dispensaries located within a city would create more convenient access for the adults that act as a conduit of marijuana to adolescents. However, creating easy access for adults through legitimate sources like dispensaries may have also shrunk the illicit market as a source for adolescents. One possibility is that the adults and adolescents that formerly supplied marijuana through the illegal market pursued other activities after losing a large proportion of their adult customers when access to dispensaries became legal. The finding that the rate of dispensaries per 10,000 population had no effect on high school students’ marijuana lifetime or recent use or perceptions of how easy it was to get marijuana was in line with research indicating that adolescents generally do not get marijuana directly from dispensaries, but rather from social sources like relatives or friends . It’s also possible that the predictions of marijuana legalization advocates are correct; that allowing easier access to marijuana through legitimate sources like dispensaries has starved the illicit market as a source.

Although this could be a factor, local research indicates that it could not be completely responsible for the results seen here. Two recent local studies have indicated that although use of dispensaries as a source for marijuana is preferred by the adult marijuana users in LA County, most of this population continues to access marijuana from illicit sources in addition to dispensaries . For example, a September 2018 community assessment published by the LA County Department of Public Health Substance Abuse and Prevention Program titled “Marijuana Use and Public Perceptions in Los Angeles County” indicates that dispensaries are still not the most common marijuana source for adult users. Instead, 58% of the LA County marijuana users surveyed cited friends as the primary source for their marijuana ,dry rack for plants whereas only 21% of respondents reported dispensaries as their primary source. However, only approximately 6% of the respondents in the 2018 study reported a “dealer” as their primary source, i.e., the illicit market. This is less than half of the proportion of marijuana users surveyed for a qualitative study of dispensary users conducted by SAPC and UCLA in 2014, which found that although dispensary customers unanimously preferred to get marijuana from dispensaries as compared to the illicit market, 13% also continued to get marijuana from the illicit market . Even if city ordinances do not have an effect on the supply of marijuana available to youth or ultimately impact their marijuana use behaviors, could they have an effect on their perceptions of risk and on youth social norms surrounding marijuana use? Attitudes toward drugs and alcohol are known to be powerful predictors of adolescent substance use , and changing attitudes to perceive cannabis use as more acceptable and less risky have been noted among youth populations . For example, qualitative research with at-risk youth in LA County indicates that many view marijuana use as having fewer negative consequences than drinking . A community assessment conducted in LA County also found that the risks of cannabis use were rated much lower among cannabis users than among non-users, indicating a potentially important relationship between perceptions of the risk of marijuana use and the willingness to use it. The results of the perceived mediation analysis indicate that while perceived risk has a strong association with the prevalence of students’ lifetime and recent marijuana use , it is not determined by their city’s dispensary policy . Perceiving great risk from frequent marijuana perceived risk could not therefore mediate the relationship between dispensary bans and student marijuana use . Perceived risk having a strong association with student marijuana use is consistent with well-known theoretical models like the Health Belief Model but it is outside of the scope of this analysis to determine what is determining students’ perception of the risks of marijuana use other than to note that it is not the dispensary ordinance in the city where they attend school and likely live.

For Research Question 4 I tested the mediating effect of the continuous distance from the study participants’ schools to the nearest dispensary in LA County. I hypothesized that dispensary bans would be associated with a greater average distance compared to cities that allowed dispensaries. I used the distance to the nearest unlicensed dispensary as the mediating variable based on a sub-analysis finding that unlicensed dispensaries had a stronger association with student marijuana use and because there were more unlicensed dispensaries located near schools. I found that dispensary bans were indeed associated with a significantly longer average distance between schools and the nearest unlicensed dispensary , and that a greater distance was in turn associated with lower rates of lifetime and recent marijuana use among students. Including the distance between schools and the nearest dispensaries in the regression equation greatly improved the model fit and the strength of the association between dispensary bans and student use, although it fell just short of statistical significance . This result indicated that to the extent that dispensary bans are effective, their effectiveness is partially determined by being associated with unlicensed dispensaries being located further from schools. The distance between schools and the nearest unlicensed dispensary has a powerful association with students’ marijuana use as well as the relationship between dispensaries and student use, suggesting that the usefulness of dispensaries in in keeping unlicensed outlets further away from schools. It’s important to note that a dispensary ban is not required to do this, but different approaches among cities that allow dispensaries may be required A sensitivity analysis using progressively smaller distances within a mile and testing for significant associations with rates of lifetime and recent marijuana use among students found that there was a statistically significant relationship between both lifetime and recent marijuana and having the nearest dispensary located within a mile. A mile is equivalent to 5,280 feet, which is more than 8 times the minimum distance the State of California requires dispensaries to be located away from schools. Interestingly, the presence of licensed dispensaries within a mile was not associated with greater likelihood of marijuana use among the study participants, but was instead significantly associated with lesser likelihood of both lifetime and recent marijuana use. The disparate effects of licensed and unlicensed dispensaries at distances within a mile of schools merits much more detailed study. How do licensed dispensaries prevent diversion to youth so much more effectively than unlicensed dispensaries, if indeed that that is the cause of the opposite effect on youth use? Could licensed dispensaries shrink the illicit market on such a localized level? Recent premise surveys conducted by the LA County Department of Public Health indicate that ID checks were nearly universal among both unlicensed and licensed dispensaries , so it’s unlikely that youth are buying it directly from unlicensed dispensaries themselves. Perhaps less easily observable differences occur with unlicensed dispensaries circumventing other regulations intended to prevent diversion to youth and the illicit market, like quantity limits on the amount customers can buy in a single transaction. Research on dispensaries business practices and compliance with state and city regulations to date is sparse but supports this possibility. For example, recent observational research among dispensaries in LA County indicates that unlicensed outlets were more likely to have violated several regulations designed to prevent youth harm, such as displaying products designed to be attractive to youth, displaying products outside of their original child resistant packaging .