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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 .

All models controlled for gender, grade, race/ethnicity, and whether the student attended a nontraditional school. I assessed the parallel trends assumption of the difference-in-difference design by graphing simple prevalence of the marijuana outcomes among Los Angeles and the control group students over the study and visually assessing whether the trends were parallel. The assumption of parallel pretreatment trends was confirmed by the notably parallel trends before Proposition D was enacted. Table 6.2 presents the results of cross tabulation frequencies of the two marijuana measures with student demographic characteristics. Most of the results were consistent with expectations based on the body of literature, such as marijuana use among males being higher than females, use among Asian students being low, and use among students attending non-traditional schools being markedly higher than among students at traditional schools. Among all students the number who reported lifetime marijuana was about one-third but varied considerably by grade, which is not unexpected given that “lifetime” is a longer period of time among eleventh grade students than among ninth grade students. Recent marijuana use was also more common among eleventh grade students, however, with 19% reporting recent use compared to 13% of ninth grade students. These frequencies are consistent with both national and state-level reports. The national Monitoring the Future Study reports that as of 2017 prevalence for lifetime use ranged between 31% for 10th grade students to 45% for 12th grade students,greenhouse racking whereas prevalence of past month use ranged from 16% for 10th grade students to 23% for 12th grade students . State-level reports using a random sample of CHKS participants from 2015-2017 surveys found that 17% of 9th grade students and 32% of 11th grade students reported lifetime marijuana use, whereas prevalence of recent use was 9% and 17%, respectively, in the 2015-2017 survey years.

Table 6.6 presents the difference-in-difference estimates from the Poisson regression comparing marijuana use over time between the City of Los Angeles and the control group cities. The difference-in-difference coefficient is an interaction term comparing the difference in change between the two groups over time and quantifies the impact of Proposition D. Presented below as a risk ratio, the value for the estimate was less than one , which indicated a lower risk of lifetime marijuana use over time and a greater decline relative to the control group cities. That the coefficient was statistically significant means the decline in students’ marijuana use in Los Angeles surpassed the declining countywide trend to such a degree that it is unlikely to have occurred by chance . This result suggests that the decline in rates of lifetime marijuana use among City of Los Angeles high school students is attributable to the stricter regulations enacted with Proposition D and supports H1.2 for lifetime marijuana use. The difference-in-difference coefficients for the covariates presented in Table 6.6 are similarly presented as risk ratios. In this case, they represent the risk of a student within a category reporting lifetime marijuana use relative to the reference group for that category and holding constant all of the other covariates in the model. For example, the risk ratio for males reporting lifetime marijuana were 1.11, or 11% higher than the risk for females reporting lifetime marijuana use. Within racial/ethnic characteristics, students within the African-American, Hispanic, and Other racial/ethnic categories had significantly higher relative risk of reporting lifetime marijuana use than the reference category, Whites. In contrast, Asian students had significantly lower relative risk compared to Whites . The relative risk of eleventh grade students reporting lifetime marijuana use significantly higher than for ninth grade students, as indicated by the 95% confidence interval not including 1. The results for non-traditional schools were in the expected direction as well, with the relative risk of lifetime marijuana among students attending these schools reporting lifetime marijuana was estimated to be almost 75% higher compared to risk of lifetime marijuana use among students attending traditional schools.

The most interesting finding among the covariates was the association between the relative risk of students reporting lifetime marijuana by time. The risk ratios for the preProposition D time periods were all greater than one, indicating that reports of lifetime marijuana use during these periods were significantly greater than baseline . In contrast, the risk ratios for the post-Proposition D time periods were all lower than one, indicating significantly less risk of students reporting lifetime marijuana use during those time periods compared to baseline. The tighter regulations enacted in Los Angeles with Proposition D were followed with lower rates of lifetime marijuana use among high school students when accounting for regional trends and covarying factors. Parallel trends were observed in Los Angeles and the control cities for both lifetime and recent marijuana use, but declines in both these measures were steeper in the City of Los Angeles following enactment of Proposition D. This result supports the hypothesis of a causal effect, although it was not large enough relative to the control group to be statistically significant for recent marijuana use. These results supported Hypothesis 1.2, that cities that enacted more restrictive dispensaries policies would see a trend of declining marijuana use among students attending school there. That a decline in student reports of marijuana use was observed among the control group was unexpected. The similar trend among the control group cities may indicate that marijuana use among high school students is driven less by whether their city allows dispensaries than by secular trends driven by the media, by state and federal laws that impact availability and legal risk for adults. This finding also justified use of the difference-in-difference design to control for background trends in the outcome variable that cannot be attributed to the policy or event of interest. By using the control cities to represent the counterfactual case for student marijuana use trends in Los Angeles had Proposition D not been enacted, I was able to isolate the effect that can be attributed to the policy change and avoid making false conclusions about its impact on students’ marijuana use behaviors. The decline in rates of lifetime and recent marijuana use among the control group cities may have been related to federal enforcement efforts that closed down over 200 dispensaries in the LA County area in 2012 ,indoor cannabis grow system but very little information is available about which dispensaries were closed down in which cities and how many dispensaries were in operation countywide before the raid. It is unknown whether the Federal raids targeted the city of LA and the control cities equally, but if they did than these raids may have played part in the declines in marijuana use that was noted among students in the control group cities as well as among Los Angeles students.

It is difficult to attribute any impacts on student marijuana use to these enforcement actions due to the limited information available but further study of these events is certainly merited. I was not able to conclude that the decline in recent marijuana use observed to occur in the post-Proposition D period was not due to chance. Recent marijuana use is a less common behavior than lifetime marijuana use, and while the effect was in the expected direction, the smaller number of students reporting this behavior produced a wider confidence interval that included a null effect. Policies take time to have a measurable effect and the City of Los Angeles has experienced significant challenges to enforcing Proposition D’s limits on the number of dispensaries. Hundreds of unlicensed storefront dispensaries continue to operate throughout Los Angeles and each one of them could be expected to weaken the impact of Proposition D. It is possible that given more time and continued investment in enforcement an effect for recent marijuana could be documented as well. An additional explanation for why an effect was not observed for recent marijuana use could be contamination, or “spillover effects”, where people living in other cities in LA County may have obtained marijuana from the many dispensaries located in the City of Los Angeles. Car culture is firmly established in Southern California and vehicle ownership is high; close to 8 million vehicles were registered in LA County last in 2017 , for a county with an estimated population in 2017 of just over 10 million . Given the geographic sprawl of Los Angeles and the many other incorporated cities and unincorporated areas it borders, it is not difficult to imagine that LA County residents who lived outside of Los Angeles obtained marijuana from dispensaries located in Los Angeles if they couldn’t get it in their own city. This could be expected to make events that impact access to marijuana in the City of Los Angeles also have an impact in the other cities. It could also be expected to weaken the impact of dispensary bans altogether, as people could obtain marijuana from other cities if it is banned in theirs.

The ability for high school youth to travel to another city to get marijuana could be less of a concern than for adults but given that youth largely obtain marijuana from adults via the illicit market or their social networks , events impacting adult access could be expected to in turn affect youth access. Additionally, even if a city is successful in enforcing dispensary bans or caps on the number of outlets like Proposition D, policies such as these that restrict access to storefront outlets may still have a limited effect on the availability of marijuana given the many other sources by which residents can obtain it, such as from delivery services or by cultivating their own. In this chapter I will present results from analyses that tested several theories for why city dispensary bans may have an indirect effect on student marijuana use . The results presented in Chapter 5 tested the focal relationship for this dissertation using a cross-sectional sample that included students from 57 cities in LA County. That analyses did not provide evidence of a direct effect between city dispensary bans and high school students’ marijuana use when controlling for student and school characteristics known to be associated with adolescent marijuana use. In contrast, the trend analysis presented in Chapter 6 showed that in the City of Los Angeles enacting and implementing a policy intended to reduce the number of dispensaries and place additional controls on their operation was followed by a decline in lifetime marijuana use among students attending the city’s public high schools. Furthermore, city dispensaries were negatively associated with student marijuana, despite the associations falling short of statistical significance. In this chapter, I therefore conducted a series of mediation analyses to elaborate on the relationship between city dispensary bans and student marijuana use and whether their effect was dependent on some factor I had not accounted for. In this chapter I investigate indirect effects; circumstances where the effect of a variable is dependent on another variable. Identifying dependent relationships is important to elucidate some of the mechanisms by which restrictive city regulations on legal, adult-use products might be effective in preventing substance use among adolescents. The analyses that follow in this chapter tested indirect mechanisms through which I theorized city dispensary policies may influence students’ marijuana behaviors, such as by preventing excessive density of dispensaries in a city, signaling to youth that marijuana use represents a health risk, and/or by preventing dispensaries from operating near their high schools. These analyses will test the hypotheses for Research Questions 2-5. As described in the methods chapter , I used a variation of Baron and Kenney’s Product Method, which described in detail in Zhou et al., 2010, to test for mediation. Baron and Kenney’s Product Method first tests for direct relationship between the independent and dependent variable as a condition of testing for mediation , but this approach has been criticized as a relationship between the independent and dependent variables can be masked by a mediating variable or competing mediators . In the case of this research, the focal relationship is the influence of dispensary bans on student marijuana use and I will investigate the effect of several different mediators on this relationship.

The city population estimates included in the city boundary shapefiles are calculated by applying mortality and migration rates to the 2010 Census count and controlling for age, race-ethnicity, and gender proportions from the Census Bureau’s LA county population estimates for the previous year. These city boundary shapefiles are available for download and public use at the County of Los Angeles GIS Portal . To quantify the impact of multiple dispensaries being located near a school, I first calculated the association between the continuous distance between the school and the nearest dispensary in within a mile and within LA County. I wanted to know at which point a dispensary was located close enough to a school to have an influence on student marijuana use, so I also conducted sensitivity tests of distance within a mile using increments of a quarter mile. These distances are much further away from schools than the state requirement of 500 feet or and the maximum distance dispensaries are required to be located away from schools by a city ordinance in LA County, which is 1,000 feet. I then constructed a series of “buffers” using ArcMap 10.4 GIS Software and recorded counts of how many dispensaries were located within each buffer. A buffer is created by specifying the length and unit of measurement for the radius around a point of interest, such as LA County public high schools. A series of 3 buffers were created for this analysis. The first buffer was 500 feet in radius; the minimum distance a city in LA County allowed dispensaries to be located near schools in 2016,cannabis drying when the city policy data was collected. I suspected that dispensaries could have an impact at greater distances from schools than at 500 feet so I tested the impact of dispensaries being located with 1,000 feet and 2,000 feet. The dispensary count within 500, 1,000, and 2,000 feet of each school were imported into SAS and matched with the data for each school that participated in the CHKS survey by CDS code.

This allowed for information about student marijuana use to be associated with the number of dispensaries within a specific radius of each school. These buffer counts were then used as independent variables in the multilevel logistic regression analyses to determine the impact of the number of dispensaries near the schools on students’ marijuana use behavior. Student characteristics assessed include gender, ethnicity , race, grade , highest level of parent education, whether the student qualified for free or reduced-price meals, and whether the student attended their school’s after school program at least one day a week. Male gender is sometimes associated with greater likelihood of and higher rates of marijuana use , whereas female gender has been associated with lower rates of use overall, but with younger ages of initiation and faster transition to regular use . Some studies have found that rates of marijuana use among people of Latino ethnicity are higher relative to other racial/ethnic groups in early adolescence but are often overtaken by rates of use by white people in later adolescence .Therefore, the analyses presented in this dissertation use the students’ grade in school as a measure of student’s age. Older age is almost universally correlated with greater substance use among adolescents , so age is an important factor to account for in any analysis of the risk of substance use among high school age youth. The analyses presented in this dissertation are based on students in the 9th and 11th grade, per CHKS study protocol. Higher grade is logically a powerful predictor of lifetime marijuana use due to it being determined by greater age, but has also been shown to be associated with a greater likelihood of recent marijuana use , which is not necessarily dependent on greater age. Participation in after-school programs was included as a covariate because it has been shown to be a protective factor against adolescent substance use in general .

The count of days each student participated in after school programs was only used in the cross-sectional analyses, as it was not available for all of the school years between the 2005/2006 and 2016/2017 school years. Eligibility for school meals and highest parent education were included as a measure of social economic status because some studies have found higher SES to be associated with greater rates of marijuana use . Self-report of receiving free and reduced-price school meals was included as the only available proxy for low family income, based on California State eligibility criteria, e.g., annual income $ 32,630 for a family of four ,” n.d.). The school meals variable was ultimately found to have a high rate of “don’t know” responses , which were grouped with “no” responses using the logic that the student would likely be receiving free-reduced price meals if they were eligible and therefore would be aware of their eligibility. This variable was only used in the cross-sectional analyses, as it was not available for all of the school years between the 2005/2006 and 2016/2017 school years. After-school program participation was operationalized using a variable in CHKS that asked “How many days a week do you usually go to your school’s after school program?” and had ordinal response categories ranging from 0 – 5 days per school week. The ordinal form of this variable was used as a covariate to account for how many days a week the student spent time at an after-school program in the regression analysis. This variable was only used in the cross-sectional analyses, as it was not available for all of the school years between the 2005/2006 and 2016/2017 school years.An indicator variable for non-traditional schools was available in the CHKS dataset and a matched more detailed descriptions of school type from the CA Department of Education School Directory. The non-traditional school indicator variable was included in all of the cross-sectional analyses to account for the expectation that students attending non-traditional schools may be more likely to likely to use marijuana. This variable was included in the trend analysis as it was available for all of the school years between the 2005/2006 and 2016/2017 school years.

The number of dispensaries within 500 feet, 1,000 feet, and 2400 was initially used to measure the density of dispensaries near the students’ schools. I defined “near” as 2,000 feet, which was quadruple the distance of 500 feet that the State of California currently requires marijuana businesses to be located from schools . The 600-foot distance from schools set by the State may be rather arbitrary, however, as no existing research has established the distance threshold at which dispensaries no longer influence students’ marijuana use. Some the LA County cities that allowed dispensaries specified that they be located greater distances from schools, such as 1,000 feet,grow trays but it is similarly unknown whether these requirements place dispensaries sufficiently far enough away from schools to prevent them from having an impact on rates of lifetime and recent marijuana use among high school students. My preliminary analyses for the trend analysis indicated that I needed to revise the first hypothesis for Research Question 1 . While testing the parallel trends assumption for the difference in difference analysis, I compared frequencies by time for both lifetime and recent marijuana use by whether the city the school was located in allowed dispensaries. Figures 4.1 and 4.2 indicate that the intervention and control groups exhibited remarkably similar trends, where lifetime and recent marijuana use increased in both groups from baseline through the 2011-2013 combined school years and was followed by a decline that was maintained through the 2015-2017 school years. The evidence of similar trends between the intervention and control groups satisfied a key assumption of difference-in-difference analyses that trends in the outcomes under study were parallel between the intervention and control groups before an event of interest has occurred. However, the similar and non-linear nature of the trends in each group indicated a need to investigate if any events had occurred in LA County that could have influenced cities that allowed dispensaries and cities that did not in similar ways. After learning more about Proposition D and the impact it had on the medical marijuana market in the City of Los Angeles it was clear that Proposition D represented a significant event that affected the intervention group and not the control group. I felt that making any conclusions about trends in marijuana use differing between cities that allowed dispensaries and those that didn’t within LA County without accounting for the impact of Proposition D on Los Angeles students would be invalid. It was less clear whether the federal raids that occurred in 2011 and 2012 affected one of the study groups more than the other, but if it did affect both groups equally, the difference-indifference study design would account for any impact the federal raids had on the marijuana use behaviors of Los Angeles students. I therefore chose to address Research Question 1 by analyzing the impact of enacting stricter regulations on dispensaries students’ marijuana use within the City of Los Angeles, using the cities that had never allowed dispensaries as a control group. Research Question 1 was therefore revised to ask “Do city restrictions on dispensaries have an influence on trends in adolescent marijuana use time?” The revised hypothesis for this question was that cities that enacted more restrictive MMDS policies would see a trend of declining marijuana use among students attending school there . To focus on the impact of Proposition D on trends in student marijuana use in the City of Los Angeles, I excluded the 2005/2007 combined school years and used 2007/2009 as the baseline time period. The 2007/2009 time period was two time periods before Proposition D was enacted and the 2015-2017 time period concluded one time period after the enactment of Proposition D. The analysis plan for Research Question 1 was changed to focus on the impact of Proposition D within the City of Los Angeles compared to cities that did not allow dispensaries . The control group for this analysis includes the 436,834 students that attended school in the 70 LA County cities that had dispensaries bans in place throughout the study period. The cities excluded were cities that had changed dispensary policies between the 2005/2006 school year and the 2016/2017 school year, which excluded cases from the cities of Diamond Bar , Huntington Park , Long Beach , Malibu , Santa Monica , South El Monte , and West Hollywood , and students from schools that could not be matched to CA Dept of Education addresses . City of Los Angeles students were chosen as the intervention group because Los Angeles and the City of West Hollywood were the only cities in LA County that allowed storefront dispensaries to operate within their borders for the entire 12-year study period. West Hollywood schools, however, did not participate in the CHKS survey during the study period and therefore could not be included in an intervention group of cities that allowed dispensaries throughout the study period. Using students who attended school in the City of Los Angeles as the intervention group was preferable for the difference-in-difference analysis of marijuana use trends because data was available for City of Los Angeles schools for every year of the study period and the population of students within this large and diverse city mirrored the population of the County as a whole for most racial/ethnic categories. Exceptions were that City of Los Angeles students were more likely to be Hispanic and less likely to be Asian or White than the control cities . The association between policy changes and subsequent outcomes is often evaluated by pre-post assessments, where outcomes after implementation of the policy are compared with conditions and outcomes from before. This design is valid only if there are no underlying time dependent trends in outcomes unrelated to the policy change . If, for example, outcomes were already improving before the policy was enacted, using a pre-post study would lead to the erroneous conclusion that the policy was associated with better outcomes. The difference-in-difference study design addresses this problem by using a comparison group that is experiencing the same trends but is not exposed to the policy change .

The Department of Public Health licenses and oversees manufacturing and testing or marijuana products, and the State Board of Equalization collects taxes from marijuana businesses. Staffing and development in the state agencies that will carry out these aims is incomplete and ongoing to date. Approaches to limiting youth exposure to marijuana products have changed dramatically as marijuana has become a legal product offered in retail settings. With the expansion of medical marijuana into the retail domain, preventing youth access to marijuana took on a new dimension, where in addition to preventing access to marijuana through illicit markets and social networks it became necessary to prevent youth from accessing it from storefronts located in communities.Explicit regulations on business practices can be applied to legal retail environments while law enforcement agencies are often the only agencies with the authority to deal with illicit markets. Within legal markets, however, prevention advocates face new and different challenges and that may require different approaches. Policy-based prevention approaches have proven to be effective at reducing adolescent substance use, despite targeting the general population and have been particularly useful to address legal substances available in retail settings . With the movement of marijuana into the legal market, regulatory controls on business practices have become necessary tools for prevention of adolescent marijuana use. Practices currently required by California law to restrict youth access include requiring employee assistance to handle products, keeping all products in their original child-resistant packaging,cannabis cultivation technology checking ID electronically or manually, and prohibiting products designed to be attractive to youth . Other recommended approaches to prevent youth marijuana use include requiring increased retailer liability and enacting stiff penalties for providing marijuana to youth .

City policies intended to prevent underage marijuana use include limiting the density of marijuana outlets in a community, preventing them from being located near sensitive areas such as schools and parks, limiting billboard advertising, and limiting the extent of exterior signage on dispensaries. Key informant interviews with LA County residents recently conducted by the LA County Dept. of Public Health indicate that keeping dispensaries a safe distance from schools and residential areas and limiting the density of outlets in the city were the top concerns stated by residents, followed by concerns about the effects of allowing dispensaries on social acceptability, particularly among youth . Perhaps intimidated by the challenge of regulating dispensaries, 75 of the 88 cities in LA County have passed local ordinances prohibiting dispensaries from locating within city limits . In Los Angeles County, the ten cities that have passed ordinances that allow and regulate dispensaries have set forth detailed rules for how dispensaries can operate. Conditions that these cities have specified to minimize the impact of dispensaries on public health include requiring them to be located a minimum distance from schools, parks, libraries and other places frequented by youth, limiting the hours of operation, and controlling their density and location . However, research indicates that preventing unlicensed dispensaries and restricting them from sensitive areas has been a problem in cities that allow dispensaries as well as in cities that ban them . Enforcement of city regulations intended to prevent adolescent substance use is an important determinant of their effectiveness in preventing substance use behaviors . A key concern for this study is the degree to which adolescents are exposed to dispensaries in the city where they attend school and likely live. Exposure to dispensaries is therefore a factor that depends not only on the city ordinances regulating dispensaries but on how effectively those ordinances are enforced.

It not yet known whether banning outlets altogether or allowing and regulating them is more effective at keeping outlets a safe distance from schools and other sensitive areas, as unlicensed dispensaries have been found in sensitive areas in cities that ban dispensaries as well as in cities that allow them. Cities that allow dispensaries are faced with enforcing limits on density and keeping outlets a defined distance away from sensitive areas such as parks and schools and some cities are more successful than others in accomplishing this. For example, unlicensed marijuana outlets were found to greatly outnumber licensed outlets within the City of Los Angeles . Among cities that have banned dispensaries the enforcement challenge has been to shut down unlicensed outlets and prevent new ones from opening in a different area of the city. This has been a problem in the unincorporated areas of LA County as well. For example, the LA County Office of Marijuana Management recently reported that it had identified 75 unlicensed outlets operating in the unincorporated areas of LA County in 2017. Seven months later, 29 of those shops had been shut down, but 31 new ones had opened in their place . Differentiating between licensed and unlicensed medical marijuana dispensaries is important because each type of dispensary may comply with regulations intended to prevent marijuana related harm and youth use to different degrees. By already existing in defiance of local law by operating in a location where they are not permitted, unlicensed dispensaries may have little incentive to comply with medical marijuana regulations. Recent observational research on compliance with regulations among dispensaries in LA County indicates that unlicensed outlets were more likely to have violated several practices designed to restrict youth access, such as displaying products designed to be attractive to youth, displaying products outside of their original child resistant packaging, or allowing onsite consumption .

The same premise survey also found that unlicensed dispensaries are more likely to be found located near sensitive areas where dispensary regulations prohibit them than licensed dispensaries . Although a robust body of literature supports the efficacy of city ordinances in preventing alcohol and tobacco use among adolescents , there exists a gap in empirical literature evaluating the effectiveness of these approaches in preventing marijuana use among adolescents . While a handful of studies have examined the impacts of the local marijuana policy environment on adult marijuana use the ability to quantify the impacts of city dispensary policies on youth marijuana use and outcomes has thus far been severely hampered by a lack of available data on youth marijuana use at the local level. Population-based national surveys like the Youth Risk Behavior Survey, the National Survey on Drug Use and Health, and state-level surveys like the California Health Interview Survey do not sample with enough density to allow for comparison of teen marijuana use between the cities within Los Angeles County . Even the Los Angeles County Health Survey, a population-based survey of health behaviors among adults and children in LA County, does not sample enough youth under 18 to provide estimates of adolescent marijuana use for geographic units smaller than the County’s health districts,indoor grow cannabis most of which span several cities . It is important to know whether the hundreds of ordinances that have been enacted to ban dispensaries in local jurisdictions across California have any impact on young people or whether the many other ways people can obtain marijuana render them primarily symbolic. Even if city ordinances do not influence the supply of marijuana available to youth or ultimately impact their marijuana use behaviors, what effect do they have on their perceptions of risk and on youth social norms surrounding marijuana use? The primary aims of this dissertation will be to answer these research questions, i.e., to learn whether local policies governing dispensaries are linked to rates of use as well as risk perceptions among Los Angeles County adolescents. Effective prevention of adolescent substance use requires an understanding of the complex etiology behind this very common behavior. Explanatory theories of adolescent substance use must elucidate relationships within the wide variety of factors that have been shown to influence substance use behavior while accounting for the unique context of adolescence. Consequently, comprehensive theoretical models that incorporate factors from multiple domains of influence have gained prominence as the field of substance abuse prevention has developed . Key themes in the etiology of adolescent substance use are that there are both distal and proximal influences at work and that a young person’s developmental stage interacts with almost every other influence . This chapter will give more emphasis to theories that apply to community and societal-level domains of influence on adolescent substance use behaviors. These theories directly address important constructs for this study, such as how and why young people’s substance use attitudes and behaviors are responsive to community contexts.

Developmental theories encompassing a wide range of biological, psychological, and experiential factors are prominent among individual-level theories and provide an explanatory framework for the most proximal influences on adolescent substance use behavior . However, even developmental theorists are increasingly looking to community contexts to explain inconsistencies and conditional relationships that have been identified in individual and relationship influences, such as the ways neighborhood effects mediate peer and family relationships . Simultaneously, an increased interest in the social determinants of health has led to more examination of how cultural and economic community characteristics and local policy may act on adolescent substance abuse . Individual-level factors represent the most proximal motivations for substance use and therefore tend to be important predictors of substance use behaviors. Generally, life experiences and psychological factors that present challenges to mental and emotional health also present risk factors for substance use and SUD . Decades of research have found that sub-groups of adolescents who experience social isolation, abuse, trauma, and mood disorders are at very high risk for SUDs and resulting health harms . The most vulnerable adolescent populations include youth who are in the child welfare system , drop out of high school , are involved with the criminal justice system , or have a minority sexual identity . Peer-reviewed empirical research of the influence of parental marijuana use on individual-level risk factors for adolescent substance use is still relatively sparse to date. Freisthler and colleagues found that parents who reported that they were current marijuana users were more likely to be physically abusive and used corporal punishment more frequently, but current marijuana use was associated with neither supervisory neglect or physical neglect. This led the authors to theorize that marijuana might be used by highly impulsive and agitated parents to relax but that their marijuana use did not appear to impair parents’ ability to care for their children’s basic needs. Intra-personal correlates of adolescent marijuana use include social and friend networks, the quality of family interactions, and the influence of school peers . Social networks are important influences on adolescents’ behavior and often mediate more distal influences like community and societal-level factors . Peer and family values, attitudes, and beliefs about substance use help shape adolescents’ values, attitudes, and beliefs about acceptable levels substance use . More accepting attitudes toward substance use among adults are correlated with a greater likelihood of substance use among adolescents and several studies have replicated these findings for marijuana use specifically . For example, in the state of Montana, Friese and Grube found that adolescents who lived in counties with a higher percentage of adult voters reporting approval for legalization of medical cannabis were more likely to have used cannabis with the last 30 days, whereas the number of medical cannabis cards that had been issued by county was not associated with higher rates of recent or lifetime cannabis use among the adolescent residents. This finding suggests that adults’ favorable attitudes toward cannabis drove the association between state policy and adolescent marijuana use rather than an increase in the availability of cannabis in the community. A marijuana-using parent could present a predisposing factor for an adolescent to use marijuana from the standpoint that they are communicating social norms accepting of marijuana use and may possibly contribute to adolescents’ perceptions that marijuana use represents little risk for health harms. To date, very little research has been dedicated to examining the relationship of parental marijuana use with adolescent marijuana use. However, a national survey of young adults ages 18 to 25 found that children of parents who smoke marijuana were more than three times more likely to use it themselves. Among young adults whose parents had used marijuana, 72% had used it, while only 20% of those whose parents had never used marijuana reported having used it themselves .

The results of this study, which have been confirmed in several subsequent reports , demarcate the pharmacological profile of AM404 from those of direct-acting cannabinoid drugs. This distinction may result from the ability of AM404 to enhance anandamide signaling in an activity-dependent manner, causing anandamide to accumulate in discrete regions of the brain and only when appropriate stimuli initiate its release. Pharmacological activation of D2 receptors may represent one such stimulus, suggesting that blockade of anandamide transport might offer an innovative strategy to correct abnormalities associated with dysfunction in dopaminergic transmission. Initial tests of this hypothesis have shown that systemic administration of AM404 normalizes movement in spontaneously hypertensive rats , an inbred line in which hyperactivity and attention deficits have been linked to a defective regulation of mesocorticolimbic dopamine pathways .FAAH was first identified as an amide hydrolase activity present in rat liver tissue, which catalyzes the hydrolysis of the fatty-acid ethanolamides palmitoylethanolamide and oleoylethanolamide . That anandamide serves as a substrate for this activity was first suggested on the basis of biochemical evidence and later demonstrated by molecular cloning, heterologous expression and generation of FAAHnull mice by homologous recombination . FAAH belongs to a group of enzymes known as ‘amidase signature family’ and catalyzes the hydrolysis not only of anandamide and other fatty-acid ethanolamides,vertical cannabis but also of primary amides such as oleamide and even of fatty-acid esters such as 2-AG . Elegant site-directed mutagenesis and X-ray diffraction studies have demonstrated that this unusually broad substrate preference is due to a novel catalytic mechanism involving the amino-acid residue lysine 142. This residue may act as a general acid catalyst, favoring the protonation and consequent detachment of reaction products from the enzyme’s active site .

Three serine residues that are conserved in all amidase signature enzymes also may be essential for enzymatic activity: serine 241 may serve as the enzyme’s catalytic nucleophile, while serine 217 and 218 may modulate catalysis through an as-yet-unidentified mechanism . Electron microscopy experiments in the rat and mouse brain have shown that FAAH is predominantly, if not exclusively localized to intracellular membrane compartments, particularly to the endoplasmic reticulum and the mitochondria . Although FAAH appears to be the predominant route of anandamide hydrolysis in the brain, other enzymes are likely to participate in the breakdown of this endocannabinoid in peripheral tissues. An acid amide hydrolase activity catalytically distinct from FAAH has been characterized in human megakaryoblastic cells and shown to be highly expressed in the rat thymus, lungs and intestine .The search for small-molecule inhibitors of intracellular FAAH activity has led to the emergence of several potent and selective agents, which include substituted sulfonyl fluorides , alpha-keto-oxazolopyridines an d carbamic acid esters . The latter were identified during structure– activity relationship studies aimed at determining whether esters of carbamic acid such as the insecticide carbaryl inhibit FAAH activity. It was found that, although carbaryl is ineffective in this regard, variations in its template result in significant inhibitory potencies. Further structural optimizations yielded a group of highly potent inhibitors, a representative example of which is provided by the compound URB597 . Kinetic and dialysis experiments indicate that URB597 interacts non-competitively with FAAH, which is suggestive of anirreversible or slowly reversible association with the enzyme. Importantly, URB597 has no notable effect on CB1 or CB2 binding, anandamide transport, or rat brain monoglyceride lipase , a cytosolic serine hydrolase that catalyzes the hydrolysis of the second endocannabinoid, 2-arachidonoylglycerol  . Following administration to rats in vivo, URB597 produces profound, dose-dependent inhibition of brain FAAH activity.

After injection of a maximal dose of compound , FAAH inhibition is rapid , persistent and associated with a 3-fold increase in brain anandamide levels. Furthermore, the inhibitor intensifies and prolongs the effects produced by exogenous anandamide, yet it elicits no overt cannabinoid-like actions when administered alone; for example, it does not cause hypothermia, hot-plate analgesia, or hyperphagia .Although URB597 does not display a typical cannabinoid profile in live animals, it exerts several pharmacological effects that might be therapeutically relevant. One such effect, the ability to reduce anxiety-like behaviors in rats, was demonstrated in two distinct experimental models: the elevated ‘zero maze’ test, and the isolation-induced ultrasonic emission test . The ‘zero maze’ consists of an elevated annular platform with two open and two closed quadrants and is based on the conflict between an animal’s instinct to explore its environment and its fear of open spaces where it may be attacked by predators . Benzodiazepines and other clinically used anxiolytic drugs increase the proportion of time spent in, and the number of entries made into, the open compartments. In a similar fashion, URB597 elicits anxiolytic-like responses at a dose that corresponds to those required to inhibit brain FAAH activity. Moreover, these effects are prevented by the CB1-selective antagonist rimonabant. Analogous results were obtained in the ultrasonic vocalizationemission test, which measures the number of stress-induced vocalizations emitted by rat pups removed from their nest . If con- firmed in further behavioral models, these findings would suggest that inhibition of intracellular FAAH activity might offer an innovative target for the treatment of anxiety , which is also a feature of marijuana withdrawal .2-AG was identified as a second endocannabinoid substance in 1995 . The multiple roles of this lipid compound incell metabolism and its high levels inbraintissue— about 200-fold higher than those of anandamide—suggest that much of cellular 2-AG may be involved in housekeeping functions. The diversity of roles played by this compound also complicates our efforts to establish biochemical route involved in its physiological formation. Nevertheless, one pathway has emerged as the most likely candidate .

This pathway starts with the phospholipase-mediated generation of 1, 2- diacylglycerol . This serves as a substrate for two enzymes: DAG kinase, which catalyzes DAG phosphorylationto phosphatidic acid; and DAG lipase , which hydrolyzes DAG to monoacylglycerol . Pharmacological inhibition of phospholipase C and DGL prevent the Ca2+-dependent accumulation of 2-AG in rat cortical neurons, which suggests a key role of this pathway in2-AG generation . However, additional routes of 2-AG synthesis also may exist, including phospholipase A1 ,cannabis drying racks hormone-sensitive lipase or a lipid phosphatase . In neurons and glia, 2-AG synthesis may be initiated by a rise incytosolic Ca2+ levels. For example, incultures of rat cortical neurons, the Ca2+ ionophore ionomycin and the glutamate receptor agonist N-methyld-aspartate stimulate 2-AG productionina Ca2+-dependent manner . Similarly, infreshly dissected hippocampal slices, electrical stimulation of the Schaffer collaterals, a glutamatergic fiber tract that connects neurons in the CA3 and CA1 fields, causes a Ca2+-dependent increase in 2-AG content . This stimulation has no effect on the levels of non-cannabinoid monoacylglycerols, such as 1-palmitoylglycerol, which indicates that 2-AG formation may not be attributed to a broad, non-specific increase in lipid turnover. Furthermore, electrical stimulationof the Scheffer collaterals does not modify hippocampal anandamide levels, suggesting that the biochemical pathways leading to the production of 2-AG and anandamide may be independently controlled . In further support of this idea, activation of D2 receptors, a potent stimulus for anandamide formation in the rat striatum, has no effect on striatal 2-AG levels .Neuronal and glial cells internalize 2-AG through a mechanism apparently similar to that implicated in anandamide transport. Thus, human astrocytoma and other tumor cells accumulate [3 H]anandamide and [ 3 H]2-AG with similar kinetic properties and this process is blocked by the anandamide transport inhibitor AM404 . In addition, anandamide and 2-AG prevent each other’s transport . Nevertheless, there also appear to be differences between anandamide and 2-AG accumulation. For example, [3 H]2-AG internalization in astrocytoma cells is reduced by exogenous arachidonic acid, whereas [3 H]anandamide internalization is not. This discrepancy may be explained in two ways: arachidonic acid may directly interfere with a 2- AG carrier distinct from anandamide’s; or the fatty acid may indirectly prevent the facilitated diffusion of 2-AG by inhibiting its enzymatic conversion to arachidonic acid. If the latter explanation is correct, agents that interfere with the arachidonic acid esterification into phospholipids, such as triacsinC , should decrease [ 3 H]2-AG uptake. This was found indeed to be the case, at least inastrocytoma cells . Thus, while anandamide and 2-AG may be internalized through similar transport mechanisms, they appear to differ in how their intracellular breakdown can affect the rate of transport into cells.After removal from the external medium, 2-AG is hydrolyzed to arachidonic acid and glycerol.

In cellfree preparations, FAAH cleaves anandamide and 2- AG at similar rates, which has led to suggest that this enzyme may contribute to the elimination of both compounds. This appears to be unlikely, however, for three reasons. First, pig brain tissue contains two distinct 2- AG-hydrolase activities, both of which are chromatographically different from FAAH . Second, inhibition of FAAH activity in intact neurons and astrocytoma cells prevents the hydrolysis of anandamide, but has no effect on 2-AG degradation . Finally, 2-AG hydrolysis is entirely preserved in FAAH-null mice . These findings suggest that, although 2-AG can be hydrolyzed by FAAH in vitro, different enzyme may be responsible for its degradation in vivo. A possible candidate for this role is MGL, a cytosolic serine hydrolase that cleaves 2- and 1-monoglycerides into fatty acid and glycerol .MGL is abundantly expressed in discrete areas of the rat brain—including the hippocampus, cortex, and cerebellum—where CB1 receptors are also found. Moreover, adenovirus-induced over expression of MGL enhances the hydrolysis of endogenously produced 2-AG in primary cultures of rat brain neurons . Finally, recent experiments indicate that silencing the MGL gene through RNA interference markedly impairs 2-AG degradation in intact HeLa cells . Although these results strongly support a role of MGL in2-AG hydrolysis, the development of additional experimental tools will be needed to demonstrate such a role unambiguously.Currently, in the United States, there are more annual deaths and disabilities from substance abuse than from any other preventable health condition . A robust body of literature suggests that prevention interventions that delay and minimize marijuana and other substance use during childhood and adolescence could have a considerable impact on morbidity and mortality in addition to reducing associated social and economic costs . Adolescent substance use is also a problem that is ideal for intervention within a public health framework, as it is both widespread and amenable to population-level approaches designed to reduce youth exposure to harmful influences. While various community-level prevention approaches have been implemented to protect underage youth from exposure to factors that encourage them to use alcohol and drugs at an age when substance use can seriously disrupt their social, physiological, and emotional development , an issue addressed in this dissertation is whether policies that restrict access to marijuana by adolescents enacted at the local level have any measurable impact. One important question is whether restrictions on marijuana have any impact on youth use in the context of high vehicle ownership, marijuana delivery services, and contrasting marijuana regulations between neighboring cities. The research literature shows that local regulations on legal substances can have a significant, if not always dramatic, effect . Given the large numbers of people impacted, if city policies cause even an incremental reduction in substance use it can have a qualitatively larger impact on a community than more intensive interventions carried out with less people . Throughout the United States , marijuana policy has been changing rapidly and following a consistent national trend of less restrictive state laws controlling access to marijuana . In an enduring legal paradox, the marijuana plant and its products remain illegal under U.S. Federal law, but the Federal government has also allowed regulations on marijuana to be determined by each state independently and these laws now contrast with Federal marijuana law in the majority of U.S. states. California’s marijuana laws are among the most permissive in the U.S., allowing for home delivery of psychoactive marijuana products, storefront medical and recreational marijuana outlets, and no limits on the THC potency of products sold . An important feature of California’s state marijuana laws, however, is that they do not preempt local regulations, meaning that local jurisdictions like cities and counties have the prerogative to enact local ordinances that further restrict access to marijuana within their borders.